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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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What is Data Interpretation? Methods, Examples & Tools

What is Data Interpretation?

• Importance of Data Interpretation in Today's World

Types of Data Interpretation

Quantitative data interpretation, qualitative data interpretation, mixed methods data interpretation, methods of data interpretation, descriptive statistics, inferential statistics, visualization techniques, benefits of data interpretation, data interpretation process, data interpretation use cases, data interpretation tools, data interpretation challenges and solutions, overcoming bias in data, dealing with missing data, addressing data privacy concerns, data interpretation examples, sales trend analysis, customer segmentation, predictive maintenance, fraud detection, data interpretation best practices, maintaining data quality, choosing the right tools, effective communication of results, ongoing learning and development, data interpretation tips.

Data interpretation is the process of making sense of data and turning it into actionable insights. With the rise of big data and advanced technologies, it has become more important than ever to be able to effectively interpret and understand data.

In today's fast-paced business environment, companies rely on data to make informed decisions and drive growth. However, with the sheer volume of data available, it can be challenging to know where to start and how to make the most of it.

This guide provides a comprehensive overview of data interpretation, covering everything from the basics of what it is to the benefits and best practices.

Data interpretation refers to the process of taking raw data and transforming it into useful information. This involves analyzing the data to identify patterns, trends, and relationships, and then presenting the results in a meaningful way. Data interpretation is an essential part of data analysis, and it is used in a wide range of fields, including business, marketing, healthcare, and many more.

Importance of Data Interpretation in Today's World

Data interpretation is critical to making informed decisions and driving growth in today's data-driven world. With the increasing availability of data, companies can now gain valuable insights into their operations, customer behavior, and market trends. Data interpretation allows businesses to make informed decisions, identify new opportunities, and improve overall efficiency.

There are three main types of data interpretation: quantitative, qualitative, and mixed methods.

Quantitative data interpretation refers to the process of analyzing numerical data. This type of data is often used to measure and quantify specific characteristics, such as sales figures, customer satisfaction ratings, and employee productivity.

Qualitative data interpretation refers to the process of analyzing non-numerical data, such as text, images, and audio. This data type is often used to gain a deeper understanding of customer attitudes and opinions and to identify patterns and trends.

Mixed methods data interpretation combines both quantitative and qualitative data to provide a more comprehensive understanding of a particular subject. This approach is particularly useful when analyzing data that has both numerical and non-numerical components, such as customer feedback data.

There are several data interpretation methods, including descriptive statistics, inferential statistics, and visualization techniques.

Descriptive statistics involve summarizing and presenting data in a way that makes it easy to understand. This can include calculating measures such as mean, median, mode, and standard deviation.

Inferential statistics involves making inferences and predictions about a population based on a sample of data. This type of data interpretation involves the use of statistical models and algorithms to identify patterns and relationships in the data.

Visualization techniques involve creating visual representations of data, such as graphs, charts, and maps. These techniques are particularly useful for communicating complex data in an easy-to-understand manner and identifying data patterns and trends.

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Data interpretation plays a crucial role in decision-making and helps organizations make informed choices. There are numerous benefits of data interpretation, including:

• Improved decision-making: Data interpretation provides organizations with the information they need to make informed decisions. By analyzing data, organizations can identify trends, patterns, and relationships that they may not have been able to see otherwise.
• Increased efficiency: By automating the data interpretation process, organizations can save time and improve their overall efficiency. With the right tools and methods, data interpretation can be completed quickly and accurately, providing organizations with the information they need to make decisions more efficiently.
• Better collaboration: Data interpretation can help organizations work more effectively with others, such as stakeholders, partners, and clients. By providing a common understanding of the data and its implications, organizations can collaborate more effectively and make better decisions.
• Increased accuracy: Data interpretation helps to ensure that data is accurate and consistent, reducing the risk of errors and miscommunication. By using data interpretation techniques, organizations can identify errors and inconsistencies in their data, making it possible to correct them and ensure the accuracy of their information.
• Enhanced transparency: Data interpretation can also increase transparency, helping organizations demonstrate their commitment to ethical and responsible data management. By providing clear and concise information, organizations can build trust and credibility with their stakeholders.
• Better resource allocation: Data interpretation can help organizations make better decisions about resource allocation. By analyzing data, organizations can identify areas where they are spending too much time or money and make adjustments to optimize their resources.
• Improved planning and forecasting: Data interpretation can also help organizations plan for the future. By analyzing historical data, organizations can identify trends and patterns that inform their forecasting and planning efforts.

Data interpretation is a process that involves several steps, including:

• Data collection: The first step in data interpretation is to collect data from various sources, such as surveys, databases, and websites. This data should be relevant to the issue or problem the organization is trying to solve.
• Data preparation: Once data is collected, it needs to be prepared for analysis. This may involve cleaning the data to remove errors, missing values, or outliers. It may also include transforming the data into a more suitable format for analysis.
• Data analysis: The next step is to analyze the data using various techniques, such as statistical analysis, visualization, and modeling. This analysis should be focused on uncovering trends, patterns, and relationships in the data.
• Data interpretation: Once the data has been analyzed, it needs to be interpreted to determine what the results mean. This may involve identifying key insights, drawing conclusions, and making recommendations.
• Data communication: The final step in the data interpretation process is to communicate the results and insights to others. This may involve creating visualizations, reports, or presentations to share the results with stakeholders.

Data interpretation can be applied in a variety of settings and industries. Here are a few examples of how data interpretation can be used:

• Marketing: Marketers use data interpretation to analyze customer behavior, preferences, and trends to inform marketing strategies and campaigns.
• Healthcare: Healthcare professionals use data interpretation to analyze patient data, including medical histories and test results, to diagnose and treat illnesses.
• Financial Services: Financial services companies use data interpretation to analyze financial data, such as investment performance, to inform investment decisions and strategies.
• Retail: Retail companies use data interpretation to analyze sales data, customer behavior, and market trends to inform merchandising and pricing strategies.
• Manufacturing: Manufacturers use data interpretation to analyze production data, such as machine performance and inventory levels, to inform production and inventory management decisions.

These are just a few examples of how data interpretation can be applied in various settings. The possibilities are endless, and data interpretation can provide valuable insights in any industry where data is collected and analyzed.

Data interpretation is a crucial step in the data analysis process, and the right tools can make a significant difference in accuracy and efficiency. Here are a few tools that can help you with data interpretation:

• Share parts of your spreadsheet, including sheets or even cell ranges, with different collaborators or stakeholders.
• Review and approve edits by collaborators to their respective sheets before merging them back with your master spreadsheet.
• Integrate popular tools and connect your tech stack to sync data from different sources, giving you a timely, holistic view of your data.
• Google Sheets: Google Sheets is a free, web-based spreadsheet application that allows users to create, edit, and format spreadsheets. It provides a range of features for data interpretation, including functions, charts, and pivot tables.
• Microsoft Excel: Microsoft Excel is a spreadsheet software widely used for data interpretation. It provides various functions and features to help you analyze and interpret data, including sorting, filtering, pivot tables, and charts.
• Tableau: Tableau is a data visualization tool that helps you see and understand your data. It allows you to connect to various data sources and create interactive dashboards and visualizations to communicate insights.
• Power BI: Power BI is a business analytics service that provides interactive visualizations and business intelligence capabilities with an easy interface for end users to create their own reports and dashboards.
• R: R is a programming language and software environment for statistical computing and graphics. It is widely used by statisticians, data scientists, and researchers to analyze and interpret data.

Each of these tools has its strengths and weaknesses, and the right tool for you will depend on your specific needs and requirements. Consider the size and complexity of your data, the analysis methods you need to use, and the level of customization you require, before making a decision.

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Data interpretation can be a complex and challenging process, but there are several solutions that can help overcome some of the most common difficulties.

Data interpretation can often be biased based on the data sources and the people who interpret it. It is important to eliminate these biases to get a clear and accurate understanding of the data. This can be achieved by diversifying the data sources, involving multiple stakeholders in the data interpretation process, and regularly reviewing the data interpretation methodology.

Missing data can often result in inaccuracies in the data interpretation process. To overcome this challenge, data scientists can use imputation methods to fill in missing data or use statistical models that can account for missing data.

Data privacy is a crucial concern in today's data-driven world. To address this, organizations should ensure that their data interpretation processes align with data privacy regulations and that the data being analyzed is adequately secured.

Data interpretation is used in a variety of industries and for a range of purposes. Here are a few examples:

Sales trend analysis is a common use of data interpretation in the business world. This type of analysis involves looking at sales data over time to identify trends and patterns, which can then be used to make informed business decisions.

Customer segmentation is a data interpretation technique that categorizes customers into segments based on common characteristics. This can be used to create more targeted marketing campaigns and to improve customer engagement.

Predictive maintenance is a data interpretation technique that uses machine learning algorithms to predict when equipment is likely to fail. This can help organizations proactively address potential issues and reduce downtime.

Fraud detection is a use case for data interpretation involving data and machine learning algorithms to identify patterns and anomalies that may indicate fraudulent activity.

To ensure that data interpretation processes are as effective and accurate as possible, it is recommended to follow some best practices.

Data quality is critical to the accuracy of data interpretation. To maintain data quality, organizations should regularly review and validate their data, eliminate data biases, and address missing data.

Choosing the right data interpretation tools is crucial to the success of the data interpretation process. Organizations should consider factors such as cost, compatibility with existing tools and processes, and the complexity of the data to be analyzed when choosing the right data interpretation tool. Layer, an add-on that equips teams with the tools to increase efficiency and data quality in their processes on top of Google Sheets, is an excellent choice for organizations looking to optimize their data interpretation process.

Data interpretation results need to be communicated effectively to stakeholders in a way they can understand. This can be achieved by using visual aids such as charts and graphs and presenting the results clearly and concisely.

The world of data interpretation is constantly evolving, and organizations must stay up to date with the latest developments and best practices. Ongoing learning and development initiatives, such as attending workshops and conferences, can help organizations stay ahead of the curve.

Regardless of the data interpretation method used, following best practices can help ensure accurate and reliable results. These best practices include:

• Validate data sources: It is essential to validate the data sources used to ensure they are accurate, up-to-date, and relevant. This helps to minimize the potential for errors in the data interpretation process.
• Use appropriate statistical techniques: The choice of statistical methods used for data interpretation should be suitable for the type of data being analyzed. For example, regression analysis is often used for analyzing trends in large data sets, while chi-square tests are used for categorical data.
• Graph and visualize data: Graphical representations of data can help to quickly identify patterns and trends. Visualization tools like histograms, scatter plots, and bar graphs can make the data more understandable and easier to interpret.
• Document and explain results: Results from data interpretation should be documented and presented in a clear and concise manner. This includes providing context for the results and explaining how they were obtained.
• Use a robust data interpretation tool: Data interpretation tools can help to automate the process and minimize the risk of errors. However, choosing a reliable, user-friendly tool that provides the features and functionalities needed to support the data interpretation process is vital.

Data interpretation is a crucial aspect of data analysis and enables organizations to turn large amounts of data into actionable insights. The guide covered the definition, importance, types, methods, benefits, process, analysis, tools, use cases, and best practices of data interpretation.

As technology continues to advance, the methods and tools used in data interpretation will also evolve. Predictive analytics and artificial intelligence will play an increasingly important role in data interpretation as organizations strive to automate and streamline their data analysis processes. In addition, big data and the Internet of Things (IoT) will lead to the generation of vast amounts of data that will need to be analyzed and interpreted effectively.

Data interpretation is a critical skill that enables organizations to make informed decisions based on data. It is essential that organizations invest in data interpretation and the development of their in-house data interpretation skills, whether through training programs or the use of specialized tools like Layer. By staying up-to-date with the latest trends and best practices in data interpretation, organizations can maximize the value of their data and drive growth and success.

Hady has a passion for tech, marketing, and spreadsheets. Besides his Computer Science degree, he has vast experience in developing, launching, and scaling content marketing processes at SaaS startups.

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Home » Data Interpretation – Process, Methods and Questions

Data Interpretation – Process, Methods and Questions

Table of Contents

Data Interpretation

Definition :

Data interpretation refers to the process of making sense of data by analyzing and drawing conclusions from it. It involves examining data in order to identify patterns, relationships, and trends that can help explain the underlying phenomena being studied. Data interpretation can be used to make informed decisions and solve problems across a wide range of fields, including business, science, and social sciences.

Data Interpretation Process

Here are the steps involved in the data interpretation process:

• Define the research question : The first step in data interpretation is to clearly define the research question. This will help you to focus your analysis and ensure that you are interpreting the data in a way that is relevant to your research objectives.
• Collect the data: The next step is to collect the data. This can be done through a variety of methods such as surveys, interviews, observation, or secondary data sources.
• Clean and organize the data : Once the data has been collected, it is important to clean and organize it. This involves checking for errors, inconsistencies, and missing data. Data cleaning can be a time-consuming process, but it is essential to ensure that the data is accurate and reliable.
• Analyze the data: The next step is to analyze the data. This can involve using statistical software or other tools to calculate summary statistics, create graphs and charts, and identify patterns in the data.
• Interpret the results: Once the data has been analyzed, it is important to interpret the results. This involves looking for patterns, trends, and relationships in the data. It also involves drawing conclusions based on the results of the analysis.
• Communicate the findings : The final step is to communicate the findings. This can involve creating reports, presentations, or visualizations that summarize the key findings of the analysis. It is important to communicate the findings in a way that is clear and concise, and that is tailored to the audience’s needs.

Types of Data Interpretation

There are various types of data interpretation techniques used for analyzing and making sense of data. Here are some of the most common types:

Descriptive Interpretation

This type of interpretation involves summarizing and describing the key features of the data. This can involve calculating measures of central tendency (such as mean, median, and mode), measures of dispersion (such as range, variance, and standard deviation), and creating visualizations such as histograms, box plots, and scatterplots.

Inferential Interpretation

This type of interpretation involves making inferences about a larger population based on a sample of the data. This can involve hypothesis testing, where you test a hypothesis about a population parameter using sample data, or confidence interval estimation, where you estimate a range of values for a population parameter based on sample data.

Predictive Interpretation

This type of interpretation involves using data to make predictions about future outcomes. This can involve building predictive models using statistical techniques such as regression analysis, time-series analysis, or machine learning algorithms.

Exploratory Interpretation

This type of interpretation involves exploring the data to identify patterns and relationships that were not previously known. This can involve data mining techniques such as clustering analysis, principal component analysis, or association rule mining.

Causal Interpretation

This type of interpretation involves identifying causal relationships between variables in the data. This can involve experimental designs, such as randomized controlled trials, or observational studies, such as regression analysis or propensity score matching.

Data Interpretation Methods

There are various methods for data interpretation that can be used to analyze and make sense of data. Here are some of the most common methods:

Statistical Analysis

This method involves using statistical techniques to analyze the data. Statistical analysis can involve descriptive statistics (such as measures of central tendency and dispersion), inferential statistics (such as hypothesis testing and confidence interval estimation), and predictive modeling (such as regression analysis and time-series analysis).

Data Visualization

This method involves using visual representations of the data to identify patterns and trends. Data visualization can involve creating charts, graphs, and other visualizations, such as heat maps or scatterplots.

Text Analysis

This method involves analyzing text data, such as survey responses or social media posts, to identify patterns and themes. Text analysis can involve techniques such as sentiment analysis, topic modeling, and natural language processing.

Machine Learning

This method involves using algorithms to identify patterns in the data and make predictions or classifications. Machine learning can involve techniques such as decision trees, neural networks, and random forests.

Qualitative Analysis

This method involves analyzing non-numeric data, such as interviews or focus group discussions, to identify themes and patterns. Qualitative analysis can involve techniques such as content analysis, grounded theory, and narrative analysis.

Geospatial Analysis

This method involves analyzing spatial data, such as maps or GPS coordinates, to identify patterns and relationships. Geospatial analysis can involve techniques such as spatial autocorrelation, hot spot analysis, and clustering.

Applications of Data Interpretation

Data interpretation has a wide range of applications across different fields, including business, healthcare, education, social sciences, and more. Here are some examples of how data interpretation is used in different applications:

• Business : Data interpretation is widely used in business to inform decision-making, identify market trends, and optimize operations. For example, businesses may analyze sales data to identify the most popular products or customer demographics, or use predictive modeling to forecast demand and adjust pricing accordingly.
• Healthcare : Data interpretation is critical in healthcare for identifying disease patterns, evaluating treatment effectiveness, and improving patient outcomes. For example, healthcare providers may use electronic health records to analyze patient data and identify risk factors for certain diseases or conditions.
• Education : Data interpretation is used in education to assess student performance, identify areas for improvement, and evaluate the effectiveness of instructional methods. For example, schools may analyze test scores to identify students who are struggling and provide targeted interventions to improve their performance.
• Social sciences : Data interpretation is used in social sciences to understand human behavior, attitudes, and perceptions. For example, researchers may analyze survey data to identify patterns in public opinion or use qualitative analysis to understand the experiences of marginalized communities.
• Sports : Data interpretation is increasingly used in sports to inform strategy and improve performance. For example, coaches may analyze performance data to identify areas for improvement or use predictive modeling to assess the likelihood of injuries or other risks.

When to use Data Interpretation

Data interpretation is used to make sense of complex data and to draw conclusions from it. It is particularly useful when working with large datasets or when trying to identify patterns or trends in the data. Data interpretation can be used in a variety of settings, including scientific research, business analysis, and public policy.

In scientific research, data interpretation is often used to draw conclusions from experiments or studies. Researchers use statistical analysis and data visualization techniques to interpret their data and to identify patterns or relationships between variables. This can help them to understand the underlying mechanisms of their research and to develop new hypotheses.

In business analysis, data interpretation is used to analyze market trends and consumer behavior. Companies can use data interpretation to identify patterns in customer buying habits, to understand market trends, and to develop marketing strategies that target specific customer segments.

In public policy, data interpretation is used to inform decision-making and to evaluate the effectiveness of policies and programs. Governments and other organizations use data interpretation to track the impact of policies and programs over time, to identify areas where improvements are needed, and to develop evidence-based policy recommendations.

In general, data interpretation is useful whenever large amounts of data need to be analyzed and understood in order to make informed decisions.

Data Interpretation Examples

Here are some real-time examples of data interpretation:

• Social media analytics : Social media platforms generate vast amounts of data every second, and businesses can use this data to analyze customer behavior, track sentiment, and identify trends. Data interpretation in social media analytics involves analyzing data in real-time to identify patterns and trends that can help businesses make informed decisions about marketing strategies and customer engagement.
• Healthcare analytics: Healthcare organizations use data interpretation to analyze patient data, track outcomes, and identify areas where improvements are needed. Real-time data interpretation can help healthcare providers make quick decisions about patient care, such as identifying patients who are at risk of developing complications or adverse events.
• Financial analysis: Real-time data interpretation is essential for financial analysis, where traders and analysts need to make quick decisions based on changing market conditions. Financial analysts use data interpretation to track market trends, identify opportunities for investment, and develop trading strategies.
• Environmental monitoring : Real-time data interpretation is important for environmental monitoring, where data is collected from various sources such as satellites, sensors, and weather stations. Data interpretation helps to identify patterns and trends that can help predict natural disasters, track changes in the environment, and inform decision-making about environmental policies.
• Traffic management: Real-time data interpretation is used for traffic management, where traffic sensors collect data on traffic flow, congestion, and accidents. Data interpretation helps to identify areas where traffic congestion is high, and helps traffic management authorities make decisions about road maintenance, traffic signal timing, and other strategies to improve traffic flow.

Data Interpretation Questions

Data Interpretation Questions samples:

• Medical : What is the correlation between a patient’s age and their risk of developing a certain disease?
• Environmental Science: What is the trend in the concentration of a certain pollutant in a particular body of water over the past 10 years?
• Finance : What is the correlation between a company’s stock price and its quarterly revenue?
• Education : What is the trend in graduation rates for a particular high school over the past 5 years?
• Marketing : What is the correlation between a company’s advertising budget and its sales revenue?
• Sports : What is the trend in the number of home runs hit by a particular baseball player over the past 3 seasons?
• Social Science: What is the correlation between a person’s level of education and their income level?

In order to answer these questions, you would need to analyze and interpret the data using statistical methods, graphs, and other visualization tools.

Purpose of Data Interpretation

The purpose of data interpretation is to make sense of complex data by analyzing and drawing insights from it. The process of data interpretation involves identifying patterns and trends, making comparisons, and drawing conclusions based on the data. The ultimate goal of data interpretation is to use the insights gained from the analysis to inform decision-making.

Data interpretation is important because it allows individuals and organizations to:

• Understand complex data : Data interpretation helps individuals and organizations to make sense of complex data sets that would otherwise be difficult to understand.
• Identify patterns and trends : Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships.
• Make informed decisions: Data interpretation provides individuals and organizations with the information they need to make informed decisions based on the insights gained from the data analysis.
• Evaluate performance : Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made.
• Communicate findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.

Characteristics of Data Interpretation

Here are some characteristics of data interpretation:

• Contextual : Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.
• Iterative : Data interpretation is an iterative process, meaning that it often involves multiple rounds of analysis and refinement as more data becomes available or as new insights are gained from the analysis.
• Subjective : Data interpretation is often subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. It is important to acknowledge and address these biases when interpreting data.
• Analytical : Data interpretation involves the use of analytical tools and techniques to analyze and draw insights from data. These may include statistical analysis, data visualization, and other data analysis methods.
• Evidence-based : Data interpretation is evidence-based, meaning that it is based on the data and the insights gained from the analysis. It is important to ensure that the data used in the analysis is accurate, relevant, and reliable.
• Actionable : Data interpretation is actionable, meaning that it provides insights that can be used to inform decision-making and to drive action. The ultimate goal of data interpretation is to use the insights gained from the analysis to improve performance or to achieve specific goals.

Advantages of Data Interpretation

Data interpretation has several advantages, including:

• Improved decision-making: Data interpretation provides insights that can be used to inform decision-making. By analyzing data and drawing insights from it, individuals and organizations can make informed decisions based on evidence rather than intuition.
• Identification of patterns and trends: Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships. This information can be used to improve performance or to achieve specific goals.
• Evaluation of performance: Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made. By analyzing data, organizations can identify strengths and weaknesses and make changes to improve their performance.
• Communication of findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.
• Better resource allocation: Data interpretation can help organizations allocate resources more efficiently by identifying areas where resources are needed most. By analyzing data, organizations can identify areas where resources are being underutilized or where additional resources are needed to improve performance.
• Improved competitiveness : Data interpretation can give organizations a competitive advantage by providing insights that help to improve performance, reduce costs, or identify new opportunities for growth.

Limitations of Data Interpretation

Data interpretation has some limitations, including:

• Limited by the quality of data: The quality of data used in data interpretation can greatly impact the accuracy of the insights gained from the analysis. Poor quality data can lead to incorrect conclusions and decisions.
• Subjectivity: Data interpretation can be subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. This can lead to different interpretations of the same data.
• Limited by analytical tools: The analytical tools and techniques used in data interpretation can also limit the accuracy of the insights gained from the analysis. Different analytical tools may yield different results, and some tools may not be suitable for certain types of data.
• Time-consuming: Data interpretation can be a time-consuming process, particularly for large and complex data sets. This can make it difficult to quickly make decisions based on the insights gained from the analysis.
• Incomplete data: Data interpretation can be limited by incomplete data sets, which may not provide a complete picture of the situation being analyzed. Incomplete data can lead to incorrect conclusions and decisions.
• Limited by context: Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.

Difference between Data Interpretation and Data Analysis

Data interpretation and data analysis are two different but closely related processes in data-driven decision-making.

Data analysis refers to the process of examining and examining data using statistical and computational methods to derive insights and conclusions from it. It involves cleaning, transforming, and modeling the data to uncover patterns, relationships, and trends that can help in understanding the underlying phenomena.

Data interpretation, on the other hand, refers to the process of making sense of the findings from the data analysis by contextualizing them within the larger problem domain. It involves identifying the key takeaways from the data analysis, assessing their relevance and significance to the problem at hand, and communicating the insights in a clear and actionable manner.

In short, data analysis is about uncovering insights from the data, while data interpretation is about making sense of those insights and translating them into actionable recommendations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Analysis and Interpretation of Data

This course is part of Market Research Specialization

Taught in English

Some content may not be translated

Instructors: Athanasia Lampraki +1 more

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For market research analysts, social media strategists, marketing managers and market researchers to enhance their knowledge of key methodologies.

What you'll learn

Familiarise with data analysis and interpretation

Use qualitative and quantitative data analysis approaches

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• Using qualitative and quantitative data analysis approaches
• Analyse and interpret data

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There are 4 modules in this course

This course focuses on the analysis and interpretation of data. The focus will be placed on data preparation and description and quantitative and qualitative data analysis. The course commences with a discussion of data preparation, scale internal consistency, appropriate data analysis and the Pearson correlation. We will look at statistics that can be used to investigate relationships and discuss statistics for investigating relationships with a focus on multiple regression. The course continues with a focus on logistic regression, exploratory factor analysis and the outcome of factor analysis. We are going to explore how to conduct an experiment and an observational study, as well as content analysis and the use of digital analytics in market research. The course ends with a consideration of digital analytics, with an emphasis on digital brand analysis, audience analysis, digital ecosystem analysis, Return on Investment (ROI), and the role of digital analytics in market research.

This week commences with a discussion of data preparation and scale internal consistency. We will then look at appropriate data analysis and the Pearson correlation. The week ends with a focus on statistics that can be used to investigate relationships.

What's included

4 videos 5 readings 6 quizzes 5 discussion prompts

4 videos • Total 14 minutes

• Manipulating the Collected Data: Reversing Negatively Worded Items • 4 minutes • Preview module
• Selecting the Appropriate Data Analysis Approach • 3 minutes
• Spearman Correlation • 2 minutes
• Regression Analysis • 3 minutes

5 readings • Total 50 minutes

• Manipulating the Collected Data: Adding Up the Total Score for the Scale • 10 minutes
• Assessing the Reliability of a Scale • 10 minutes
• Pearson Correlation • 10 minutes
• Investigating Relationships • 10 minutes
• Selecting the Right Statistic • 10 minutes

6 quizzes • Total 180 minutes

• Refresh Your Knowledge • 30 minutes
• Test Your Knowledge • 30 minutes

5 discussion prompts • Total 50 minutes

• Obtaining Descriptives • 10 minutes
• The Decision Making Approach • 10 minutes
• Bivariate Regression • 10 minutes
• Enhance Retention and Knowledge Transfer • 10 minutes

The week commences with a discussion of statistics for investigating relationships with a focus on multiple regression. The week continues with a focus on logistic regression and exploratory factor analysis. The week ends with a discussion of the outcome of factor analysis.

4 videos 5 readings 7 quizzes 4 discussion prompts

4 videos • Total 16 minutes

• Multiple Regression • 4 minutes • Preview module
• Logistic Regression • 4 minutes
• Exploratory Factor Analysis • 3 minutes
• Interpret the Outcome of Factor Analysis • 3 minutes
• Assumptions in Multiple Regression • 10 minutes
• Run a Multiple Regression Analysis • 10 minutes
• Interpreting the Results of Logistic Regression • 10 minutes
• Stages in the Factor Analysis • 10 minutes
• Run a Factor Analysis • 10 minutes

7 quizzes • Total 210 minutes

4 discussion prompts • total 40 minutes.

• Multiple Regression • 10 minutes
• Using Logistic Regression • 10 minutes
• Factor Analysis • 10 minutes

The week commences with a discussion of how to conduct an experiment and an observational study. The week ends with an exploration of content analysis and the use of digital analytics in market research.

• Conducting an Experiment • 3 minutes • Preview module
• Participant Observation • 3 minutes
• Content Analysis • 6 minutes
• Instagram and Snapchat • 3 minutes
• Conducting an Observational Study • 10 minutes
• The Relationship Between Observer and Participant • 10 minutes
• Structured Observation • 10 minutes
• Twitter • 10 minutes
• YouTube and Pinterest • 10 minutes
• Observation • 10 minutes
• Digital Metrics • 10 minutes
• Instagram • 10 minutes

This week explores digital analytics, with an emphasis on digital brand analysis, audience analysis, digital ecosystem analysis, Return on Investment (ROI), and the role of digital analytics in market research.

4 videos 4 readings 8 quizzes 1 peer review 3 discussion prompts

• LinkedIn and Earned Media Metrics • 5 minutes • Preview module
• Digital Brand Analysis • 3 minutes
• Ecosystem Analysis • 4 minutes
• The Role of Digital Analytics • 2 minutes

4 readings • Total 40 minutes

• Web Analytics and Types of Digital Advertising • 10 minutes
• Metrics in Search Analytics • 10 minutes
• Audience Analysis • 10 minutes
• Return on Investment in the Digital Environment • 10 minutes

8 quizzes • Total 240 minutes

1 peer review • total 60 minutes.

• Synthesise Your Knowledge • 60 minutes

3 discussion prompts • Total 30 minutes

• Tools for Audience Analysis • 10 minutes
• Conducting and Ecosystem Analysis • 10 minutes

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Research Techniques for Computer Science, Information Systems and Cybersecurity pp 115–138 Cite as

Data Collection, Presentation and Analysis

• Uche M. Mbanaso 4 ,
• Lucienne Abrahams 5 &
• Kennedy Chinedu Okafor 6  
• First Online: 25 May 2023

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This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.

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Mbanaso, U.M., Abrahams, L., Okafor, K.C. (2023). Data Collection, Presentation and Analysis. In: Research Techniques for Computer Science, Information Systems and Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-031-30031-8_7

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Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

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DATA ANALYSIS, INTERPRETATION, AND PRESENTATION

8.1 introduction.

• 8.2 Qualitative and Quantitative
• 8.3 Simple Quantitative Analysis
• 8.4 Simple Qualitative Analysis
• 8.5 Tools to Support Data Analysis
• 8.6 Using Theoretical Frameworks
• 8.7 Presenting the Findings

The main aims of this chapter are to:

• Discuss the difference between qualitative and quantitative data and analysis.
• Enable you to analyze data gathered from questionnaires.
• Enable you to analyze data gathered from interviews.
• Enable you to analyze data gathered from observation studies.
• Make you aware of software packages that are available to help your analysis.
• Identify some of the common pitfalls in data analysis, interpretation, and presentation.
• Enable you to be able to interpret and present your findings in a meaningful and appropriate manner.

The kind of analysis that can be performed on a set of data will be influenced by the goals identified at the outset, and the data actually gathered. Broadly speaking, you may take a qualitative analysis approach or a quantitative analysis approach, or a combination of qualitative and quantitative. The last of these is very common as it provides a more comprehensive account of the behavior being observed or performance being measured.

Most analysis, whether it is quantitative or qualitative, begins with initial reactions or observations from the data. This might involve identifying patterns or calculating simple numerical values such ...

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data analysis, interpretation, and presentation

Depends on data.

quantitative

qualitative

mixed methods

translating raw data

questionnaires

observations

begins with initial reactions or observations

identify patterns

calculating values

data cleansing: check for errors

use structured frameworks and theories

interpretation

parallel with analysis

results interpreted different ways

make sure data supports conclusion

avoid biases

avoid over claiming

presentation

different methods, depends on goals

affects interpretation

quantitative analysis

Statistical analysis.

percentages

individual differences

task times: 45, 50, 55, 55, 60, 65

task times: 45, 50, 55, 55, 60, 300

task times: 10, 10, 50, 55, 60, 300

median: 52.5

data format

table: rows and columns

how to represent responses and measures

participants as rows

responses: single or multiple

spreadsheet applications

analysis tools: R, SPSS, SAS

scatter plots

presenting percentages

almost always better ways

comparison difficult when percentages similar

include percentages as text

provide overview with other representations

qualitative analysis

Initial approach.

gain overall understanding

look for interesting features

highlight common, record surprises

inductive vs deductive

inductive: extract concepts from the data

deductive: use existing theory to categorize

depends on data and goals

inductive, then deductive

reliable analysis

replication

training towards consensus

inter-rater reliability or agreement

Cohen's kappa

transcription: oTranscribe , Otter.ai , Trint

coding and analysis: Dedoose, Atlas.ai, Nvivo

identifying themes

Categorizing data.

analyzing critical incidents

thematic analysis

identify, analyze, and report patterns

themes represent important, relevant, or unexpected patterns

open coding: initial pass

axial coding: themes across participants, connections, categories

selective coding: validate relationships, consistency, themes

find further themes

step back, look at big picture

affinity diagrams

organize ideas and insights into hierarchy

groupings emerge through data

analysis frame chosen beforehand

study goals

categorization schemes

evolve with analysis

• Verbalizations show evidence of dissatisfaction about an aspect of the interface.
• Verbalizations show evidence of confusion/uncertainty about an aspect of the interface.
• Verbalizations show evidence of dissatisfaction about aspects of the content of the electronic text.

critical incident analysis

help deal with lots of data

identify significant subsets

critical events

focus on important or unique events

apply in both data gathering and analysis

useful when cause of problem unknown

disadvantages

may miss routine incidents

not as good for general task analysis

interpreting qualitative data

Analytic frameworks, conversation analysis, discourse analysis, content analysis, interaction analysis, grounded theory.

semantics of conversation in detail

focus on how conversation conducted

compare conversations across different media

voice-assisted technologies

voice assistants

conversational interactions

how do these devices change social behaviors? in home? at work?

do these devices change the way we talk?

interaction interwoven with other activities Martin Porcheron et al. [2018]

interleaving conversations with people and devices

instructing rather than conversing

communication tools

zoom, discord

mediate much of our communication

do these tools change the way we talk to each other?

the ways we collaborate?

meaning of what is said and how words are used

context important

strongly interpretive

no objective scientific truth

how people use language to construct perspectives

identify subtle and implicit meaning

communication: e-mails, social media, interviews

scraping data

time consuming

analysis via statistics and visualizations

tag / word clouds

classifying data into themes

studying frequency of theme occurrence

used for a range of media

used in conjunction with other approaches

interactions of humans with each other and objects

verbal and non-verbal

data: video recordings

knowledge and action are social

goal: derive generalizations of activities based on actions

interaction analysis: first step

teams suggest general patterns from multiple observations

collaborative

first: create content logs summarizing happenings

categories emerge through repeated play and discussion

intentions, motivations, and understandings

cannibalizing videos

extracting interesting material

reclassifying in term of material

instances assembled in playlist

derive theory grounded in data

from sociology

theory: set of concepts that constitute a framework for explaining or predicting phenomena

grounded theory: method

alternating data collection and analysis

identify themes, then refine with more data

continues until saturation, no new emergent themes

balance objectivity and sensitivity

goal: define properties and dimensions of relevant themes (categories)

grounded theory: data gathering

focus on analysis

interviews and observations

written records and diagrammatic representations

physical code books

grounded theory: coding

open coding: categories, properties, and dimensions discovered

axial coding: flesh out categories and relate to subcategories

selecting coding: refining and integrating categories

grounded theory: analytic tools

question the data: different perspectives, escape ruts

analysis of a word, phrase, or sentence: different perspectives

comparison: between objects or categories → alternative interpretations

interpreting and presenting findings

Presenting findings.

tables of numbers and text

graphical devices, such as charts and diagrams

set of themes or categories

choice depends on the data and analytic method

details of data collection and analysis

set of complementary representations

structured notations

analyze, capture, and present information

clear syntax and semantics

structured notation: advantages

meaning of symbols well-defined

highlights what to look for

enforces precision in expression

structured notation: disadvantages

by highlighting, ignores other aspects

precise expression lost on an unfamiliar audience

combine with other more accessible formats

storytelling

easy and intuitive approach

stories told by participants during data gathering

stories based on observation

stories constructed from snippets or repeated episodes in data

scenarios derived from stories collected during data gathering

summarizing findings

combination of presentation formats

careful interpretation and presentation important

not specific to interaction design

common pitfall: over-generalizing results

reading for next class

Chapter 12: Design, Prototyping, and Construction Interaction Design: Beyond Human-Computer Interaction

Data Analysis and Interpretation

Data analysis and interpretation is the next stage after collecting data from empirical methods. The dividing line between the analysis of data and interpretation is difficult to draw as the two processes are symbolic and merge imperceptibly. Interpretation is inextricably interwoven with analysis.

The analysis is a critical examination of the assembled data. Analysis of data leads to generalization.

Interpretation refers to the analysis of generalizations and results. A generalization involves concluding a whole group or category based on information drawn from particular instances or examples.

Interpretation is a search for the broader meaning of research findings. Analysis of data is to be made regarding the purpose of the study.

Data should be analyzed in light of hypothesis or research questions and organized to yield answers to the research questions.

Data analysis can be both descriptive as well as a graphic in presentation. It can be presented in charts, diagrams, and tables.

The data analysis includes various processes, including data classification, coding, tabulation, statistical analysis of data, and inference about causal relations among variables.

Proper analysis helps classify and organize unorganized data and gives scientific shape. In addition, it helps study the trends and changes that occur in a particular period.

What is the primary distinction between data analysis and interpretation?

Data analysis is a critical examination of the assembled data, leading to generalization. In contrast, interpretation refers to the analysis of these generalizations and results, searching for the broader meaning of research findings.

3 How is a hypothesis related to research objectives?

A well-formulated, testable research hypothesis is the best expression of a research objective. It is an unproven statement or proposition that can be refuted or supported by empirical data, asserting a possible answer to a research question.

What are the four basic research designs a researcher can use?

The four basic research designs are survey, experiment, secondary data study, and observational study.

What are the steps involved in the processing of interpretation?

The steps include editing the data, coding or converting data to a numerical form, arranging data according to characteristics and attributes, presenting data in tabular form or graphs, and directing the reader to its component, especially striking from the point of view of research questions.

Steps for processing the interpretation

• Firstly, data should be edited. Since all the data collected is irrelevant to the study, irrelevant data should be separated from the relevant ones. Careful editing is essential to avoid possible errors that may distort data analysis and interpretation. But the exclusion of data should be done with an objective view and free from bias and prejudices.
• The next step is coding or converting data to a numerical form and presenting it on the coding matrix. Coding reduces the huge quantity of data to a manageable proportion.
• Thirdly, all data should be arranged according to characteristics and attributes. The data should then be properly classified to become simple and clear.
• Thirdly, data should be presented in tabular form or graphs. But any tabulation of data should be accompanied by comments as to why the particular data finding is important.
• Finally, the researcher should direct the reader to its component, especially striking from the point of view of research questions.

Three key concepts of analysis and interpretation of data

Why is data editing essential in the research process.

Data editing is essential to ensure consistency across respondents, locate omissions, reduce errors in recording, improve legibility, and clarify unclear and inappropriate responses.

What are the three key concepts regarding the analysis and interpretation of data?

The three key concepts are Reliability (referring to consistency), Validity (ensuring the data collected is a true picture of what is being studied), and Representativeness (ensuring the group or situation studied is typical of others).

Reliability

It refers to consistency. In other words, if a method of collecting evidence is reliable, it means that anybody else is using this method, or the same person using it at another time, would come with the same results.

In other words, reliability is concerned with the extent that an experiment can be repeated or how far a given measurement will provide the same results on different occasions.

It refers to whether the data collected is a true picture of what is being studied. It means that the data collected should be a product of the research method used rather than studied.

Representativeness

This refers to whether the group of people or the situation we are studying are typical’ of others.’

The following conditions should be considered to draw reliable and valid inferences from the data.

• Reliable inference can only be drawn when the statistics are strictly comparable, and data are complete and consistent.’ Thus, to ensure comparability of different situations, the data should be homogenous; data should be complete and adequate, and the data should be appropriate.
• An ideal sample must adequately represent the whole population. Thus, when the number of units is huge, the researcher should choose those samples with the same set of qualities and features as found in the whole data.

Data Analysis, Interpretation, and Presentation Techniques: A Guide to Making Sense of Your Research Data

by Prince Kumar

Last updated: 27 February 2023

Table of Contents

Data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. Data analysis involves processing and analyzing the data to derive meaningful insights, while data interpretation involves making sense of the insights and drawing conclusions. Data presentation involves presenting the data in a clear and concise way to communicate the research findings. In this article, we will discuss the techniques for data analysis, interpretation, and presentation.

1. Data Analysis Techniques

Data analysis techniques involve processing and analyzing the data to derive meaningful insights. The choice of data analysis technique depends on the research question and objectives. Some common data analysis techniques are:

a. Descriptive Statistics

Descriptive statistics involves summarizing and describing the data using measures such as mean, median, and standard deviation.

b. Inferential Statistics

Inferential statistics involves making inferences about the population based on the sample data. This technique involves hypothesis testing, confidence intervals, and regression analysis.

c. Content Analysis

Content analysis involves analyzing the text, images, or videos to identify patterns and themes.

d. Data Mining

Data mining involves using statistical and machine learning techniques to analyze large datasets and identify patterns.

2. Data Interpretation Techniques

Data interpretation involves making sense of the insights derived from the data analysis. The choice of data interpretation technique depends on the research question and objectives. Some common data interpretation techniques are:

a. Data Visualization

Data visualization involves presenting the data in a visual format, such as charts, graphs, or tables, to communicate the insights effectively.

b. Storytelling

Storytelling involves presenting the data in a narrative format, such as a story, to make the insights more relatable and memorable.

c. Comparative Analysis

Comparative analysis involves comparing the research findings with the existing literature or benchmarks to draw conclusions.

3. Data Presentation Techniques

Data presentation involves presenting the data in a clear and concise way to communicate the research findings. The choice of data presentation technique depends on the research question and objectives. Some common data presentation techniques are:

a. Tables and Graphs

Tables and graphs are effective data presentation techniques for presenting numerical data.

b. Infographics

Infographics are effective data presentation techniques for presenting complex data in a visual and easy-to-understand format.

c. Data Storytelling

Data storytelling involves presenting the data in a narrative format to communicate the research findings effectively.

In conclusion, data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. By using the appropriate data analysis, interpretation, and presentation techniques, researchers can derive meaningful insights, make sense of the insights, and communicate the research findings effectively. By conducting high-quality data analysis, interpretation, and presentation in research, researchers can provide valuable insights into the research question and objectives.

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Syllabus – Research Methodology

01 Introduction To Research Methodology

• Meaning and objectives of Research
• Types of Research
• Research Approaches
• Significance of Research
• Research methods vs Methodology
• Research Process
• Criteria of Good Research
• Problems faced by Researchers
• Techniques Involved in defining a problem

02 Research Design

• Meaning and Need for Research Design
• Features and important concepts relating to research design
• Different Research design
• Important Experimental Designs

03 Sample Design

• Introduction to Sample design
• Censure and sample survey
• Implications of Sample design
• Steps in sampling design
• Criteria for selecting a sampling procedure
• Characteristics of a good sample design
• Different types of Sample design
• Measurement Scales
• Important scaling Techniques

04 Methods of Data Collection

• Introduction
• Collection of Primary Data
• Collection through Questionnaire and schedule collection of secondary data
• Differences in Questionnaire and schedule
• Different methods to collect secondary data

05 Data Analysis Interpretation and Presentation Techniques

• Hypothesis Testing
• Basic concepts concerning Hypothesis Testing
• Procedure and flow diagram for Hypothesis Testing
• Test of Significance
• Chi-Square Analysis
• Report Presentation Techniques

Analysis and Interpretation of Data

PPT on Analysis and interpretation of Data Tahira Rafiq (Principal at Multan Post Graduate College, Multan). Read less

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• 1. Presented by : TAHIRA RAFIQ Reg. # 161-FSS/PHDEDU/F19 Presented to : Dr. SHAMSAAZIZ
• 2. The Research Report  Why write a report?  The writing process (pre-writing, composing, re-writing)  Audience  Style and tone  Organizing thoughts  Back to the library  Plagiarism
• 3. Qualitative and Quantitative Research Qualitative research involves the use of qualitative data such as: Interviews, Documents, Observation data . In qualitative research different type of approaches, methods and techniques are used. Quantitative research is an inquiry to identify the problem, based on testing a theory, measured with numbers and analyzed using statistical techniques
• 4. Qualitative Report Writing Format 1. Preliminary pages 2. Main body of research 3. Reference section Appendices
• 5. Preliminary Pages 1. Title page 2. Inner title page 3. Supervisor page 4. Certificate by supervisor page 5. Dedication page 6. List of abbreviations 7. List of tables 8. List of appendices 9. Acknowledgment 10. Declaration 11. Abstract 12. Table of contents/content list
• 6. Main Body of Report Introduction  Rationale of study  Statement of problem  Objective of the study  Hypothesis of the study  Significance of the study  Delimitation of the study  Operational definitions of the major terms  Conceptual framework
• 7. Literature Review  Relevant previous researches with APA style ResearchMethodology  Research design  Population of study  Sample and sampling technique  Data collection tool development  Pilot testing  Data collection procedure
• 8. Data analysis and interpretation  ANALYSIS means the ordering, manipulating, and summarizing of data to obtain answers to research questions. Its purpose is to reduce data to intelligible and interpretable form so that the relations of research problems can be studied and tested.  INTERPRETATION gives the results of analysis, makes inferences pertinent to the research relations studied, and draws conclusions about these relations.
• 9. Data Analysis  In general terms it is the description of the strategies which a researcher used for data analysis. Researcher will specify the data will be analyzed by manually or by computer. For computer analysis, identify the program and the statistical procedures to perform for the data and also identify the main variables for cross tabulation.
• 10. STEPS IN DATA PROCESSING STEPS IN QUANTITATIVE STUDIES Developing a code book Pre-testing the code book Developing a Frame of Analysis Coding the data Verifying the coded data Computer Manual 1. SPSS x 2. SAS Raw data Editing Coding Analysis
• 11. STEPS IN QUALITATIVE STUDIES Interviews Developing themes Content analysis Questionnaires Observations In-depth/focus group interviews Computer Analysis: Manual Secondary sources Thematic Ethnograph Analysis Raw data Editing Coding Analysis
• 12. Qualitative Data Analysis  Case study (intrinsic, instrumental, multiple)  Thematic analysis  Grounded theory  Discourse analysis  Domain analysis (cultural, folk, mixed)  Phenomenological analysis  Coding (open, axial, selective)
• 13. Qualitative Data Analysis cont…  Narrative analysis (narrative style, narrative inquiry)  Content analysis  Historic analysis  Ethnographic analysis  Framework analysis  Parametric statistics (T-test, Z-test etc.)  Non-parametric statistics (Chi-square)
• 14.  STATISTICS is simply a tool in research. In fact, according to Leedy (1974:21), statistics is a language which, through its own special symbols and grammar, takes the numerical facts of life and translates them meaningfully.  Statistics thus gathers numerical data. The variations of the data gathered are abstracted based on group characteristics and combined to interpretation, serve the purpose of description, analysis, and possible generalization. According to McGuigan (1987), in research, this is known as the process of concatenation where the statements are “chained together” with other statements.
• 15. Types of Statistics There are two types of statistics:  DESCRIPTIVE STATISTICS – allows the researcher to describe the population or sample used in the study.  INFERENTIAL STATISTICS – draws inferences from sample data and actually deals with answering the research questions postulated which are, in some cases, cause and effect relationship.
• 16. DESCRIPTIVE STATISTICS Descriptive statistics describes the characteristics of the population or the sample. To make them meaningful, they are grouped according to the following measures:  Measures of Central Tendency orAverages  Measures of Dispersion or Variability  Measures of Non-central Location  Measures of Symmetry and/orAsymmetry  Measures of Peakedness or Flatness
• 17. Measures of Central Tendency or Averages These include the mean, mode and the median. The mean is a measure obtained by adding all the values in a population or sample and dividing by the number of values that are added (Daniel, 1991:19-20). The mode is simply the most frequent score of scores (Blalock, 1972:72). The median is the value above and below which one half of the observations fall (Norusis, (1984:B- 63)
• 18. Measures of Dispersion or Variability. These include deviation, and the variance, standard the range. According to Daniel (1991:24), a measure of dispersion conveys information regarding the amount of variability present in a set of idea.
• 19. The VARIANCE is a measure of the dispersion of the set of scores. It tells us how much the scores are spread out. Thus, the variance is a measure of the spread of the scores; it describes the extent to which the scores differ from each other about their mean. STANDARD DEVIATION thus refers to the deviation of scores from the mean. Where ordinal measures of 1-5 scales (low-high) are used, data most likely have standard deviations of less than one unless, the responses are extremes (i.e., all ones and all fives). The RANGE is defined as the difference between the highest and lowest scores (Blalock, 1972:77)
• 20. Measures of Non-CentralLocation  These include the quantiles (i.e. percentiles, deciles, quartiles). The word percent means “per hundred”. Therefore, in using percentages, size is standardized by calculating the number of individuals who would be in a given category if the total number of cases were 100 and if the proportion in each category remains unchanged. Since proportions must add to unity, it is obvious that percentages will sum up to 100 unless the categories are not mutually exclusive or exhaustive (Blalock, 1972:33)
• 21.  Measures of Symmetry and/or Asymmetry These include the skewness of the frequency distribution. If a distribution is asymmetrical and the larger frequencies tend to be concentrated toward the low end of the variable and the smaller frequencies toward the high end, it is said to be positively skewed. If the opposite holds, the larger frequencies being concentrated toward the high end of the variable and the smaller frequencies toward the low end, the distribution is said to be negatively skewed (Ferguson and Takane, 1989:30).
• 22.  Measures of Peakedness or Flatness Measures of Peakedness or Flatness of one distribution in relation to another is referred to as Kurtosis. If one distribution is more peaked than another, it may be spoken of as more leptokurtic. If it is less peaked, it is said to be more platykurtic.
• 23. NON-PARAMETRIC TESTS Two types of data are recognized in the application of statistical treatments, these are: parametric data and non-parametric data.  Parametric data are measured data and parametric statistical tests assume that the data are normally, or nearly normally, distributed (Best and Kahn, 1998:338).  Non-parametric data are distribution free samples which implies that they are free, of independent of the population distribution (Ferguson and Takane, 1989:431). The tests on these data do not rest on the more stringent assumption of normally distributed population (Best and Kahn, 1998:338).
• 24.  Kolmogorov – Smirnov test - fulfills the function of chi- square in testing goodness of fit and of the Wilcoxon rank sum test in determining whether random samples are from the same population.  Sign Test - is important in determining the significance of differences between two correlated samples. The “signs” of the test are the algebraic plus or minus values of the difference of the paired scores.  Median Test - is a sign test for two independent samples in contradistinction to two correlated samples, as is the case with the sign test.  Spearman rank order correlation - sometimes called Spearman rho (p) or Spearman’s rank difference correlation, is a nonparametric statistic that has its counterpart in parametric calculations in the Pearson product moment correlation.
• 25. The Non-Parametric Tests Available are:  Two types of data are recognized in the application of statistical treatments, these are: parametric data and nonparametric data. Parametric data are measured data and parametric statistical tests assume that the data are normally, or nearly normally, distributed (Best and Kahn, 1998:338). Nonparametric data are distribution free samples which implies that they are free, of independent of the population distribution (Ferguson and Takane, 1989:431). The tests on these data do not rest on the more stringent assumption of normally distributed population (Best and Kahn, 1998:338).
• 26.  APPROPRIATE STATISTICAL METHODS BASED ON THE RESEARCH PROBLEM AND LEVELS OF MEASUREMENT The statistical methods appropriate to any studies are always determined by the research problem and the measurement scale of the variables used in the study.  CHI – SQUARE The most commonly used nonparametric test. It is employed in instances where a comparison between observed and theoretical frequencies is present, or in testing the mathematical fit of a frequency curve to an observed frequency distribution.
• 27. Tests provides the capability of computing student’s t and probability levels for testing whether or not the difference between two samples means is significant (Nie, et al., 1975:267). This type of analysis is the comparison of two groups of subjects, with the group means as the basis for comparison. Two types of T-tests may be performed:  Independent Samples - cases are classified into 2 groups and a test of mean differences is performed for specified variables;  Paired Samples – for paired observations arrange casewise, a test of treatment effects is performed.
• 28. What is Coding  People are constantly talking about it, but what is it? Code and Instructions  In the most simplified terms, coding is the act of writing instructions that computers can understand.  Having cleaned the data, the next step is to code it.  Coding is the process of naming or labeling things, categories, and properties.
• 29. Types of Coding 1. Open Coding: a first coding of qualitative data in which a researcher examines the data to condense them into preliminary analytical categorize or codes 2. Axial Coding: A second stage of coding of qualitative data in which a researcher organizes the codes, links them and discovers key analytical categories 3. Selective Coding: A late stage in coding qualitative data in which a researcher examines previous codes to identify and select data that will support the conceptual coding categorize that were developed
• 30. The Method of Coding 1. The way a variable has been measure (measurement scale) in your research instrument (if a response to a question is descriptive, categorical or quantitative) 2. The way you want to communicate the findings about a variable to your readers All responses can be classified into the following three categories:  Quantitative responses  Categorical responses  Descriptive responses-transforming into numerical values called codes
• 31. Coding Quantitative/Categorical (Qualitative and Quantitative) data For coding quantitative/categorical (qualitative and quantitative) data you need to go through the following steps:  Developing a code book  Pre-testing code book  Coding the data  Verifying the coded data
• 32. Thematic Analysis  Thematic analysis is the most common form of analysis in qualitative research  It emphasizes pinpointing, examining, and recording patterns (themes) within data  Themes are patterns across data sets that are important to the description of a phenomenon and are associated to a specific research question  The themes become the categories for analysis  Thematic analysis is performed through the process of coding in six phases to create established, meaningful patterns. These phases are: familiarization with data, generating initial codes, searching for themes among codes, reviewing themes, defining and naming themes, and producing the final report.
• 33. Doing Thematic Analysis  Reading and re-reading material until researcher is comfortable is crucial to the initial phase of analysis. While becoming familiar with material, note- taking is a crucial part of this step in order begin developing potential codes.  The second step in thematic analysis is generating an initial list of items from data set that have a reoccurring pattern. This systematic way of organizing and gaining meaningful parts of data as it relates to research question is called coding. The coding process evolves through an inductive analysis and is not considered to be linear process, but a cyclical process in which codes emerge throughout the research process. This cyclical process involves going back and forth between phases of data analysis as needed until you are satisfied with final themes. The coding process is rarely completed the first time. Each time, researchers should strive to refine codes by adding, subtracting, combining or splitting potential codes.
• 34.  In this phase, it is important to begin by examining how codes combine to form themes in the data. Researchers begin considering how relationships are formed between codes and themes and between different levels of existing themes. Themes differ from codes in that themes are phrases or sentences that identifies what the data means. They describe an outcome of coding for analytic reflection. Themes consist of ideas and descriptions within a culture that can be used to explain causal events, statements, and morals derived from the participants' stories.  After finalizing the themes to be discussed, researcher should name and define themes.
• 35. Discourse Analysis What is discourse? Discourse can be defined in three ways: 1. Language beyond the level of a sentence 2. Language behaviors linked to social practices 3. Language as a system of thought. Discourse analysis is usually defined as the analysis of language 'beyond the sentence'. And the analysis of discourse is typically concerned with the study of language in text and conversation Written by MaCarthy et al. in An Introduction to Applied Linguistics. Schmitt, N. 2012
• 36. Basic ideas in Discourse analysis  Text analysis ( writing) structure of a discourse speech events  Conversation analysis (speaking) Turn-taking The cooperative principle Background knowledge Basics of Text Analysis  Cohesion  Coherence  Speech events
• 37. Cohesion, Coherence and Speech Events  Cohesion is the grammatical and/or lexical relationships between the different elements of a text.  Coherence is the relationships which link the meanings of utterances in a discourse or of the sentences in a text. Cohesion and coherence :  Cohesion helps to create coherence  Cohesion does not entail coherence  Coherence can be made with/out cohesive ties/ devises  Speech event includes interactions such as a conversation at a party or ordering a meal. Any speech event comprises several components. debate, lecture, interview, game, daily routine, etc.
• 38. Domain Analysis A method of qualitative data analysis in which a researcher describes the structure of a cultural domain. It is a comprehensive approach for qualitative data 1. Cultural domain (a cultural setting in which people regularly interact and develop a set shared understandings or “mini culture” that can be analyzed) 2. Folk domain (a cultural domain based on the categorize used by the people being studied in the field site) 3. Mix domain ( a cultural domain that combine the categories of members under study with categories developed by a researcher for analysis
• 39. Domain Model  Represents meaningful conceptual classes in a problem domain.  Represents real-world concepts (the problem), not software components (the solution).  May be shared by different projects.
• 40. Narrative Analysis Both a type of historical writing that tells a story and a type of qualitative data analysis that presents a chronologically linked chain of events’ in which individual or collective social actors have an important role  Narrative inquiry is method of investigation of data collection about the people ordinary lived experiences  Narrative style it is sometimes called story telling (Berger and Quency, 2004)
• 41. Tools of Narrative Analysis  Path dependency refers to how a unique beginning can trigger a sequence of events and create a deterministic path that is followed in the chain of subsequent events. Types of path dependency Two types of path dependency  Self reinforcing (explanation looks at how, one’s set into motion, events continue to operate on their own, events in a direction that resist external factors)  Reactive sequence (focus on how each event respond to an pre-coding 1
• 42. Periodization  Dividing the flow of time in social reality into segments or periods. A field researcher might discover parts or periods in an ongoing process (e.g. typical day, yearly cycle) Historical Contingency  Analytical idea in narrative analysis that explain a process, event or situation by referring to the specific combination of the factors that came together in a particular time and place
• 43. GROUNDED THEORY DESIGNS IN QUALITATIVE ANALYSIS  ‘‘Grounded Theory is the study of a concept! It is not a descriptive study of a descriptive problem’’ (Glaser, 2010). ‘‘Most grounded theorists believe they are theorizing about how the world *is* rather than how respondents see it’’ (Steve Borgatti).
• 44.  A grounded theory design is a systematic, qualitative procedure used to generate a theory that explains, at a broad conceptual level, a process, an action, or an interaction about a substantive topic (Creswell, 2008). The phrase "grounded theory" refers to theory that is developed inductively from a corpus of data. ‘‘Grounded Theory is the most common, widely used, and popular analytic technique in qualitative analysis’’ (the evidence is: the number of book published on it) (Gibbs, 2010). • It is mainly used for qualitative research, but is also applicable to other data (e.g., quantitative data; Glaser, 1967, chapter VIII).
• 45. When do you use Grounded Theory?  When you need a broad theory or explanation of a process.  Especially helpful when current theories about a phenomenon are either inadequate or nonexistent (Creswell, 2008).  When you wish to study some process, such as how students develop as writers (Neff, 1998) or how high- achieving African American and Caucasian women’s career develop.
• 46. Methods The basic idea of the grounded theory approach is to read a textual database and "discover" or label variables (called categories, concepts and properties) and their interrelationships. The data do not have to be literally textual they could be observations of behavior, such as interactions and events in a restaurant. Often they are in the form of field notes, which are like diary entries. Data Collection  Interviews  Observations  Documents  Historical Records  Video tapes
• 47. CORRELATIONANALYSIS  Correlation is used when one is interested to know the relationship between two or more paired variables. According to Blalock (1972:361), this is where interest is focused primarily on the exploratory task of finding out which variables are related to a given variable.
• 48. Thank You

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Scope and purpose Principles Guidelines Quality indicators References

Scope and purpose

Data analysis is the process of developing answers to questions through the examination and interpretation of data.  The basic steps in the analytic process consist of identifying issues, determining the availability of suitable data, deciding on which methods are appropriate for answering the questions of interest, applying the methods and evaluating, summarizing and communicating the results.  

Analytical results underscore the usefulness of data sources by shedding light on relevant issues. Some Statistics Canada programs depend on analytical output as a major data product because, for confidentiality reasons, it is not possible to release the microdata to the public. Data analysis also plays a key role in data quality assessment by pointing to data quality problems in a given survey. Analysis can thus influence future improvements to the survey process.

Data analysis is essential for understanding results from surveys, administrative sources and pilot studies; for providing information on data gaps; for designing and redesigning surveys; for planning new statistical activities; and for formulating quality objectives.

Results of data analysis are often published or summarized in official Statistics Canada releases. 

A statistical agency is concerned with the relevance and usefulness to users of the information contained in its data. Analysis is the principal tool for obtaining information from the data.

Data from a survey can be used for descriptive or analytic studies. Descriptive studies are directed at the estimation of summary measures of a target population, for example, the average profits of owner-operated businesses in 2005 or the proportion of 2007 high school graduates who went on to higher education in the next twelve months.  Analytical studies may be used to explain the behaviour of and relationships among characteristics; for example, a study of risk factors for obesity in children would be analytic. 

To be effective, the analyst needs to understand the relevant issues both current and those likely to emerge in the future and how to present the results to the audience. The study of background information allows the analyst to choose suitable data sources and appropriate statistical methods. Any conclusions presented in an analysis, including those that can impact public policy, must be supported by the data being analyzed.

Initial preparation

Prior to conducting an analytical study the following questions should be addressed:

Objectives. What are the objectives of this analysis? What issue am I addressing? What question(s) will I answer?

Justification. Why is this issue interesting?  How will these answers contribute to existing knowledge? How is this study relevant?

Data. What data am I using? Why it is the best source for this analysis? Are there any limitations?

Analytical methods. What statistical techniques are appropriate? Will they satisfy the objectives?

Audience. Who is interested in this issue and why?

  Suitable data

Ensure that the data are appropriate for the analysis to be carried out.  This requires investigation of a wide range of details such as whether the target population of the data source is sufficiently related to the target population of the analysis, whether the source variables and their concepts and definitions are relevant to the study, whether the longitudinal or cross-sectional nature of the data source is appropriate for the analysis, whether the sample size in the study domain is sufficient to obtain meaningful results and whether the quality of the data, as outlined in the survey documentation or assessed through analysis is sufficient.

 If more than one data source is being used for the analysis, investigate whether the sources are consistent and how they may be appropriately integrated into the analysis.

Appropriate methods and tools

Choose an analytical approach that is appropriate for the question being investigated and the data to be analyzed. 

When analyzing data from a probability sample, analytical methods that ignore the survey design can be appropriate, provided that sufficient model conditions for analysis are met. (See Binder and Roberts, 2003.) However, methods that incorporate the sample design information will generally be effective even when some aspects of the model are incorrectly specified.

Assess whether the survey design information can be incorporated into the analysis and if so how this should be done such as using design-based methods.  See Binder and Roberts (2009) and Thompson (1997) for discussion of approaches to inferences on data from a probability sample.

See Chambers and Skinner (2003), Korn and Graubard (1999), Lehtonen and Pahkinen (1995), Lohr (1999), and Skinner, Holt and Smith (1989) for a number of examples illustrating design-based analytical methods.

For a design-based analysis consult the survey documentation about the recommended approach for variance estimation for the survey. If the data from more than one survey are included in the same analysis, determine whether or not the different samples were independently selected and how this would impact the appropriate approach to variance estimation.

The data files for probability surveys frequently contain more than one weight variable, particularly if the survey is longitudinal or if it has both cross-sectional and longitudinal purposes. Consult the survey documentation and survey experts if it is not obvious as to which might be the best weight to be used in any particular design-based analysis.

When analyzing data from a probability survey, there may be insufficient design information available to carry out analyses using a full design-based approach.  Assess the alternatives.

Consult with experts on the subject matter, on the data source and on the statistical methods if any of these is unfamiliar to you.

Having determined the appropriate analytical method for the data, investigate the software choices that are available to apply the method. If analyzing data from a probability sample by design-based methods, use software specifically for survey data since standard analytical software packages that can produce weighted point estimates do not correctly calculate variances for survey-weighted estimates.

It is advisable to use commercial software, if suitable, for implementing the chosen analyses, since these software packages have usually undergone more testing than non-commercial software.

Determine whether it is necessary to reformat your data in order to use the selected software.

Include a variety of diagnostics among your analytical methods if you are fitting any models to your data.

Refer to the documentation about the data source to determine the degree and types of missing data and the processing of missing data that has been performed.  This information will be a starting point for what further work may be required.

Consider how unit and/or item nonresponse could be handled in the analysis, taking into consideration the degree and types of missing data in the data sources being used.

Consider whether imputed values should be included in the analysis and if so, how they should be handled.  If imputed values are not used, consideration must be given to what other methods may be used to properly account for the effect of nonresponse in the analysis.

If the analysis includes modelling, it could be appropriate to include some aspects of nonresponse in the analytical model.

Report any caveats about how the approaches used to handle missing data could have impact on results

Interpretation of results

Since most analyses are based on observational studies rather than on the results of a controlled experiment, avoid drawing conclusions concerning causality.

When studying changes over time, beware of focusing on short-term trends without inspecting them in light of medium-and long-term trends. Frequently, short-term trends are merely minor fluctuations around a more important medium- and/or long-term trend.

Where possible, avoid arbitrary time reference points. Instead, use meaningful points of reference, such as the last major turning point for economic data, generation-to-generation differences for demographic statistics, and legislative changes for social statistics.

Presentation of results

Focus the article on the important variables and topics. Trying to be too comprehensive will often interfere with a strong story line.

Arrange ideas in a logical order and in order of relevance or importance. Use headings, subheadings and sidebars to strengthen the organization of the article.

Keep the language as simple as the subject permits. Depending on the targeted audience for the article, some loss of precision may sometimes be an acceptable trade-off for more readable text.

Use graphs in addition to text and tables to communicate the message. Use headings that capture the meaning ( e.g. "Women's earnings still trail men's") in preference to traditional chart titles ( e.g. "Income by age and sex"). Always help readers understand the information in the tables and charts by discussing it in the text.

When tables are used, take care that the overall format contributes to the clarity of the data in the tables and prevents misinterpretation.  This includes spacing; the wording, placement and appearance of titles; row and column headings and other labeling. 

Explain rounding practices or procedures. In the presentation of rounded data, do not use more significant digits than are consistent with the accuracy of the data.

Satisfy any confidentiality requirements ( e.g. minimum cell sizes) imposed by the surveys or administrative sources whose data are being analysed.

Include information about the data sources used and any shortcomings in the data that may have affected the analysis.  Either have a section in the paper about the data or a reference to where the reader can get the details.

Include information about the analytical methods and tools used.  Either have a section on methods or a reference to where the reader can get the details.

Include information regarding the quality of the results. Standard errors, confidence intervals and/or coefficients of variation provide the reader important information about data quality. The choice of indicator may vary depending on where the article is published.

Ensure that all references are accurate, consistent and are referenced in the text.

Check for errors in the article. Check details such as the consistency of figures used in the text, tables and charts, the accuracy of external data, and simple arithmetic.

Ensure that the intentions stated in the introduction are fulfilled by the rest of the article. Make sure that the conclusions are consistent with the evidence.

Have the article reviewed by others for relevance, accuracy and comprehensibility, regardless of where it is to be disseminated.  As a good practice, ask someone from the data providing division to review how the data were used.  If the article is to be disseminated outside of Statistics Canada, it must undergo institutional and peer review as specified in the Policy on the Review of Information Products (Statistics Canada, 2003). 

If the article is to be disseminated in a Statistics Canada publication make sure that it complies with the current Statistics Canada Publishing Standards. These standards affect graphs, tables and style, among other things.

As a good practice, consider presenting the results to peers prior to finalizing the text. This is another kind of peer review that can help improve the article. Always do a dry run of presentations involving external audiences.

Refer to available documents that could provide further guidance for improvement of your article, such as Guidelines on Writing Analytical Articles (Statistics Canada 2008 ) and the Style Guide (Statistics Canada 2004)

Quality indicators

Main quality elements:  relevance, interpretability, accuracy, accessibility

An analytical product is relevant if there is an audience who is (or will be) interested in the results of the study.

For the interpretability of an analytical article to be high, the style of writing must suit the intended audience. As well, sufficient details must be provided that another person, if allowed access to the data, could replicate the results.

For an analytical product to be accurate, appropriate methods and tools need to be used to produce the results.

For an analytical product to be accessible, it must be available to people for whom the research results would be useful.

Binder, D.A. and G.R. Roberts. 2003. "Design-based methods for estimating model parameters."  In Analysis of Survey Data. R.L. Chambers and C.J. Skinner ( eds. ) Chichester. Wiley. p. 29-48.

Binder, D.A. and G. Roberts. 2009. "Design and Model Based Inference for Model Parameters." In Handbook of Statistics 29B: Sample Surveys: Inference and Analysis. Pfeffermann, D. and Rao, C.R. ( eds. ) Vol. 29B. Chapter 24. Amsterdam.Elsevier. 666 p.

Chambers, R.L. and C.J. Skinner ( eds. ) 2003. Analysis of Survey Data. Chichester. Wiley. 398 p.

Korn, E.L. and B.I. Graubard. 1999. Analysis of Health Surveys. New York. Wiley. 408 p.

Lehtonen, R. and E.J. Pahkinen. 2004. Practical Methods for Design and Analysis of Complex Surveys.Second edition. Chichester. Wiley.

Lohr, S.L. 1999. Sampling: Design and Analysis. Duxbury Press. 512 p.

Skinner, C.K., D.Holt and T.M.F. Smith. 1989. Analysis of Complex Surveys. Chichester. Wiley. 328 p.

Thompson, M.E. 1997. Theory of Sample Surveys. London. Chapman and Hall. 312 p.

Statistics Canada. 2003. "Policy on the Review of Information Products." Statistics Canada Policy Manual. Section 2.5. Last updated March 4, 2009.

Statistics Canada. 2004. Style Guide.  Last updated October 6, 2004.

Statistics Canada. 2008. Guidelines on Writing Analytical Articles. Last updated September 16, 2008.