data analysis in quantitative research types

Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng Published: May 18, 2022

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These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

• Mean:   This calculates the numerical average of a set of values.
• Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
• Mode: This is used to find the most commonly occurring value in a dataset.
• Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
• Frequency: This indicates the number of times a value is found.
• Range: This shows the highest and lowest values in a dataset.
• Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
• Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

• Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
• Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
• Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
• Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
• Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
• MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
• Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
• Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
• SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem is a freelance writer specializing in data-related topics, who has expertise in translating complex concepts. With a focus on data science, analytics, and emerging technologies.

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations.

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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74 Comments

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Based on the given learning points, this is inferential analysis, thus, use ‘t-tests, ANOVA, correlation and regression analysis’

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Quantitative Methods

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• Juwel Rana 2 , 3 , 4 ,
• Patricia Luna Gutierrez 5 &
• John C. Oldroyd 6  

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Babones S (2016) Interpretive quantitative methods for the social sciences. Sociology. https://doi.org/10.1177/0038038515583637

Balnaves M, Caputi P (2001) Introduction to quantitative research methods: an investigative approach. Sage, London

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Brenner PS (2020) Understanding survey methodology: sociological theory and applications. Springer, Boston

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Creswell JW (2014) Research design: qualitative, quantitative, and mixed methods approaches. Sage, London

Leavy P (2017) Research design. The Gilford Press, New York

Mertens W, Pugliese A, Recker J (2018) Quantitative data analysis, research methods: information, systems, and contexts: second edition. https://doi.org/10.1016/B978-0-08-102220-7.00018-2

Neuman LW (2014) Social research methods: qualitative and quantitative approaches. Pearson Education Limited, Edinburgh

Treiman DJ (2009) Quantitative data analysis: doing social research to test ideas. Jossey-Bass, San Francisco

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Department of Public Health, School of Health and Life Sciences, North South University, Dhaka, Bangladesh

Department of Biostatistics and Epidemiology, School of Health and Health Sciences, University of Massachusetts Amherst, MA, USA

Department of Research and Innovation, South Asia Institute for Social Transformation (SAIST), Dhaka, Bangladesh

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Rana, J., Gutierrez, P.L., Oldroyd, J.C. (2021). Quantitative Methods. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_460-1

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Data & Finance for Work & Life

Data Analysis: Types, Methods & Techniques (a Complete List)

( Updated Version )

While the term sounds intimidating, “data analysis” is nothing more than making sense of information in a table. It consists of filtering, sorting, grouping, and manipulating data tables with basic algebra and statistics.

In fact, you don’t need experience to understand the basics. You have already worked with data extensively in your life, and “analysis” is nothing more than a fancy word for good sense and basic logic.

Over time, people have intuitively categorized the best logical practices for treating data. These categories are what we call today types , methods , and techniques .

This article provides a comprehensive list of types, methods, and techniques, and explains the difference between them.

For a practical intro to data analysis (including types, methods, & techniques), check out our Intro to Data Analysis eBook for free.

Descriptive, Diagnostic, Predictive, & Prescriptive Analysis

If you Google “types of data analysis,” the first few results will explore descriptive , diagnostic , predictive , and prescriptive analysis. Why? Because these names are easy to understand and are used a lot in “the real world.”

Descriptive analysis is an informational method, diagnostic analysis explains “why” a phenomenon occurs, predictive analysis seeks to forecast the result of an action, and prescriptive analysis identifies solutions to a specific problem.

That said, these are only four branches of a larger analytical tree.

Good data analysts know how to position these four types within other analytical methods and tactics, allowing them to leverage strengths and weaknesses in each to uproot the most valuable insights.

Let’s explore the full analytical tree to understand how to appropriately assess and apply these four traditional types.

Tree diagram of Data Analysis Types, Methods, and Techniques

Here’s a picture to visualize the structure and hierarchy of data analysis types, methods, and techniques.

If it’s too small you can view the picture in a new tab . Open it to follow along!

Note: basic descriptive statistics such as mean , median , and mode , as well as standard deviation , are not shown because most people are already familiar with them. In the diagram, they would fall under the “descriptive” analysis type.

Tree Diagram Explained

The highest-level classification of data analysis is quantitative vs qualitative . Quantitative implies numbers while qualitative implies information other than numbers.

Quantitative data analysis then splits into mathematical analysis and artificial intelligence (AI) analysis . Mathematical types then branch into descriptive , diagnostic , predictive , and prescriptive .

Methods falling under mathematical analysis include clustering , classification , forecasting , and optimization . Qualitative data analysis methods include content analysis , narrative analysis , discourse analysis , framework analysis , and/or grounded theory .

Moreover, mathematical techniques include regression , Nïave Bayes , Simple Exponential Smoothing , cohorts , factors , linear discriminants , and more, whereas techniques falling under the AI type include artificial neural networks , decision trees , evolutionary programming , and fuzzy logic . Techniques under qualitative analysis include text analysis , coding , idea pattern analysis , and word frequency .

It’s a lot to remember! Don’t worry, once you understand the relationship and motive behind all these terms, it’ll be like riding a bike.

We’ll move down the list from top to bottom and I encourage you to open the tree diagram above in a new tab so you can follow along .

But first, let’s just address the elephant in the room: what’s the difference between methods and techniques anyway?

Difference between methods and techniques

Though often used interchangeably, methods ands techniques are not the same. By definition, methods are the process by which techniques are applied, and techniques are the practical application of those methods.

For example, consider driving. Methods include staying in your lane, stopping at a red light, and parking in a spot. Techniques include turning the steering wheel, braking, and pushing the gas pedal.

Data sets: observations and fields

It’s important to understand the basic structure of data tables to comprehend the rest of the article. A data set consists of one far-left column containing observations, then a series of columns containing the fields (aka “traits” or “characteristics”) that describe each observations. For example, imagine we want a data table for fruit. It might look like this:

Now let’s turn to types, methods, and techniques. Each heading below consists of a description, relative importance, the nature of data it explores, and the motivation for using it.

Quantitative Analysis

• It accounts for more than 50% of all data analysis and is by far the most widespread and well-known type of data analysis.
• As you have seen, it holds descriptive, diagnostic, predictive, and prescriptive methods, which in turn hold some of the most important techniques available today, such as clustering and forecasting.
• It can be broken down into mathematical and AI analysis.
• Importance : Very high . Quantitative analysis is a must for anyone interesting in becoming or improving as a data analyst.
• Nature of Data: data treated under quantitative analysis is, quite simply, quantitative. It encompasses all numeric data.
• Motive: to extract insights. (Note: we’re at the top of the pyramid, this gets more insightful as we move down.)

Qualitative Analysis

• It accounts for less than 30% of all data analysis and is common in social sciences .
• It can refer to the simple recognition of qualitative elements, which is not analytic in any way, but most often refers to methods that assign numeric values to non-numeric data for analysis.
• Because of this, some argue that it’s ultimately a quantitative type.
• Importance: Medium. In general, knowing qualitative data analysis is not common or even necessary for corporate roles. However, for researchers working in social sciences, its importance is very high .
• Nature of Data: data treated under qualitative analysis is non-numeric. However, as part of the analysis, analysts turn non-numeric data into numbers, at which point many argue it is no longer qualitative analysis.
• Motive: to extract insights. (This will be more important as we move down the pyramid.)

Mathematical Analysis

• Description: mathematical data analysis is a subtype of qualitative data analysis that designates methods and techniques based on statistics, algebra, and logical reasoning to extract insights. It stands in opposition to artificial intelligence analysis.
• Importance: Very High. The most widespread methods and techniques fall under mathematical analysis. In fact, it’s so common that many people use “quantitative” and “mathematical” analysis interchangeably.
• Nature of Data: numeric. By definition, all data under mathematical analysis are numbers.
• Motive: to extract measurable insights that can be used to act upon.

Artificial Intelligence & Machine Learning Analysis

• Description: artificial intelligence and machine learning analyses designate techniques based on the titular skills. They are not traditionally mathematical, but they are quantitative since they use numbers. Applications of AI & ML analysis techniques are developing, but they’re not yet mainstream enough to show promise across the field.
• Importance: Medium . As of today (September 2020), you don’t need to be fluent in AI & ML data analysis to be a great analyst. BUT, if it’s a field that interests you, learn it. Many believe that in 10 year’s time its importance will be very high .
• Nature of Data: numeric.
• Motive: to create calculations that build on themselves in order and extract insights without direct input from a human.

Descriptive Analysis

• Description: descriptive analysis is a subtype of mathematical data analysis that uses methods and techniques to provide information about the size, dispersion, groupings, and behavior of data sets. This may sounds complicated, but just think about mean, median, and mode: all three are types of descriptive analysis. They provide information about the data set. We’ll look at specific techniques below.
• Importance: Very high. Descriptive analysis is among the most commonly used data analyses in both corporations and research today.
• Nature of Data: the nature of data under descriptive statistics is sets. A set is simply a collection of numbers that behaves in predictable ways. Data reflects real life, and there are patterns everywhere to be found. Descriptive analysis describes those patterns.
• Motive: the motive behind descriptive analysis is to understand how numbers in a set group together, how far apart they are from each other, and how often they occur. As with most statistical analysis, the more data points there are, the easier it is to describe the set.

Diagnostic Analysis

• Description: diagnostic analysis answers the question “why did it happen?” It is an advanced type of mathematical data analysis that manipulates multiple techniques, but does not own any single one. Analysts engage in diagnostic analysis when they try to explain why.
• Importance: Very high. Diagnostics are probably the most important type of data analysis for people who don’t do analysis because they’re valuable to anyone who’s curious. They’re most common in corporations, as managers often only want to know the “why.”
• Nature of Data : data under diagnostic analysis are data sets. These sets in themselves are not enough under diagnostic analysis. Instead, the analyst must know what’s behind the numbers in order to explain “why.” That’s what makes diagnostics so challenging yet so valuable.
• Motive: the motive behind diagnostics is to diagnose — to understand why.

Predictive Analysis

• Description: predictive analysis uses past data to project future data. It’s very often one of the first kinds of analysis new researchers and corporate analysts use because it is intuitive. It is a subtype of the mathematical type of data analysis, and its three notable techniques are regression, moving average, and exponential smoothing.
• Importance: Very high. Predictive analysis is critical for any data analyst working in a corporate environment. Companies always want to know what the future will hold — especially for their revenue.
• Nature of Data: Because past and future imply time, predictive data always includes an element of time. Whether it’s minutes, hours, days, months, or years, we call this time series data . In fact, this data is so important that I’ll mention it twice so you don’t forget: predictive analysis uses time series data .
• Motive: the motive for investigating time series data with predictive analysis is to predict the future in the most analytical way possible.

Prescriptive Analysis

• Description: prescriptive analysis is a subtype of mathematical analysis that answers the question “what will happen if we do X?” It’s largely underestimated in the data analysis world because it requires diagnostic and descriptive analyses to be done before it even starts. More than simple predictive analysis, prescriptive analysis builds entire data models to show how a simple change could impact the ensemble.
• Importance: High. Prescriptive analysis is most common under the finance function in many companies. Financial analysts use it to build a financial model of the financial statements that show how that data will change given alternative inputs.
• Nature of Data: the nature of data in prescriptive analysis is data sets. These data sets contain patterns that respond differently to various inputs. Data that is useful for prescriptive analysis contains correlations between different variables. It’s through these correlations that we establish patterns and prescribe action on this basis. This analysis cannot be performed on data that exists in a vacuum — it must be viewed on the backdrop of the tangibles behind it.
• Motive: the motive for prescriptive analysis is to establish, with an acceptable degree of certainty, what results we can expect given a certain action. As you might expect, this necessitates that the analyst or researcher be aware of the world behind the data, not just the data itself.

Clustering Method

• Description: the clustering method groups data points together based on their relativeness closeness to further explore and treat them based on these groupings. There are two ways to group clusters: intuitively and statistically (or K-means).
• Importance: Very high. Though most corporate roles group clusters intuitively based on management criteria, a solid understanding of how to group them mathematically is an excellent descriptive and diagnostic approach to allow for prescriptive analysis thereafter.
• Nature of Data : the nature of data useful for clustering is sets with 1 or more data fields. While most people are used to looking at only two dimensions (x and y), clustering becomes more accurate the more fields there are.
• Motive: the motive for clustering is to understand how data sets group and to explore them further based on those groups.
• Here’s an example set:

Classification Method

• Description: the classification method aims to separate and group data points based on common characteristics . This can be done intuitively or statistically.
• Importance: High. While simple on the surface, classification can become quite complex. It’s very valuable in corporate and research environments, but can feel like its not worth the work. A good analyst can execute it quickly to deliver results.
• Nature of Data: the nature of data useful for classification is data sets. As we will see, it can be used on qualitative data as well as quantitative. This method requires knowledge of the substance behind the data, not just the numbers themselves.
• Motive: the motive for classification is group data not based on mathematical relationships (which would be clustering), but by predetermined outputs. This is why it’s less useful for diagnostic analysis, and more useful for prescriptive analysis.

Forecasting Method

• Description: the forecasting method uses time past series data to forecast the future.
• Importance: Very high. Forecasting falls under predictive analysis and is arguably the most common and most important method in the corporate world. It is less useful in research, which prefers to understand the known rather than speculate about the future.
• Nature of Data: data useful for forecasting is time series data, which, as we’ve noted, always includes a variable of time.
• Motive: the motive for the forecasting method is the same as that of prescriptive analysis: the confidently estimate future values.

Optimization Method

• Description: the optimization method maximized or minimizes values in a set given a set of criteria. It is arguably most common in prescriptive analysis. In mathematical terms, it is maximizing or minimizing a function given certain constraints.
• Importance: Very high. The idea of optimization applies to more analysis types than any other method. In fact, some argue that it is the fundamental driver behind data analysis. You would use it everywhere in research and in a corporation.
• Nature of Data: the nature of optimizable data is a data set of at least two points.
• Motive: the motive behind optimization is to achieve the best result possible given certain conditions.

Content Analysis Method

• Description: content analysis is a method of qualitative analysis that quantifies textual data to track themes across a document. It’s most common in academic fields and in social sciences, where written content is the subject of inquiry.
• Importance: High. In a corporate setting, content analysis as such is less common. If anything Nïave Bayes (a technique we’ll look at below) is the closest corporations come to text. However, it is of the utmost importance for researchers. If you’re a researcher, check out this article on content analysis .
• Nature of Data: data useful for content analysis is textual data.
• Motive: the motive behind content analysis is to understand themes expressed in a large text

Narrative Analysis Method

• Description: narrative analysis is a method of qualitative analysis that quantifies stories to trace themes in them. It’s differs from content analysis because it focuses on stories rather than research documents, and the techniques used are slightly different from those in content analysis (very nuances and outside the scope of this article).
• Importance: Low. Unless you are highly specialized in working with stories, narrative analysis rare.
• Nature of Data: the nature of the data useful for the narrative analysis method is narrative text.
• Motive: the motive for narrative analysis is to uncover hidden patterns in narrative text.

Discourse Analysis Method

• Description: the discourse analysis method falls under qualitative analysis and uses thematic coding to trace patterns in real-life discourse. That said, real-life discourse is oral, so it must first be transcribed into text.
• Importance: Low. Unless you are focused on understand real-world idea sharing in a research setting, this kind of analysis is less common than the others on this list.
• Nature of Data: the nature of data useful in discourse analysis is first audio files, then transcriptions of those audio files.
• Motive: the motive behind discourse analysis is to trace patterns of real-world discussions. (As a spooky sidenote, have you ever felt like your phone microphone was listening to you and making reading suggestions? If it was, the method was discourse analysis.)

Framework Analysis Method

• Description: the framework analysis method falls under qualitative analysis and uses similar thematic coding techniques to content analysis. However, where content analysis aims to discover themes, framework analysis starts with a framework and only considers elements that fall in its purview.
• Importance: Low. As with the other textual analysis methods, framework analysis is less common in corporate settings. Even in the world of research, only some use it. Strangely, it’s very common for legislative and political research.
• Nature of Data: the nature of data useful for framework analysis is textual.
• Motive: the motive behind framework analysis is to understand what themes and parts of a text match your search criteria.

Grounded Theory Method

• Description: the grounded theory method falls under qualitative analysis and uses thematic coding to build theories around those themes.
• Importance: Low. Like other qualitative analysis techniques, grounded theory is less common in the corporate world. Even among researchers, you would be hard pressed to find many using it. Though powerful, it’s simply too rare to spend time learning.
• Nature of Data: the nature of data useful in the grounded theory method is textual.
• Motive: the motive of grounded theory method is to establish a series of theories based on themes uncovered from a text.

Clustering Technique: K-Means

• Description: k-means is a clustering technique in which data points are grouped in clusters that have the closest means. Though not considered AI or ML, it inherently requires the use of supervised learning to reevaluate clusters as data points are added. Clustering techniques can be used in diagnostic, descriptive, & prescriptive data analyses.
• Importance: Very important. If you only take 3 things from this article, k-means clustering should be part of it. It is useful in any situation where n observations have multiple characteristics and we want to put them in groups.
• Nature of Data: the nature of data is at least one characteristic per observation, but the more the merrier.
• Motive: the motive for clustering techniques such as k-means is to group observations together and either understand or react to them.

Regression Technique

• Description: simple and multivariable regressions use either one independent variable or combination of multiple independent variables to calculate a correlation to a single dependent variable using constants. Regressions are almost synonymous with correlation today.
• Importance: Very high. Along with clustering, if you only take 3 things from this article, regression techniques should be part of it. They’re everywhere in corporate and research fields alike.
• Nature of Data: the nature of data used is regressions is data sets with “n” number of observations and as many variables as are reasonable. It’s important, however, to distinguish between time series data and regression data. You cannot use regressions or time series data without accounting for time. The easier way is to use techniques under the forecasting method.
• Motive: The motive behind regression techniques is to understand correlations between independent variable(s) and a dependent one.

Nïave Bayes Technique

• Description: Nïave Bayes is a classification technique that uses simple probability to classify items based previous classifications. In plain English, the formula would be “the chance that thing with trait x belongs to class c depends on (=) the overall chance of trait x belonging to class c, multiplied by the overall chance of class c, divided by the overall chance of getting trait x.” As a formula, it’s P(c|x) = P(x|c) * P(c) / P(x).
• Importance: High. Nïave Bayes is a very common, simplistic classification techniques because it’s effective with large data sets and it can be applied to any instant in which there is a class. Google, for example, might use it to group webpages into groups for certain search engine queries.
• Nature of Data: the nature of data for Nïave Bayes is at least one class and at least two traits in a data set.
• Motive: the motive behind Nïave Bayes is to classify observations based on previous data. It’s thus considered part of predictive analysis.

Cohorts Technique

• Description: cohorts technique is a type of clustering method used in behavioral sciences to separate users by common traits. As with clustering, it can be done intuitively or mathematically, the latter of which would simply be k-means.
• Importance: Very high. With regard to resembles k-means, the cohort technique is more of a high-level counterpart. In fact, most people are familiar with it as a part of Google Analytics. It’s most common in marketing departments in corporations, rather than in research.
• Nature of Data: the nature of cohort data is data sets in which users are the observation and other fields are used as defining traits for each cohort.
• Motive: the motive for cohort analysis techniques is to group similar users and analyze how you retain them and how the churn.

Factor Technique

• Description: the factor analysis technique is a way of grouping many traits into a single factor to expedite analysis. For example, factors can be used as traits for Nïave Bayes classifications instead of more general fields.
• Importance: High. While not commonly employed in corporations, factor analysis is hugely valuable. Good data analysts use it to simplify their projects and communicate them more clearly.
• Nature of Data: the nature of data useful in factor analysis techniques is data sets with a large number of fields on its observations.
• Motive: the motive for using factor analysis techniques is to reduce the number of fields in order to more quickly analyze and communicate findings.

Linear Discriminants Technique

• Description: linear discriminant analysis techniques are similar to regressions in that they use one or more independent variable to determine a dependent variable; however, the linear discriminant technique falls under a classifier method since it uses traits as independent variables and class as a dependent variable. In this way, it becomes a classifying method AND a predictive method.
• Importance: High. Though the analyst world speaks of and uses linear discriminants less commonly, it’s a highly valuable technique to keep in mind as you progress in data analysis.
• Nature of Data: the nature of data useful for the linear discriminant technique is data sets with many fields.
• Motive: the motive for using linear discriminants is to classify observations that would be otherwise too complex for simple techniques like Nïave Bayes.

Exponential Smoothing Technique

• Description: exponential smoothing is a technique falling under the forecasting method that uses a smoothing factor on prior data in order to predict future values. It can be linear or adjusted for seasonality. The basic principle behind exponential smoothing is to use a percent weight (value between 0 and 1 called alpha) on more recent values in a series and a smaller percent weight on less recent values. The formula is f(x) = current period value * alpha + previous period value * 1-alpha.
• Importance: High. Most analysts still use the moving average technique (covered next) for forecasting, though it is less efficient than exponential moving, because it’s easy to understand. However, good analysts will have exponential smoothing techniques in their pocket to increase the value of their forecasts.
• Nature of Data: the nature of data useful for exponential smoothing is time series data . Time series data has time as part of its fields .
• Motive: the motive for exponential smoothing is to forecast future values with a smoothing variable.

Moving Average Technique

• Description: the moving average technique falls under the forecasting method and uses an average of recent values to predict future ones. For example, to predict rainfall in April, you would take the average of rainfall from January to March. It’s simple, yet highly effective.
• Importance: Very high. While I’m personally not a huge fan of moving averages due to their simplistic nature and lack of consideration for seasonality, they’re the most common forecasting technique and therefore very important.
• Nature of Data: the nature of data useful for moving averages is time series data .
• Motive: the motive for moving averages is to predict future values is a simple, easy-to-communicate way.

Neural Networks Technique

• Description: neural networks are a highly complex artificial intelligence technique that replicate a human’s neural analysis through a series of hyper-rapid computations and comparisons that evolve in real time. This technique is so complex that an analyst must use computer programs to perform it.
• Importance: Medium. While the potential for neural networks is theoretically unlimited, it’s still little understood and therefore uncommon. You do not need to know it by any means in order to be a data analyst.
• Nature of Data: the nature of data useful for neural networks is data sets of astronomical size, meaning with 100s of 1000s of fields and the same number of row at a minimum .
• Motive: the motive for neural networks is to understand wildly complex phenomenon and data to thereafter act on it.

Decision Tree Technique

• Description: the decision tree technique uses artificial intelligence algorithms to rapidly calculate possible decision pathways and their outcomes on a real-time basis. It’s so complex that computer programs are needed to perform it.
• Importance: Medium. As with neural networks, decision trees with AI are too little understood and are therefore uncommon in corporate and research settings alike.
• Nature of Data: the nature of data useful for the decision tree technique is hierarchical data sets that show multiple optional fields for each preceding field.
• Motive: the motive for decision tree techniques is to compute the optimal choices to make in order to achieve a desired result.

Evolutionary Programming Technique

• Description: the evolutionary programming technique uses a series of neural networks, sees how well each one fits a desired outcome, and selects only the best to test and retest. It’s called evolutionary because is resembles the process of natural selection by weeding out weaker options.
• Importance: Medium. As with the other AI techniques, evolutionary programming just isn’t well-understood enough to be usable in many cases. It’s complexity also makes it hard to explain in corporate settings and difficult to defend in research settings.
• Nature of Data: the nature of data in evolutionary programming is data sets of neural networks, or data sets of data sets.
• Motive: the motive for using evolutionary programming is similar to decision trees: understanding the best possible option from complex data.
• Video example :

Fuzzy Logic Technique

• Description: fuzzy logic is a type of computing based on “approximate truths” rather than simple truths such as “true” and “false.” It is essentially two tiers of classification. For example, to say whether “Apples are good,” you need to first classify that “Good is x, y, z.” Only then can you say apples are good. Another way to see it helping a computer see truth like humans do: “definitely true, probably true, maybe true, probably false, definitely false.”
• Importance: Medium. Like the other AI techniques, fuzzy logic is uncommon in both research and corporate settings, which means it’s less important in today’s world.
• Nature of Data: the nature of fuzzy logic data is huge data tables that include other huge data tables with a hierarchy including multiple subfields for each preceding field.
• Motive: the motive of fuzzy logic to replicate human truth valuations in a computer is to model human decisions based on past data. The obvious possible application is marketing.

Text Analysis Technique

• Description: text analysis techniques fall under the qualitative data analysis type and use text to extract insights.
• Importance: Medium. Text analysis techniques, like all the qualitative analysis type, are most valuable for researchers.
• Nature of Data: the nature of data useful in text analysis is words.
• Motive: the motive for text analysis is to trace themes in a text across sets of very long documents, such as books.

Coding Technique

• Description: the coding technique is used in textual analysis to turn ideas into uniform phrases and analyze the number of times and the ways in which those ideas appear. For this reason, some consider it a quantitative technique as well. You can learn more about coding and the other qualitative techniques here .
• Importance: Very high. If you’re a researcher working in social sciences, coding is THE analysis techniques, and for good reason. It’s a great way to add rigor to analysis. That said, it’s less common in corporate settings.
• Nature of Data: the nature of data useful for coding is long text documents.
• Motive: the motive for coding is to make tracing ideas on paper more than an exercise of the mind by quantifying it and understanding is through descriptive methods.

Idea Pattern Technique

• Description: the idea pattern analysis technique fits into coding as the second step of the process. Once themes and ideas are coded, simple descriptive analysis tests may be run. Some people even cluster the ideas!
• Importance: Very high. If you’re a researcher, idea pattern analysis is as important as the coding itself.
• Nature of Data: the nature of data useful for idea pattern analysis is already coded themes.
• Motive: the motive for the idea pattern technique is to trace ideas in otherwise unmanageably-large documents.

Word Frequency Technique

• Description: word frequency is a qualitative technique that stands in opposition to coding and uses an inductive approach to locate specific words in a document in order to understand its relevance. Word frequency is essentially the descriptive analysis of qualitative data because it uses stats like mean, median, and mode to gather insights.
• Importance: High. As with the other qualitative approaches, word frequency is very important in social science research, but less so in corporate settings.
• Nature of Data: the nature of data useful for word frequency is long, informative documents.
• Motive: the motive for word frequency is to locate target words to determine the relevance of a document in question.

Types of data analysis in research

Types of data analysis in research methodology include every item discussed in this article. As a list, they are:

• Quantitative
• Qualitative
• Mathematical
• Machine Learning and AI
• Descriptive
• Prescriptive
• Classification
• Forecasting
• Optimization
• Grounded theory
• Artificial Neural Networks
• Decision Trees
• Evolutionary Programming
• Fuzzy Logic
• Text analysis
• Idea Pattern Analysis
• Word Frequency Analysis
• Nïave Bayes
• Exponential smoothing
• Moving average
• Linear discriminant

Types of data analysis in qualitative research

As a list, the types of data analysis in qualitative research are the following methods:

Types of data analysis in quantitative research

As a list, the types of data analysis in quantitative research are:

Data analysis methods

As a list, data analysis methods are:

• Content (qualitative)
• Narrative (qualitative)
• Discourse (qualitative)
• Framework (qualitative)
• Grounded theory (qualitative)

Quantitative data analysis methods

As a list, quantitative data analysis methods are:

Tabular View of Data Analysis Types, Methods, and Techniques

About the author.

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Quantitative Data: What It Is, Types & Examples

When we’re asking questions like “ How many? “, “ How often? ” or “ How much? ” we’re talking about the kind of hard-hitting, verifiable data that can be analyzed with mathematical techniques. It’s the kind of stuff that would make a statistician’s heart skip a beat. Let’s discuss quantitative data.

Thankfully, online surveys are the go-to tool for collecting this kind of data in the internet age. With the ability to reach more people in less time and gather honest responses for later analysis, online surveys are the ultimate quantitative data-gathering machine. Plus, let’s be real: who doesn’t love taking a good survey?

What is Quantitative Data?

Quantitative data is the value of data in the form of counts or numbers where each data set has a unique numerical value. This data is any quantifiable information that researchers can use for mathematical calculations and statistical analysis to make real-life decisions based on these mathematical derivations.

For example, there are quantities corresponding to various parameters. For instance, “How much did that laptop cost?” is a question that will collect quantitative data. Values are associated with most measuring parameters, such as pounds or kilograms for weight, dollars for cost, etc.

It makes measuring various parameters controllable due to the ease of mathematical derivations they come with. It is usually collected for statistical analysis plans using surveys , polls, or questionnaires sent across to a specific section of a population. Researches can establish the retrieved results across a population.

Types of Quantitative Data with Examples

Quantitative data is integral to the research process, providing valuable insights into various phenomena. Let’s explore the most common types of quantitative data and their applications in various fields. The most common types are listed below:

• Counter: Count equated with entities—for example, the number of people downloading a particular application from the App Store.
• Measurement of physical objects: Calculating measurement of any physical thing. For example, the HR executive carefully measures the size of each cubicle assigned to the newly joined employees.
• Sensory calculation: Mechanism to naturally “sense” the measured parameters to create a constant source of information. For example, a digital camera converts electromagnetic information to a string of numerical data.
• Projection of data: Future data projections can be made using algorithms and other mathematical analysis tools. For example, a marketer will predict an increase in sales after launching a new product with a thorough analysis.
• Quantification of qualitative entities: Identify numbers to qualitative information. For example, asking respondents of an online survey to share the likelihood of recommendation on a scale of 0-10.

Quantitative Data: Collection Methods

As quantitative data is in the form of numbers, mathematical and statistical analysis of these numbers can lead to establishing some conclusive results.

There are two main Quantitative Data Collection Methods :

01. Surveys

Traditionally, surveys were conducted using paper-based methods and have gradually evolved into online mediums. Closed-ended questions form a major part of these surveys as they are more effective in collecting data.

The survey includes answer options they think are the most appropriate for a particular question. Surveys are integral in collecting feedback from an audience larger than the conventional size. A critical factor about surveys is that the responses collected should be such that they can be generalized to the entire population without significant discrepancies.

Based on the time involved in completing surveys, they are classified into the following:

• Longitudinal Studies: A type of observational research in which the market researcher conducts surveys from one time period to another, i.e., over a considerable course of time, is called a longitudinal survey . This survey is often implemented for trend analysis or studies where the primary objective is to collect and analyze a pattern in data.
• Cross-sectional Studies: A type of observational research in which market research conducts surveys at a particular time period across the target sample is known as a cross-sectional survey . This survey type implements a questionnaire to understand a specific subject from the sample at a definite time period.

To administer a survey to collect quantitative data, the following principles are to be followed.

• Fundamental Levels of Measurement – Nominal, Ordinal, Interval, and Ratio Scales: Four measurement scales are fundamental to creating a multiple-choice question in a survey in collecting quantitative data. They are  nominal, ordinal, interval, and ratio  measurement scales without the fundamentals of which no multiple-choice questions can be created.
• Use of Different Question Types:  To collect quantitative data,  close-ended questions have to be used in a survey. They can be a mix of multiple  question types , including  multiple-choice questions  like  semantic differential scale questions ,  rating scale questions , etc., that can help collect data that can be analyzed and made sense of.
• Email:  Sending a survey via email is the most commonly used and most effective survey distribution method. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents:  Another effective way to distribute a survey and collect quantitative data is to use a sample. Since the respondents are knowledgeable and also are open to participating in research studies, the responses are much higher.
• Embed survey in a website:  Embedding a survey in a website increases the number of responses as the respondent is already near the brand when the survey pops up.
• Social distribution:  Using  social media to distribute the survey  aids in collecting a higher number of responses from the people who are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can  print/publish this code  in magazines, signs, business cards, or on just about any object/medium.
• SMS survey:  A quick and time-effective way of conducting a survey to collect a high number of responses is the  SMS survey .
• QuestionPro app:  The  QuestionPro App  allows the quick creation of surveys, and the responses can be collected both online and  offline .
• API integration:  You can use the  API integration  of the QuestionPro platform for potential respondents to take your survey.

02. One-on-one Interviews

This quantitative data collection method was also traditionally conducted face-to-face but has shifted to telephonic and online platforms. Interviews offer a marketer the opportunity to gather extensive data from the participants. Quantitative interviews are immensely structured and play a key role in collecting information. There are three major sections of these online interviews:

• Face-to-Face Interviews: An interviewer can prepare a list of important interview questions in addition to the already asked survey questions . This way, interviewees provide exhaustive details about the topic under discussion. An interviewer can manage to bond with the interviewee on a personal level which will help him/her to collect more details about the topic due to which the responses also improve. Interviewers can also ask for an explanation from the interviewees about unclear answers.
• Online/Telephonic Interviews: Telephone-based interviews are no more a novelty but these quantitative interviews have also moved to online mediums such as Skype or Zoom. Irrespective of the distance between the interviewer and the interviewee and their corresponding time zones, communication becomes one-click away with online interviews. In case of telephone interviews, the interview is merely a phone call away.
• Computer Assisted Personal Interview: This is a one-on-one interview technique where the interviewer enters all the collected data directly into a laptop or any other similar device. The processing time is reduced and also the interviewers don’t have to carry physical questionnaires and merely enter the answers in the laptop.

All of the above quantitative data collection methods can be achieved by using surveys , questionnaires and online polls .

Quantitative Data: Analysis Methods

Data collection forms a major part of the research process. This data, however, has to be analyzed to make sense of. There are multiple methods of analyzing quantitative data collected in surveys . They are:

• Cross-tabulation: Cross-tabulation is the most widely used quantitative data analysis methods. It is a preferred method since it uses a basic tabular form to draw inferences between different data-sets in the research study. It contains data that is mutually exclusive or have some connection with each other.
• Trend analysis: Trend analysis is a statistical analysis method that provides the ability to look at quantitative data that has been collected over a long period of time. This data analysis method helps collect feedback about data changes over time and if aims to understand the change in variables considering one variable remains unchanged.
• MaxDiff analysis: The MaxDiff analysis is a quantitative data analysis method that is used to gauge customer preferences for a purchase and what parameters rank higher than the others in this process. In a simplistic form, this method is also called the “best-worst” method. This method is very similar to conjoint analysis but is much easier to implement and can be interchangeably used.  
• Conjoint analysis: Like in the above method, conjoint analysis is a similar quantitative data analysis method that analyzes parameters behind a purchasing decision. This method possesses the ability to collect and analyze advanced metrics which provide an in-depth insight into purchasing decisions as well as the parameters that rank the most important.
• TURF analysis: TURF analysis or Total Unduplicated Reach and Basic Frequency Analysis, is a quantitative data analysis methodology that assesses the total market reach of a product or service or a mix of both. This method is used by organizations to understand the frequency and the avenues at which their messaging reaches customers and prospective customers which helps them tweak their go-to-market strategies.
• Gap analysis: Gap analysis uses a side-by-side matrix to depict data that helps measure the difference between expected performance and actual performance. This data gap analysis helps measure gaps in performance and the things that are required to be done to bridge this gap.
• SWOT analysis: SWOT analysis , is a quantitative data analysis methods that assigns numerical values to indicate strength, weaknesses, opportunities and threats of an organization or product or service which in turn provides a holistic picture about competition. This method helps to create effective business strategies.
• Text analysis: Text analysis is an advanced statistical method where intelligent tools make sense of and quantify or fashion qualitative observation and open-ended data into easily understandable data. This method is used when the raw survey data is unstructured but has to be brought into a structure that makes sense.

Steps to conduct Quantitative Data Analysis

For Quantitative Data, raw information has to presented in a meaningful manner using data analysis methods. This data should be analyzed to find evidential data that would help in the research process. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.

• Relate measurement scales with variables:  Associate measurement scales such as Nominal, Ordinal, Interval and Ratio with the variables. This step is important to arrange the data in proper order. Data can be entered into an excel sheet to organize it in a specific format.
• Mean- An average of values for a specific variable
• Median- A midpoint of the value scale for a variable
• Mode- For a variable, the most common value
• Frequency- Number of times a particular value is observed in the scale
• Minimum and Maximum Values- Lowest and highest values for a scale
• Percentages- Format to express scores and set of values for variables
• Decide a measurement scale:  It is important to decide the measurement scale to conclude descriptive statistics for the variable. For instance, a nominal data variable score will never have a mean or median, so the descriptive statistics will correspondingly vary. Descriptive statistics suffice in situations where the results are not to be generalized to the population.
• Select appropriate tables to represent data and analyze collected data: After deciding on a suitable measurement scale, researchers can use a tabular format to represent data. This data can be analyzed using various techniques such as Cross-tabulation or TURF .  

Quantitative Data Examples

Listed below are some examples of quantitative data that can help understand exactly what this pertains:

• I updated my phone 6 times in a quarter.
• My teenager grew by 3 inches last year.
• 83 people downloaded the latest mobile application.
• My aunt lost 18 pounds last year.
• 150 respondents were of the opinion that the new product feature will fail to be successful.
• There will be 30% increase in revenue with the inclusion of a new product.
• 500 people attended the seminar.
• 54% people prefer shopping online instead of going to the mall.
• She has 10 holidays in this year.
• Product X costs $1000 .

As you can see in the above 10 examples, there is a numerical value assigned to each parameter and this is known as, quantitative data.

Advantages of Quantitative Data

Some of the advantages of quantitative data are:

• Conduct in-depth research: Since quantitative data can be statistically analyzed, it is highly likely that the research will be detailed.
• Minimum bias: There are instances in research, where personal bias is involved which leads to incorrect results. Due to the numerical nature of quantitative data, personal bias is reduced to a great extent.
• Accurate results: As the results obtained are objective in nature, they are extremely accurate.

Disadvantages of Quantitative Data

Some of disadvantages of quantitative data, are:

• Restricted information: Because quantitative data is not descriptive, it becomes difficult for researchers to make decisions based solely on the collected information.
• Depends on question types: Bias in results is dependent on the question types included to collect quantitative data. The researcher’s knowledge of questions and the objective of research are exceedingly important while collecting quantitative data.

Differences between Quantitative and Qualitative Data

There are some stark differences between quantitative data and qualitative data . While quantitative data deals with numbers and measures and quantifies a specific phenomenon, qualitative data focuses on non-numerical information, such as opinions and observations.

The two types of data have different purposes, strengths, and limitations, which are important in understanding a given subject completely. Understanding the differences between these two forms of data is crucial in choosing the right research methods, analyzing the results, and making informed decisions. Let’s explore the differences:

Using quantitative data in an investigation is one of the best strategies to guarantee reliable results that allow better decisions. In summary, quantitative data is the basis of statistical analysis.

Data that can be measured and verified gives us information about quantities; that is, information that can be measured and written with numbers. Quantitative data defines a number, while qualitative data collection is descriptive. You can also get quantitative data from qualitative by using semantic analysis .

QuestionPro is a software created to collect quantitative data using a powerful platform with preloaded questionnaires. In addition, you will be able to analyze your data with advanced analysis tools such as cross tables, Likert scales, infographics, and much more.

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Part II: Data Analysis Methods in Quantitative Research

Data analysis methods in quantitative research.

We started this module with levels of measurement as a way to categorize our data. Data analysis is directed toward answering the original research question and achieving the study purpose (or aim). Now, we are going to delve into two main statistical analyses to describe our data and make inferences about our data:

Descriptive Statistics and Inferential Statistics.

Descriptive Statistics:

Before you panic, we will not be going into statistical analyses very deeply. We want to simply get a good overview of some of the types of general statistical analyses so that it makes some sense to us when we read results in published research articles.

Descriptive statistics   summarize or describe the characteristics of a data set. This is a method of simply organizing and describing our data. Why? Because data that are not organized in some fashion are super difficult to interpret.

Let’s say our sample is golden retrievers (population “canines”). Our descriptive statistics  tell us more about the same.

• 37% of our sample is male, 43% female
• The mean age is 4 years
• Mode is 6 years
• Median age is 5.5 years

Let’s explore some of the types of descriptive statistics.

Frequency Distributions : A frequency distribution describes the number of observations for each possible value of a measured variable. The numbers are arranged from lowest to highest and features a count of how many times each value occurred.

For example, if 18 students have pet dogs, dog ownership has a frequency of 18.

We might see what other types of pets that students have. Maybe cats, fish, and hamsters. We find that 2 students have hamsters, 9 have fish, 1 has a cat.

You can see that it is very difficult to interpret the various pets into any meaningful interpretation, yes?

Now, let’s take those same pets and place them in a frequency distribution table.                          

As we can now see, this is much easier to interpret.

Let’s say that we want to know how many books our sample population of  students have read in the last year. We collect our data and find this:

We can then take that table and plot it out on a frequency distribution graph. This makes it much easier to see how the numbers are disbursed. Easier on the eyes, yes?

Here’s another example of symmetrical, positive skew, and negative skew:

Correlation : Relationships between two research variables are called correlations . Remember, correlation is not cause-and-effect. Correlations  simply measure the extent of relationship between two variables. To measure correlation in descriptive statistics, the statistical analysis called Pearson’s correlation coefficient I is often used.  You do not need to know how to calculate this for this course. But, do remember that analysis test because you will often see this in published research articles. There really are no set guidelines on what measurement constitutes a “strong” or “weak” correlation, as it really depends on the variables being measured.

However, possible values for correlation coefficients range from -1.00 through .00 to +1.00. A value of +1 means that the two variables are positively correlated, as one variable goes up, the other goes up. A value of r = 0 means that the two variables are not linearly related.

Often, the data will be presented on a scatter plot. Here, we can view the data and there appears to be a straight line (linear) trend between height and weight. The association (or correlation) is positive. That means, that there is a weight increase with height. The Pearson correlation coefficient in this case was r = 0.56.

A type I error is made by rejecting a null hypothesis that is true. This means that there was no difference but the researcher concluded that the hypothesis was true.

A type II error is made by accepting that the null hypothesis is true when, in fact, it was false. Meaning there was actually a difference but the researcher did not think their hypothesis was supported.

Hypothesis Testing Procedures : In a general sense, the overall testing of a hypothesis has a systematic methodology. Remember, a hypothesis is an educated guess about the outcome. If we guess wrong, we might set up the tests incorrectly and might get results that are invalid. Sometimes, this is super difficult to get right. The main purpose of statistics is to test a hypothesis.

• Selecting a statistical test. Lots of factors go into this, including levels of measurement of the variables.
• Specifying the level of significance. Usually 0.05 is chosen.
• Computing a test statistic. Lots of software programs to help with this.
• Determining degrees of freedom ( df ). This refers to the number of observations free to vary about a parameter. Computing this is easy (but you don’t need to know how for this course).
• Comparing the test statistic to a theoretical value. Theoretical values exist for all test statistics, which is compared to the study statistics to help establish significance.

Some of the common inferential statistics you will see include:

Comparison tests: Comparison tests look for differences among group means. They can be used to test the effect of a categorical variable on the mean value of some other characteristic.

T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults).

• t -tests (compares differences in two groups) – either paired t-test (example: What is the effect of two different test prep programs on the average exam scores for students from the same class?) or independent t-test (example: What is the difference in average exam scores for students from two different schools?)
• analysis of variance (ANOVA, which compares differences in three or more groups) (example: What is the difference in average pain levels among post-surgical patients given three different painkillers?) or MANOVA (compares differences in three or more groups, and 2 or more outcomes) (example: What is the effect of flower species on petal length, petal width, and stem length?)

Correlation tests: Correlation tests check whether variables are related without hypothesizing a cause-and-effect relationship.

• Pearson r (measures the strength and direction of the relationship between two variables) (example: How are latitude and temperature related?)

Nonparametric tests: Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.

• chi-squared ( X 2 ) test (measures differences in proportions). Chi-square tests are often used to test hypotheses. The chi-square statistic compares the size of any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship. For example, the results of tossing a fair coin meet these criteria. We can apply a chi-square test to determine which type of candy is most popular and make sure that our shelves are well stocked. Or maybe you’re a scientist studying the offspring of cats to determine the likelihood of certain genetic traits being passed to a litter of kittens.

Inferential Versus Descriptive Statistics Summary Table

Statistical Significance Versus Clinical Significance

Finally, when it comes to statistical significance  in hypothesis testing, the normal probability value in nursing is <0.05. A p=value (probability) is a statistical measurement used to validate a hypothesis against measured data in the study. Meaning, it measures the likelihood that the results were actually observed due to the intervention, or if the results were just due by chance. The p-value, in measuring the probability of obtaining the observed results, assumes the null hypothesis is true.

The lower the p-value, the greater the statistical significance of the observed difference.

In the example earlier about our diabetic patients receiving online diet education, let’s say we had p = 0.05. Would that be a statistically significant result?

If you answered yes, you are correct!

What if our result was p = 0.8?

Not significant. Good job!

That’s pretty straightforward, right? Below 0.05, significant. Over 0.05 not   significant.

Could we have significance clinically even if we do not have statistically significant results? Yes. Let’s explore this a bit.

Statistical hypothesis testing provides little information for interpretation purposes. It’s pretty mathematical and we can still get it wrong. Additionally, attaining statistical significance does not really state whether a finding is clinically meaningful. With a large enough sample, even a small very tiny relationship may be statistically significant. But, clinical significance  is the practical importance of research. Meaning, we need to ask what the palpable effects may be on the lives of patients or healthcare decisions.

Remember, hypothesis testing cannot prove. It also cannot tell us much other than “yeah, it’s probably likely that there would be some change with this intervention”. Hypothesis testing tells us the likelihood that the outcome was due to an intervention or influence and not just by chance. Also, as nurses and clinicians, we are not concerned with a group of people – we are concerned at the individual, holistic level. The goal of evidence-based practice is to use best evidence for decisions about specific individual needs.

Additionally, begin your Discussion section. What are the implications to practice? Is there little evidence or a lot? Would you recommend additional studies? If so, what type of study would you recommend, and why?

• Were all the important results discussed?
• Did the researchers discuss any study limitations and their possible effects on the credibility of the findings? In discussing limitations, were key threats to the study’s validity and possible biases reviewed? Did the interpretations take limitations into account?
• What types of evidence were offered in support of the interpretation, and was that evidence persuasive? Were results interpreted in light of findings from other studies?
• Did the researchers make any unjustifiable causal inferences? Were alternative explanations for the findings considered? Were the rationales for rejecting these alternatives convincing?
• Did the interpretation consider the precision of the results and/or the magnitude of effects?
• Did the researchers draw any unwarranted conclusions about the generalizability of the results?
• Did the researchers discuss the study’s implications for clinical practice or future nursing research? Did they make specific recommendations?
• If yes, are the stated implications appropriate, given the study’s limitations and the magnitude of the effects as well as evidence from other studies? Are there important implications that the report neglected to include?
• Did the researchers mention or assess clinical significance? Did they make a distinction between statistical and clinical significance?
• If clinical significance was examined, was it assessed in terms of group-level information (e.g., effect sizes) or individual-level results? How was clinical significance operationalized?

References & Attribution

“ Green check mark ” by rawpixel licensed CC0 .

“ Magnifying glass ” by rawpixel licensed CC0

“ Orange flame ” by rawpixel licensed CC0 .

Polit, D. & Beck, C. (2021).  Lippincott CoursePoint Enhanced for Polit’s Essentials of Nursing Research  (10th ed.). Wolters Kluwer Health 

Vaid, N. K. (2019) Statistical performance measures. Medium. https://neeraj-kumar-vaid.medium.com/statistical-performance-measures-12bad66694b7

Evidence-Based Practice & Research Methodologies Copyright © by Tracy Fawns is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The ultimate guide to quantitative data analysis

Numbers help us make sense of the world. We collect quantitative data on our speed and distance as we drive, the number of hours we spend on our cell phones, and how much we save at the grocery store.

Our businesses run on numbers, too. We spend hours poring over key performance indicators (KPIs) like lead-to-client conversions, net profit margins, and bounce and churn rates.

But all of this quantitative data can feel overwhelming and confusing. Lists and spreadsheets of numbers don’t tell you much on their own—you have to conduct quantitative data analysis to understand them and make informed decisions.

Last updated

Reading time.

This guide explains what quantitative data analysis is and why it’s important, and gives you a four-step process to conduct a quantitative data analysis, so you know exactly what’s happening in your business and what your users need .

Collect quantitative customer data with Hotjar

Use Hotjar’s tools to gather the customer insights you need to make quantitative data analysis a breeze.

What is quantitative data analysis? 

Quantitative data analysis is the process of analyzing and interpreting numerical data. It helps you make sense of information by identifying patterns, trends, and relationships between variables through mathematical calculations and statistical tests. 

With quantitative data analysis, you turn spreadsheets of individual data points into meaningful insights to drive informed decisions. Columns of numbers from an experiment or survey transform into useful insights—like which marketing campaign asset your average customer prefers or which website factors are most closely connected to your bounce rate. 

Without analytics, data is just noise. Analyzing data helps you make decisions which are informed and free from bias.

What quantitative data analysis is not

But as powerful as quantitative data analysis is, it’s not without its limitations. It only gives you the what, not the why . For example, it can tell you how many website visitors or conversions you have on an average day, but it can’t tell you why users visited your site or made a purchase.

For the why behind user behavior, you need qualitative data analysis , a process for making sense of qualitative research like open-ended survey responses, interview clips, or behavioral observations. By analyzing non-numerical data, you gain useful contextual insights to shape your strategy, product, and messaging. 

Quantitative data analysis vs. qualitative data analysis 

Let’s take an even deeper dive into the differences between quantitative data analysis and qualitative data analysis to explore what they do and when you need them.

The bottom line: quantitative data analysis and qualitative data analysis are complementary processes. They work hand-in-hand to tell you what’s happening in your business and why.  

💡 Pro tip: easily toggle between quantitative and qualitative data analysis with Hotjar Funnels . 

The Funnels tool helps you visualize quantitative metrics like drop-off and conversion rates in your sales or conversion funnel to understand when and where users leave your website. You can break down your data even further to compare conversion performance by user segment.

Spot a potential issue? A single click takes you to relevant session recordings , where you see user behaviors like mouse movements, scrolls, and clicks. With this qualitative data to provide context, you'll better understand what you need to optimize to streamline the user experience (UX) and increase conversions .

Hotjar Funnels lets you quickly explore the story behind the quantitative data

4 benefits of quantitative data analysis

There’s a reason product, web design, and marketing teams take time to analyze metrics: the process pays off big time. 

Four major benefits of quantitative data analysis include:

1. Make confident decisions 

With quantitative data analysis, you know you’ve got data-driven insights to back up your decisions . For example, if you launch a concept testing survey to gauge user reactions to a new logo design, and 92% of users rate it ‘very good’—you'll feel certain when you give the designer the green light. 

Since you’re relying less on intuition and more on facts, you reduce the risks of making the wrong decision. (You’ll also find it way easier to get buy-in from team members and stakeholders for your next proposed project. 🙌)

2. Reduce costs

By crunching the numbers, you can spot opportunities to reduce spend . For example, if an ad campaign has lower-than-average click-through rates , you might decide to cut your losses and invest your budget elsewhere. 

Or, by analyzing ecommerce metrics , like website traffic by source, you may find you’re getting very little return on investment from a certain social media channel—and scale back spending in that area.

3. Personalize the user experience

Quantitative data analysis helps you map the customer journey , so you get a better sense of customers’ demographics, what page elements they interact with on your site, and where they drop off or convert . 

These insights let you better personalize your website, product, or communication, so you can segment ads, emails, and website content for specific user personas or target groups.

4. Improve user satisfaction and delight

Quantitative data analysis lets you see where your website or product is doing well—and where it falls short for your users . For example, you might see stellar results from KPIs like time on page, but conversion rates for that page are low. 

These quantitative insights encourage you to dive deeper into qualitative data to see why that’s happening—looking for moments of confusion or frustration on session recordings, for example—so you can make adjustments and optimize your conversions by improving customer satisfaction and delight.

💡Pro tip: use Net Promoter Score® (NPS) surveys to capture quantifiable customer satisfaction data that’s easy for you to analyze and interpret. 

With an NPS tool like Hotjar, you can create an on-page survey to ask users how likely they are to recommend you to others on a scale from 0 to 10. (And for added context, you can ask follow-up questions about why customers selected the rating they did—rich qualitative data is always a bonus!)

Hotjar graphs your quantitative NPS data to show changes over time

4 steps to effective quantitative data analysis 

Quantitative data analysis sounds way more intimidating than it actually is. Here’s how to make sense of your company’s numbers in just four steps:

1. Collect data

Before you can actually start the analysis process, you need data to analyze. This involves conducting quantitative research and collecting numerical data from various sources, including: 

Interviews or focus groups 

Website analytics

Observations, from tools like heatmaps or session recordings

Questionnaires, like surveys or on-page feedback widgets

Just ensure the questions you ask in your surveys are close-ended questions—providing respondents with select choices to choose from instead of open-ended questions that allow for free responses.

Hotjar’s pricing plans survey template provides close-ended questions

 2. Clean data

Once you’ve collected your data, it’s time to clean it up. Look through your results to find errors, duplicates, and omissions. Keep an eye out for outliers, too. Outliers are data points that differ significantly from the rest of the set—and they can skew your results if you don’t remove them.

By taking the time to clean your data set, you ensure your data is accurate, consistent, and relevant before it’s time to analyze. 

3. Analyze and interpret data

At this point, your data’s all cleaned up and ready for the main event. This step involves crunching the numbers to find patterns and trends via mathematical and statistical methods. 

Two main branches of quantitative data analysis exist: 

Descriptive analysis : methods to summarize or describe attributes of your data set. For example, you may calculate key stats like distribution and frequency, or mean, median, and mode.

Inferential analysis : methods that let you draw conclusions from statistics—like analyzing the relationship between variables or making predictions. These methods include t-tests, cross-tabulation, and factor analysis. (For more detailed explanations and how-tos, head to our guide on quantitative data analysis methods.)

Then, interpret your data to determine the best course of action. What does the data suggest you do ? For example, if your analysis shows a strong correlation between email open rate and time sent, you may explore optimal send times for each user segment.

4. Visualize and share data

Once you’ve analyzed and interpreted your data, create easy-to-read, engaging data visualizations—like charts, graphs, and tables—to present your results to team members and stakeholders. Data visualizations highlight similarities and differences between data sets and show the relationships between variables.

Software can do this part for you. For example, the Hotjar Dashboard shows all of your key metrics in one place—and automatically creates bar graphs to show how your top pages’ performance compares. And with just one click, you can navigate to the Trends tool to analyze product metrics for different segments on a single chart. 

Hotjar Trends lets you compare metrics across segments

Discover rich user insights with quantitative data analysis

Conducting quantitative data analysis takes a little bit of time and know-how, but it’s much more manageable than you might think. 

By choosing the right methods and following clear steps, you gain insights into product performance and customer experience —and you’ll be well on your way to making better decisions and creating more customer satisfaction and loyalty.

FAQs about quantitative data analysis

What is quantitative data analysis.

Quantitative data analysis is the process of making sense of numerical data through mathematical calculations and statistical tests. It helps you identify patterns, relationships, and trends to make better decisions.

How is quantitative data analysis different from qualitative data analysis?

Quantitative and qualitative data analysis are both essential processes for making sense of quantitative and qualitative research .

Quantitative data analysis helps you summarize and interpret numerical results from close-ended questions to understand what is happening. Qualitative data analysis helps you summarize and interpret non-numerical results, like opinions or behavior, to understand why the numbers look like they do.

 If you want to make strong data-driven decisions, you need both.

What are some benefits of quantitative data analysis?

Quantitative data analysis turns numbers into rich insights. Some benefits of this process include: 

Making more confident decisions

Identifying ways to cut costs

Personalizing the user experience

Improving customer satisfaction

What methods can I use to analyze quantitative data?

Quantitative data analysis has two branches: descriptive statistics and inferential statistics. 

Descriptive statistics provide a snapshot of the data’s features by calculating measures like mean, median, and mode. 

Inferential statistics , as the name implies, involves making inferences about what the data means. Dozens of methods exist for this branch of quantitative data analysis, but three commonly used techniques are: 

Cross tabulation

Factor analysis

Educational resources and simple solutions for your research journey

What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

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Home » Quantitative Data – Types, Methods and Examples

Quantitative Data – Types, Methods and Examples

Table of Contents

Quantitative Data

Definition:

Quantitative data refers to numerical data that can be measured or counted. This type of data is often used in scientific research and is typically collected through methods such as surveys, experiments, and statistical analysis.

Quantitative Data Types

There are two main types of quantitative data: discrete and continuous.

• Discrete data: Discrete data refers to numerical values that can only take on specific, distinct values. This type of data is typically represented as whole numbers and cannot be broken down into smaller units. Examples of discrete data include the number of students in a class, the number of cars in a parking lot, and the number of children in a family.
• Continuous data: Continuous data refers to numerical values that can take on any value within a certain range or interval. This type of data is typically represented as decimal or fractional values and can be broken down into smaller units. Examples of continuous data include measurements of height, weight, temperature, and time.

Quantitative Data Collection Methods

There are several common methods for collecting quantitative data. Some of these methods include:

• Surveys : Surveys involve asking a set of standardized questions to a large number of people. Surveys can be conducted in person, over the phone, via email or online, and can be used to collect data on a wide range of topics.
• Experiments : Experiments involve manipulating one or more variables and observing the effects on a specific outcome. Experiments can be conducted in a controlled laboratory setting or in the real world.
• Observational studies : Observational studies involve observing and collecting data on a specific phenomenon without intervening or manipulating any variables. Observational studies can be conducted in a natural setting or in a laboratory.
• Secondary data analysis : Secondary data analysis involves using existing data that was collected for a different purpose to answer a new research question. This method can be cost-effective and efficient, but it is important to ensure that the data is appropriate for the research question being studied.
• Physiological measures: Physiological measures involve collecting data on biological or physiological processes, such as heart rate, blood pressure, or brain activity.
• Computerized tracking: Computerized tracking involves collecting data automatically from electronic sources, such as social media, online purchases, or website analytics.

Quantitative Data Analysis Methods

There are several methods for analyzing quantitative data, including:

• Descriptive statistics: Descriptive statistics are used to summarize and describe the basic features of the data, such as the mean, median, mode, standard deviation, and range.
• Inferential statistics : Inferential statistics are used to make generalizations about a population based on a sample of data. These methods include hypothesis testing, confidence intervals, and regression analysis.
• Data visualization: Data visualization involves creating charts, graphs, and other visual representations of the data to help identify patterns and trends. Common types of data visualization include histograms, scatterplots, and bar charts.
• Time series analysis: Time series analysis involves analyzing data that is collected over time to identify patterns and trends in the data.
• Multivariate analysis : Multivariate analysis involves analyzing data with multiple variables to identify relationships between the variables.
• Factor analysis : Factor analysis involves identifying underlying factors or dimensions that explain the variation in the data.
• Cluster analysis: Cluster analysis involves identifying groups or clusters of observations that are similar to each other based on multiple variables.

Quantitative Data Formats

Quantitative data can be represented in different formats, depending on the nature of the data and the purpose of the analysis. Here are some common formats:

• Tables : Tables are a common way to present quantitative data, particularly when the data involves multiple variables. Tables can be used to show the frequency or percentage of data in different categories or to display summary statistics.
• Charts and graphs: Charts and graphs are useful for visualizing quantitative data and can be used to highlight patterns and trends in the data. Some common types of charts and graphs include line charts, bar charts, scatterplots, and pie charts.
• Databases : Quantitative data can be stored in databases, which allow for easy sorting, filtering, and analysis of large amounts of data.
• Spreadsheets : Spreadsheets can be used to organize and analyze quantitative data, particularly when the data is relatively small in size. Spreadsheets allow for calculations and data manipulation, as well as the creation of charts and graphs.
• Statistical software : Statistical software, such as SPSS, R, and SAS, can be used to analyze quantitative data. These programs allow for more advanced statistical analyses and data modeling, as well as the creation of charts and graphs.

Quantitative Data Gathering Guide

Here is a basic guide for gathering quantitative data:

• Define the research question: The first step in gathering quantitative data is to clearly define the research question. This will help determine the type of data to be collected, the sample size, and the methods of data analysis.
• Choose the data collection method: Select the appropriate method for collecting data based on the research question and available resources. This could include surveys, experiments, observational studies, or other methods.
• Determine the sample size: Determine the appropriate sample size for the research question. This will depend on the level of precision needed and the variability of the population being studied.
• Develop the data collection instrument: Develop a questionnaire or survey instrument that will be used to collect the data. The instrument should be designed to gather the specific information needed to answer the research question.
• Pilot test the data collection instrument : Before collecting data from the entire sample, pilot test the instrument on a small group to identify any potential problems or issues.
• Collect the data: Collect the data from the selected sample using the chosen data collection method.
• Clean and organize the data : Organize the data into a format that can be easily analyzed. This may involve checking for missing data, outliers, or errors.
• Analyze the data: Analyze the data using appropriate statistical methods. This may involve descriptive statistics, inferential statistics, or other types of analysis.
• Interpret the results: Interpret the results of the analysis in the context of the research question. Identify any patterns, trends, or relationships in the data and draw conclusions based on the findings.
• Communicate the findings: Communicate the findings of the analysis in a clear and concise manner, using appropriate tables, graphs, and other visual aids as necessary. The results should be presented in a way that is accessible to the intended audience.

Examples of Quantitative Data

Here are some examples of quantitative data:

• Height of a person (measured in inches or centimeters)
• Weight of a person (measured in pounds or kilograms)
• Temperature (measured in Fahrenheit or Celsius)
• Age of a person (measured in years)
• Number of cars sold in a month
• Amount of rainfall in a specific area (measured in inches or millimeters)
• Number of hours worked in a week
• GPA (grade point average) of a student
• Sales figures for a product
• Time taken to complete a task.
• Distance traveled (measured in miles or kilometers)
• Speed of an object (measured in miles per hour or kilometers per hour)
• Number of people attending an event
• Price of a product (measured in dollars or other currency)
• Blood pressure (measured in millimeters of mercury)
• Amount of sugar in a food item (measured in grams)
• Test scores (measured on a numerical scale)
• Number of website visitors per day
• Stock prices (measured in dollars)
• Crime rates (measured by the number of crimes per 100,000 people)

Applications of Quantitative Data

Quantitative data has a wide range of applications across various fields, including:

• Scientific research: Quantitative data is used extensively in scientific research to test hypotheses and draw conclusions. For example, in biology, researchers might use quantitative data to measure the growth rate of cells or the effectiveness of a drug treatment.
• Business and economics: Quantitative data is used to analyze business and economic trends, forecast future performance, and make data-driven decisions. For example, a company might use quantitative data to analyze sales figures and customer demographics to determine which products are most popular among which segments of their customer base.
• Education: Quantitative data is used in education to measure student performance, evaluate teaching methods, and identify areas where improvement is needed. For example, a teacher might use quantitative data to track the progress of their students over the course of a semester and adjust their teaching methods accordingly.
• Public policy: Quantitative data is used in public policy to evaluate the effectiveness of policies and programs, identify areas where improvement is needed, and develop evidence-based solutions. For example, a government agency might use quantitative data to evaluate the impact of a social welfare program on poverty rates.
• Healthcare : Quantitative data is used in healthcare to evaluate the effectiveness of medical treatments, track the spread of diseases, and identify risk factors for various health conditions. For example, a doctor might use quantitative data to monitor the blood pressure levels of their patients over time and adjust their treatment plan accordingly.

Purpose of Quantitative Data

The purpose of quantitative data is to provide a numerical representation of a phenomenon or observation. Quantitative data is used to measure and describe the characteristics of a population or sample, and to test hypotheses and draw conclusions based on statistical analysis. Some of the key purposes of quantitative data include:

• Measuring and describing : Quantitative data is used to measure and describe the characteristics of a population or sample, such as age, income, or education level. This allows researchers to better understand the population they are studying.
• Testing hypotheses: Quantitative data is often used to test hypotheses and theories by collecting numerical data and analyzing it using statistical methods. This can help researchers determine whether there is a statistically significant relationship between variables or whether there is support for a particular theory.
• Making predictions : Quantitative data can be used to make predictions about future events or trends based on past data. This is often done through statistical modeling or time series analysis.
• Evaluating programs and policies: Quantitative data is often used to evaluate the effectiveness of programs and policies. This can help policymakers and program managers identify areas where improvements can be made and make evidence-based decisions about future programs and policies.

When to use Quantitative Data

Quantitative data is appropriate to use when you want to collect and analyze numerical data that can be measured and analyzed using statistical methods. Here are some situations where quantitative data is typically used:

• When you want to measure a characteristic or behavior : If you want to measure something like the height or weight of a population or the number of people who smoke, you would use quantitative data to collect this information.
• When you want to compare groups: If you want to compare two or more groups, such as comparing the effectiveness of two different medical treatments, you would use quantitative data to collect and analyze the data.
• When you want to test a hypothesis : If you have a hypothesis or theory that you want to test, you would use quantitative data to collect data that can be analyzed statistically to determine whether your hypothesis is supported by the data.
• When you want to make predictions: If you want to make predictions about future trends or events, such as predicting sales for a new product, you would use quantitative data to collect and analyze data from past trends to make your prediction.
• When you want to evaluate a program or policy : If you want to evaluate the effectiveness of a program or policy, you would use quantitative data to collect data about the program or policy and analyze it statistically to determine whether it has had the intended effect.

Characteristics of Quantitative Data

Quantitative data is characterized by several key features, including:

• Numerical values : Quantitative data consists of numerical values that can be measured and counted. These values are often expressed in terms of units, such as dollars, centimeters, or kilograms.
• Continuous or discrete : Quantitative data can be either continuous or discrete. Continuous data can take on any value within a certain range, while discrete data can only take on certain values.
• Objective: Quantitative data is objective, meaning that it is not influenced by personal biases or opinions. It is based on empirical evidence that can be measured and analyzed using statistical methods.
• Large sample size: Quantitative data is often collected from a large sample size in order to ensure that the results are statistically significant and representative of the population being studied.
• Statistical analysis: Quantitative data is typically analyzed using statistical methods to determine patterns, relationships, and other characteristics of the data. This allows researchers to make more objective conclusions based on empirical evidence.
• Precision : Quantitative data is often very precise, with measurements taken to multiple decimal points or significant figures. This precision allows for more accurate analysis and interpretation of the data.

Advantages of Quantitative Data

Some advantages of quantitative data are:

• Objectivity : Quantitative data is usually objective because it is based on measurable and observable variables. This means that different people who collect the same data will generally get the same results.
• Precision : Quantitative data provides precise measurements of variables. This means that it is easier to make comparisons and draw conclusions from quantitative data.
• Replicability : Since quantitative data is based on objective measurements, it is often easier to replicate research studies using the same or similar data.
• Generalizability : Quantitative data allows researchers to generalize findings to a larger population. This is because quantitative data is often collected using random sampling methods, which help to ensure that the data is representative of the population being studied.
• Statistical analysis : Quantitative data can be analyzed using statistical methods, which allows researchers to test hypotheses and draw conclusions about the relationships between variables.
• Efficiency : Quantitative data can often be collected quickly and efficiently using surveys or other standardized instruments, which makes it a cost-effective way to gather large amounts of data.

Limitations of Quantitative Data

Some Limitations of Quantitative Data are as follows:

• Limited context: Quantitative data does not provide information about the context in which the data was collected. This can make it difficult to understand the meaning behind the numbers.
• Limited depth: Quantitative data is often limited to predetermined variables and questions, which may not capture the complexity of the phenomenon being studied.
• Difficulty in capturing qualitative aspects: Quantitative data is unable to capture the subjective experiences and qualitative aspects of human behavior, such as emotions, attitudes, and motivations.
• Possibility of bias: The collection and interpretation of quantitative data can be influenced by biases, such as sampling bias, measurement bias, or researcher bias.
• Simplification of complex phenomena: Quantitative data may oversimplify complex phenomena by reducing them to numerical measurements and statistical analyses.
• Lack of flexibility: Quantitative data collection methods may not allow for changes or adaptations in the research process, which can limit the ability to respond to unexpected findings or new insights.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Analysis of Quantitative data enables you to transform raw data points, typically organised in spreadsheets, into actionable insights. Refer to the article to know more!

Analysis of Quantitative Data : Data, data everywhere — it’s impossible to escape it in today’s digitally connected world. With business and personal activities leaving digital footprints, vast amounts of quantitative data are being generated every second of every day. While data on its own may seem impersonal and cold, in the right hands it can be transformed into valuable insights that drive meaningful decision-making. In this article, we will discuss analysis of quantitative data types and examples!

If you are looking to acquire hands-on experience in quantitative data analysis, look no further than Physics Wallah’s Data Analytics Course . And as a token of appreciation for reading this blog post until the end, use our exclusive coupon code “READER” to get a discount on the course fee.

Table of Contents

What is the Quantitative Analysis Method?

Quantitative Analysis refers to a mathematical approach that gathers and evaluates measurable and verifiable data. This method is utilized to assess performance and various aspects of a business or research. It involves the use of mathematical and statistical techniques to analyze data. Quantitative methods emphasize objective measurements, focusing on statistical, analytical, or numerical analysis of data. It collects data and studies it to derive insights or conclusions.

In a business context, it helps in evaluating the performance and efficiency of operations. Quantitative analysis can be applied across various domains, including finance, research, and chemistry, where data can be converted into numbers for analysis.

Also Read: Analysis vs. Analytics: How Are They Different?

What is the Best Analysis for Quantitative Data?

The “best” analysis for quantitative data largely depends on the specific research objectives, the nature of the data collected, the research questions posed, and the context in which the analysis is conducted. Quantitative data analysis encompasses a wide range of techniques, each suited for different purposes. Here are some commonly employed methods, along with scenarios where they might be considered most appropriate:

1) Descriptive Statistics:

• When to Use: To summarize and describe the basic features of the dataset, providing simple summaries about the sample and measures of central tendency and variability.
• Example: Calculating means, medians, standard deviations, and ranges to describe a dataset.

2) Inferential Statistics:

• When to Use: When you want to make predictions or inferences about a population based on a sample, testing hypotheses, or determining relationships between variables.
• Example: Conducting t-tests to compare means between two groups or performing regression analysis to understand the relationship between an independent variable and a dependent variable.

3) Correlation and Regression Analysis:

• When to Use: To examine relationships between variables, determining the strength and direction of associations, or predicting one variable based on another.
• Example: Assessing the correlation between customer satisfaction scores and sales revenue or predicting house prices based on variables like location, size, and amenities.

4) Factor Analysis:

• When to Use: When dealing with a large set of variables and aiming to identify underlying relationships or latent factors that explain patterns of correlations within the data.
• Example: Exploring underlying constructs influencing employee engagement using survey responses across multiple indicators.

5) Time Series Analysis:

• When to Use: When analyzing data points collected or recorded at successive time intervals to identify patterns, trends, seasonality, or forecast future values.
• Example: Analyzing monthly sales data over several years to detect seasonal trends or forecasting stock prices based on historical data patterns.

6) Cluster Analysis:

• When to Use: To segment a dataset into distinct groups or clusters based on similarities, enabling pattern recognition, customer segmentation, or data reduction.
• Example: Segmenting customers into distinct groups based on purchasing behavior, demographic factors, or preferences.

The “best” analysis for quantitative data is not one-size-fits-all but rather depends on the research objectives, hypotheses, data characteristics, and contextual factors. Often, a combination of analytical techniques may be employed to derive comprehensive insights and address multifaceted research questions effectively. Therefore, selecting the appropriate analysis requires careful consideration of the research goals, methodological rigor, and interpretative relevance to ensure valid, reliable, and actionable outcomes.

Analysis of Quantitative Data in Quantitative Research

Analyzing quantitative data in quantitative research involves a systematic process of examining numerical information to uncover patterns, relationships, and insights that address specific research questions or objectives. Here’s a structured overview of the analysis process:

1) Data Preparation:

• Data Cleaning: Identify and address errors, inconsistencies, missing values, and outliers in the dataset to ensure its integrity and reliability.
• Variable Transformation: Convert variables into appropriate formats or scales, if necessary, for analysis (e.g., normalization, standardization).

2) Descriptive Statistics:

• Central Tendency: Calculate measures like mean, median, and mode to describe the central position of the data.
• Variability: Assess the spread or dispersion of data using measures such as range, variance, standard deviation, and interquartile range.
• Frequency Distribution: Create tables, histograms, or bar charts to display the distribution of values for categorical or discrete variables.

3) Exploratory Data Analysis (EDA):

• Data Visualization: Generate graphical representations like scatter plots, box plots, histograms, or heatmaps to visualize relationships, distributions, and patterns in the data.
• Correlation Analysis: Examine the strength and direction of relationships between variables using correlation coefficients.

4) Inferential Statistics:

• Hypothesis Testing: Formulate null and alternative hypotheses based on research questions, selecting appropriate statistical tests (e.g., t-tests, ANOVA, chi-square tests) to assess differences, associations, or effects.
• Confidence Intervals: Estimate population parameters using sample statistics and determine the range within which the true parameter is likely to fall.

5) Regression Analysis:

• Linear Regression: Identify and quantify relationships between an outcome variable and one or more predictor variables, assessing the strength, direction, and significance of associations.
• Multiple Regression: Evaluate the combined effect of multiple independent variables on a dependent variable, controlling for confounding factors.

6) Factor Analysis and Structural Equation Modeling:

• Factor Analysis: Identify underlying dimensions or constructs that explain patterns of correlations among observed variables, reducing data complexity.
• Structural Equation Modeling (SEM): Examine complex relationships between observed and latent variables, assessing direct and indirect effects within a hypothesized model.

7) Time Series Analysis and Forecasting:

• Trend Analysis: Analyze patterns, trends, and seasonality in time-ordered data to understand historical patterns and predict future values.
• Forecasting Models: Develop predictive models (e.g., ARIMA, exponential smoothing) to anticipate future trends, demand, or outcomes based on historical data patterns.

8) Interpretation and Reporting:

• Interpret Results: Translate statistical findings into meaningful insights, discussing implications, limitations, and conclusions in the context of the research objectives.
• Documentation: Document the analysis process, methodologies, assumptions, and findings systematically for transparency, reproducibility, and peer review.

Also Read: Learning Path to Become a Data Analyst in 2024

Analysis of Quantitative Data Examples

Analyzing quantitative data involves various statistical methods and techniques to derive meaningful insights from numerical data. Here are some examples illustrating the analysis of quantitative data across different contexts:

How to Write Data Analysis in Quantitative Research Proposal?

Writing the data analysis section in a quantitative research proposal requires careful planning and organization to convey a clear, concise, and methodologically sound approach to analyzing the collected data. Here’s a step-by-step guide on how to write the data analysis section effectively:

Step 1: Begin with an Introduction

• Contextualize : Briefly reintroduce the research objectives, questions, and the significance of the study.
• Purpose Statement : Clearly state the purpose of the data analysis section, outlining what readers can expect in this part of the proposal.

Step 2: Describe Data Collection Methods

• Detail Collection Techniques : Provide a concise overview of the methods used for data collection (e.g., surveys, experiments, observations).
• Instrumentation : Mention any tools, instruments, or software employed for data gathering and its relevance.

Step 3 : Discuss Data Cleaning Procedures

• Data Cleaning : Describe the procedures for cleaning and pre-processing the data.
• Handling Outliers & Missing Data : Explain how outliers, missing values, and other inconsistencies will be managed to ensure data quality.

Step 4 : Present Analytical Techniques

• Descriptive Statistics : Outline the descriptive statistics that will be calculated to summarize the data (e.g., mean, median, mode, standard deviation).
• Inferential Statistics : Specify the inferential statistical tests or models planned for deeper analysis (e.g., t-tests, ANOVA, regression).

Step 5: State Hypotheses & Testing Procedures

• Hypothesis Formulation : Clearly state the null and alternative hypotheses based on the research questions or objectives.
• Testing Strategy : Detail the procedures for hypothesis testing, including the chosen significance level (e.g., α = 0.05) and statistical criteria.

Step 6 : Provide a Sample Analysis Plan

• Step-by-Step Plan : Offer a sample plan detailing the sequence of steps involved in the data analysis process.
• Software & Tools : Mention any specific statistical software or tools that will be utilized for analysis.

Step 7 : Address Validity & Reliability

• Validity : Discuss how you will ensure the validity of the data analysis methods and results.
• Reliability : Explain measures taken to enhance the reliability and replicability of the study findings.

Step 8 : Discuss Ethical Considerations

• Ethical Compliance : Address ethical considerations related to data privacy, confidentiality, and informed consent.
• Compliance with Guidelines : Ensure that your data analysis methods align with ethical guidelines and institutional policies.

Step 9 : Acknowledge Limitations

• Limitations : Acknowledge potential limitations in the data analysis methods or data set.
• Mitigation Strategies : Offer strategies or alternative approaches to mitigate identified limitations.

Step 10 : Conclude the Section

• Summary : Summarize the key points discussed in the data analysis section.
• Transition : Provide a smooth transition to subsequent sections of the research proposal, such as the conclusion or references.

Step 11 : Proofread & Revise

• Review : Carefully review the data analysis section for clarity, coherence, and consistency.
• Feedback : Seek feedback from peers, advisors, or mentors to refine your approach and ensure methodological rigor.

What are the 4 Types of Quantitative Analysis?

Quantitative analysis encompasses various methods to evaluate and interpret numerical data. While the specific categorization can vary based on context, here are four broad types of quantitative analysis commonly recognized:

• Descriptive Analysis: This involves summarizing and presenting data to describe its main features, such as mean, median, mode, standard deviation, and range. Descriptive statistics provide a straightforward overview of the dataset’s characteristics.
• Inferential Analysis: This type of analysis uses sample data to make predictions or inferences about a larger population. Techniques like hypothesis testing, regression analysis, and confidence intervals fall under this category. The goal is to draw conclusions that extend beyond the immediate data collected.
• Time-Series Analysis: In this method, data points are collected, recorded, and analyzed over successive time intervals. Time-series analysis helps identify patterns, trends, and seasonal variations within the data. It’s particularly useful in forecasting future values based on historical trends.
• Causal or Experimental Research: This involves establishing a cause-and-effect relationship between variables. Through experimental designs, researchers manipulate one variable to observe the effect on another variable while controlling for external factors. Randomized controlled trials are a common method within this type of quantitative analysis.

Each type of quantitative analysis serves specific purposes and is applied based on the nature of the data and the research objectives.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Steps to Effective Quantitative Data Analysis 

Quantitative data analysis need not be daunting; it’s a systematic process that anyone can master. To harness actionable insights from your company’s data, follow these structured steps:

Step 1 : Gather Data Strategically

Initiating the analysis journey requires a foundation of relevant data. Employ quantitative research methods to accumulate numerical insights from diverse channels such as:

• Interviews or Focus Groups: Engage directly with stakeholders or customers to gather specific numerical feedback.
• Digital Analytics: Utilize tools like Google Analytics to extract metrics related to website traffic, user behavior, and conversions.
• Observational Tools: Leverage heatmaps, click-through rates, or session recordings to capture user interactions and preferences.
• Structured Questionnaires: Deploy surveys or feedback mechanisms that employ close-ended questions for precise responses.

Ensure that your data collection methods align with your research objectives, focusing on granularity and accuracy.

Step 2 : Refine and Cleanse Your Data

Raw data often comes with imperfections. Scrutinize your dataset to identify and rectify:

• Errors and Inconsistencies: Address any inaccuracies or discrepancies that could mislead your analysis.
• Duplicates: Eliminate repeated data points that can skew results.
• Outliers: Identify and assess outliers, determining whether they should be adjusted or excluded based on contextual relevance.

Cleaning your dataset ensures that subsequent analyses are based on reliable and consistent information, enhancing the credibility of your findings.

Step 3 : Delve into Analysis with Precision

With a refined dataset at your disposal, transition into the analytical phase. Employ both descriptive and inferential analysis techniques:

• Descriptive Analysis: Summarize key attributes of your dataset, computing metrics like averages, distributions, and frequencies.
• Inferential Analysis: Leverage statistical methodologies to derive insights, explore relationships between variables, or formulate predictions.

The objective is not just number crunching but deriving actionable insights. Interpret your findings to discern underlying patterns, correlations, or trends that inform strategic decision-making. For instance, if data indicates a notable relationship between user engagement metrics and specific website features, consider optimizing those features for enhanced user experience.

Step 4 : Visual Representation and Communication

Transforming your analytical outcomes into comprehensible narratives is crucial for organizational alignment and decision-making. Leverage visualization tools and techniques to:

• Craft Engaging Visuals: Develop charts, graphs, or dashboards that encapsulate key findings and insights.
• Highlight Insights: Use visual elements to emphasize critical data points, trends, or comparative metrics effectively.
• Facilitate Stakeholder Engagement: Share your visual representations with relevant stakeholders, ensuring clarity and fostering informed discussions.

Tools like Tableau, Power BI, or specialized platforms like Hotjar can simplify the visualization process, enabling seamless representation and dissemination of your quantitative insights.

Also Read: Top 10 Must Use AI Tools for Data Analysis [2024 Edition]

Statistical Analysis in Quantitative Research

Statistical analysis is a cornerstone of quantitative research, providing the tools and techniques to interpret numerical data systematically. By applying statistical methods, researchers can identify patterns, relationships, and trends within datasets, enabling evidence-based conclusions and informed decision-making. Here’s an overview of the key aspects and methodologies involved in statistical analysis within quantitative research:

• Mean, Median, Mode: Measures of central tendency that summarize the average, middle, and most frequent values in a dataset, respectively.
• Standard Deviation, Variance: Indicators of data dispersion or variability around the mean.
• Frequency Distributions: Tabular or graphical representations that display the distribution of data values or categories.
• Hypothesis Testing: Formal methodologies to test hypotheses or assumptions about population parameters using sample data. Common tests include t-tests, chi-square tests, ANOVA, and regression analysis.
• Confidence Intervals: Estimation techniques that provide a range of values within which a population parameter is likely to lie, based on sample data.
• Correlation and Regression Analysis: Techniques to explore relationships between variables, determining the strength and direction of associations. Regression analysis further enables prediction and modeling based on observed data patterns.

3) Probability Distributions:

• Normal Distribution: A bell-shaped distribution often observed in naturally occurring phenomena, forming the basis for many statistical tests.
• Binomial, Poisson, and Exponential Distributions: Specific probability distributions applicable to discrete or continuous random variables, depending on the nature of the research data.

4) Multivariate Analysis:

• Factor Analysis: A technique to identify underlying relationships between observed variables, often used in survey research or data reduction scenarios.
• Cluster Analysis: Methodologies that group similar objects or individuals based on predefined criteria, enabling segmentation or pattern recognition within datasets.
• Multivariate Regression: Extending regression analysis to multiple independent variables, assessing their collective impact on a dependent variable.

5) Data Modeling and Forecasting:

• Time Series Analysis: Analyzing data points collected or recorded at specific time intervals to identify patterns, trends, or seasonality.
• Predictive Analytics: Leveraging statistical models and machine learning algorithms to forecast future trends, outcomes, or behaviors based on historical data.

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Analysis of Quantitative Data FAQs

What is quantitative data analysis.

Quantitative data analysis involves the systematic process of collecting, cleaning, interpreting, and presenting numerical data to identify patterns, trends, and relationships through statistical methods and mathematical calculations.

What are the main steps involved in quantitative data analysis?

The primary steps include data collection, data cleaning, statistical analysis (descriptive and inferential), interpretation of results, and visualization of findings using graphs or charts.

What is the difference between descriptive and inferential analysis?

Descriptive analysis summarizes and describes the main aspects of the dataset (e.g., mean, median, mode), while inferential analysis draws conclusions or predictions about a population based on a sample, using statistical tests and models.

How do I handle outliers in my quantitative data?

Outliers can be managed by identifying them through statistical methods, understanding their nature (error or valid data), and deciding whether to remove them, transform them, or conduct separate analyses to understand their impact.

Which statistical tests should I use for my quantitative research?

The choice of statistical tests depends on your research design, data type, and research questions. Common tests include t-tests, ANOVA, regression analysis, chi-square tests, and correlation analysis, among others.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Quantitative Data: Types, Analysis & Examples

‘ Quantitative data ’ can be understood as something that can be counted and measured. It is a simple concept that offers insight into the number of required variables.

As a business, you have been using qualitative data for different purposes. Think of the time when you wished to know how many repeat customers you have or what percentage of customers buy items with a value exceeding $1000. 

Quantitative research can allow you to study a wider audience, get rich insights, and make data-backed decisions in a manner of minutes. So if you are still wondering “ what is quantitative data ” and wish to explore its various attributes, collection methods, advantages, or types, this blog is for you. 

But before we dive into deep waters, let us first start with the quantitative data definition .

What is Quantitative Data?

Quantitative data refers to data that can be expressed in numerical terms. Answers to questions like ‘ How much ’, ‘ How many ’, ‘ What percentage ’, and ‘ How often ’ are what constitutes quantitative data . Such data cannot be used for statistical analysis. However, you must try and identify relevant groups and descriptions to make sense of the data.

Quantitative Data Types with Examples

There are two main types of quantitative data . They are:

In simple terms, Discrete data is countable and Continuous data is measurable. Let’s explore the two types of data in detail.

Discrete data is data that can be expressed in specific values. These values are typically counted in whole numbers and cannot be broken down into smaller units. Discrete data is also known as attribute data. Thus, you can easily identify discrete quantitative data by questioning whether the given data can be counted or not. This type of data is usually represented using tally charts, bar charts, and pie charts.

A few examples of discrete data include:

• Number of members in a team
• Number of toffees in a packet
• Number of questions in a test paper
• Monthly profit of a business
• Shoe size number

On the other hand, continuous data is data that can take any value. This value has a tendency to fluctuate over time. Thus, the value will vary over a given period of time, depending on when you seek the data. This type of quantitative data is usually represented using a line graph as a line graph aptly illustrates the data changes occurring over a period of time.

Continuous data can be further divided into two types, namely, ratio data and interval data. Statistically, the geometric or harmonic mean is calculated in ratio data while the arithmetic mean is calculated in interval data.

A few examples of continuous data include:

• The daily temperature of a place
• Height of a baby
• Weight of a child
• Length of a leaf

Quantitative Data Collection Methods

You can collect quantitative data in many different ways. Let’s have a look at a few of them.

1. Probability Sampling

Probability sampling is a great way to eliminate sampling bias. It allows you to reach out to your target population and collect data in the most effective way possible with a representative sample.

Additionally, you can opt for any of the following sampling techniques as per your convenience and requirement.

• Simple random sampling – In this, each member of the targeted population has an equal probability of being selected for sampling.
• Systematic random sampling – In this, each member of the population is selected from a preset or ordered sampling frame. For example, you select the first target member for sampling randomly and then select the rest in a predetermined fashion thereafter, say every third member of the group or say, every fifth member of the population.
• Stratified random sampling -In this method, you divide the population into smaller sub-groups called strata. These strata are made using a common attribute that defines that set of people, for example, income or occupation.

2. Questionnaires and Surveys

This is a great way to collect quantitative data as it involves quick and to-the-point questions and answers. It comprises surveys, checklists, and ratings. You must have often come across surveys asking you about how many times you buy a certain product or service. These surveys are commonly used to understand customer value and monitor their dependency on the product or service. 

• Web-based questionnaires often come in the form of a survey link in your mail and include some close-ended questions that aim to collect information about a specific topic. These surveys can be created using secure online survey tools that are easy to collect and can be accessed anywhere and anytime.
• Mail questionnaires are sent out to the targeted population with a cover sheet that enlightens the audience about the topic. It allows the researcher to connect with a vast number of people, giving them time to get acquainted with the topic and respond to the questionnaire at their convenience. The researcher may also offer an incentive to people for responding to the mail with the complete survey.

3. Interviews

For interviews, the researcher asks a standard set of questions to the interviewee. The interview may be telephonic or face-to-face. In this, the interviewer prepares a set of questions and puts these in front of the interviewee to answer.  The face-to-face is often the most preferred method of interviewing as it is more interactive and allows the interviewer to build rapport. The researcher can also extract insight into answers by observing the body language of the interviewee, etc.

4. Open Source Datasets

In the age of the internet, getting information on any topic is no more a hassle. Whether you’re seeking information on finance, communication, dentistry, commerce, or the internet itself, there’s an overflow of information that you can access 24×7. You can easily access free and reliable information from a wide range of open datasets online.

5. Experiments

This data collection method involves making some changes to variables and then observing their effect on other variables. For this, you need to be vigilant and be prepared to fail as the success of this method is secured only after trials and errors. Thus, here, the researcher primarily aims to understand the cause and effect relationship of a specific situation.

Quantitative Data Analysis Methods

If you’re still looking for an answer to how to analyze quantitative data , we’re here to help. Analyzing quantitative data is easy, provided you collect the right quantitative research data and incorporate the right technique to analyze that data.

Here, we will look at a few quantitative data analysis methods that you can choose to analyze your next data research project effectively.

1. Cross-Tabulation

This method utilizes a basic tabular format to draw inferences between the collected data. It involves gathering multiple variables and understanding the correlation between them. This method is also known as contingency table or cross tabs and is apt for extracting relevant information from large data sets

2. MaxDiff Analysis

MaxDiff analysis, also called the ‘best-worst’ method, aims to gauge the preferences of the respondents. So whether you need to know which purchase was more fulfilling for the customer or what parameters the customer ranks more, this method is excellent to adopt in such a scenario.

3. TURF Analysis

TURF, an acronym for Total Unduplicated Reach and Frequency Analysis, aims to determine the market strategy for a business. It involves analyzing which platform offers the maximum reach so that you can direct your team efforts in the right direction

4. Gap Analysis

A gap analysis simply aims to identify gaps in attaining the desired results. It helps identify gaps and bottlenecks, paving the way for improved data and ultimately, better business performance.

5. SWOT Analysis

A SWOT analysis helps upi identify the various strengths, weaknesses, opportunities, and threats of a product, service, or organization. It helps you visualize the bigger picture and identify which areas need improvement and which areas can be leveraged to improve overall performance.

6. Text Analysis

Text analysis is apt for transforming and making sense of unstructured data. This process helps you extract valuable information from a large dataset, easing data collection and improving decision making.

Steps to Conduct Quantitative Data Analysis

Now that you have become familiar with quantitative data definition along with data collection and analysis methods, here is how you can conduct quantitative data analysis in 5 simple steps.

• Validating Data – The first step to conducting data analysis is to validate your data. Is the data relevant? Is it free of personal bias? These are common questions that you must ask yourself before you set out to analyze the data.
• Data Cleaning – Now, once the data has been validated for accuracy and bias, you must edit the data for consistency and relevancy. For instance, a respondent may have omitted to answer all questions. This is a case for incomplete data that will not give the required details for complete data analysis.
• Analyze the Data – Now is when you sit down to analyze the data . Look for descriptive statistics such as Mean, Median, and Percentage and establish a common pattern of evaluation.
• Interpret the Results – In this stage, you transform the data so that it can be easily understood by key stakeholders. Determine a measurement scale and decide how you are going to represent the collected data.

Quantitative Data Examples

Identifying quantitative data is simple as there is a numerical value assigned to the data. Let’s look at a few quantitative data examples in order to grasp a better understanding of it.

• I grew by 2 inches this year.
• The 43 children attended the event last night.
• He lost 20 pounds after the training.
• We have availed 12 holidays this year.
• The smartphone costs $1500 .
• About 34% of people prefer staying in on weekends.
• The jar holds 10 gallons of water.
• The room is 30 feet in width.

In all of the above examples, there is a numerical value in each data.

Advantages of Quantitative Data

The main advantages of quantitative data are:

• For Extensive Research – Statistical analysis comes easy with quantitative data . Such data offer a detailed and better understanding of the subject matter, allowing you to gain insight into the pertaining numerical pattern for further research.
• Remove Personal Bias – Personal preferences influence the quality of respondents’ information. It impacts the interpretation of data received. In the case of quantitative data, which is concrete, it eliminates any scope for personal bias, lending credibility to the data.
• Precise Outcome – Quantifying the data provides specific, accurate, and reliable results. This data is free from incomplete descriptions, offering information that is easy to analyze and interpret.
• Summarises Data – Summarized data in the form of quantitative data helps interpret the data quickly, saving both time and effort. Thus, such data helps in extracting relevant and crisp information from a well-analyzed set of data effortlessly.

Disadvantages of Quantitative Data

Everything has its pros and cons. Similarly, quantitative data too has its own share of disadvantages. Let’s look at them below.

• Inadequate Data – Since data is only in quantifiable terms, it is possible to omit the descriptive aspect of the final result.
• Misleading Results – Results of assessing quantitative data such as questionnaires and surveys can be misleading as there is a risk of bias creeping in due to prejudiced assumptions.

Difference Between Quantitative Data and Qualitative Data

We can understand the difference between quantitative and qualitative data clearly with the below table:

Ensure Precision with Solid Quantitative Data Collection

Data collection is an integral part of any research project. While you may find a particular data collection method convenient to use, the overall gist of collecting data to extract specific, relevant information will remain the same.

Quantitative data collection offers you data in numerical terms, making it easier for you to support or reject an assumption and arrive at a conclusion. With proven quantitative data collection methods such as surveys, questionnaires, probability sampling, interviews, and experiments,  you can get the most relevant answers that help move your research forward.

Now that you are familiar with everything that encapsulates the collection and analysis of quantitative data , you can enhance the overall data collection and analysis efficiency with a secure and collaborative research solution – ProProfs Survey Maker . Choose from hundreds of ready-to-use and professionally designed templates to get started with your next survey. You can even create surveys , forms, tests, and quizzes and share these with your respondents as a link, via social media, or even embed them on your website.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Qualitative vs. quantitative data

Removing bias in employee evaluations with actionable data-driven metrics.

Digital marketing and data management Businessman use laptops to work marketing analysis chart strategic planning for sustainable development and financial and investment competition digital marketing

NongAsimo/Getty Images/iStockphoto

By Joe Locke, AAS; Kelly Wright, MS

The benefits of using data to make decisions and drive change outweigh the costs of collection or analysis. Data is impartial, objective and emotionless, and it transforms into information only with context.

Care must be taken to ensure that data is not sought to support or disprove a hypothesis; instead, the data itself leads to conclusions that can be recreated empirically to validate that hypothesis. Empirical research – based on observations and experiences, rather than theory or belief – takes one of two forms: qualitative or quantitative .

Qualitative data

In qualitative research, observations or judgments are made about a thing or action, which are recorded as descriptive words. Examples include condition (e.g., good, fair, poor) or risk severity (e.g., low, moderate, high, critical).

From an analytic perspective, it is difficult to measure or rank qualitative data against other qualitative data because of the subjectivity of the criteria being used – who decides what is good, fair or poor?

Quantitative data

Opposingly, quantitative research is empirical research in which data is in the form of numbers and lends itself well to comparative analysis.

Converting qualitative judgments into quantitative data is a powerful analysis tool, making it possible to generate meaningful, actionable information.

Evaluating employee performance

Without the ability to convert qualitative data to quantitative data, it can be easy for intentional or unconscious bias to be introduced into a qualitative assessment like employee performance. Assessment of employee performance based on metrics is increasingly common as it enables employers to empirically quantify how well or poorly an employee performs with data in the form of numbers. When the employee is an EMS provider and poor performance could potentially affect patient outcomes, it is vital that an organization’s leadership implements impartial assessment and objectivity when assessing work quality.

If strict adherence to the data collection methodology is not maintained, outcomes can be manipulated to favor or disfavor individuals through intent or negligence, potentially perpetuating harmful systemic power structures. Organizations look toward data-driven metrics to evaluate individual and team performance to prevent inklings of nepotism or favoritism and promote transparency and accountability across a group. This provides leadership with concrete data-turned-actionable information to provide critical feedback to individual EMS providers, suggestions for improvement across the department, or opportunities to redefine the assessment criteria if results are inconclusive or irrelevant.

The knowledge that these assessments are being performed department-wide can lessen the sting of constructive criticism at the individual level, offer the EMS provider concrete steps to improve patient outcomes, help the department recognize appropriate EMS billing for services provided, and engender a feeling of responsibility and place within the organization.

About the authors Joe Locke, AAS, is EMS coordinator, City of Monroe Fire department. Kelly Wright, MS, is a City of Monroe GIS specialist.

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• Published: 12 April 2024

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

• Qiuyue Yuan 1 &
• Zhana Duren   ORCID: orcid.org/0000-0003-4685-811X 1  

Nature Biotechnology ( 2024 ) Cite this article

Metrics details

• Dynamic networks
• Gene regulatory networks

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

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GRNs 1 , 2 are collections of molecular regulators that interact with each other and determine gene activation and silencing in specific cellular contexts. A comprehensive understanding of gene regulation is fundamental to explain how cells perform diverse functions, how cells alter gene expression in response to different environments and how noncoding genetic variants cause disease. GRNs are composed of transcription factors (TFs) that bind DNA regulatory elements to activate or repress the expression of target genes.

Inference of GRNs is a central problem 2 , 3 , 4 , and there have been many attempts to approach this issue 2 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . Co-expression-based methods such as WGCNA 14 , ARACNe 9 and GENIE3 (ref. 15 ) infer the TF–TG trans -regulation from gene expression by capturing the TF–TG covariation. Such networks have undirected edges, preventing distinction of direction from a TF A –TF B edge. Moreover, co-expressions are interpreted as correlations rather than causal regulations 16 . Genome-wide measurements of chromatin accessibility, such as DNase-seq 17 and assay for transposase-accessible chromatin sequencing (ATAC-seq) 18 , locate REs, enabling TF–RE connections by motif matching and connecting REs to their nearby TGs 19 . However, TF footprint approaches cannot distinguish within-family TFs sharing motifs. To overcome this limitation, we developed a statistical model, PECA 20 , to fit TG expression by TF expression and RE accessibility across a diverse panel of cell types. However, the problem still has not been fully resolved because heterogeneity of cell types in bulk data limits the accuracy of inference.

The advent of single-cell sequencing technology has enabled highly accurate regulatory analysis at the level of individual cell types. Single-cell RNA sequencing (scRNA-seq) data enables cell type-specific trans -regulation inference through co-expression analysis such as PIDC and SCENIC 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 . Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) can be used to infer trans -regulation, as in DeepTFni 31 . Many methods integrate unpaired scRNA-seq and scATAC-seq data to infer trans -regulation. Those methods, including IReNA 32 , SOMatic 33 , UnpairReg 34 , CoupledNMF 35 , 36 , DC3 (ref. 36 ) and others 37 link TFs to REs by motif matching and link REs to TGs using the covariation of RE–TG or physical base pair distance. Recently, scJoint 38 was developed to transfer labels from scRNA-seq to scATAC-seq data, which may enable improved cell GRN inference. Despite extensive efforts, GRN inference accuracy has remained disappointingly low, marginally exceeding random predictions 39 .

Recent advances in single-cell sequencing 40 provide opportunities to address these challenges 41 , exemplified by SCENIC+ 42 . However, three major challenges persist in GRN inference. First, learning such a complex mechanism from limited data points remains a challenge. Although single-cell data offers a large number of cells, most of them are not independent. Second, incorporating prior knowledge such as motif matching into non-linear models is challenging. Third, inferred GRN accuracy assessed by experimental data is only marginally better than random prediction 39 .

To overcome these challenges, we propose a method called LINGER (Lifelong neural network for gene regulation). This research paper contributes to the field of GRN inference in multiple ways. First, LINGER uses lifelong learning, a previously defined concept 43 that incorporates large-scale external bulk data, mitigating the challenge of limited data but extensive parameters. Second, LINGER integrates TF–RE motif matching knowledge through manifold regularization, enabling prior knowledge incorporation into the model. Third, the accuracy of LINGER represents a fourfold to sevenfold relative increase. Fourth, LINGER enables the estimation of TF activity solely from gene expression data, identifying driver regulators.

LINGER: using bulk data to infer GRNs from single-cell multiome data

LINGER is a computational framework designed to infer GRNs from single-cell multiome data (Fig. 1 and Methods ). Using count matrices of gene expression and chromatin accessibility along with cell type annotation as input, it provides a cell population GRN, cell type-specific GRNs and cell-level GRNs. Each GRN contains three types of interactions, namely, trans -regulation (TF–TG), cis -regulation (RE–TG) and TF-binding (TF–RE). Note that TF–TF interactions are included in TF–TG pairs but TF self-regulation, which is challenging to model without additional data, is not considered. LINGER is distinguished by its ability to integrate the comprehensive gene regulatory profile from external bulk data. This is achieved through lifelong machine learning, also called continuous learning. The concept of lifelong learning is that the knowledge learned in the past helps us learn new things with little data or effort 44 . Lifelong learning has been proven to leverage the knowledge learned in previous tasks to learn the new task better 45 .

a , Schematic illustration of LINGER: a model predicting gene expression by TF expression and chromatin accessibility using a neural network model. LINGER pre-trains on the atlas-scale external bulk data and retains parameters by lifelong learning. The population-level GRN is generated from the neural network using the Shapley value. b , Strategy for constructing cell type-specific and cell-level GRNs. Cell type-specific and cell-level GRNs are inferred by an identical strategy, which combines consistent information across all cells, including regulatory strength, motif binding affinity and RE–TG distance, with context-specific information on gene expression and chromatin accessibility. c , Downstream analyses enabled by LINGER-inferred GRNs, including identifying complex regulatory landscape of GWAS traits and driver regulator identification.

LINGER leverages external data to enhance the inference from single-cell multiome data, incorporating three key steps: training on external bulk data, refining on single-cell data and extracting regulatory information using interpretable artificial intelligence techniques. In our approach, we use a neural network model to fit the expression of TGs, taking as input TF expression and the accessibility of REs. The second layer of the neural network model consists of weighted sums of TFs and REs, forming regulatory modules guided by TF–RE motif matching by incorporating manifold regularization. This leads to the enrichment of TF motifs binding to REs that belong to the same regulatory module. First, we pre-train using external bulk data obtained from the ENCODE project 46 , which contains hundreds of samples covering diverse cellular contexts, referred to as BulkNN.

For refinement on single-cell data, we apply elastic weight consolidation (EWC) loss, using bulk data parameters as a prior. The magnitude of parameter deviation is determined by the Fisher information, which reflects the sensitivity of the loss function to parameter changes. In the Bayesian context, knowledge gained from the bulk data is the prior distribution, forming our initial beliefs about the model parameters. As the model trains on new single-cell data, the posterior distribution is updated, combining the prior knowledge with the likelihood of the new data. EWC regularization encourages the posterior to remain close to the prior, retaining knowledge while adapting, preventing excessive changes and ensuring a more stable learning process 47 . After training the neural network model on single-cell data, we infer the regulatory strength of TF–TG and RE–TG interactions using the Shapley value, which estimates the contribution of each feature for each gene. The TF–RE binding strength is generated by the correlation of TF and RE parameters learned in the second layer (Fig. 1a ). LINGER then constructs the cell type-specific and cell-level GRNs based on the general GRN and the cell type-specific profiles (Fig. 1b and Methods ).

We will use independent datasets to validate the inference of GRN and then perform several downstream analyses: first, identification of the disease or trait-related cell type, TFs and GRN combining genome-wide association studies (GWAS) data; second, constructing regulon activity on external expression data and identifying driver regulators as differentially active TFs (Fig. 1c ).

LINGER improves the cellular population GRN inference

To assess the performance of LINGER, we used a public multiome dataset of peripheral blood mononuclear cells (PBMCs) from 10× Genomics (see Methods for details). To investigate whether a linear model is adequate for modeling gene expression or whether a non-linear model is necessary, we conducted a comparative analysis between two models. The first model employs an elastic net to predict the expression of TG by TFs and REs. The second model, single-cell neural network (scNN), is a three-layer neural network model sharing LINGER’s architecture. We assessed the gene expression prediction ability of the two models using fivefold cross-validation. We found that scNN modeled gene expression better than elastic net, with −log 10 P  = 572.09, especially for those substantial proportions of genes that show negative Pearson’s correlation coefficient (PCC) in elastic net predictions (−log 10 P  = 1,060.17; Fig. 2a ). This demonstrates that the three-layer neural network model scNN outperforms the elastic net model in predicting gene expression.

a , Correlation between predicted and real gene expression, showing higher accuracy for scNN than elastic net. The x axis represents the PCC of genes predicted by elastic net and real gene expression across cells, while the y axis gives the PCC for scNN. The points represent genes and the color of the points represents the density. The color of distribution in b – e indicates the different methods: orange, LINGER; gray, elastic net; dark green, scNN; blue, BulkNN; light blue, PCC. Null hypothesis testing results in a t -statistic with an effect size of 53.46, df = 15,659, −log 10 P  = 572.09 and 95% confidence interval of [0.058, 0.063] from a two-sided paired t -test. b , Boxplot of the performance metric AUC for the predicted trans -regulatory strength across all ground truth data. The ground truth data for b and c are putative targets of TFs from 20 ChIP–seq data points from blood cells ( n  = 20 independent samples). PCC denotes Pearson’s correlation coefficient between the chromatin accessibility of RE and the expression of TG. Note that all boxplots in this study present minima and maxima, the smallest and largest value that is not considered an outlier; center, median; bounds of box, 25th (Q1) to 75th (Q3) percentile; whiskers, 1.5 times the (Q3–Q1). In this study, we use the following convention for symbols indicating statistical significance: ns, P  > 0.05; * P  ≤ 0.05; ** P  ≤ 0.01; *** P  ≤ 0.001; **** P  ≤ 0.0001. We hide the ns symbol when displaying significance levels. In detail, P  = 8.32 × 10 −6 for LINGER and scNN, P  = 8.57 × 10 −5 for LINGER and BulkNN and P  = 1.24 × 10 −3 for LINGER and PCC. c , Boxplot of the performance metric AUPR ratio for the predicted trans -regulatory strength. P  values in b and c are from one-sided paired t -tests. In detail, P  = 3.49 × 10 −3 for LINGER and scNN, P  = 2.13 × 10 −4 for LINGER and BulkNN and P  = 4.53 × 10 −4 for LINGER and PCC. d , AUC for cis -regulatory strength inferred by LINGER. The ground truth data for d and e are the variant-gene links from eQTLGen. We divide RE–TG pairs into different groups based on the distance of the RE from the TSS of TG. e , AUPR ratio for cis -regulatory strength. f , Classification of the trans -dominant or cis -dominant gene. TFs contribute more to predicting the expression of trans -dominant genes, while REs contribute more to cis -dominant genes. g , Probability of trans -dominant and cis -dominant being loss-of-function (LoF)-intolerant genes. Points show estimated success probability from binomial distribution, at 0.26 and 0.09 for trans -dominant and cis -dominant, respectively. n  = 317 and n  = 693 independent sample size for trans -dominant and cis -dominant, respectively. Data are presented as means ± 1.96 × s.d.

To show the utility and effectiveness of integrating external bulk data, we compared LINGER to scNN, BulkNN and PCC. To evaluate the performance of trans -regulatory strength, we collected putative targets of TFs from chromatin immunoprecipitation followed by sequencing (ChIP–seq) data using a systematical standard ( Methods ) and, in total, obtained 20 data sets in blood cells as ground truth 48 (Supplementary Table 1 ). For each ground truth, we calculated the area under the receiver operating characteristic curve (AUC) and the area under the precision–recall curve (AUPR) ratio (see Methods ) by sliding the trans -regulatory predictions. Results show that scNN performs better than PCC and BulkNN. Compared to other methods, LINGER performs better, with a significantly higher AUC (Fig. 2b ) and AUPR ratio (Fig. 2c ) across all ground truth data.

To validate the cis -regulatory inference of LINGER, we calculated the consistency of the cis -regulatory coefficients with expression quantitative trait loci (eQTL) studies that link genotype variants to their TGs. We downloaded variant-gene links defined by eQTL in whole blood from GTEx 49 and eQTLGen 50 (Supplementary Table 2 ) as ground truth. As the distance between RE and TG is important for the prediction, we divided RE–TG pairs into different distance groups. LINGER achieved a higher AUC and AUPR ratio than scNN in all different distance groups in eQTLGen (Fig. 2d,e ) as well as GTEx (Extended Data Fig. 1a,b ). The above results show that LINGER improves the cis -regulatory and trans -regulatory strength inference by leveraging external data.

We next sought to investigate the dominant regulation for genes; that is, whether a gene is mainly regulated by cis -regulation or trans -regulation. To shed light on this question, we compared the average of cis -regulatory and trans -regulatory strength Shapley values by a two-sided unpaired t -test and performed Bonferroni P  value correction. Our findings reveal that most genes exhibit no significant difference in cis -regulation and trans -regulation dominance. Specifically, 4.37% of genes are cis -regulation dominant, while 2.00% are trans -regulation dominant (Fig. 2f ). To discern evolutionary distinctions between trans -dominant and cis -dominant genes, we compared their strength of selection using pLI, which is an estimate of the ‘probability of being loss of function intolerant’ 51 . We observed that the percentage of selectively constrained genes with high pLI (>0.9) in the trans -dominant group was approximately three times higher than that in the cis -dominant group (Fig. 2g ). A previous study found that disease-associated genes from GWAS were enriched in selectively constrained genes, while eQTL genes were depleted in selectively constrained genes 52 . These observations highlight the importance of the trans -regulatory network in understanding complex diseases. Functional enrichment analysis 53 shows that the cis -regulatory dominant genes were significantly enriched in 38 GTEx aging signatures (Supplementary Table 3 ), which aligns with the conclusion that chromatin accessibility alterations occur in age-related macular degeneration 54 .

To gain an understanding of parameter sensitivity, we systematically evaluated the effects of TF–RE motif matching, cis -REs transcription start site (TSS) distance, activation function, number of nodes in hidden layers and metacell-generating method on the scNN. Note that the sigmoid activation function would not improve the gene expression prediction but would improve the GRN inference (Extended Data Fig. 2a ). Using motif matching information by manifold regularization loss properly by setting the weight will improve the performance. Compared to 0, weight 0.01 improved the performance on 100% (Extended Data Fig. 2c ) and 80% (Extended Data Fig. 2d ) of ground truth data based on the AUC and AUPR ratio, respectively. The performance of weight 10 decreases compared to 0.01 (Extended Data Fig. 2c,d ). To verify the robustness of our method to alternative metacell-generation approaches (see ‘PBMC 10× data’ in Methods ), we used metacells generated by the SEACells as a substitute for our original metacells. There were no significant differences in the performance between SEACells metacells and our original metacells (two-sided paired t -test, P  = 0.89; Extended Data Fig. 2e ). Using REs within 1 Mb is the best across 200 kb, 500 kb, 1 Mb and 2 Mb (Extended Data Fig. 2f,g ).

We evaluated LINGER’s capability for lifelong learning by leveraging additional data sources. We split the ENCODE data into two batches (ENCODE1, ENCODE2) and applied two rounds of pre-training, then trained on PBMCs single-cell multiome data (ENCODE1+ENCODE2+sc). We compared the results with those obtained by using one batch of ENCODE data as pre-training (ENCODE1+sc). Extended Data Fig. 2h shows that compared to single pre-training, the addition of the second round of pre-training improved the performance of TF–TG inference for 85.5% (17 out of 20) and 75% (15 out of 20) of ChIP–seq data based on the AUC and AUPR ratio, respectively. This validates LINGER’s capability for continuous refinement through incremental learning from diverse datasets.

LINGER improves the cell type-specific GRN inference

We evaluated the cell type-specific GRN inference ( Methods ) of LINGER in PBMCs sc-multiome data as well as an in-silico mixture of H1, BJ, GM12878 and K562 cell lines from single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq) data 55 . To assess TF–RE binding prediction, we used ChIP–seq data as ground truth, including 20 TFs from four cell types within the blood and 33 TFs from the H1 cell line 48 (Supplementary Table 4 ). The putative target of TF from the ChIP–seq data serves as ground truth for the trans -regulatory potential. For the cis -regulatory potential, we incorporated promoter-capture Hi-C data of three primary blood cell types (Supplementary Table 5 ) 56 and single-cell eQTL 57 , including six immune cell types as ground truth for PBMCs.

To assess the TF–RE binding potential, we compared our method with TF–RE correlation (PCC) and motif binding affinity. For example, in naive CD4 T cells, LINGER achieves an AUC of 0.92 and an AUPR ratio of 5.17 for ETS1 , which is an improvement over PCC (AUC, 0.78; AUPR ratio, 2.71) and motif binding affinity (AUC, 0.70; AUPR ratio, 1.92) (Fig. 3a,e ). For binding sites of MYC in the H1 cell line, LINGER outperforms PCC and motif binding affinity-based predictions (Extended Data Fig. 3a,b ). For all 20 TFs in PBMCs, LINGER consistently exhibits the highest AUC and AUPR ratios, and the overall distributions are significantly higher than others in PBMCs ( P  ≤ 8.72 × 10 −5 ; Fig. 3b,c and Supplementary Table 6 ). LINGER also outperforms other methods for H1 data ( P  ≤ 6.68 × 10 −6 ; Extended Data Fig. 3c,d ). Furthermore, we compared LINGER with a state-of-the-art method, SCENIC+ 42 , which predicts TF–RE pairs from multiome single-cell data. Given that SCENIC+ does not provide a continuous score for all REs, we used the F1 score as a measure of accuracy. Fig. 3d shows that LINGER performs better for all 20 TFs binding site predictions.

a , e , Receiver operating characteristic curve and precision–recall curve of binding potential for ETS1 in naive CD4 T cells. The ground truth for a and e is the ChIP–seq data of ETS1 in naive CD4 + T cells. The color in a – e represents the different methods used to predict TF–RE regulation. Orange, LINGER; green, PCC between the expression of TF and the chromatin accessibility of RE; blue, motif binding affinity of TF to RE. b , c , Violin plot of the AUC and AUPR ratio values of binding potential across diverse TFs and cell types. The ground truth is the ChIP–seq data for 20 TFs from different cell types in blood. The original data is in Supplementary Table 6 . The null hypothesis testing in b , comparing the AUC of LINGER with PCC and binding, results in t -statistics (one-sided paired t -test) with effect size, 8.99; df, 19; P  = 1.42 × 10 −8 , 95% confidence intervals, [0.17, Inf] and effect size, 18.25; df, 19; P  = 8.34 × 10 −14 ; 95% confidence intervals, [0.17, Inf], respectively. The null hypothesis testing in c , comparing the AUPR ratio of LINGER with PCC and binding, results in t-statistics (one-sided paired t -test) with effect size, 4.65; df, 19; P  = 8.72 × 10 −5 ; 95% confidence intervals, [1.31, Inf] and effect size, 5.44, df, 19; P  = 1.49 × 10 −5 ; 95% confidence intervals, [1.51, Inf], respectively. d , The performance metrics F1 score of binding potential. Each point represents ground truth data ( n  = 20 independent samples). The P  values for d , h and k are based on one-sided paired t -tests. f , g , AUC and AUPR ratio of cis -regulatory potential in naive CD4 + cells. The ground truth for f – h is promoter-capture Hi-C data. RE–TG pairs are divided into six distance groups ranging from 0–5 kb to 100–200 kb. PCC is calculated between the expression of TG and the chromatin accessibility of RE. Distance denotes the decay function of the distance to the TSS. Random denotes the uniform distribution from 0 to 1. h , F1 score of cis -regulatory in naive CD4 + cells for LINGER and SCENIC+ ( n  = 9 independent samples). i , j , AUC and AUPR ratio of cis -regulatory potential. The ground truth is eQTL data from six immune cell types. k , F1 score of cis -regulatory potential in naive B cells. The ground truth is eQTL data from naive B cells ( n  = 9 independent samples). This figure corresponds to the PBMC data.

To assess the cis -regulatory potential, we compared LINGER with four baseline methods, including distance-based methods, RE–TG correlation (PCC), random predictions, and SCENIC+. We divided RE–TG pairs of Hi-C data into six distance groups ranging from 0–5 kb to 100–200 kb. In naive CD4 T cells, LINGER achieves AUC ranging from 0.66 to 0.70 (Fig. 3f ) and AUPR ratio ranging from 1.81 to 2.16 (Fig. 3g ) across all distance groups, while other methods are close to random. In other cell types, LINGER exhibits consistent superiority over the baseline methods (Extended Data Fig. 3e–h ). All eQTL pairs were considered positive labels owing to the insufficient pairs available for division into distance groups. In all cell types, the AUC and AUPR ratio of LINGER are higher than the baseline methods (Fig. 3i,j ). We also compared our method with SCENIC+, which outputs predicted RE–TG pairs without importance scores. We selected the same number of top-ranking RE–TG pairs and calculated the F1 score using nine cutoffs corresponding to quantiles ranging from the 10th to the 90th percentile. As a result, LINGER attains significantly higher F1 scores than SCENIC+ in all cell types (Fig. 3h and Extended Data Fig. 3i,j ) based on Hi-C data. Taking eQTL as ground truth, the F1 score of LINGER is significantly higher than SCENIC+ (Fig. 3k ) and other cell types (Extended Data Fig. 3k–o ).

To evaluate the accuracy of trans -regulatory potential, we chose GENIE3 (ref. 15 ) and PIDC 21 for comparison based on the benchmarking literature of GRN inference from single-cell data 39 that we chose in previous work 58 (see Methods ). In addition, we compared LINGER with PCC and SCENIC+. For STAT1 in classical monocytes, LINGER improves the prediction performance, as evidenced by an AUC of 0.76 versus 0.57–0.59 and an AUPR ratio of 2.60 versus 1.26–1.36 (Fig. 4a,b ). A similar improvement is observed for CTCF in H1 (Extended Data Fig. 3p,q ). The average AUPR ratio across ground truth datasets for other methods was 1.17–1.29, 0.17–0.29 units above random prediction, whereas LINGER achieves 1.25 units above random prediction, indicating a fourfold to sevenfold relative increase (Fig. 4d ). Overall, LINGER consistently performs better than other methods for all 20 TFs in PBMCs, with a significantly higher AUC and AUPR ratio ( P  ≤ 9.49 × 10 −9 ; Fig. 4c,d and Supplementary Table 7 ). LINGER outperforms other competitors in the H1 cell line ( P   ≤ 3.00 × 10 −8 ; Extended Data Fig. 3r ). Unlike GENIE3 and PIDC, which solely use scRNA-seq data, our method effectively doubles the cell data by integrating both scRNA-seq and scATAC-seq. For a fairer comparison, we removed pre-training and used only half as many cells as input (scNN_half). Comparing to other competitors showed that scNN_half continued to significantly outperform all other methods (Extended Data Fig. 2b ). We also evaluated cell type-specific trans -regulatory potential to predict direct differentially expressed genes (DEGs) under perturbation of the TF, using perturbation experiment data as ground truth. We collected eight datasets for PBMCs (Supplementary Table 8 ) from the KnockTF database 59 . Extended Data Fig. 4a,b shows that LINGER outperforms all other methods ( P  ≤ 3.72 × 10 −4 ).

a , b , Receiver operating characteristic curve and precision–recall curve of trans -regulatory potential inference of STAT1 in classical monocytes. The ground truth data in a – d are putative targets of TFs from ChIP–seq data for the corresponding cell types in PBMCs. c , d , Violin plot of AUC and AUPR ratio values of trans -regulatory potential performance across diverse TFs and cell types. The original data is in Supplementary Table 7 . The sample size for the one-sided paired t -test is 20. For c , −log 10 ( P  values) are 11.12, 7.72, 11,13 and 10.17 for GENIE3, PCC, PIDC and SCENIC+, respectively. For d , −log 10 ( P  values) are 9.59, 8.02, 9.22 and 8.47, respectively. e , Uniform manifold approximation and projection (UMAP) of PBMCs including 14 cell types. NK cells, natural killer cells; MAIT, mucosal-associated invariant T cells; DCs; dendritic cells. f , UMAP of RUNX1 expression across PBMCs. g , UMAP of cell level trans -regulatory potential for RUNX1 (TF) –SPI1 (TG) across PBMCs. h , UMAP of cell level trans -regulatory potential for RUNX1 (TF) –PRKCQ (TG) across PBMCs. i , Violin plot of cell level trans -regulatory potential from different cell types. The sample size for each boxplot is the number of cells of each cell type, ranging from 98 to 1,848. This figure corresponds to the PBMCs.

The rationale for constructing a single-cell-level GRN is the same as a cell type-specific GRN, replacing the cell type-specific term with the single-cell term ( Methods ). We show the result of trans -regulation, taking RUNX1 as an example. RUNX1 is critical for establishing definitive hematopoiesis 60 and expresses at high levels in almost all PBMC cell types (Fig. 4e,f ). RUNX1 regulates SPI1 in monocytes (classical, non-classical and intermediate) and myeloid dendritic cells (Fig. 4g,i ), while regulates PRKCQ in CD56 dim natural killer cells, effector CD8 T cells, mucosal-associated invariant T cells, memory CD4 T cells, naive CD4 T cells and naive CD8 T cells (Fig. 4h,i ). This example illustrates the capability of LINGER to visualize gene regulation at the single-cell level.

LINGER reveals the regulatory landscape of GWAS traits

GWASs have identified thousands of disease variants, but the active cells and functions involving variant-regulated genes remain largely unknown 61 . We integrate GWAS summary statistics and cell type-specific GRN to identify the relevant cell types, key TFs and sub-GRN ( Methods ). We define a trait regulation score for TFs in each cell type, measuring the enrichment of GWAS genes downstream of TFs. In trait-relevant cell types, TFs with high trait regulation scores should be expressed to perform their function. We identify the trait-relevant cell types by assessing the concordance between TF expression and the trait regulation score.

In our specific study on inflammatory bowel disease (IBD), we collected the risk loci based on a GWAS meta-analysis of about 330,000 individuals from the NHGRI-EBI GWAS catalog 62 for study GCST90225550 63 . Figure 5a shows that in classical monocytes, trait regulation scores for the top-expressed TF are significantly higher than randomly selected TFs ( P  = 8.9 × 10 −29 , one-sided unpaired t -test), while there is no significant difference for non-relevant cell types such as CD56 dim natural killer cells. The most relevant cell types in PBMCs are monocytes and myeloid dendritic cells (Fig. 5b ). These findings align with previous studies linking monocytes to the pathogenesis of IBD 64 , 65 .

a , Distribution of the number of TGs for top expression TFs and randomly selected TFs in classical monocytes (top) and CD56 dim NK cells (bottom). The 100 top-expression TFs and 100 randomly selected TFs are used to generate the distribution. b , Enrichment of IBD GWAS to cell types in PBMCs. The color of the bubbles corresponds to the odds ratio of the number of TGs between top expression and randomly selected TFs. The x axis is the −log 10 ( P  value) from the one-sided unpaired t -test for the number of TGs between top expression and randomly selected TFs. c , Key IBD-associated regulators in classical monocytes. The x axis is the z -score of the expression of TFs across all TFs. The y axis is the regulation score of TFs. The TFs in red are the top-ranked TFs according to the summation of the expression level and regulation score. d , Enrichment of GWAS IBD genes among STAT1 targets in classical monocytes. The violin plot is generated by randomly choosing 1,000 TFs; the number of overlapping genes for STAT1 is marked by a star. The different violin plots correspond to taking the top 200–5,000 genes as the TG for each TF, respectively. e , Enrichment of DEGs between inflamed biopsies and non-inflamed biopsies among STAT1 targets in classical monocytes. The details are the same as in d . f , Sub-network of IBD-relevant TFs from classical monocytes trans -regulatory network. The size of the TF or TG nodes corresponds to their degree in the network. The color of TF denotes the trait-relevant score, and the color of TG denotes the −log 10 ( P  value) of GWAS SNP assigned to the gene. g , Cis -regulatory network at locus around SLC24A4 . The interaction denotes significant RE–TG links, and we use the location of the promoter to represent the gene.

We next identified key TFs by the sum of the expression level and trait regulation score. Figure 5c lists the top eight candidate TFs in classical monocytes. These TFs have been previously reported to be associated with IBD in the literature. FOS can increase the risk of recurrence of IBD 66 ; one variant identified in the IBD cohort is located at the exon of ETV6 ; IRF1 and ETV6 are key TFs with activity differences in IBD 67 ; genes FOS , FOSB and JUN encode potent mediators of IBD 68 ; CUX1 is induced in IBD 69 ; and STAT1 epigenetically contribute to the pathogenesis of IBD 70 .

To investigate the downstream targets of key TFs, we chose STAT1 as an example. Among the top 200 TGs regulated by STAT1 in classical monocytes, 67 of them overlap with the GWAS genes, which is statistically significant with a P value of less than 0.01 based on a background distribution from a random selection of TFs (one-sided bootstrap hypothesis testing). The numbers of overlapped TGs are all significant for the top 500, 1,000, 2,000 and 5,000 TGs (Fig. 5d ). Apart from GWAS-relevant genes, we collected the DEGs between inflamed biopsies and non-inflamed biopsies 71 and we found that these DEGs significantly overlapped with the top-ranked TGs of STAT1 (one-sided bootstrap hypothesis testing; Fig. 5e ). The lack of significant overlap between DEGs and GWAS genes ( P  = 0.15, two-sided Fisher’s exact test) but the significant overlap of both DEGs and GWAS with the top-ranked TGs of STAT1 indicates the robustness and unbiased nature of our method.

Finally, we extracted the sub-network of the eight candidate TFs from the classical monocyte trans -regulatory network for IBD (Fig. 5f ). We also observed that the cis -regulatory network of SLC24A4 (Fig. 5g ), 46 kb from a risk single nucleotide polymorphism (SNP) rs11626366 ( P  = 7.4 × 10 −3 ), is specifically dense in the IBD-relevant cell types, which shows the complex regulatory landscape of disease genes across different cell types.

Identify driver regulators based on transcription profiles

Researchers often identify DEGs between cases and controls using bulk or single-cell expression data, but the underlying regulatory drivers remain elusive. TF activity, focusing on the DNA-binding component of TF proteins, is a more reliable metric than mRNA for identifying driver regulators. One feasible approach is to estimate TF activity based on the expression patterns of downstream TGs, which necessitates the availability of an accurate GRN. Assuming that the GRN structure is consistent for the same cell type across individuals, we employed LINGER-inferred GRNs from single-cell multiome data of a single individual to estimate the TF activity of other individuals using gene expression data alone from the same cell type. By comparing TF activity between cases and controls, we identified driver regulators. This approach is valuable, as it leverages limited single-cell multiome data to estimate TF activity in multiple individuals using only gene expression data (see Methods ). We present two illustrative examples showcasing its utility.

Example 1: We collected the bulk gene expression data from 26 patients with acute myeloid leukemia (AML) and 38 healthy donors 72 . We calculated the TF activity for these samples based on the LINGER-inferred cell population GRN from PBMCs and found that FOXN1 is significantly less active in patients with AML than in healthy donors, and it is not differentially expressed (Fig. 6a,b ). In addition, we calculated the TF activity of the transcriptome profile (bulk RNA-seq data) of 671 individuals with AML 73 and performed survival analysis, which indicated that individuals with high FOXN1 activity level tend to have a higher survival probability (Fig. 6c ). Furthermore, FOXN1 has been reported as a tumor suppressor 74 .

a , Violin plot of FOXN1 expression across healthy donors ( n  = 38 independent samples) and patients with AML ( n  = 26 independent samples), respectively. There is no significant difference in the mean expression (two-sided unpaired t -test). b , Violin plot of regulon activity of FOXN1 across healthy donors ( n  = 38 independent samples) and patients with AML ( n  = 26 independent samples), respectively (two-sided unpaired t -test, P  = 0.035). c , AML survival by the regulon activity of FOXN1 ( P  value is from a two-sided log-rank test). d , The heatmap of regulon activity and gene expression in response to TCR stimulation at 0 h and 8 h. Two-sided unpaired t -test for the difference in regulon activity, P  = 0.0057 and P  = 0.00081 for FOXK1 and NR4A1 , respectively; the P  value for gene expression is >0.05. Heatmap is scaled by row. e , Heatmap of whole protein (wProtein) and phosphoproteomics (pProtein) expression in response to TCR stimulation at 0 h, 2 h, 8 h and 16 h. There are two biological replicates, represented by a and b . The wProtein and pProtein expression of FOXK1 and NR4A1 is higher at 8 h than at 0 h. The heatmap is scaled by row.

Example 2: We also present an example of the naive CD4 + T cell response upon T cell receptor (TCR) stimulation 75 , which induces T cell differentiation into various effector cells and activates T lymphocytes. We calculated the TF activity based on the GRN of naive CD4 + T cells and identified differentially active regulators in response to TCR stimulation at 8 h versus 0 h. FOXK2 and NR4A1 are activated at 8 h based on regulon activity (Fig. 6d ), which is consistent with the whole proteomics and phosphoproteomics data (Fig. 6e ) 76 . Other studies have also shown that FOXK2 affects the activation of T lymphocytes 77 , 78 and revealed the essential roles of NR4A1 in regulatory T cell differentiation 79 , 80 , suggesting that the identified TFs have important roles in naive CD4 + T cell response upon TCR stimulation. However, FOXK2 and NR4A1 show no significant differences in expression at 8 h versus 0 h (Fig. 6d ).

In silico perturbation

We performed in silico perturbation to predict the gene expression after knocking out TFs. To do so, we changed the expression of an individual TF or combinations of TFs to zero and used the predicted gene expression as the in silico perturbation gene expression. We used the expression difference before and after in silico perturbation to infer the TG. To assess the performance of the prediction, we collected perturbation data for eight TFs in blood cells from the KnockTF 59 database (Supplementary Table 8 ) as ground truth. We performed the in silico individual TF perturbation of the eight TFs using LINGER. As a comparison, we performed identical computational perturbation experiments using the CellOracle 81 and SCENIC+ 42 methods. The results, shown in Extended Data Fig. 4c,d , demonstrate that LINGER is more accurate than the alternative approaches ( P   ≤ 3.72 × 10 −4 ).

To assess LINGER’s capability to infer differentiation behavior, we leveraged CellOracle 81 as a downstream analytical tool. We used the LINGER-inferred GRN as an input to CellOracle. This allowed us to investigate the capacity of LINGER-derived networks to recapitulate differentiation responses. Examining bone marrow mononuclear cell data 82 , which contains progenitor populations, we performed an in silico knockout of GATA1 , a known key regulator of erythroid and megakaryocytic differentiation 83 . CellOracle predictions based on the LINGER GRN showed that GATA1 knockout shifted proerythroblasts to a megakaryocytic or erythroid progenitor state (Extended Data Fig. 4e ), consistent with the functional role of GATA1 in inhibiting erythroblast maturation. These results demonstrate that LINGER can not only predict gene expression under perturbation but also enable downstream characterizations of differentiation trajectories through integration with complementary analytical frameworks like CellOracle.

Conclusions and discussions

LINGER is an neural network-based method that infers GRNs from paired single-cell multiomic data by incorporating bulk datasets and knowledge of TF–RE motif matching. Compared to existing tools, LINGER achieves substantially higher GRN inference accuracy. A key innovation is lifelong machine learning to leverage diverse cellular contexts, continually updating the model as new data emerge. This addresses historic challenges from limited single-cell datasets and vast parameter spaces hindering complex model fitting. LINGER’s lifelong learning approach has the advantage of pre-training on bulk collections, allowing users to easily retrain the model for their own studies while capitalizing on publicly available resources without direct access. Traditionally, GRN inference performance is assessed by gene expression prediction. However, the use of lifelong learning to leverage external data does not lead to improved gene expression prediction but does improve the GRN inference. This finding challenges the traditional strategy of evaluating GRN inference solely based on gene expression prediction and highlights the importance of considering the overall network structure and regulatory interactions.

The lifelong learning mechanism will encourage the model to retain prior knowledge from the bulk data when adapting to the new single-cell data. It is a tradeoff between retaining prior knowledge and fitting new data. The flexibility of the variation in prior knowledge is not constrained when fitting the new data. The extent to which the final result deviates from the prior knowledge depends on the loss incurred in fitting the new data. LINGER will learn this tradeoff automatically to obtain a maximized usage of the information from both datasets.

GRN inference by lifelong learning

LINGER is a computational framework to infer GRNs—pairwise regulation among TGs, REs and TFs—from single-cell multiome data. Overall, LINGER predicts gene expression by the TF expression and chromatin accessibility of REs based on neural network models. The contribution of each feature is estimated by the Shapley value of the neural network models, enabling the inference of the GRNs. To capture key information from the majority of tissue lineages, LINGER uses lifelong machine learning (continuous learning). Moreover, LINGER integrates motif binding data by incorporating a manifold regularization into the loss function.

The inputs for full training of LINGER are external bulk and single-cell paired gene expression and chromatin accessibility data. However, we provided a bulk data pre-trained LINGER model so that users can retrain it for their own single-cell data without accessing external bulk data. We collected paired bulk data—gene expression profiles and chromatin accessibility matrices—from 201 samples from diverse cellular contexts 84 from the ENCODE project 46 . Single-cell data are raw count matrices of multiome single-cell data (gene counts for RNA-seq and RE counts for ATAC-seq). LINGER trains individual models for each gene using a neural network architecture that includes an input layer and two fully connected hidden layers. The input layer has dimensions equal to the number of features, containing all TFs and REs within 1 Mb of the TSS for the gene to be predicted. The first hidden layer has 64 neurons with rectified linear unit activation that can capture regulatory modules, each of which contains multiple TFs and REs. These regulatory modules are characterized by enriched motifs of the TFs on the corresponding REs. The second hidden layer has 16 neurons with rectified linear unit activation. The output layer is a single neuron, which outputs a real value for gene expression prediction.

We first construct neural network models based on bulk data, using the same architecture described above. We extract the TF expression matrix \({\widetilde{E}}_{{\rm{TF}}}\in {{\mathbb{R}}}^{{N}_{{\rm{TF}}}\times {N}_{b}}\) from the bulk gene expression matrix \(\widetilde{E}\in {{\mathbb{R}}}^{{N}_{{\rm{TG}}}\times {N}_{b}}\) , with \({N}_{{\rm{TG}}}\) representing the number of genes, \({N}_{{\rm{TF}}}\) representing the number of TFs and \({N}_{b}\) representing the number of tissues. The loss function consists of mean squared error (MSE) and L1 regularization, which, for the i th gene is:

where \(\widetilde{O}\in {{\mathbb{R}}}^{{N}_{{\rm{RE}}}^{(i)}\times {N}_{b}}\) represents the chromatin accessibility matrix, with \({N}_{{\rm{RE}}}^{(i)}\) REs within 1 Mb of the TSS of the i th gene, and \(f\left({\left({\widetilde{E}}_{{\rm{TF}}}\right)}_{\bullet ,n},{{\widetilde{O}}^{(i)}}_{\bullet ,n},{\theta }_{b}^{(i)}\right)\) is the predicted gene expression from the neural network of sample n . The neural network is parametrized by a set of weights and biases, collectively denoted by \({\theta }_{b}^{(i)}\) . The weight λ 0 is a tuning parameter.

The loss function of LINGER is composed of MSE, L1 regularization, manifold regularization and EWC loss: \({{\mathcal{L}}}_{{\rm{LINGER}}}={{{\lambda }}_{1}{\mathcal{L}}}_{{\rm{MSE}}}\) \(+{{\lambda }}_{2}{{\mathcal{L}}}_{L1}+{{\lambda }}_{3}{{\mathcal{L}}}_{{\rm{Laplace}}}+{{{\lambda }}_{4}{\mathcal{L}}}_{{\rm{EWC}}}\) . \({{\mathcal{L}}}_{{\rm{Laplace}}}\) represents the manifold regularization because a Laplacian matrix is used to generate this regularization term. The loss function terms correspond to gene i , and for simplicity, we omit subscripts \((i)\) for the chromatin accessibility matrix ( \(O\) ), parameters for the bulk model ( \({\theta }_{b}\) ) and parameters for LINGER ( \({\theta }_{l}\) ).

Here, \({E}_{{\rm{TF}}}\in {{\mathbb{R}}}^{{N}_{{\rm{TF}}}\times {N}_{{\rm{sc}}}}\) represents the TF expression matrix from the single-cell RNA-seq data, consisting of \({N}_{{\rm{sc}}}\) cells; \(O\in {{\mathbb{R}}}^{{N}_{{\rm{RE}}}^{(i)}\times {N}_{{\rm{sc}}}}\) represents the RE chromatin accessibility matrix of the single-cell ATAC-seq data; \(E\in {{\mathbb{R}}}^{{{N}_{{\rm{TG}}}\times N}_{{\rm{sc}}}}\) represents the expression of the genes across cells; and \({\theta }_{l}\) represents the parameters in the neural network. We use metacells to train the models; therefore, \({N}_{{\rm{sc}}}\) is the number of cells from metacell data.

L1 regularization

Laplacian loss (manifold regularization)

We generate the adjacency matrix as: \({B}^{* }\in {{\mathbb{R}}}^{\left({N}_{{\rm{TF}}}+{N}_{{\rm{RE}}}^{(i)}\right)}\) \({\times \left({N}_{{\rm{TF}}}+{N}_{{\rm{RE}}}^{(i)}\right)}\) , where \({B}_{k,{N}_{{\rm{TF}}}+j}^{* }\) and \({B}_{{N}_{{\rm{TF}}}+j,k}^{* }\) represent the binding affinity of the TF \(k\) and the RE \(j\) , which is elaborated in the following sections. \({L}^{{\rm{Norm}}}\in {{\mathbb{R}}}^{\left({N}_{{\rm{TF}}}+{N}_{{\rm{RE}}}^{(i)}\right)\times \left({N}_{{\rm{TF}}}+{N}_{{\rm{RE}}}^{(i)}\right)}\) is the normalized Laplacian matrix based on the adjacency matrix.

where \({\theta }_{l}^{\left(1\right)}\in {{\mathbb{R}}}^{\left({N}_{{\rm{TF}}}+{N}_{{\rm{RE}}}^{(i)}\right)\times 64}\) is the parameter matrix of the first hidden layer, which can capture the densely connected TF–RE modules.

EWC loss. EWC constrains the parameters of the first layer to stay in a region of \({\theta }_{b}^{\left(1\right)}\) , which is previously learned from the bulk data 45 . To do so, EWC uses MSE between the parameters \({\theta }_{l}^{\left(1\right)}\) and \({\theta }_{b}^{\left(1\right)}\) , weighted by the Fisher information, a metric of how important the parameter is, allowing the model to protect the performance, both for single-cell data and bulk data 45 .

where \(F\) is the fisher information matrix, which is detailed below, and \({\theta }_{l}^{\left(1\right)}\in {{\mathbb{R}}}^{\left({N}_{{\rm{TF}}}+K\right)\times 64}\) is the parameter matrix of the first hidden layer.

To construct a normalized Laplacian matrix, we first generate the TF–RE binding affinity matrix for all REs from the single-cell ATAC-seq data. We extract the REs 1 Mb from the TSS for the gene to be predicted. Let \({N}_{{\rm{RE}}}^{(i)}\) be the number of these REs and \(B\in {{\mathbb{R}}}^{{N}_{{\rm{TF}}}\times {N}_{{\rm{RE}}}^{(i)}}\) be the TF–RE binding affinity matrix, where \({B}_{{kj}}\) represents the binding affinity for the TF \(k\) and RE \(j\) . We construct a graph, taking TFs as the first \({N}_{{\rm{TF}}}\) nodes, REs as the remaining \({N}_{{\rm{RE}}}^{\;(i)}\) nodes and binding affinity as the edge weight between TF and RE. The edge weights of TF–TF and RE–RE are set to zero. Then the adjacency matrix \({B}^{* }\in {{\mathbb{R}}}^{\left({N}_{{\rm{TF}}}+{N}_{{\rm{RE}}}^{(i)}\right)\times \left({N}_{{\rm{TF}}}+{N}_{{\rm{RE}}}^{(i)}\right)}\) is defined as:

The Fisher information matrix is calculated based on the neural network trained on bulk data:

GRN inference by Shapley value

The Shapley value measures the contribution of features in a machine-learning model and is widely used in algorithms such as deep learning, graphical models and reinforcement learning 85 . We use the average of absolute Shapley values across samples to infer the regulation strength of TF and RE to TGs, generating the RE–TG cis -regulatory strength and the TF–TG trans -regulatory strength. Let \({\beta }_{{ij}}\) represent the cis -regulatory strength of RE \(j\) and TG i , and \({\gamma }_{{ki}}\) represent the trans -regulatory strength. To generate the TF–RE binding strength, we use the weights from the input layer (TFs and REs) to all nodes in the second layer of the neural network model to embed the TF or RE. The TF–RE binding strength is calculated by the PCC between the TF and RE based on this embedding. \({\alpha }_{{kj}}\) represents the TF–RE binding strength.

Constructing cell type-specific GRNs

The TF–RE regulatory potential for a certain cell type is given by:

where \({\rm{TFB}}_{{kj}}\) is the TF–RE regulation potential of TF \(k\) and RE \(j\) ; \({s}_{k}\) is an importance score of TF \(k\) in the cell type to measure the preference of TF for activating cell type-specific open chromatin regions (which will be described in ‘TF importance score’ below); \({C}_{{kj}}\) is the PCC of TF \(k\) and RE \(j\) ; \({O}_{j}\) is the average chromatin accessibility across cells in the cell type; \({B}_{{kj}}\) is the binding affinity between TF \(k\) and RE \(j\) ; and \({\alpha }_{{kj}}\) is the TF–RE binding strength.

The RE–TG cis -regulatory potential is defined as:

where \({\rm{CRP}}_{{ij}}\) is the cis -regulatory potential of TG i and RE \(j\) ; \({\beta }_{{ij}}\) is the cis -regulatory strength of RE \(j\) and TG i ; \({O}_{j}\) is the average chromatin accessibility; \({E}_{i}\) is the average gene expression across cells in the cell type; \({d}_{{ij}}\) is the distance between genomic locations of TG i and RE \(j\) ; and \({d}_{0}\) is a fixed value used to scale the distance, which is set to 25,000 in this paper.

The TF–TG trans -regulatory potential is defined as the cumulative effect of corresponding REs on the TG:

where \({\gamma }_{{ki}}\) is the TF–TG trans -regulatory strength of TF \(k\) and TG i ; \({S}_{i}\) is the set of REs within 1 Mb from the TSS for TG i ; \({\rm{CRP}}_{{ij}}\) is the cis -regulatory potential of TG i and RE \(j\) ; and \({\rm{TFB}}_{{kj}}\) is the TF–RE regulation potential of TF \(k\) and RE \(j\) .

Constructing cell-level GRNs

Cell-level GRNs are inferred by integrating information consistent across all cells, such as regulatory strength, binding affinity and RE–TG distance, with cell-level information, such as gene expression and chromatin accessibility. This approach is similar to inferring cell type-specific GRNs, with the key difference that cell-level GRNs use cell-level TF expression \({E}_{{\rm{TF}}}\) , chromatin accessibility \(O\) and gene expression \(E\) rather than cell type-averaged data. This allows us to infer the network for each individual cell based on its specific characteristics rather than grouping cells into predefined types.

TF importance score

To systematically identify TFs playing a pivotal role in controlling the chromatin accessibility of cell type, we introduce a TF importance score. The score is designed to measure the preference of TFs for activating cell type-specific REs. The input is multiome single-cell data with known cell type annotations. There are four steps to generate the TF importance score:

Motif enrichment. We perform the motif enrichment analysis 86 to identify the motifs significantly enriched in the binding sites of the top 5,000 cell type-specific REs. We use the P  value to measure the significant level of motif enrichment.

TF–RE correlation. To avoid dropouts in single-cell data, we recover the original count matrix by an average of the observed count of nearby cells. We calculate PCC between the TF expression and cell type-specific RE chromatin accessibility, with \({r}_{{kj}}\) representing the PCC of the TF \(k\) and the RE \(j\) . To mitigate the bias in the distribution of TF expression and REs chromatin accessibility so that the PCC is comparable across different TF–RE pairs, we permute the cell barcode in the gene expression data and then calculate, generating a background PCC distribution for each TF–RE pair. We generate a z -score for \({r}_{{kj}}\) ,

where \({\mu }_{{kj}}\) and \({\sigma }_{{kj}}^{2}\) are the mean and the variance of the background PCC distribution between \({\rm{TF}}_{k}\) and \({\rm{RE}}_{j}\) .

The co-activity score of the TF-motif pair. To pair TFs with their motifs, we match 713 TFs and 1,331 motifs, yielding 8,793 TF-motif pairs 84 . Let \(\left(k,m\right)\) denote the TF-motif pair of TF \(k\) and motif \(m\) . We then calculate a co-activity score for a TF-motif pair for \(\left(k,m\right)\) , defined as the average z -score across cell type-specific REs with at least one motif binding site. That is \({z}_{k,m}^{\;{co}}=\frac{1}{{N}_{m}}\sum _{j\in {\left\{{\rm{RE}}\;\right\}}_{m}}{z}_{{kj}}\) , where \({\left\{{\rm{RE}}\right\}}_{m}\) is the set of REs with the \(m\) -th motif binding; and \({N}_{m}=\left|{\left\{{\rm{RE}}\right\}}_{m}\right|\) is the number of REs in \({\left\{{\rm{RE}}\right\}}_{m}\) .

TF importance score. The score of the TF-motif pair, \(\left(k,m\right)\) , is given by:

where \({p}_{m}\) is the P  value of the \(m\) th motif from the motif-enrichment analysis and \({s}_{(k,m)}\) is the importance score of the TF-motif pair \((k,m)\) . The TF importance score for the TF \(k\) is the average TF-motif pair TF importance score across motifs, omitting NA:

where \({N}_{(k,m)}=\left|\{{{m|s}}_{(k,m)}\ne {\rm{NA}}\}\right|\) is the number of the TF-motif pair of the TF \(k\) , whose CECI score is not NA.

TF–RE binding affinity matrix

We download 713 TF position weight matrices for the known motifs from GitHub page of PECA2 84 , which is collected from widely used databases including JASPAR, TRANSFAC, UniPROBE and Taipale. Given a list of REs, we calculate the binding affinity score for each TF by motif scan using Homer 86 , as a quantitative measure of the strength of the interaction between TF and RE 20 .

Identify motif-binding REs

We identify the REs with motif binding by motif scan using Homer 86 .

ChIP–seq-based validation

Given that the choice of TFs for benchmarking may affect the final results, we use the following standard to collect all ChIP–seq data from the Cistrome database that satisfies the following criteria.

The procedure for choosing ChIP–seq data for PBMC is as follows.

We downloaded all human TF ChIP–seq information, including 11,349 datasets.

We filtered samples that did not pass quality control, and 4,657 datasets remained.

We chose samples in blood tissue, and 609 datasets remained.

We filtered the cell line data that is not consistent with PBMC cell types, and 63 datasets remained.

We chose the TF expressed in single-cell data and with known motifs available, and 39 datasets remained.

We chose the experiments that were done in one of the 14 cell types detected in the PBMC data, and 20 datasets remained.

The procedure for choosing ChIP–seq data for the H1 cell line is as follows:

We chose the H1 cell line, and 42 datasets remained.

We chose the TF expressed in single-cell data and with known motifs available, and 33 datasets remained.

Perturbation-based validation

The criteria for choosing ground truth from the KnockTF database is similar to ChIP–seq data.

The procedure for choosing knockdown data for PBMC is as follows.

We selected the molecular type as ‘TF’ and chose the ‘Peripheral_blood’ tissue type, with 21 cases remaining.

There are 11 datasets included in the PBMCs cell type in the single-cell data.

We chose the TF expressed in single-cell data and with known motifs available, and 8 datasets remained.

PBMC 10× data

We download the PBMC 10K data from the 10× Genomics website ( https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets ). Note that it contains 11,909 cells, and the granulocytes were removed by cell sorting of this dataset. We use the filtered cells by features matrix from the output of 10× Genomics software Cell Ranger ARC as input and perform the downstream analysis. First, we perform weighted nearest neighbor analysis in Seurat (version 4.0) 87 , and it removes 1,497 cells. We also remove the cells that do not have surrogate ground truth and it results in 9,543 cells. We generate metacells data by randomly selecting the square root of the number of cells in each cell type and averaging the expression levels and chromatin accessibility of the 100 nearest cells to produce the gene expression and chromatin accessibility values of the selected cells. The metacells data were directly input into LINGER for analysis.

To measure the accuracy of a predictor, we defined the AUPR ratio as the ratio of the AUPR of a method to that of a random predictor. For a random predictor, the AUPR equals the fraction of positive samples in the dataset. The AUPR ratio is defined as \({\rm{AUPR}\frac{\#\,{sample}}{\#\,{real}\,{positive}}}\) , representing the fold change of the accuracy of a predictor compared to the random prediction.

We propose a method to integrate GWAS summary statistics data and cell type-specific GRNs to identify the relevant cell types, key TFs and sub-GRNs responsible for GWAS variants. To identify relevant cell types, we first project the risk SNP identified from GWAS summary data to a gene. We then link the gene within the 200 kb region centering on the SNP and assign the most significant P  value of linked SNPs to each gene. In this study, the trait-related genes are defined as those with P  < 0.01 after multiple testing adjustments. We then calculate a trait regulation score for each TF in each cell type, measuring the enrichment of GWAS genes downstream of the TF based on the cell type-specific GRN. We choose 1,000 top-ranked genes according to the trans -regulation as the TG of each TF and count the number of overlapping genes with trait-related genes. The enrichment of cell types to the GWAS traits is measured by a t -test comparing the number of overlapping genes between the 100 top-expressed and 100 randomly chosen TFs.

To identify key TFs of GWAS traits, we combine the trait regulation score and the gene expression level of TFs in each cell type. The trait regulation score is the z -score of the number of overlapping genes of a TF across all TFs. The expression level is also transformed to a z -score based on the gene expression. The final importance of key TFs is the summation of the expression level and trait regulation score.

To measure the activity of each TF on the independent transcriptional profiles, we first constructed a TG set for each TF based on the corresponding GRN. We perform quantile normalization to the trans -regulation score of each gene across all TFs. We then rank the genes for each TF and choose the top 1,000 genes as the target. Next, we use the R package AUCell 22 to calculate whether the TGs are enriched within the expressed genes for each sample, which defines the TF activity.

Benchmark the trans -regulatory potential

We compare LINGER’s performance of the trans -regulation prediction using PCC, SCENIC+, GENIE3 and PIDC as competitors to LINGER. Owing to the time-consuming nature of PIDC’s mutual information-based algorithm, we used the 5,000 most variable genes as input. As a result, there are 9 TFs and 14 TFs in ground truth data left for PBMCs and the H1 cell line, respectively.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The PBMC data used during this study was downloaded from the 10× Genomics website ( https://s3-us-west-2.amazonaws.com/10x.files/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_fastqs.tar ) 40 . SNARE-seq was downloaded from NCBI Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ) under accession number GSE126074 (ref. 55 ).

Code availability

The software is available at GitHub 88 ( https://github.com/Durenlab/LINGER ) and the Zenodo repository under the GPLv3 license 89 . We used Python and R for this study.

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Acknowledgements

The authors are supported by National Institutes of Health grants P20 GM139769 and R35 GM150513. The language in the text has been polished by GPT-3.5 and Grammarly.

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Qiuyue Yuan & Zhana Duren

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Contributions

Z.D. conceived the LINGER method. Z.D. and Q.Y. designed the analytical approach. Q.Y. performed the data analysis. Q.Y. wrote the software. Q.Y. and Z.D. wrote, revised and contributed to the final manuscript. The authors read and approved the final manuscript.

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Correspondence to Zhana Duren .

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Extended data

Extended data fig. 1 assessing the performance of cis -regulatory strength inferred by linger taking eqtl data for gtex as ground truth..

A . AUC for cis -regulatory strength inferred by LINGER. The ground truth for A and B is the variant-gene links from GTEx. We divide RE-TG pairs into different groups based on the distance of RE and the TSS of TG. B . AUPR ratio for cis -regulatory strength.

Extended Data Fig. 2 Parameter sensitivity.

A . Sensitivity of neural network structure and active function. B . Violin plot of AUC and AUPR ratio values of trans -regulatory potential performance across diverse TFs and cell types (n=20 independent sample). One-sided paired t-test result in -log 10 P-value 10.73, 7.11, 10.85, and 9.61 compared with GENIE3, PCC, PIDC, and SCENIC+ in terms of AUC, respectively. For AUPR ratio, -log 10 P-values are 8.94, 7.03, 8.48, and 7.57, respectively. C , D . Bar plot of AUC and AUPR ratio difference of different motif matching weight. The upper and lower figures refer to the difference in weight 0.01 to 0 and 0.01 to 10. The x-axis of C , D , and H refers to the ground truth data named by the TF name and Cistrome database ID. E . Scatter plot of AUC of original metacells and SEACells metacells as input. Each point refers to each ChIP-seq ground truth data. F, G . Box plot of AUPR ratio and AUC of defining regulatory element within different TSS distances from 200 Kb to 2 Mb (n = 20 independent sample). Two-sided paired t-test result in p-value 0.055(2 Mb and 1 Mb), 0.088(2 Mb and 500 Kb), 0.028(2 Mb and 200 Kb), 0.025(1 Mb and 500 Kb), 0.0056(1 Mb and 200 Kb), and 0.70(500 Kb and 200 Kb) in terms of AUC. For AUPR ratio, p-values are 0.0017(2 Mb and 1 Mb), 0.093(2 Mb and 500 Kb), 0.12(2 Mb and 200 Kb), 0.00048(1 Mb and 500 Kb), 0.00075(1 Mb and 200 Kb), and 0.64(500 Kb and 200 Kb). H . Bar plot of AUC and AUPR ratio difference of two rounds pre-train and single round pre-train.

Extended Data Fig. 3 Systematic benchmarking of cell type-specific GRN.

A, B . ROC curve and PR curve of binding potential for MYC in H1 cell line. The ground truth for A to D is the ChIP-seq data of MYC in the H1 cell line. The color in A to D represents the different competitors to predict TF-RE regulation. Orange represents LINGER, green represents PCC between the expression of TF and the chromatin accessibility of RE, and blue represents motif binding affinity of TF to RE. C, D . Violin plot of AUC and AUPR ratio values of binding potential across diverse TFs. The ground truth is ChIP-seq data for 33 TFs (n=33 independent sample). One-sided paired t-test is performed to test whether there is significant difference. In C , -log 10 P-values are 11.36 and 12.27 compared with PCC and TFBS, respectively. In D , -log P-values are 6.21 and 5.18, respectively. E , F . AUC and AUPR ratio of cis -regulatory potential in naïve CD8 T cells. The ground truth for E to J is promoter capture HiC data. RE-TG pairs are divided into six distance groups ranging from 0-5k to 100-200 kb. PCC is calculated between the expression of TG and the chromatin accessibility of RE. Distance denotes the decay function of the distance to the TSS. Random denotes the uniform distribution. G , H . AUC and AUPR ratio of cis -regulatory potential in naïve B cells. I , J . F1 score of cis -regulatory in naïve CD8 T cells and naïve B cells for LINGER and SCENIC+. P-values are from one-sided paired t-test with n=9 independent sample. K to O , F1 score of cis -regulatory potential in classical monocytes, effector CD8 T cells, memory B cells, non-classical monocytes, and plasmacytoid DC cells for LINGER and SCENIC+. The ground truth is eQTL data (n=9 independent sample). P-values are from one-sided paired t-test. P , Q . ROC curve and PR curve of trans -regulatory potential inference of CTCF in H1 cell line. The ground truth of P to R is putative targets of TFs from ChIP-seq data in the H1 cell line. R Violin plot of AUC and AUPR ratio values of trans -regulatory potential performance across diverse TFs in H1 cell line (n=33 independent sample). One-sided unpaired t-test result in -log 10 P-value 15.89, 15.64, 16.36, and 15.54 compared with GENIE3, PCC, PIDC, and SCENIC+ in terms of AUC, respectively. For AUPR ratio, -log 10 P-values are 11.01, 10.64, 11.20, and 11.17, respectively.

Extended Data Fig. 4 In silico perturbation.

A , B . Violin plot of AUC and AUPR ratio values of trans -regulatory potential performance across diverse TFs and cell types for PBMCs. The ground truth of A to D is 8 experimental perturbation data from KnockTF database (n=8 independent sample). One-sided paired t-test are performed to test the difference. For AUC, -log 10 P-values are 3.74, 3.43, 3.64, and 3.86 compared with GENIE3, PCC, PIDC, and SCENIC+, respectively. For AUPR ratio, -log 10 P-values are 3.36, 2.14, 1.69 and 1.80, respectively. C , D . Box plot of AUC and AUPR ratio values of in silico perturbation predicted target gene. P-values are from one-sided paired t-test with 8 independent samples. E . Differentiation behavior prediction on BMMC data after knocking out GATA1.

Supplementary information

Reporting summary, supplementary tables 1–8.

Table 1: Information of ground truth data for the trans -regulation and TF–RE binding potential for PBMC data. Table 2: Details of eQTL data as ground truth for the cis -regulation for PBMCs. Table 3: Functional enrichment of cis -regulatory dominant gene. Table 4: Ground truth data for the trans- regulation and TF-RE binding potential for H1 cell line. Table 5: Details of Hi-C data as ground truth for the cis -regulation for PBMCs. Table 6: Details of Fig. 3b,c Table 7: Details of Fig. 4c,d. Table 8: Ground truth data information of the trans -regulation for PBMC data from the KnockTF database.

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Yuan, Q., Duren, Z. Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02182-7

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Received : 04 August 2023

Accepted : 26 February 2024

Published : 12 April 2024

DOI : https://doi.org/10.1038/s41587-024-02182-7

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