what is data analysis in scientific research

Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
• Qualitative Analysis Tools
• Mixed Methods Analysis
• Geospatial Analysis
• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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• Last Updated: May 3, 2024 9:38 AM
• URL: https://guides.library.georgetown.edu/data-analysis

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Data Analysis and Interpretation: Revealing and explaining trends

by Anne E. Egger, Ph.D., Anthony Carpi, Ph.D.

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Did you know that scientists don't always agree on what data mean? Different scientists can look at the same set of data and come up with different explanations for it, and disagreement among scientists doesn't point to bad science.

Data collection is the systematic recording of information; data analysis involves working to uncover patterns and trends in datasets; data interpretation involves explaining those patterns and trends.

Scientists interpret data based on their background knowledge and experience; thus, different scientists can interpret the same data in different ways.

By publishing their data and the techniques they used to analyze and interpret those data, scientists give the community the opportunity to both review the data and use them in future research.

Before you decide what to wear in the morning, you collect a variety of data: the season of the year, what the forecast says the weather is going to be like, which clothes are clean and which are dirty, and what you will be doing during the day. You then analyze those data . Perhaps you think, "It's summer, so it's usually warm." That analysis helps you determine the best course of action, and you base your apparel decision on your interpretation of the information. You might choose a t-shirt and shorts on a summer day when you know you'll be outside, but bring a sweater with you if you know you'll be in an air-conditioned building.

Though this example may seem simplistic, it reflects the way scientists pursue data collection, analysis , and interpretation . Data (the plural form of the word datum) are scientific observations and measurements that, once analyzed and interpreted, can be developed into evidence to address a question. Data lie at the heart of all scientific investigations, and all scientists collect data in one form or another. The weather forecast that helped you decide what to wear, for example, was an interpretation made by a meteorologist who analyzed data collected by satellites. Data may take the form of the number of bacteria colonies growing in soup broth (see our Experimentation in Science module), a series of drawings or photographs of the different layers of rock that form a mountain range (see our Description in Science module), a tally of lung cancer victims in populations of cigarette smokers and non-smokers (see our Comparison in Science module), or the changes in average annual temperature predicted by a model of global climate (see our Modeling in Science module).

Scientific data collection involves more care than you might use in a casual glance at the thermometer to see what you should wear. Because scientists build on their own work and the work of others, it is important that they are systematic and consistent in their data collection methods and make detailed records so that others can see and use the data they collect.

But collecting data is only one step in a scientific investigation, and scientific knowledge is much more than a simple compilation of data points. The world is full of observations that can be made, but not every observation constitutes a useful piece of data. For example, your meteorologist could record the outside air temperature every second of the day, but would that make the forecast any more accurate than recording it once an hour? Probably not. All scientists make choices about which data are most relevant to their research and what to do with those data: how to turn a collection of measurements into a useful dataset through processing and analysis , and how to interpret those analyzed data in the context of what they already know. The thoughtful and systematic collection, analysis, and interpretation of data allow them to be developed into evidence that supports scientific ideas, arguments, and hypotheses .

Data collection, analysis , and interpretation: Weather and climate

The weather has long been a subject of widespread data collection, analysis , and interpretation . Accurate measurements of air temperature became possible in the mid-1700s when Daniel Gabriel Fahrenheit invented the first standardized mercury thermometer in 1714 (see our Temperature module). Air temperature, wind speed, and wind direction are all critical navigational information for sailors on the ocean, but in the late 1700s and early 1800s, as sailing expeditions became common, this information was not easy to come by. The lack of reliable data was of great concern to Matthew Fontaine Maury, the superintendent of the Depot of Charts and Instruments of the US Navy. As a result, Maury organized the first international Maritime Conference , held in Brussels, Belgium, in 1853. At this meeting, international standards for taking weather measurements on ships were established and a system for sharing this information between countries was founded.

Defining uniform data collection standards was an important step in producing a truly global dataset of meteorological information, allowing data collected by many different people in different parts of the world to be gathered together into a single database. Maury's compilation of sailors' standardized data on wind and currents is shown in Figure 1. The early international cooperation and investment in weather-related data collection has produced a valuable long-term record of air temperature that goes back to the 1850s.

Figure 1: Plate XV from Maury, Matthew F. 1858. The Winds. Chapter in Explanations and Sailing Directions. Washington: Hon. Isaac Toucey.

This vast store of information is considered "raw" data: tables of numbers (dates and temperatures), descriptions (cloud cover), location, etc. Raw data can be useful in and of itself – for example, if you wanted to know the air temperature in London on June 5, 1801. But the data alone cannot tell you anything about how temperature has changed in London over the past two hundred years, or how that information is related to global-scale climate change. In order for patterns and trends to be seen, data must be analyzed and interpreted first. The analyzed and interpreted data may then be used as evidence in scientific arguments, to support a hypothesis or a theory .

Good data are a potential treasure trove – they can be mined by scientists at any time – and thus an important part of any scientific investigation is accurate and consistent recording of data and the methods used to collect those data. The weather data collected since the 1850s have been just such a treasure trove, based in part upon the standards established by Matthew Maury . These standards provided guidelines for data collections and recording that assured consistency within the dataset . At the time, ship captains were able to utilize the data to determine the most reliable routes to sail across the oceans. Many modern scientists studying climate change have taken advantage of this same dataset to understand how global air temperatures have changed over the recent past. In neither case can one simply look at the table of numbers and observations and answer the question – which route to take, or how global climate has changed. Instead, both questions require analysis and interpretation of the data.

Comprehension Checkpoint

• Data analysis: A complex and challenging process

Though it may sound straightforward to take 150 years of air temperature data and describe how global climate has changed, the process of analyzing and interpreting those data is actually quite complex. Consider the range of temperatures around the world on any given day in January (see Figure 2): In Johannesburg, South Africa, where it is summer, the air temperature can reach 35° C (95° F), and in Fairbanks, Alaska at that same time of year, it is the middle of winter and air temperatures might be -35° C (-31° F). Now consider that over huge expanses of the ocean, where no consistent measurements are available. One could simply take an average of all of the available measurements for a single day to get a global air temperature average for that day, but that number would not take into account the natural variability within and uneven distribution of those measurements.

Figure 2: Satellite image composite of average air temperatures (in degrees Celsius) across the globe on January 2, 2008 (http://www.ssec.wisc.edu/data/).

Defining a single global average temperature requires scientists to make several decisions about how to process all of those data into a meaningful set of numbers. In 1986, climatologists Phil Jones, Tom Wigley, and Peter Wright published one of the first attempts to assess changes in global mean surface air temperature from 1861 to 1984 (Jones, Wigley, & Wright, 1986). The majority of their paper – three out of five pages – describes the processing techniques they used to correct for the problems and inconsistencies in the historical data that would not be related to climate. For example, the authors note:

Early SSTs [sea surface temperatures] were measured using water collected in uninsulated, canvas buckets, while more recent data come either from insulated bucket or cooling water intake measurements, with the latter considered to be 0.3-0.7° C warmer than uninsulated bucket measurements.

Correcting for this bias may seem simple, just adding ~0.5° C to early canvas bucket measurements, but it becomes more complicated than that because, the authors continue, the majority of SST data do not include a description of what kind of bucket or system was used.

Similar problems were encountered with marine air temperature data . Historical air temperature measurements over the ocean were taken aboard ships, but the type and size of ship could affect the measurement because size "determines the height at which observations were taken." Air temperature can change rapidly with height above the ocean. The authors therefore applied a correction for ship size in their data. Once Jones, Wigley, and Wright had made several of these kinds of corrections, they analyzed their data using a spatial averaging technique that placed measurements within grid cells on the Earth's surface in order to account for the fact that there were many more measurements taken on land than over the oceans.

Developing this grid required many decisions based on their experience and judgment, such as how large each grid cell needed to be and how to distribute the cells over the Earth. They then calculated the mean temperature within each grid cell, and combined all of these means to calculate a global average air temperature for each year. Statistical techniques such as averaging are commonly used in the research process and can help identify trends and relationships within and between datasets (see our Statistics in Science module). Once these spatially averaged global mean temperatures were calculated, the authors compared the means over time from 1861 to 1984.

A common method for analyzing data that occur in a series, such as temperature measurements over time, is to look at anomalies, or differences from a pre-defined reference value . In this case, the authors compared their temperature values to the mean of the years 1970-1979 (see Figure 3). This reference mean is subtracted from each annual mean to produce the jagged lines in Figure 3, which display positive or negative anomalies (values greater or less than zero). Though this may seem to be a circular or complex way to display these data, it is useful because the goal is to show change in mean temperatures rather than absolute values.

Figure 3: The black line shows global temperature anomalies, or differences between averaged yearly temperature measurements and the reference value for the entire globe. The smooth, red line is a filtered 10-year average. (Based on Figure 5 in Jones et al., 1986).

Putting data into a visual format can facilitate additional analysis (see our Using Graphs and Visual Data module). Figure 3 shows a lot of variability in the data: There are a number of spikes and dips in global temperature throughout the period examined. It can be challenging to see trends in data that have so much variability; our eyes are drawn to the extreme values in the jagged lines like the large spike in temperature around 1876 or the significant dip around 1918. However, these extremes do not necessarily reflect long-term trends in the data.

In order to more clearly see long-term patterns and trends, Jones and his co-authors used another processing technique and applied a filter to the data by calculating a 10-year running average to smooth the data. The smooth lines in the graph represent the filtered data. The smooth line follows the data closely, but it does not reach the extreme values .

Data processing and analysis are sometimes misinterpreted as manipulating data to achieve the desired results, but in reality, the goal of these methods is to make the data clearer, not to change it fundamentally. As described above, in addition to reporting data, scientists report the data processing and analysis methods they use when they publish their work (see our Understanding Scientific Journals and Articles module), allowing their peers the opportunity to assess both the raw data and the techniques used to analyze them.

• Data interpretation: Uncovering and explaining trends in the data

The analyzed data can then be interpreted and explained. In general, when scientists interpret data, they attempt to explain the patterns and trends uncovered through analysis , bringing all of their background knowledge, experience, and skills to bear on the question and relating their data to existing scientific ideas. Given the personal nature of the knowledge they draw upon, this step can be subjective, but that subjectivity is scrutinized through the peer review process (see our Peer Review in Science module). Based on the smoothed curves, Jones, Wigley, and Wright interpreted their data to show a long-term warming trend. They note that the three warmest years in the entire dataset are 1980, 1981, and 1983. They do not go further in their interpretation to suggest possible causes for the temperature increase, however, but merely state that the results are "extremely interesting when viewed in the light of recent ideas of the causes of climate change."

• Making data available

The process of data collection, analysis , and interpretation happens on multiple scales. It occurs over the course of a day, a year, or many years, and may involve one or many scientists whose priorities change over time. One of the fundamentally important components of the practice of science is therefore the publication of data in the scientific literature (see our Utilizing the Scientific Literature module). Properly collected and archived data continues to be useful as new research questions emerge. In fact, some research involves re-analysis of data with new techniques, different ways of looking at the data, or combining the results of several studies.

For example, in 1997, the Collaborative Group on Hormonal Factors in Breast Cancer published a widely-publicized study in the prestigious medical journal The Lancet entitled, "Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer" (Collaborative Group on Hormonal Factors in Breast Cancer, 1997). The possible link between breast cancer and hormone replacement therapy (HRT) had been studied for years, with mixed results: Some scientists suggested a small increase of cancer risk associated with HRT as early as 1981 (Brinton et al., 1981), but later research suggested no increased risk (Kaufman et al., 1984). By bringing together results from numerous studies and reanalyzing the data together, the researchers concluded that women who were treated with hormone replacement therapy were more like to develop breast cancer. In describing why the reanalysis was used, the authors write:

The increase in the relative risk of breast cancer associated with each year of [HRT] use in current and recent users is small, so inevitably some studies would, by chance alone, show significant associations and others would not. Combination of the results across many studies has the obvious advantage of reducing such random fluctuations.

In many cases, data collected for other purposes can be used to address new questions. The initial reason for collecting weather data, for example, was to better predict winds and storms to help assure safe travel for trading ships. It is only more recently that interest shifted to long-term changes in the weather, but the same data easily contribute to answering both of those questions.

• Technology for sharing data advances science

One of the most exciting advances in science today is the development of public databases of scientific information that can be accessed and used by anyone. For example, climatic and oceanographic data , which are generally very expensive to obtain because they require large-scale operations like drilling ice cores or establishing a network of buoys across the Pacific Ocean, are shared online through several web sites run by agencies responsible for maintaining and distributing those data, such as the Carbon Dioxide Information Analysis Center run by the US Department of Energy (see Research under the Resources tab). Anyone can download those data to conduct their own analyses and make interpretations . Likewise, the Human Genome Project has a searchable database of the human genome, where researchers can both upload and download their data (see Research under the Resources tab).

The number of these widely available datasets has grown to the point where the National Institute of Standards and Technology actually maintains a database of databases. Some organizations require their participants to make their data publicly available, such as the Incorporated Research Institutions for Seismology (IRIS): The instrumentation branch of IRIS provides support for researchers by offering seismic instrumentation, equipment maintenance and training, and logistical field support for experiments . Anyone can apply to use the instruments as long as they provide IRIS with the data they collect during their seismic experiments. IRIS then makes these data available to the public.

Making data available to other scientists is not a new idea, but having those data available on the Internet in a searchable format has revolutionized the way that scientists can interact with the data, allowing for research efforts that would have been impossible before. This collective pooling of data also allows for new kinds of analysis and interpretation on global scales and over long periods of time. In addition, making data easily accessible helps promote interdisciplinary research by opening the doors to exploration by diverse scientists in many fields.

Table of Contents

• Data collection, analysis, and interpretation: Weather and climate
• Different interpretations in the scientific community
• Debate over data interpretation spurs further research

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Data Analysis in Quantitative Research

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Quantitative data analysis serves as part of an essential process of evidence-making in health and social sciences. It is adopted for any types of research question and design whether it is descriptive, explanatory, or causal. However, compared with qualitative counterpart, quantitative data analysis has less flexibility. Conducting quantitative data analysis requires a prerequisite understanding of the statistical knowledge and skills. It also requires rigor in the choice of appropriate analysis model and the interpretation of the analysis outcomes. Basically, the choice of appropriate analysis techniques is determined by the type of research question and the nature of the data. In addition, different analysis techniques require different assumptions of data. This chapter provides introductory guides for readers to assist them with their informed decision-making in choosing the correct analysis models. To this end, it begins with discussion of the levels of measure: nominal, ordinal, and scale. Some commonly used analysis techniques in univariate, bivariate, and multivariate data analysis are presented for practical examples. Example analysis outcomes are produced by the use of SPSS (Statistical Package for Social Sciences).

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Jung, Y.M. (2019). Data Analysis in Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_109

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

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Scientific Research and Big Data

Big Data promises to revolutionise the production of knowledge within and beyond science, by enabling novel, highly efficient ways to plan, conduct, disseminate and assess research. The last few decades have witnessed the creation of novel ways to produce, store, and analyse data, culminating in the emergence of the field of data science , which brings together computational, algorithmic, statistical and mathematical techniques towards extrapolating knowledge from big data. At the same time, the Open Data movement—emerging from policy trends such as the push for Open Government and Open Science—has encouraged the sharing and interlinking of heterogeneous research data via large digital infrastructures. The availability of vast amounts of data in machine-readable formats provides an incentive to create efficient procedures to collect, organise, visualise and model these data. These infrastructures, in turn, serve as platforms for the development of artificial intelligence, with an eye to increasing the reliability, speed and transparency of processes of knowledge creation. Researchers across all disciplines see the newfound ability to link and cross-reference data from diverse sources as improving the accuracy and predictive power of scientific findings and helping to identify future directions of inquiry, thus ultimately providing a novel starting point for empirical investigation. As exemplified by the rise of dedicated funding, training programmes and publication venues, big data are widely viewed as ushering in a new way of performing research and challenging existing understandings of what counts as scientific knowledge.

This entry explores these claims in relation to the use of big data within scientific research, and with an emphasis on the philosophical issues emerging from such use. To this aim, the entry discusses how the emergence of big data—and related technologies, institutions and norms—informs the analysis of the following themes:

• how statistics, formal and computational models help to extrapolate patterns from data, and with which consequences;
• the role of critical scrutiny (human intelligence) in machine learning, and its relation to the intelligibility of research processes;
• the nature of data as research components;
• the relation between data and evidence, and the role of data as source of empirical insight;
• the view of knowledge as theory-centric;
• understandings of the relation between prediction and causality;
• the separation of fact and value; and
• the risks and ethics of data science.

These are areas where attention to research practices revolving around big data can benefit philosophy, and particularly work in the epistemology and methodology of science. This entry doesn’t cover the vast scholarship in the history and social studies of science that has emerged in recent years on this topic, though references to some of that literature can be found when conceptually relevant. Complementing historical and social scientific work in data studies, the philosophical analysis of data practices can also elicit significant challenges to the hype surrounding data science and foster a critical understanding of the role of data-fuelled artificial intelligence in research.

1. What Are Big Data?

2. extrapolating data patterns: the role of statistics and software, 3. human and artificial intelligence, 4. the nature of (big) data, 5. big data and evidence, 6. big data, knowledge and inquiry, 7. big data between causation and prediction, 8. the fact/value distinction, 9. big data risks and the ethics of data science, 10. conclusion: big data and good science, other internet resources, related entries.

We are witnessing a progressive “datafication” of social life. Human activities and interactions with the environment are being monitored and recorded with increasing effectiveness, generating an enormous digital footprint. The resulting “big data” are a treasure trove for research, with ever more sophisticated computational tools being developed to extract knowledge from such data. One example is the use of various different types of data acquired from cancer patients, including genomic sequences, physiological measurements and individual responses to treatment, to improve diagnosis and treatment. Another example is the integration of data on traffic flow, environmental and geographical conditions, and human behaviour to produce safety measures for driverless vehicles, so that when confronted with unforeseen events (such as a child suddenly darting into the street on a very cold day), the data can be promptly analysed to identify and generate an appropriate response (the car swerving enough to avoid the child while also minimising the risk of skidding on ice and damaging to other vehicles). Yet another instance is the understanding of the nutritional status and needs of a particular population that can be extracted from combining data on food consumption generated by commercial services (e.g., supermarkets, social media and restaurants) with data coming from public health and social services, such as blood test results and hospital intakes linked to malnutrition. In each of these cases, the availability of data and related analytic tools is creating novel opportunities for research and for the development of new forms of inquiry, which are widely perceived as having a transformative effect on science as a whole.

A useful starting point in reflecting on the significance of such cases for a philosophical understanding of research is to consider what the term “big data” actually refers to within contemporary scientific discourse. There are multiple ways to define big data (Kitchin 2014, Kitchin & McArdle 2016). Perhaps the most straightforward characterisation is as large datasets that are produced in a digital form and can be analysed through computational tools. Hence the two features most commonly associated with Big Data are volume and velocity. Volume refers to the size of the files used to archive and spread data. Velocity refers to the pressing speed with which data is generated and processed. The body of digital data created by research is growing at breakneck pace and in ways that are arguably impossible for the human cognitive system to grasp and thus require some form of automated analysis.

Volume and velocity are also, however, the most disputed features of big data. What may be perceived as “large volume” or “high velocity” depends on rapidly evolving technologies to generate, store, disseminate and visualise the data. This is exemplified by the high-throughput production, storage and dissemination of genomic sequencing and gene expression data, where both data volume and velocity have dramatically increased within the last two decades. Similarly, current understandings of big data as “anything that cannot be easily captured in an Excel spreadsheet” are bound to shift rapidly as new analytic software becomes established, and the very idea of using spreadsheets to capture data becomes a thing of the past. Moreover, data size and speed do not take account of the diversity of data types used by researchers, which may include data that are not generated in digital formats or whose format is not computationally tractable, and which underscores the importance of data provenance (that is, the conditions under which data were generated and disseminated) to processes of inference and interpretation. And as discussed below, the emphasis on physical features of data obscures the continuing dependence of data interpretation on circumstances of data use, including specific queries, values, skills and research situations.

An alternative is to define big data not by reference to their physical attributes, but rather by virtue of what can and cannot be done with them. In this view, big data is a heterogeneous ensemble of data collected from a variety of different sources, typically (but not always) in digital formats suitable for algorithmic processing, in order to generate new knowledge. For example boyd and Crawford (2012: 663) identify big data with “the capacity to search, aggregate and cross-reference large datasets”, while O’Malley and Soyer (2012) focus on the ability to interrogate and interrelate diverse types of data, with the aim to be able to consult them as a single body of evidence. The examples of transformative “big data research” given above are all easily fitted into this view: it is not the mere fact that lots of data are available that makes a different in those cases, but rather the fact that lots of data can be mobilised from a wide variety of sources (medical records, environmental surveys, weather measurements, consumer behaviour). This account makes sense of other characteristic “v-words” that have been associated with big data, including:

• Variety in the formats and purposes of data, which may include objects as different as samples of animal tissue, free-text observations, humidity measurements, GPS coordinates, and the results of blood tests;
• Veracity , understood as the extent to which the quality and reliability of big data can be guaranteed. Data with high volume, velocity and variety are at significant risk of containing inaccuracies, errors and unaccounted-for bias. In the absence of appropriate validation and quality checks, this could result in a misleading or outright incorrect evidence base for knowledge claims (Floridi & Illari 2014; Cai & Zhu 2015; Leonelli 2017);
• Validity , which indicates the selection of appropriate data with respect to the intended use. The choice of a specific dataset as evidence base requires adequate and explicit justification, including recourse to relevant background knowledge to ground the identification of what counts as data in that context (e.g., Loettgers 2009, Bogen 2010);
• Volatility , i.e., the extent to which data can be relied upon to remain available, accessible and re-interpretable despite changes in archival technologies. This is significant given the tendency of formats and tools used to generate and analyse data to become obsolete, and the efforts required to update data infrastructures so as to guarantee data access in the long term (Bowker 2006; Edwards 2010; Lagoze 2014; Borgman 2015);
• Value , i.e., the multifaceted forms of significance attributed to big data by different sections of society, which depend as much on the intended use of the data as on historical, social and geographical circumstances (Leonelli 2016, D’Ignazio and Klein 2020). Alongside scientific value, researchers may impute financial, ethical, reputational and even affective value to data, depending on their intended use as well as the historical, social and geographical circumstances of their use. The institutions involved in governing and funding research also have ways of valuing data, which may not always overlap with the priorities of researchers (Tempini 2017).

This list of features, though not exhaustive, highlights how big data is not simply “a lot of data”. The epistemic power of big data lies in their capacity to bridge between different research communities, methodological approaches and theoretical frameworks that are difficult to link due to conceptual fragmentation, social barriers and technical difficulties (Leonelli 2019a). And indeed, appeals to big data often emerge from situations of inquiry that are at once technically, conceptually and socially challenging, and where existing methods and resources have proved insufficient or inadequate (Sterner & Franz 2017; Sterner, Franz, & Witteveen 2020).

This understanding of big data is rooted in a long history of researchers grappling with large and complex datasets, as exemplified by fields like astronomy, meteorology, taxonomy and demography (see the collections assembled by Daston 2017; Anorova et al. 2017; Porter & Chaderavian 2018; as well as Anorova et al. 2010, Sepkoski 2013, Stevens 2016, Strasser 2019 among others). Similarly, biomedical research—and particularly subfields such as epidemiology, pharmacology and public health—has an extensive tradition of tackling data of high volume, velocity, variety and volatility, and whose validity, veracity and value are regularly negotiated and contested by patients, governments, funders, pharmaceutical companies, insurances and public institutions (Bauer 2008). Throughout the twentieth century, these efforts spurred the development of techniques, institutions and instruments to collect, order, visualise and analyse data, such as: standard classification systems and formats; guidelines, tools and legislation for the management and security of sensitive data; and infrastructures to integrate and sustain data collections over long periods of time (Daston 2017).

This work culminated in the application of computational technologies, modelling tools and statistical methods to big data (Porter 1995; Humphreys 2004; Edwards 2010), increasingly pushing the boundaries of data analytics thanks to supervised learning, model fitting, deep neural networks, search and optimisation methods, complex data visualisations and various other tools now associated with artificial intelligence. Many of these tools are based on algorithms whose functioning and results are tested against specific data samples (a process called “training”). These algorithms are programmed to “learn” from each interaction with novel data: in other words, they have the capacity to change themselves in response to new information being inputted into the system, thus becoming more attuned to the phenomena they are analysing and improving their ability to predict future behaviour. The scope and extent of such changes is shaped by the assumptions used to build the algorithms and the capability of related software and hardware to identify, access and process information of relevance to the learning in question. There is however a degree of unpredictability and opacity to these systems, which can evolve to the point of defying human understanding (more on this below).

New institutions, communication platforms and regulatory frameworks also emerged to assemble, prepare and maintain data for such uses (Kitchin 2014), such as various forms of digital data infrastructures, organisations aiming to coordinate and improve the global data landscape (e.g., the Research Data Alliance), and novel measures for data protection, like the General Data Protection Regulation launched in 2017 by the European Union. Together, these techniques and institutions afford the opportunity to assemble and interpret data at a much broader scale, while also promising to deliver finer levels of granularity in data analysis. [ 1 ] They increase the scope of any investigation by making it possible for researchers to link their own findings to those of countless others across the world, both within and beyond the academic sphere. By enhancing the mobility of data, they facilitate their repurposing for a variety of goals that may have been unforeseeable when the data were originally generated. And by transforming the role of data within research, they heighten their status as valuable research outputs in and of themselves. These technological and methodological developments have significant implications for philosophical conceptualisations of data, inferential processes and scientific knowledge, as well as for how research is conducted, organised, governed and assessed. It is to these philosophical concerns that I now turn.

Big data are often associated to the idea of data-driven research, where learning happens through the accumulation of data and the application of methods to extract meaningful patterns from those data. Within data-driven inquiry, researchers are expected to use data as their starting point for inductive inference, without relying on theoretical preconceptions—a situation described by advocates as “the end of theory”, in contrast to theory-driven approaches where research consists of testing a hypothesis (Anderson 2008, Hey et al. 2009). In principle at least, big data constitute the largest pool of data ever assembled and thus a strong starting point to search for correlations (Mayer-Schönberger & Cukier 2013). Crucial to the credibility of the data-driven approach is the efficacy of the methods used to extrapolate patterns from data and evaluate whether or not such patterns are meaningful, and what “meaning” may involve in the first place. Hence, some philosophers and data scholars have argued that

the most important and distinctive characteristic of Big Data [is] its use of statistical methods and computational means of analysis, (Symons & Alvarado 2016: 4)

such as for instance machine learning tools, deep neural networks and other “intelligent” practices of data handling.

The emphasis on statistics as key adjudicator of validity and reliability of patterns extracted from data is not novel. Exponents of logical empiricism looked for logically watertight methods to secure and justify inference from data, and their efforts to develop a theory of probability proceeded in parallel with the entrenchment of statistical reasoning in the sciences in the first half of the twentieth century (Romeijn 2017). In the early 1960s, Patrick Suppes offered a seminal link between statistical methods and the philosophy of science through his work on the production and interpretation of data models. As a philosopher deeply embedded in experimental practice, Suppes was interested in the means and motivations of key statistical procedures for data analysis such as data reduction and curve fitting. He argued that once data are adequately prepared for statistical modelling, all the concerns and choices that motivated data processing become irrelevant to their analysis and interpretation. This inspired him to differentiate between models of theory, models of experiment and models of data, noting that such different components of inquiry are governed by different logics and cannot be compared in a straightforward way. For instance,

the precise definition of models of the data for any given experiment requires that there be a theory of the data in the sense of the experimental procedure, as well as in the ordinary sense of the empirical theory of the phenomena being studied. (Suppes 1962: 253)

Suppes viewed data models as necessarily statistical: that is, as objects

designed to incorporate all the information about the experiment which can be used in statistical tests of the adequacy of the theory. (Suppes 1962: 258)

His formal definition of data models reflects this decision, with statistical requirements such as homogeneity, stationarity and order identified as the ultimate criteria to identify a data model Z and evaluate its adequacy:

Z is an N-fold model of the data for experiment Y if and only if there is a set Y and a probability measure P on subsets of Y such that \(Y = \langle Y, P\rangle\) is a model of the theory of the experiment, Z is an N-tuple of elements of Y , and Z satisfies the statistical tests of homogeneity, stationarity and order. (1962: 259)

This analysis of data models portrayed statistical methods as key conduits between data and theory, and hence as crucial components of inferential reasoning.

The focus on statistics as entry point to discussions of inference from data was widely promoted in subsequent philosophical work. Prominent examples include Deborah Mayo, who in her book Error and the Growth of Experimental Knowledge asked:

What should be included in data models? The overriding constraint is the need for data models that permit the statistical assessment of fit (between prediction and actual data); (Mayo 1996: 136)

and Bas van Fraassen, who also embraced the idea of data models as “summarizing relative frequencies found in data” (Van Fraassen 2008: 167). Closely related is the emphasis on statistics as means to detect error within datasets in relation to specific hypotheses, most prominently endorsed by the error-statistical approach to inference championed by Mayo and Aris Spanos (Mayo & Spanos 2009a). This approach aligns with the emphasis on computational methods for data analysis within big data research, and supports the idea that the better the inferential tools and methods, the better the chance to extract reliable knowledge from data.

When it comes to addressing methodological challenges arising from the computational analysis of big data, however, statistical expertise needs to be complemented by computational savvy in the training and application of algorithms associated to artificial intelligence, including machine learning but also other mathematical procedures for operating upon data (Bringsjord & Govindarajulu 2018). Consider for instance the problem of overfitting, i.e., the mistaken identification of patterns in a dataset, which can be greatly amplified by the training techniques employed by machine learning algorithms. There is no guarantee that an algorithm trained to successfully extrapolate patterns from a given dataset will be as successful when applied to other data. Common approaches to this problem involve the re-ordering and partitioning of both data and training methods, so that it is possible to compare the application of the same algorithms to different subsets of the data (“cross-validation”), combine predictions arising from differently trained algorithms (“ensembling”) or use hyperparameters (parameters whose value is set prior to data training) to prepare the data for analysis.

Handling these issues, in turn, requires

familiarity with the mathematical operations in question, their implementations in code, and the hardware architectures underlying such implementations. (Lowrie 2017: 3)

For instance, machine learning

aims to build programs that develop their own analytic or descriptive approaches to a body of data, rather than employing ready-made solutions such as rule-based deduction or the regressions of more traditional statistics. (Lowrie 2017: 4)

In other words, statistics and mathematics need to be complemented by expertise in programming and computer engineering. The ensemble of skills thus construed results in a specific epistemological approach to research, which is broadly characterised by an emphasis on the means of inquiry as the most significant driver of research goals and outputs. This approach, which Sabina Leonelli characterised as data-centric , involves “focusing more on the processes through which research is carried out than on its ultimate outcomes” (Leonelli 2016: 170). In this view, procedures, techniques, methods, software and hardware are the prime motors of inquiry and the chief influence on its outcomes. Focusing more specifically on computational systems, John Symons and Jack Horner argued that much of big data research consists of software-intensive science rather than data-driven research: that is, science that depends on software for its design, development, deployment and use, and thus encompasses procedures, types of reasoning and errors that are unique to software, such as for example the problems generated by attempts to map real-world quantities to discrete-state machines, or approximating numerical operations (Symons & Horner 2014: 473). Software-intensive science is arguably supported by an algorithmic rationality focused on the feasibility, practicality and efficiency of algorithms, which is typically assessed by reference to concrete situations of inquiry (Lowrie 2017).

Algorithms are enormously varied in their mathematical structures and underpinning conceptual commitments, and more philosophical work needs to be carried out on the specifics of computational tools and software used in data science and related applications—with emerging work in philosophy of computer science providing an excellent way forward (Turner & Angius 2019). Nevertheless, it is clear that whether or not a given algorithm successfully applies to the data at hand depends on factors that cannot be controlled through statistical or even computational methods: for instance, the size, structure and format of the data, the nature of the classifiers used to partition the data, the complexity of decision boundaries and the very goals of the investigation.

In a forceful critique informed by the philosophy of mathematics, Christian Calude and Giuseppe Longo argued that there is a fundamental problem with the assumption that more data will necessarily yield more information:

very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. (Calude & Longo 2017: 595)

They conclude that big data analysis is by definition unable to distinguish spurious from meaningful correlations and is therefore a threat to scientific research. A related worry, sometimes dubbed “the curse of dimensionality” by data scientists, concerns the extent to which the analysis of a given dataset can be scaled up in complexity and in the number of variables being considered. It is well known that the more dimensions one considers in classifying samples, for example, the larger the dataset on which such dimensions can be accurately generalised. This demonstrates the continuing, tight dependence between the volume and quality of data on the one hand, and the type and breadth of research questions for which data need to serve as evidence on the other hand.

Determining the fit between inferential methods and data requires high levels of expertise and contextual judgement (a situation known within machine learning as the “no free lunch theorem”). Indeed, overreliance on software for inference and data modelling can yield highly problematic results. Symons and Horner note that the use of complex software in big data analysis makes margins of error unknowable, because there is no clear way to test them statistically (Symons & Horner 2014: 473). The path complexity of programs with high conditionality imposes limits on standard error correction techniques. As a consequence, there is no effective method for characterising the error distribution in the software except by testing all paths in the code, which is unrealistic and intractable in the vast majority of cases due to the complexity of the code.

Rather than acting as a substitute, the effective and responsible use of artificial intelligence tools in big data analysis requires the strategic exercise of human intelligence—but for this to happen, AI systems applied to big data need to be accessible to scrutiny and modification. Whether or not this is the case, and who is best qualified to exercise such scrutiny, is under dispute. Thomas Nickles argued that the increasingly complex and distributed algorithms used for data analysis follow in the footsteps of long-standing scientific attempts to transcend the limits of human cognition. The resulting epistemic systems may no longer be intelligible to humans: an “alien intelligence” within which “human abilities are no longer the ultimate criteria of epistemic success” (Nickles forthcoming). Such unbound cognition holds the promise of enabling powerful inferential reasoning from previously unimaginable volumes of data. The difficulties in contextualising and scrutinising such reasoning, however, sheds doubt on the reliability of the results. It is not only machine learning algorithms that are becoming increasingly inaccessible to evaluation: beyond the complexities of programming code, computational data analysis requires a whole ecosystem of classifications, models, networks and inference tools which typically have different histories and purposes, and whose relation to each other—and effects when they are used together—are far from understood and may well be untraceable.

This raises the question of whether the knowledge produced by such data analytic systems is at all intelligible to humans, and if so, what forms of intelligibility it yields. It is certainly the case that deriving knowledge from big data may not involve an increase in human understanding, especially if understanding is understood as an epistemic skill (de Regt 2017). This may not be a problem to those who await the rise of a new species of intelligent machines, who may master new cognitive tools in a way that humans cannot. But as Nickles, Nicholas Rescher (1984), Werner Callebaut (2012) and others pointed out, even in that case “we would not have arrived at perspective-free science” (Nickles forthcoming). While the human histories and assumptions interwoven into these systems may be hard to disentangle, they still affect their outcomes; and whether or not these processes of inquiry are open to critical scrutiny, their telos, implications and significance for life on the planet arguably should be. As argued by Dan McQuillan (2018), the increasing automation of big data analytics may foster acceptance of a Neoplatonist machinic metaphysics , within which mathematical structures “uncovered” by AI would trump any appeal to human experience. Luciano Floridi echoes this intuition in his analysis of what he calls the infosphere :

The great opportunities offered by Information and Communication Technologies come with a huge intellectual responsibility to understand them and take advantage of them in the right way. (2014: vii)

These considerations parallel Paul Humphreys’s long-standing critique of computer simulations as epistemically opaque (Humphreys 2004, 2009)—and particularly his definition of what he calls essential epistemic opacity:

A process is essentially epistemically opaque to X if and only if it is impossible , given the nature of X , for X to know all of the epistemically relevant elements of the process. (Humphreys 2009: 618)

Different facets of the general problem of epistemic opacity are stressed within the vast philosophical scholarship on the role of modelling, computing and simulations in the sciences: the implications of lacking experimental access to the concrete parts of the world being modelled, for instance (Morgan 2005; Parker 2009; Radder 2009); the difficulties in testing the reliability of computational methods used within simulations (Winsberg 2010; Morrison 2015); the relation between opacity and justification (Durán & Formanek 2018); the forms of black-boxing associated to mechanistic reasoning implemented in computational analysis (Craver and Darden 2013; Bechtel 2016); and the debate over the intrinsic limits of computational approaches and related expertise (Collins 1990; Dreyfus 1992). Roman Frigg and Julian Reiss argued that such issues do not constitute fundamental challenges to the nature of inquiry and modelling, and in fact exist in a continuum with traditional methodological issues well-known within the sciences (Frigg & Reiss 2009). Whether or not one agrees with this position (Humphreys 2009; Beisbart 2012), big data analysis is clearly pushing computational and statistical methods to their limit, thus highlighting the boundaries to what even technologically augmented human beings are capable of knowing and understanding.

Research on big data analysis thus sheds light on elements of the research process that cannot be fully controlled, rationalised or even considered through recourse to formal tools.

One such element is the work required to present empirical data in a machine-readable format that is compatible with the software and analytic tools at hand. Data need to be selected, cleaned and prepared to be subjected to statistical and computational analysis. The processes involved in separating data from noise, clustering data so that it is tractable, and integrating data of different formats turn out to be highly sophisticated and theoretically structured, as demonstrated for instance by James McAllister’s (1997, 2007, 2011) and Uljana Feest’s (2011) work on data patterns, Marcel Boumans’s and Leonelli’s comparison of clustering principles across fields (forthcoming), and James Griesemer’s (forthcoming) and Mary Morgan’s (forthcoming) analyses of the peculiarities of datasets. Suppes was so concerned by what he called the “bewildering complexity” of data production and processing activities, that he worried that philosophers would not appreciate the ways in which statistics can and does help scientists to abstract data away from such complexity. He described the large group of research components and activities used to prepare data for modelling as “pragmatic aspects” encompassing “every intuitive consideration of experimental design that involved no formal statistics” (Suppes 1962: 258), and positioned them as the lowest step of his hierarchy of models—at the opposite end of its pinnacle, which are models of theory. Despite recent efforts to rehabilitate the methodology of inductive-statistical modelling and inference (Mayo & Spanos 2009b), this approach has been shared by many philosophers who regard processes of data production and processing as so chaotic as to defy systematic analysis. This explains why data have received so little consideration in philosophy of science when compared to models and theory.

The question of how data are defined and identified, however, is crucial for understanding the role of big data in scientific research. Let us now consider two philosophical views—the representational view and the relational view —that are both compatible with the emergence of big data, and yet place emphasis on different aspects of that phenomenon, with significant implications for understanding the role of data within inferential reasoning and, as we shall see in the next section, as evidence. The representational view construes data as reliable representations of reality which are produced via the interaction between humans and the world. The interactions that generate data can take place in any social setting regardless of research purposes. Examples range from a biologist measuring the circumference of a cell in the lab and noting the result in an Excel file, to a teacher counting the number of students in her class and transcribing it in the class register. What counts as data in these interactions are the objects created in the process of description and/or measurement of the world. These objects can be digital (the Excel file) or physical (the class register) and form a footprint of a specific interaction with the natural world. This footprint—“trace” or “mark”, in the words of Ian Hacking (1992) and Hans-Jörg Rheinberger (2011), respectively—constitutes a crucial reference point for analytic study and for the extraction of new insights. This is the reason why data forms a legitimate foundation to empirical knowledge: the production of data is equivalent to “capturing” features of the world that can be used for systematic study. According to the representative approach, data are objects with fixed and unchangeable content, whose meaning, in virtue of being representations of reality, needs to be investigated and revealed step-by-step through adequate inferential methods. The data documenting cell shape can be modelled to test the relevance of shape to the elasticity, permeability and resilience of cells, producing an evidence base to understand cell-to-cell signalling and development. The data produced counting students in class can be aggregated with similar data collected in other schools, producing an evidence base to evaluate the density of students in the area and their school attendance frequency.

This reflects the intuition that data, especially when they come in the form of numerical measurements or images such as photographs, somehow mirror the phenomena that they are created to document, thus providing a snapshot of those phenomena that is amenable to study under the controlled conditions of research. It also reflects the idea of data as “raw” products of research, which are as close as it gets to unmediated knowledge of reality. This makes sense of the truth-value sometimes assigned to data as irrefutable sources of evidence—the Popperian idea that if data are found to support a given claim, then that claim is corroborated as true at least as long as no other data are found to disprove it. Data in this view represent an objective foundation for the acquisition of knowledge and this very objectivity—the ability to derive knowledge from human experience while transcending it—is what makes knowledge empirical. This position is well-aligned with the idea that big data is valuable to science because it facilitates the (broadly understood) inductive accumulation of knowledge: gathering data collected via reliable methods produces a mountain of facts ready to be analysed and, the more facts are produced and connected with each other, the more knowledge can be extracted.

Philosophers have long acknowledged that data do not speak for themselves and different types of data require different tools for analysis and preparation to be interpreted (Bogen 2009 [2013]). According to the representative view, there are correct and incorrect ways of interpreting data, which those responsible for data analysis need to uncover. But what is a “correct” interpretation in the realm of big data, where data are consistently treated as mobile entities that can, at least in principle, be reused in countless different ways and towards different objectives? Perhaps more than at any other time in the history of science, the current mobilisation and re-use of big data highlights the degree to which data interpretation—and with it, whatever data is taken to represent—may differ depending on the conceptual, material and social conditions of inquiry. The analysis of how big data travels across contexts shows that the expectations and abilities of those involved determine not only the way data are interpreted, but also what is regarded as “data” in the first place (Leonelli & Tempini forthcoming). The representative view of data as objects with fixed and contextually independent meaning is at odds with these observations.

An alternative approach is to embrace these findings and abandon the idea of data as fixed representations of reality altogether. Within the relational view , data are objects that are treated as potential or actual evidence for scientific claims in ways that can, at least in principle, be scrutinised and accounted for (Leonelli 2016). The meaning assigned to data depends on their provenance, their physical features and what these features are taken to represent, and the motivations and instruments used to visualise them and to defend specific interpretations. The reliability of data thus depends on the credibility and strictness of the processes used to produce and analyse them. The presentation of data; the way they are identified, selected, and included (or excluded) in databases; and the information provided to users to re-contextualise them are fundamental to producing knowledge and significantly influence its content. For instance, changes in data format—as most obviously involved in digitisation, data compression or archival procedures— can have a significant impact on where, when, and who uses the data as source of knowledge.

This framework acknowledges that any object can be used as a datum, or stop being used as such, depending on the circumstances—a consideration familiar to big data analysts used to pick and mix data coming from a vast variety of sources. The relational view also explains how, depending on the research perspective interpreting it, the same dataset may be used to represent different aspects of the world (“phenomena” as famously characterised by James Bogen and James Woodward, 1988). When considering the full cycle of scientific inquiry from the viewpoint of data production and analysis, it is at the stage of data modelling that a specific representational value is attributed to data (Leonelli 2019b).

The relational view of data encourages attention to the history of data, highlighting their continual evolution and sometimes radical alteration, and the impact of this feature on the power of data to confirm or refute hypotheses. It explains the critical importance of documenting data management and transformation processes, especially with big data that transit far and wide over digital channels and are grouped and interpreted in different ways and formats. It also explains the increasing recognition of the expertise of those who produce, curate, and analyse data as indispensable to the effective interpretation of big data within and beyond the sciences; and the inextricable link between social and ethical concerns around the potential impact of data sharing and scientific concerns around the quality, validity, and security of data (boyd & Crawford 2012; Tempini & Leonelli, 2018).

Depending on which view on data one takes, expectations around what big data can do for science will vary dramatically. The representational view accommodates the idea of big data as providing the most comprehensive, reliable and generative knowledge base ever witnessed in the history of science, by virtue of its sheer size and heterogeneity. The relational view makes no such commitment, focusing instead on what inferences are being drawn from such data at any given point, how and why.

One thing that the representational and relational views agree on is the key epistemic role of data as empirical evidence for knowledge claims or interventions. While there is a large philosophical literature on the nature of evidence (e.g., Achinstein 2001; Reiss 2015; Kelly 2016), however, the relation between data and evidence has received less attention. This is arguably due to an implicit acceptance, by many philosophers, of the representational view of data. Within the representational view, the identification of what counts as data is prior to the study of what those data can be evidence for: in other words, data are “givens”, as the etymology of the word indicates, and inferential methods are responsible for determining whether and how the data available to investigators can be used as evidence, and for what. The focus of philosophical attention is thus on formal methods to single out errors and misleading interpretations, and the probabilistic and/or explanatory relation between what is unproblematically taken to be a body of evidence and a given hypothesis. Hence much of the expansive philosophical work on evidence avoids the term “data” altogether. Peter Achinstein’s seminal work is a case in point: it discusses observed facts and experimental results, and whether and under which conditions scientists would have reasons to believe such facts, but it makes no mention of data and related processing practices (Achinstein 2001).

By contrast, within the relational view an object can only be identified as datum when it is viewed as having value as evidence. Evidence becomes a category of data identification, rather than a category of data use as in the representational view (Canali 2019). Evidence is thus constitutive of the very notion of data and cannot be disentangled from it. This involves accepting that the conditions under which a given object can serve as evidence—and thus be viewed as datum - may change; and that should this evidential role stop altogether, the object would revert back into an ordinary, non-datum item. For example, the photograph of a plant taken by a tourist in a remote region may become relevant as evidence for an inquiry into the morphology of plants from that particular locality; yet most photographs of plants are never considered as evidence for an inquiry into the features and functioning of the world, and of those who are, many may subsequently be discarded as uninteresting or no longer pertinent to the questions being asked.

This view accounts for the mobility and repurposing that characterises big data use, and for the possibility that objects that were not originally generated in order to serve as evidence may be subsequently adopted as such. Consider Mayo and Spanos’s “minimal scientific principle for evidence”, which they define as follows:

Data x 0 provide poor evidence for H if they result from a method or procedure that has little or no ability of finding flaws in H , even if H is false. (Mayo & Spanos 2009b)

This principle is compatible with the relational view of data since it incorporates cases where the methods used to generate and process data may not have been geared towards the testing of a hypothesis H: all it asks is that such methods can be made relevant to the testing of H, at the point in which data are used as evidence for H (I shall come back to the role of hypotheses in the handling of evidence in the next section).

The relational view also highlights the relevance of practices of data formatting and manipulation to the treatment of data as evidence, thus taking attention away from the characteristics of the data objects alone and focusing instead on the agency attached to and enabled by those characteristics. Nora Boyd has provided a way to conceptualise data processing as an integral part of inferential processes, and thus of how we should understand evidence. To this aim she introduced the notion of “line of evidence”, which she defines as:

a sequence of empirical results including the records of data collection and all subsequent products of data processing generated on the way to some final empirical constraint. (Boyd 2018:406)

She thus proposes a conception of evidence that embraces both data and the way in which data are handled, and indeed emphasises the importance of auxiliary information used when assessing data for interpretation, which includes

the metadata regarding the provenance of the data records and the processing workflow that transforms them. (2018: 407)

As she concludes,

together, a line of evidence and its associated metadata compose what I am calling an “enriched line of evidence”. The evidential corpus is then to be made up of many such enriched lines of evidence. (2018: 407)

The relational view thus fosters a functional and contextualist approach to evidence as the manner through which one or more objects are used as warrant for particular knowledge items (which can be propositional claims, but also actions such as specific decisions or modes of conduct/ways of operating). This chimes with the contextual view of evidence defended by Reiss (2015), John Norton’s work on the multiple, tangled lines of inferential reasoning underpinning appeals to induction (2003), and Hasok Chang’s emphasis on the epistemic activities required to ground evidential claims (2012). Building on these ideas and on Stephen Toulmin’s seminal work on research schemas (1958), Alison Wylie has gone one step further in evaluating the inferential scaffolding that researchers (and particularly archaeologists, who so often are called to re-evaluate the same data as evidence for new claims; Wylie 2017) need to make sense of their data, interpret them in ways that are robust to potential challenges, and modify interpretations in the face of new findings. This analysis enabled Wylie to formulate a set of conditions for robust evidential reasoning, which include epistemic security in the chain of evidence, causal anchoring and causal independence of the data used as evidence, as well as the explicit articulation of the grounds for calibration of the instruments and methods involved (Chapman & Wylie 2016; Wylie forthcoming). A similar conclusion is reached by Jessey Wright’s evaluation of the diverse data analysis techniques that neuroscientists use to make sense of functional magnetic resonance imaging of the brain (fMRI scans):

different data analysis techniques reveal different patterns in the data. Through the use of multiple data analysis techniques, researchers can produce results that are locally robust. (Wright 2017: 1179)

Wylie’s and Wright’s analyses exemplify how a relational approach to data fosters a normative understanding of “good evidence” which is anchored in situated judgement—the arguably human prerogative to contextualise and assess the significance of evidential claims. The advantages of this view of evidence are eloquently expressed by Nancy Cartwright’s critique of both philosophical theories and policy approaches that do not recognise the local and contextual nature of evidential reasoning. As she notes,

we need a concept that can give guidance about what is relevant to consider in deciding on the probability of the hypothesis, not one that requires that we already know significant facts about the probability of the hypothesis on various pieces of evidence. (Cartwright 2013: 6)

Thus she argues for a notion of evidence that is not too restrictive, takes account of the difficulties in combining and selecting evidence, and allows for contextual judgement on what types of evidence are best suited to the inquiry at hand (Cartwright 2013, 2019). Reiss’s proposal of a pragmatic theory of evidence similarly aims to

takes scientific practice [..] seriously, both in terms of its greater use of knowledge about the conditions under which science is practised and in terms of its goal to develop insights that are relevant to practising scientists. (Reiss 2015: 361)

A better characterisation of the relation between data and evidence, predicated on the study of how data are processed and aggregated, may go a long way towards addressing these demands. As aptly argued by James Woodward, the evidential relationship between data and claims is not a “a purely formal, logical, or a priori matter” (Woodward 2000: S172–173). This again sits uneasily with the expectation that big data analysis may automate scientific discovery and make human judgement redundant.

Let us now return to the idea of data-driven inquiry, often suggested as a counterpoint to hypothesis-driven science (e.g., Hey et al. 2009). Kevin Elliot and colleagues have offered a brief history of hypothesis-driven inquiry (Elliott et al. 2016), emphasising how scientific institutions (including funding programmes and publication venues) have pushed researchers towards a Popperian conceptualisation of inquiry as the formulation and testing of a strong hypothesis. Big data analysis clearly points to a different and arguably Baconian understanding of the role of hypothesis in science. Theoretical expectations are no longer seen as driving the process of inquiry and empirical input is recognised as primary in determining the direction of research and the phenomena—and related hypotheses—considered by researchers.

The emphasis on data as a central component of research poses a significant challenge to one of the best-established philosophical views on scientific knowledge. According to this view, which I shall label the theory-centric view of science, scientific knowledge consists of justified true beliefs about the world. These beliefs are obtained through empirical methods aiming to test the validity and reliability of statements that describe or explain aspects of reality. Hence scientific knowledge is conceptualised as inherently propositional: what counts as an output are claims published in books and journals, which are also typically presented as solutions to hypothesis-driven inquiry. This view acknowledges the significance of methods, data, models, instruments and materials within scientific investigations, but ultimately regards them as means towards one end: the achievement of true claims about the world. Reichenbach’s seminal distinction between contexts of discovery and justification exemplifies this position (Reichenbach 1938). Theory-centrism recognises research components such as data and related practical skills as essential to discovery, and more specifically to the messy, irrational part of scientific work that involves value judgements, trial-and-error, intuition and exploration and within which the very phenomena to be investigated may not have been stabilised. The justification of claims, by contrast, involves the rational reconstruction of the research that has been performed, so that it conforms to established norms of inferential reasoning. Importantly, within the context of justification, only data that support the claims of interest are explicitly reported and discussed: everything else—including the vast majority of data produced in the course of inquiry—is lost to the chaotic context of discovery. [ 2 ]

Much recent philosophy of science, and particularly modelling and experimentation, has challenged theory-centrism by highlighting the role of models, methods and modes of intervention as research outputs rather than simple tools, and stressing the importance of expanding philosophical understandings of scientific knowledge to include these elements alongside propositional claims. The rise of big data offers another opportunity to reframe understandings of scientific knowledge as not necessarily centred on theories and to include non-propositional components—thus, in Cartwright’s paraphrase of Gilbert Ryle’s famous distinction, refocusing on knowing-how over knowing-that (Cartwright 2019). One way to construe data-centric methods is indeed to embrace a conception of knowledge as ability, such as promoted by early pragmatists like John Dewey and more recently reprised by Chang, who specifically highlighted it as the broader category within which the understanding of knowledge-as-information needs to be placed (Chang 2017).

Another way to interpret the rise of big data is as a vindication of inductivism in the face of the barrage of philosophical criticism levelled against theory-free reasoning over the centuries. For instance, Jon Williamson (2004: 88) has argued that advances in automation, combined with the emergence of big data, lend plausibility to inductivist philosophy of science. Wolfgang Pietsch agrees with this view and provided a sophisticated framework to understand just what kind of inductive reasoning is instigated by big data and related machine learning methods such as decision trees (Pietsch 2015). Following John Stuart Mill, he calls this approach variational induction and presents it as common to both big data approaches and exploratory experimentation, though the former can handle a much larger number of variables (Pietsch 2015: 913). Pietsch concludes that the problem of theory-ladenness in machine learning can be addressed by determining under which theoretical assumptions variational induction works (2015: 910ff).

Others are less inclined to see theory-ladenness as a problem that can be mitigated by data-intensive methods, and rather see it as a constitutive part of the process of empirical inquiry. Arching back to the extensive literature on perspectivism and experimentation (Gooding 1990; Giere 2006; Radder 2006; Massimi 2012), Werner Callebaut has forcefully argued that the most sophisticated and standardised measurements embody a specific theoretical perspective, and this is no less true of big data (Callebaut 2012). Elliott and colleagues emphasise that conceptualising big data analysis as atheoretical risks encouraging unsophisticated attitudes to empirical investigation as a

“fishing expedition”, having a high probability of leading to nonsense results or spurious correlations, being reliant on scientists who do not have adequate expertise in data analysis, and yielding data biased by the mode of collection. (Elliott et al. 2016: 880)

To address related worries in genetic analysis, Ken Waters has provided the useful characterisation of “theory-informed” inquiry (Waters 2007), which can be invoked to stress how theory informs the methods used to extract meaningful patterns from big data, and yet does not necessarily determine either the starting point or the outcomes of data-intensive science. This does not resolve the question of what role theory actually plays. Rob Kitchin (2014) has proposed to see big data as linked to a new mode of hypothesis generation within a hypothetical-deductive framework. Leonelli is more sceptical of attempts to match big data approaches, which are many and diverse, with a specific type of inferential logic. She rather focused on the extent to which the theoretical apparatus at work within big data analysis rests on conceptual decisions about how to order and classify data—and proposed that such decisions can give rise to a particular form of theorization, which she calls classificatory theory (Leonelli 2016).

These disagreements point to big data as eliciting diverse understandings of the nature of knowledge and inquiry, and the complex iterations through which different inferential methods build on each other. Again, in the words of Elliot and colleagues,

attempting to draw a sharp distinction between hypothesis-driven and data-intensive science is misleading; these modes of research are not in fact orthogonal and often intertwine in actual scientific practice. (Elliott et al. 2016: 881, see also O’Malley et al. 2009, Elliott 2012)

Another epistemological debate strongly linked to reflection on big data concerns the specific kinds of knowledge emerging from data-centric forms of inquiry, and particularly the relation between predictive and causal knowledge.

Big data science is widely seen as revolutionary in the scale and power of predictions that it can support. Unsurprisingly perhaps, a philosophically sophisticated defence of this position comes from the philosophy of mathematics, where Marco Panza, Domenico Napoletani and Daniele Struppa argued for big data science as occasioning a momentous shift in the predictive knowledge that mathematical analysis can yield, and thus its role within broader processes of knowledge production. The whole point of big data analysis, they posit, is its disregard for causal knowledge:

answers are found through a process of automatic fitting of the data to models that do not carry any structural understanding beyond the actual solution of the problem itself. (Napoletani, Panza, & Struppa 2014: 486)

This view differs from simplistic popular discourse on “the death of theory” (Anderson 2008) and the “power of correlations” (Mayer-Schoenberg and Cukier 2013) insofar as it does not side-step the constraints associated with knowledge and generalisations that can be extracted from big data analysis. Napoletani, Panza and Struppa recognise that there are inescapable tensions around the ability of mathematical reasoning to overdetermine empirical input, to the point of providing a justification for any and every possible interpretation of the data. In their words,

the problem arises of how we can gain meaningful understanding of historical phenomena, given the tremendous potential variability of their developmental processes. (Napoletani et al. 2014: 487)

Their solution is to clarify that understanding phenomena is not the goal of predictive reasoning, which is rather a form of agnostic science : “the possibility of forecasting and analysing without a structured and general understanding” (Napoletani et al. 2011: 12). The opacity of algorithmic rationality thus becomes its key virtue and the reason for the extraordinary epistemic success of forecasting grounded on big data. While “the phenomenon may forever re-main hidden to our understanding”(ibid.: 5), the application of mathematical models and algorithms to big data can still provide meaningful and reliable answers to well-specified problems—similarly to what has been argued in the case of false models (Wimsatt 2007). Examples include the use of “forcing” methods such as regularisation or diffusion geometry to facilitate the extraction of useful insights from messy datasets.

This view is at odds with accounts that posit scientific understanding as a key aim of science (de Regt 2017), and the intuition that what researchers are ultimately interested in is

whether the opaque data-model generated by machine-learning technologies count as explanations for the relationships found between input and output. (Boon 2020: 44)

Within the philosophy of biology, for example, it is well recognised that big data facilitates effective extraction of patterns and trends, and that being able to model and predict how an organism or ecosystem may behave in the future is of great importance, particularly within more applied fields such as biomedicine or conservation science. At the same time, researchers are interested in understanding the reasons for observed correlations, and typically use predictive patterns as heuristics to explore, develop and verify causal claims about the structure and functioning of entities and processes. Emanuele Ratti (2015) has argued that big data mining within genome-wide association studies often used in cancer genomics can actually underpin mechanistic reasoning, for instance by supporting eliminative inference to develop mechanistic hypotheses and by helping to explore and evaluate generalisations used to analyse the data. In a similar vein, Pietsch (2016) proposed to use variational induction as a method to establish what counts as causal relationships among big data patterns, by focusing on which analytic strategies allow for reliable prediction and effective manipulation of a phenomenon.

Through the study of data sourcing and processing in epidemiology, Stefano Canali has instead highlighted the difficulties of deriving mechanistic claims from big data analysis, particularly where data are varied and embodying incompatible perspectives and methodological approaches (Canali 2016, 2019). Relatedly, the semantic and logistical challenges of organising big data give reason to doubt the reliability of causal claims extracted from such data. In terms of logistics, having a lot of data is not the same as having all of them, and cultivating illusions of comprehensiveness is a risky and potentially misleading strategy, particularly given the challenges encountered in developing and applying curatorial standards for data other than the high-throughput results of “omics” approaches (see also the next section). The constant worry about the partiality and reliability of data is reflected in the care put by database curators in enabling database users to assess such properties; and in the importance given by researchers themselves, particularly in the biological and environmental sciences, to evaluating the quality of data found on the internet (Leonelli 2014, Fleming et al. 2017). In terms of semantics, we are back to the role of data classifications as theoretical scaffolding for big data analysis that we discussed in the previous section. Taxonomic efforts to order and visualise data inform causal reasoning extracted from such data (Sterner & Franz 2017), and can themselves constitute a bottom-up method—grounded in comparative reasoning—for assigning meaning to data models, particularly in situation where a full-blown theory or explanation for the phenomenon under investigation is not available (Sterner 2014).

It is no coincidence that much philosophical work on the relation between causal and predictive knowledge extracted from big data comes from the philosophy of the life sciences, where the absence of axiomatized theories has elicited sophisticated views on the diversity of forms and functions of theory within inferential reasoning. Moreover, biological data are heterogeneous both in their content and in their format; are curated and re-purposed to address the needs of highly disparate and fragmented epistemic communities; and present curators with specific challenges to do with tracking complex, diverse and evolving organismal structures and behaviours, whose relation to an ever-changing environment is hard to pinpoint with any stability (e.g., Shavit & Griesemer 2009). Hence in this domain, some of the core methods and epistemic concerns of experimental research—including exploratory experimentation, sampling and the search for causal mechanisms—remain crucial parts of data-centric inquiry.

At the start of this entry I listed “value” as a major characteristic of big data and pointed to the crucial role of valuing procedures in identifying, processing, modelling and interpreting data as evidence. Identifying and negotiating different forms of data value is an unavoidable part of big data analysis, since these valuation practices determine which data is made available to whom, under which conditions and for which purposes. What researchers choose to consider as reliable data (and data sources) is closely intertwined not only with their research goals and interpretive methods, but also with their approach to data production, packaging, storage and sharing. Thus, researchers need to consider what value their data may have for future research by themselves and others, and how to enhance that value—such as through decisions around which data to make public, how, when and in which format; or, whenever dealing with data already in the public domain (such as personal data on social media), decisions around whether the data should be shared and used at all, and how.

No matter how one conceptualises value practices, it is clear that their key role in data management and analysis prevents facile distinctions between values and “facts” (understood as propositional claims for which data provide evidential warrant). For example, consider a researcher who values both openness —and related practices of widespread data sharing—and scientific rigour —which requires a strict monitoring of the credibility and validity of conditions under which data are interpreted. The scale and manner of big data mobilisation and analysis create tensions between these two values. While the commitment to openness may prompt interest in data sharing, the commitment to rigour may hamper it, since once data are freely circulated online it becomes very difficult to retain control over how they are interpreted, by whom and with which knowledge, skills and tools. How a researcher responds to this conflict affects which data are made available for big data analysis, and under which conditions. Similarly, the extent to which diverse datasets may be triangulated and compared depends on the intellectual property regimes under which the data—and related analytic tools—have been produced. Privately owned data are often unavailable to publicly funded researchers; and many algorithms, cloud systems and computing facilities used in big data analytics are only accessible to those with enough resources to buy relevant access and training. Whatever claims result from big data analysis are, therefore, strongly dependent on social, financial and cultural constraints that condition the data pool and its analysis.

This prominent role of values in shaping data-related epistemic practices is not surprising given existing philosophical critiques of the fact/value distinction (e.g., Douglas 2009), and the existing literature on values in science—such as Helen Longino’s seminal distinction between constitutive and contextual values, as presented in her 1990 book Science as Social Knowledge —may well apply in this case too. Similarly, it is well-established that the technological and social conditions of research strongly condition its design and outcomes. What is particularly worrying in the case of big data is the temptation, prompted by hyped expectations around the power of data analytics, to hide or side-line the valuing choices that underpin the methods, infrastructures and algorithms used for big data extraction.

Consider the use of high-throughput data production tools, which enable researchers to easily generate a large volume of data in formats already geared to computational analysis. Just as in the case of other technologies, researchers have a strong incentive to adopt such tools for data generation; and may do so even in cases where such tools are not good or even appropriate means to pursue the investigation. Ulrich Krohs uses the term convenience experimentation to refer to experimental designs that are adopted not because they are the most appropriate ways of pursuing a given investigation, but because they are easily and widely available and usable, and thus “convenient” means for researchers to pursue their goals (Krohs 2012).

Appeals to convenience can extend to other aspects of data-intensive analysis. Not all data are equally easy to digitally collect, disseminate and link through existing algorithms, which makes some data types and formats more convenient than others for computational analysis. For example, research databases often display the outputs of well-resourced labs within research traditions which deal with “tractable” data formats (such as “omics”). And indeed, the existing distribution of resources, infrastructure and skills determines high levels of inequality in the production, dissemination and use of big data for research. Big players with large financial and technical resources are leading the development and uptake of data analytics tools, leaving much publicly funded research around the world at the receiving end of innovation in this area. Contrary to popular depictions of the data revolution as harbinger of transparency, democracy and social equality, the digital divide between those who can access and use data technologies, and those who cannot, continues to widen. A result of such divides is the scarcity of data relating to certain subgroups and geographical locations, which again limits the comprehensiveness of available data resources.

In the vast ecosystem of big data infrastructures, it is difficult to keep track of such distortions and assess their significance for data interpretation, especially in situations where heterogeneous data sources structured through appeal to different values are mashed together. Thus, the systematic aggregation of convenient datasets and analytic tools over others often results in a big data pool where the relevant sources and forms of bias are impossible to locate and account for (Pasquale 2015; O’Neill 2016; Zuboff 2017; Leonelli 2019a). In such a landscape, arguments for a separation between fact and value—and even a clear distinction between the role of epistemic and non-epistemic values in knowledge production—become very difficult to maintain without discrediting the whole edifice of big data science. Given the extent to which this approach has penetrated research in all domains, it is arguably impossible, however, to critique the value-laden structure of big data science without calling into question the legitimacy of science itself. A more constructive approach is to embrace the extent to which big data science is anchored in human choices, interests and values, and ascertain how this affects philosophical views on knowledge, truth and method.

In closing, it is important to consider at least some of the risks and related ethical questions raised by research with big data. As already mentioned in the previous section, reliance on big data collected by powerful institutions or corporations risks raises significant social concerns. Contrary to the view that sees big and open data as harbingers of democratic social participation in research, the way that scientific research is governed and financed is not challenged by big data. Rather, the increasing commodification and large value attributed to certain kinds of data (e.g., personal data) is associated to an increase in inequality of power and visibility between different nations, segments of the population and scientific communities (O’Neill 2016; Zuboff 2017; D’Ignazio and Klein 2020). The digital gap between those who not only can access data, but can also use it, is widening, leading from a state of digital divide to a condition of “data divide” (Bezuidenout et al. 2017).

Moreover, the privatisation of data has serious implications for the world of research and the knowledge it produces. Firstly, it affects which data are disseminated, and with which expectations. Corporations usually only release data that they regard as having lesser commercial value and that they need public sector assistance to interpret. This introduces another distortion on the sources and types of data that are accessible online while more expensive and complex data are kept secret. Even many of the ways in which citizens -researchers included - are encouraged to interact with databases and data interpretation sites tend to encourage participation that generates further commercial value. Sociologists have recently described this type of social participation as a form of exploitation (Prainsack & Buyx 2017; Srnicek 2017). In turn, these ways of exploiting data strengthen their economic value over their scientific value. When it comes to the commerce of personal data between companies working in analysis, the value of the data as commercial products -which includes the evaluation of the speed and efficiency with which access to certain data can help develop new products - often has priority over scientific issues such as for example, representativity and reliability of the data and the ways they were analysed. This can result in decisions that pose a problem scientifically or that simply are not interested in investigating the consequences of the assumptions made and the processes used. This lack of interest easily translates into ignorance of discrimination, inequality and potential errors in the data considered. This type of ignorance is highly strategic and economically productive since it enables the use of data without concerns over social and scientific implications. In this scenario the evaluation on the quality of data shrinks to an evaluation of their usefulness towards short-term analyses or forecasting required by the client. There are no incentives in this system to encourage evaluation of the long-term implications of data analysis. The risk here is that the commerce of data is accompanied by an increasing divergence between data and their context. The interest in the history of the transit of data, the plurality of their emotional or scientific value and the re-evaluation of their origins tend to disappear over time, to be substituted by the increasing hold of the financial value of data.

The multiplicity of data sources and tools for aggregation also creates risks. The complexity of the data landscape is making it harder to identify which parts of the infrastructure require updating or have been put in doubt by new scientific developments. The situation worsens when considering the number of databases that populate every area of scientific research, each containing assumptions that influence the circulation and interoperability of data and that often are not updated in a reliable and regular way. Just to provide an idea of the numbers involved, the prestigious scientific publication Nucleic Acids Research publishes a special issue on new databases that are relevant to molecular biology every year and included: 56 new infrastructures in 2015, 62 in 2016, 54 in 2017 and 82 in 2018. These are just a small proportion of the hundreds of databases that are developed each year in the life sciences sector alone. The fact that these databases rely on short term funding means that a growing percentage of resources remain available to consult online although they are long dead. This is a condition that is not always visible to users of the database who trust them without checking whether they are actively maintained or not. At what point do these infrastructures become obsolete? What are the risks involved in weaving an ever more extensive tapestry of infrastructures that depend on each other, given the disparity in the ways they are managed and the challenges in identifying and comparing their prerequisite conditions, the theories and scaffolding used to build them? One of these risks is rampant conservativism: the insistence on recycling old data whose features and management elements become increasingly murky as time goes by, instead of encouraging the production of new data with features that specifically respond to the requirements and the circumstances of their users. In disciplines such as biology and medicine that study living beings and therefore are by definition continually evolving and developing, such trust in old data is particularly alarming. It is not the case, for example, that data collected on fungi ten, twenty or even a hundred years ago is reliable to explain the behaviour of the same species of fungi now or in the future (Leonelli 2018).

Researchers of what Luciano Floridi calls the infosphere —the way in which the introduction of digital technologies is changing the world - are becoming aware of the destructive potential of big data and the urgent need to focus efforts for management and use of data in active and thoughtful ways towards the improvement of the human condition. In Floridi’s own words:

ICT yields great opportunity which, however, entails the enormous intellectual responsibility of understanding this technology to use it in the most appropriate way. (Floridi 2014: vii; see also British Academy & Royal Society 2017)

In light of these findings, it is essential that ethical and social issues are seen as a core part of the technical and scientific requirements associated with data management and analysis. The ethical management of data is not obtained exclusively by regulating the commerce of research and management of personal data nor with the introduction of monitoring of research financing, even though these are important strategies. To guarantee that big data are used in the most scientifically and socially forward-thinking way it is necessary to transcend the concept of ethics as something external and alien to research. An analysis of the ethical implications of data science should become a basic component of the background and activity of those who take care of data and the methods used to view and analyse it. Ethical evaluations and choices are hidden in every aspect of data management, including those choices that may seem purely technical.

This entry stressed how the emerging emphasis on big data signals the rise of a data-centric approach to research, in which efforts to mobilise, integrate, disseminate and visualise data are viewed as central contributions to discovery. The emergence of data-centrism highlights the challenges involved in gathering, classifying and interpreting data, and the concepts, technologies and institutions that surround these processes. Tools such as high-throughput measurement instruments and apps for smartphones are fast generating large volumes of data in digital formats. In principle, these data are immediately available for dissemination through internet platforms, which can make them accessible to anybody with a broadband connection in a matter of seconds. In practice, however, access to data is fraught with conceptual, technical, legal and ethical implications; and even when access can be granted, it does not guarantee that the data can be fruitfully used to spur further research. Furthermore, the mathematical and computational tools developed to analyse big data are often opaque in their functioning and assumptions, leading to results whose scientific meaning and credibility may be difficult to assess. This increases the worry that big data science may be grounded upon, and ultimately supporting, the process of making human ingenuity hostage to an alien, artificial and ultimately unintelligible intelligence.

Perhaps the most confronting aspect of big data science as discussed in this entry is the extent to which it deviates from understandings of rationality grounded on individual agency and cognitive abilities (on which much of contemporary philosophy of science is predicated). The power of any one dataset to yield knowledge lies in the extent to which it can be linked with others: this is what lends high epistemic value to digital objects such as GPS locations or sequencing data, and what makes extensive data aggregation from a variety of sources into a highly effective surveillance tool. Data production and dissemination channels such as social media, governmental databases and research repositories operate in a globalised, interlinked and distributed network, whose functioning requires a wide variety of skills and expertise. The distributed nature of decision-making involved in developing big data infrastructures and analytics makes it impossible for any one individual to retain oversight over the quality, scientific significance and potential social impact of the knowledge being produced.

Big data analysis may therefore constitute the ultimate instance of a distributed cognitive system. Where does this leave accountability questions? Many individuals, groups and institutions end up sharing responsibility for the conceptual interpretation and social outcomes of specific data uses. A key challenge for big data governance is to find mechanisms for allocating responsibilities across this complex network, so that erroneous and unwarranted decisions—as well as outright fraudulent, unethical, abusive, discriminatory or misguided actions—can be singled out, corrected and appropriately sanctioned. Thinking about the complex history, processing and use of data can encourage philosophers to avoid ahistorical, uncontextualized approaches to questions of evidence, and instead consider the methods, skills, technologies and practices involved in handling data—and particularly big data—as crucial to understanding empirical knowledge-making.

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Acknowledgments

The research underpinning this entry was funded by the European Research Council (grant award 335925) and the Alan Turing Institute (EPSRC Grant EP/N510129/1).

Copyright © 2020 by Sabina Leonelli < s . leonelli @ exeter . ac . uk >

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What Is a Data Scientist? Salary, Skills, and How to Become One

A data scientist uses data to understand and explain the phenomena around them, and help organizations make better decisions.

Working as a data scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new technological advances. Data scientists have become more common and in demand, as big data continues to be increasingly important to the way organizations make decisions. Here’s a closer look at what they are and do—and how to become one.

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What does a data scientist do?

Data scientists determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting.

A data scientist might do the following tasks on a day-to-day basis:

Find patterns and trends in datasets to uncover insights

Create algorithms and data models to forecast outcomes

Use machine learning techniques to improve the quality of data or product offerings

Communicate recommendations to other teams and senior staff

Deploy data tools such as Python , R , SAS, or SQL in data analysis

Stay on top of innovations in the data science field

Data analyst vs data scientist: What’s the difference?

The work of data analysts and data scientists can seem similar—both find trends or patterns in data to reveal new ways for organizations to make better decisions about operations. But data scientists tend to have more responsibility and are generally considered more senior than data analysts. 

Data scientists are often expected to form their own questions about the data, while data analysts might support teams that already have set goals in mind. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyze data.

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

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Data scientist salary and job growth

A data scientist earns an average salary of $108,659 in the United States, according to Lightcast™ [1]. 

Demand is high for data professionals—data scientists occupations are expected to grow by 36 percent in the next 10 years (much faster than average), according to the US Bureau of Labor Statistics (BLS) [ 2 ].

The high demand has been linked to the rise of big data and its increasing importance to businesses and other organizations. 

How to become a data scientist

Becoming a data scientist generally requires some formal training. Here are some steps to consider.

1. Earn a data science degree.

Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. That said, a related bachelor’s degree can certainly help—try studying data science, statistics, or computer science to get a leg up in the field.

Already have a bachelor's degree?

Consider getting a master’s in data science. At a master’s degree program, you can dive deeper into your understanding of statistics, machine learning, algorithms, modeling, and forecasting, and potentially conduct your own research on a topic you care about. Several data science master’s degrees are available online .

2. Sharpen relevant skills.

If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant bootcamp. Here are some of the skills you’ll want to have under your belt.

Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include:

Data visualization: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work:

Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.

Big data: Some employers may want to see that you have some familiarity in grappling with big data. Some of the software frameworks used to process big data include Hadoop and Apache Spark.

Communication: The most brilliant data scientists won’t be able to affect any change if they aren’t able to communicate their findings well. The ability to share ideas and results verbally and in written language is an often-sought skill for data scientists.

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3. Get an entry-level data analytics job.

Though there are many paths to becoming a data scientist, starting in a related entry-level job can be an excellent first step. Seek positions that work heavily with data, such as data analyst , business intelligence analyst , statistician, or data engineer . From there, you can work your way up to becoming a scientist as you expand your knowledge and skills.

4. Prepare for data science interviews.

With a few years of experience working with data analytics, you might feel ready to move into data science. Once you’ve scored an interview, prepare answers to likely interview questions. 

Data scientist positions can be highly technical, so you may encounter technical and behavioral questions. Anticipate both, and practice by speaking your answer aloud. Preparing examples from your past work or academic experiences can help you appear confident and knowledgeable to interviewers.

Here are a few questions you might encounter:

What are the pros and cons of a linear model?

What is a random forest?

How would you use SQL to find all duplicates in a data set?

Describe your experience with machine learning.

Give an example of a time you encountered a problem you didn’t know how to solve. What did you do?

Read more: SQL Interview Questions: A Guide for Data Analysts

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Article sources

Lightcast™ Analyst. "Occupation Summary for Data Scientist." Accessed April 13, 2023.

US Bureau of Labor Statistics. " Data Scientists , https://www.bls.gov/ooh/math/data-scientists.htm." Accessed April 13, 2023.

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.

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This report is a collaborative effort based on the input and analysis of the following individuals:

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Sensitive technology research areas, pdf version.

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Introduction

The list of Sensitive Technology Research Areas consists of advanced and emerging technologies that are important to Canadian research and development, but may also be of interest to foreign state, state-sponsored, and non-state actors, seeking to misappropriate Canada’s technological advantages to our detriment.

While advancement in each of these areas is crucial for Canadian innovation, it is equally important to ensure that open and collaborative research funded by the Government of Canada does not cause injury to Canada’s national security or defence.

The list covers research areas and includes technologies at various stages of development. Of specific concern is the advancement of a technology during the course of the research . This list is not intended to cover the use of any technology that may already be ubiquitous in the course of a research project. Each high-level technology category is complemented by sub-categories which provide researchers with further specificity regarding where the main concerns lie.

The list will be reviewed on a regular basis and updated as technology areas evolve and mature, and as new information and insights are provided by scientific and technical experts across the Government of Canada, allied countries, and the academic research community.

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1. Advanced Digital Infrastructure Technology

Advanced digital infrastructure technology refers to the devices, systems and technologies which compute, process, store, transmit and secure a growing amount of information and data that support an increasingly digital and data-driven world.

Advanced communications technology

Technologies that enable fast, secure and reliable wireless communication to facilitate growing demand for connectivity and faster processing and transmission of data and information. These technologies could also enable communications in remote environments or adverse conditions where conventional methods are ineffective, or in spectrum-congested areas. Examples include: adaptive/cognitive/intelligent radios; massive multiple input/multiple output; millimeter-wave spectrum, open/virtualized radio access networks, optical/photonic communications and wideband high frequency communications.

Advanced computing technology

Computing systems with high computational power that enable the processing of complex calculations that are data- or compute-intensive. Examples include: context-aware computing, edge computing, high performance computing and neuromorphic computing.

Cryptography

Methods and technologies that enable secure communications by transforming, transmitting or storing data in a secure format that can only be deciphered by the intended recipient. Examples of emerging capabilities in cryptography that may replace or enhance current encryption methods include: biometric encryption, DNA-based encryption, post-quantum cryptography, homomorphic encryption and optical stealth encryption.

Cyber security technology

Technologies that protect the integrity, confidentiality and availability of internet-connected systems, including their hardware, software, as well as data from unauthorized access or malicious activities. Examples include: cyber defence tools, cross domain solutions and moving target defence technology.

Data storage technology

The methods, tools, platforms, and infrastructure for storing data or information securely in a digital format. Examples include: five-dimensional (5D) optical storage, DNA storage, single-molecule magnets.

Distributed ledger technology

Digital ledgers or databases that track assets or records transactions in multiple locations at the same time, with no centralized or single point of control or storage. Examples include: blockchain, cryptocurrencies, digital currencies and non-fungible tokens.

Microelectronics

Microelectronics encompasses the development and manufacturing of very small electronic designs on a substrate. It incorporates semiconductors as well as more conventional components such as surface mount technology with the goal of producing smaller and faster products. As microelectronics reach the limit for integration, photonic components are making their way into this field. Examples of semiconductor components include: memory-centric logic, multi-chip module, systems-on-chip and stacked memory on chip.

Next-generation network technology

Fifth and future generations of communications networks that use high frequency spectrums to enable significantly faster processing and transmission speeds for larger amounts of data. Advancements in networking could allow for integrated communication across air, land, space and sea using terrestrial and non-terrestrial networks, as well as increased data speed and capacity for network traffic. It could also pave the way for new AI- and big data-driven applications and services, and its massive data processing capabilities could enable the Internet of Everything.

2. Advanced Energy Technology

Advanced energy technology refers to technologies and processes that enable improved generation, storage and transmission of energy, as well as operating in remote or adverse environments where power sources may not be readily available, but are required to support permanent or temporary infrastructure and power vehicles, equipment and devices.

Advanced energy storage technology

Technologies that store energy, such as batteries, with new or enhanced properties, including improved energy density, compact size and low weight to enable portability, survivability in harsh conditions and the ability to recharge quickly. Examples include: fuel cells, novel batteries (biodegradable batteries; graphene aluminium-ion batteries; lithium-air batteries; room-temperature all-liquid-metal batteries; solid-state batteries; structural batteries) and supercapacitors (or ultracapacitors).

Advanced nuclear generation technology

New reactors and technologies that are smaller in size than conventional nuclear reactors and are developed to be less capital-intensive, therefore minimizing risks faced during construction. Examples include: nuclear fusion and small modular reactors.

Wireless power transfer technology

Enables the transmission of electricity without using wire over extended distances that vary greatly and could be up to several kilometres. Examples include recharging zones (analogous to Wi-Fi zones) that allow for electric devices, such as vehicles, to be recharged within a large radius, as well as for recharging space-based objects, such as satellites.

3. Advanced Materials and Manufacturing

Advanced materials.

Advanced materials refer to high-value products, components or materials with new or enhanced structural or functional properties. They may rely on advanced manufacturing processes or novel approaches for their production.

Augmented conventional materials

Conventional materials such as high strength steel or aluminum and magnesium alloys – products that are already widely used – which are augmented to have unconventional or extraordinary properties. Examples of these properties could include improved durability or high temperature strength, corrosion resistance, flexibility, weldability, or reduced weight, among others.

Auxetic materials

Materials that have a negative Poisson’s ratio, meaning that when stretched horizontally, they thicken or expand vertically (rather than thinning as most materials do when stretched), and do the opposite when compressed horizontally. These materials possess unique properties, such as energy-absorption, high rigidity, improved energy/impact absorption and resistance to fracture.

High-entropy materials

Special materials, including high-entropy alloys, high-entropy oxides or other high-entropy compounds, comprised of several elements or components. Depending on their composition, high-entropy materials can enhance fracture toughness, strength, conductivity, corrosion resistance, hardness and other desired properties. Due to the breadth of the theoretically available combinations and their respective properties, these materials can be used in several industries, including aerospace. Additionally, high-entropy oxides are being considered for applications in energy production and storage, as well as thermal barrier coatings.

Metamaterials

Structured materials that are not found or easily obtained in nature. Metamaterials often have unique interactions with electromagnetic radiation (i.e. light or microwaves) or sound waves.

Multifunctional/smart materials

Materials that can transform in response to external stimuli (e.g. heat, water, light, etc.) within a given amount of time. Examples include: magnetorheological fluid, shape memory alloys, shape memory polymers and self-assembled materials.

Nanomaterials

Nanomaterial materials have dimensions of less than 100 nanometers and exhibit certain properties or unique characteristics such as increased durability or self-repair. A subset of nanomaterials, nano-energetic materials are energetic materials synthesized and fabricated at the nano-level that have a small particle size and high surface area between particles, which enable faster or more efficient reaction pathways when exposed to other substances.

Powder materials for additive manufacturing

Powders that typically consist of metal, polymer, ceramic and composite materials. These powders enable additive manufacturing processes, also referred to as 3D printing. Research into novel powder materials can lead to manufactured parts with enhanced mechanical properties and other desired characteristics.

Superconducting materials

Materials that can transmit electricity with no resistance, ultimately eliminating power losses associated with electrical resistivity that normally occurs in conductors. Manufacturing of superconducting electronic circuits is one of the most promising approaches to implementing quantum computers.

Two-dimensional (2D) materials

Materials with a thickness of roughly one atomic layer. One of the most well-known 2D materials, for which there are currently production/fabrication technologies, is graphene. Other examples of 2D materials include: silicene, germanene, stantene, metal chalcogenides and others, which are currently being researched with potential applications in sensors, miniaturized electronic devices, semiconductors and more.

Advanced Manufacturing

Advanced manufacturing refers to enhanced or novel technologies, tools and processes used to develop and manufacture advanced materials or components. This could include using specialized software, artificial intelligence, sensors and high performance tools, among others, to facilitate process automation or closed-loop automated machining and create new materials or components.

Additive manufacturing (3D printing)

Various processes in which solid three-dimensional objects are constructed using computer-aided-design (CAD) software to build an object, ranging from simple geometric shapes to parts for commercial airplanes. 3D printing could be used to accelerate the development through rapid prototyping of customized equipment, spare tools or novel shapes or objects that are stronger and lighter. Approaches are also being developed for multi-material additive manufacturing and volumetric additive manufacturing, as well as additive manufacturing for repair and restoration.

Advanced semiconductor manufacturing

Methods, materials and processes related to the manufacturing of semiconductor devices. Examples of techniques include: advancements in deposition, coating, lithography, ionization/doping, and other core and supporting processes, such as thermal management techniques. Recent technological advancements include developments in Extreme Ultraviolet (EUV) lithography, which is an advanced method for fabricating intricate patterns on a substrate to produce a semiconductor device with extremely small features.

Critical materials manufacturing

Up and midstream technologies necessary to extract, process, upgrade, and recycle/recover critical materials (e.g. rare earth elements, scandium, lithium, etc.) and establish and maintain secure domestic and allied supply chains. More information about critical minerals can be found in Canada’s Critical Minerals List .

Four-dimensional (4D) printing

Production and manufacture of 3D products using multifunctional or “smart” materials that are programmed to transform in response to external stimuli (e.g. heat, water, light, etc.) within a given amount of time. Recent developments have also been made in creating reversible 4D printed objects, which can return to their original shape without human involvement.

Nano-manufacturing

Production and manufacture of nanoscale materials, structures, devices and systems in a scaled-up, reliable and cost-effective manner.

Two-dimensional (2D) materials manufacturing

Standardized, scalable and cost-effective large-scale production of 2D materials.

4. Advanced Sensing and Surveillance

Advanced sensing and surveillance refers to a large array of advanced technologies that detect, measure or monitor physical, chemical, biological or environmental conditions and generate data or information about them. Advanced surveillance technologies, in particular, are used to monitor and observe the activities and communications of specific individuals or groups for national security or law enforcement purposes, but have also been used for mass surveillance with increased accuracy and scale.

Advanced biometric recognition technologies

Technologies that identify individuals based on their distinctive physical identifiers (e.g. face, fingerprint or DNA) or behavioural identifiers (e.g. gait, keystroke pattern and voice). These technologies are becoming more advanced due to improving sensing capabilities, as well as integrating artificial intelligence to identify/verify an individual more quickly and accurately.

Advanced radar technologies

Radar is a system that uses radio waves to detect moving objects and measure their distance, speed and direction. Advancements in radar technology could enable improved detection and surveillance in different environments and over greater distances. Examples include: active electronically-scanned arrays, cognitive radars, high frequency skywave radar (or over-the-horizon radar), passive radar and synthetic aperture radar.

Atomic interferometer sensors

Sensors that perform sensitive interferometric measurements using the wave character of atomic particles and quantum gases. These sensors can detect small changes in inertial forces and can be used in gravimetry. They can also improve accuracy in navigation and provide position information in environments where the Global Positioning System (GPS) is unavailable.

Cross-cueing sensors

Systems that enable multiple sensors to cue one another. Cross cueing can be used in satellites for data validation, objection tracking, enhanced reliability (i.e. in the event of a sensor failure) and earth observations.

Electric field sensors

Sensors that detect variations in electric fields and use low amounts of power. They are useful for detecting power lines or lightning, as well as locating power grids or damaged components in the aftermath of a natural disaster.

Imaging and optical devices and sensors

Devices and sensors that provide a visual depiction of the physical structure of an object beyond the typical capabilities of consumer grade imaging techniques such as cameras, cellphones, and visible light-imaging. Such technologies typically make use of electromagnetic radiation beyond the visible spectrum, or use advanced techniques and materials to improve optical capabilities, such as enabling more precise imaging from a greater distance. This sensitive research area also includes sensitive infrared sensors.

Magnetic field sensors (or magnetometers)

Sensors that are used to detect or measure changes in a magnetic field, or its intensity or direction.

Micro (or nano) electro-mechanical systems (M/NEMS)

Miniaturized, lightweight electro-mechanical devices that integrate mechanical and electrical functionality at the microscopic or nano level. A potential use of M/NEMS could be as ‘smart dust’, or a group of M/NEMs, made up of various components, including sensors, circuits, communications technology and a power supply, that function as a single digital entity. Smart dust could be light enough to float in the air and detect vibrations, light, pressure and temperature, among other things, to capture a great deal of information about a particular environment.

Position, navigation and timing (PNT) technology

Systems, platforms or capabilities that enable accurate and timely calculation of positioning, navigation and timing. These technologies are critical to a wide-range of applications, most notably for enabling the Global Navigation Satellite System (GNSS), one of which is the widely-used Global Positioning System (GPS), but also for enabling navigation in areas where GPS or GNSS do not work. Examples include: chip-scale advanced atomic clocks, gravity-aided inertial navigation system, long-range underwater navigation system, magnetic anomaly navigation, precision inertial navigation system.

Side scan sonar

An active sonar system that uses a transducer array to send and receive acoustic pulses in swaths laterally from the tow-body or vessel, enabling it to quickly scan a large area in a body of water to produce an image of the sea floor beneath the tow-body or vessel.

Synthetic aperture sonar (SAS)

An active sonar system that produces high resolution images of the sea floor along the track of the vessel or tow body. SAS can send continuous sonar signals to capture images underwater at 30 times the resolution of traditional sonar systems, as well as up to 10 times the range and area coverage.

Underwater (wireless) sensor network

Network of sensors and autonomous/uncrewed underwater vehicles that use acoustic waves to communicate with each other, or with underwater sinks that collect and transmit data from deep ocean sensors, to enable remote sensing, surveillance and ocean exploration, observation and monitoring.

5. Advanced Weapons

Emerging or improved weapons used by military, and in some instances law enforcement, for defence and national security purposes. Advancements in materials, manufacturing, propulsion, energy and other technologies have brought weapons like directed energy weapons and hypersonic weapons closer to reality, while nanotechnology, synthetic biology, artificial intelligence and sensing technologies, among others, have provided enhancements to existing weapons, such as biological/chemical weapons and autonomous weapons.

6. Aerospace, Space and Satellite Technology

Aerospace technology refers to the technology that enables the design, production, testing, operation and maintenance of aircraft, spacecraft and their respective components, as well as other aeronautics. Space and satellite technology refers to technologies that enable travel, research and exploration in space, as well as weather-tracking, advanced PNT, communications, remote sensing and other capabilities using satellites and other space-based assets.

Advanced wind tunnels

Technological advancements in systems related to wind tunnel infrastructure. Existing facilities are used to simulate various flight conditions and speeds ranging from subsonic, transonic, supersonic and hypersonic.

On-orbit servicing, assembly and manufacturing systems

Systems and equipment that are used for space-based servicing, assembly and manufacturing. On-orbit servicing, assembly and manufacturing systems can be used to optimize space logistics, increase efficiencies, mitigate debris threats and to modernize space asset capabilities.

Lower cost satellite payloads with increased performance that can meet the needs of various markets. This will require several technology improvements, such as light weight apertures, antennas, panels, transceivers, control actuators, optical/infrared sensor and multi-spectral imagers, to meet the growing demand and ever-increasing technical requirements.

Propulsion technologies

Components and systems that produce a powerful thrust to push an object forward, which is essential to launching aircraft, spacecraft, rockets or missiles. Innovations could range from new designs or advanced materials to enable improved performance, speed, energy-efficiency and other enhanced properties, as well as reduced aircraft production times and emissions. Examples include: electrified aircraft propulsion, solar electric propulsion, pulse detonation engines, nuclear thermal propulsion systems, nuclear pulse propulsion systems and nuclear electric propulsion systems, among others.

Artificial or human-made, including (semi-)autonomous, objects placed into orbit. Depending on their specific function, satellites typically consist of an antenna, radio communications system, a power source and a computer, but their exact composition may vary. Continued developments have led to smaller satellites that are less costly to manufacture and deploy compared to large satellites, resulting in faster development times and increased accessibility to space. Examples include: remote sensing and communications satellites.

Space-based positioning, navigation and timing technology

Global Navigation Satellite System (GNSS)-based satellites and technologies that will improve the accuracy, agility and resilience of GNSS and the Global Positioning System (GPS).

Space stations

Space-based facility that can act as an orbital outpost while having the ability to support extended human operations. Space stations can be used as a hub to support other space-based activities including assembly, manufacturing, research, experimentations, training, space vehicle docking and storage. Examples of innovations in space stations could include the ability to extend further out into space or enhanced life support systems that can be used to prolong human missions.

Zero-emission/fuel aircraft

Aircraft powered by energy sources that do not emit polluting emissions that disrupt the environment or do not require fuel to fly. While still in early stages, these advances in powering aircraft could support cleaner air travel, as well as enable flight over greater distances and to remote areas without the need for refueling (for zero-fuel aircraft).

7. Artificial Intelligence and Big Data Technology

Artificial intelligence (AI) is a broad field encompassing the science of making computers behave in a manner that simulates human behaviour/intelligence using data and algorithms. Big data refers to information and data that is large and complex in volume, velocity and variety, and as such, requires specialized tools, techniques and technologies to process, analyze and visualize it. AI and big data technology may be considered cross-cutting given how important they are in enabling developments in other technology areas, including biotechnology, advanced materials and manufacturing, robotics and autonomous systems and others.

AI chipsets

Custom-designed chips meant to process large amounts of data and information that enable algorithms to perform calculations more efficiently, simultaneously and using less energy than general-purpose chips. AI chips have unique design features specialized for AI, which may make them more cost-effective to use for AI development.

Computer vision

Field of AI that allows computers to see and extract meaning from the content of digital images such as photos and videos. Examples of computer vision techniques include: image classification, object detection, depth perception and others.

Data science and big data technology

Enables the autonomous or semi-autonomous analysis of data, namely large and/or complex sets of data when it comes to big data technology. It also includes the extraction or generation of deeper insights, predictions or recommendations to inform decision-making. Examples include: AI-enabled data analytics, big data technology (i.e. data warehouse, data mining, data correlation) and predictive analytics.

Digital twin technology

Virtual representations of physical objects or systems that combine real-time sensor data, big data processing and artificial intelligence (namely machine learning) to create an interactive model and predict the object or system’s future behaviour or performance. Advancements in digital twin technology could enable the growth and integration of an immersive digital experience (e.g. the metaverse) into daily life.

Machine learning (ML)

Branch of AI where computer programs are trained using algorithms and data to improve their decisions when introduced to a new set of data without necessarily being programmed to do so. Types of ML include: deep learning, evolutionary computation and neural networks.

Natural language processing

An area of AI that allows computers to process and make sense of, or ‘translate’, natural human language using speech and audio recognition to identify, analyze and interpret human voices and other types of audio. Examples include: syntactic and semantic analysis, tokenization, text classification and others, which enable capabilities like virtual assistants, chatbots, machine translation, predictive text, sentiment analysis and automatic summarization.

8. Human-Machine Integration

Human-machine integration refers to the pairing of operators with technology to enhance or optimize human capability. The nature of the integration can vary widely, with an important dimension being the invasive nature of the pairing.

Brain-computer interfaces

Interfaces that allow a human to interact with a computer directly via input from the brain through a device that senses brain activity, allowing for research, mapping, assistance or augmentation of human brain functions that could enable improved cognitive performance or communication with digital devices.

Exoskeletons

External devices or ‘wearable robots’ that can assist or augment the physical and physiological performance/capabilities of an individual or a group.

Neuroprosthetic/cybernetic devices

Implanted and worn devices that interact with the nervous system to enhance or restore motor, sensory, cognitive, visual, auditory or communicative functions, often resulting from brain injury. This includes cybernetic limbs or devices that go beyond medical use to contribute to human performance enhancement.

Virtual/augmented/mixed reality

Immersive technologies that combine elements of the virtual world with the real world to create an interactive virtual experience. An application of these technologies that several companies are developing is the ‘metaverse’ which is an immersive digital experience that integrates the physical world with the digital one and allows users to interact and perform a variety of activities like shopping and gaming, seamlessly in one virtual ecosystem. While still being explored, this could potentially translate into a digital economy with its own currency, property and other goods.

Wearable neurotechnology

Brain-computer interfaces that are wearable and non-invasive (i.e. do not need to be implanted). These wearable brain devices can be used for medical uses, such as tracking brain health and sending data to a doctor to inform treatment, as well as for non-medical applications related to human optimization, augmentation or enhancement, such as user-drowsiness, cognitive load monitoring or early reaction detection, among others.

9. Life Science Technology

Life science technology is a broad term that encompasses a wide array of technologies that enhance living organisms, such as biotechnology and medical and healthcare technologies.

Biotechnology

Biotechnology uses living systems, processes and organisms, or parts of them, to develop new or improved products, processes or services. It often integrates other areas of technology, such as nanotechnology, artificial intelligence, computing and others, to create novel solutions to problems, including in the area of human performance enhancement.

Biomanufacturing

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Combination of biology and engineering to create new biological entities, such as cells or enzymes, or redesign existing biological systems, with new functions like sensing or producing a specific substance. Synthetic biology is expected to enable advancements in many areas, such as antibiotic, drug and vaccine development, biocomputers, biofuel, novel drug delivery platforms, novel chemicals, synthetic food, and synthetic life.

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Gene therapy

Use of gene manipulation or modification in humans to prevent, treat or cure disease, either by replacing or disabling disease-causing genes or inserting new or modified genes.

Nanomedicine

Use of nanomaterials to diagnose, monitor, prevent and/or treat disease. Examples of nanomedicine include nanoparticles for targeted drug delivery, smart imaging using nanomaterials, as well as nano-engineered implants to support tissue engineering and regenerative medicine.

Tissue engineering and regenerative medicine

Methods of regenerating or rebuilding cells, tissues or organs to allow normal, biological functions to be restored. Regenerative medicine includes self-healing, where the body is able to use its own tools or other biological materials to regrow tissues or cells, whereas tissue engineering largely focuses on the use of synthetic and biological materials, such as stem cells, to build function constructs or supports that help heal or restore damaged tissues or organs.

10. Quantum Science and Technology

Quantum science and technology refers to a new generation of devices that use quantum effects to significantly enhance the performance over those of existing, ‘classical’, technologies. This technology is expected to deliver sensing and imaging, communications, and computing capabilities that far exceed those of conventional technologies in certain cases, well as new materials with extraordinary properties and many useful applications. Quantum science and technology may be considered cross-cutting, given that quantum-enhanced technologies are expected to enable advancements or improvements in most other technology areas, including biotechnology, advanced materials, robotics and autonomous systems, aerospace, space and satellite technology and others.

Quantum communications

Use of quantum physics to enable secure communications and protect data using quantum cryptography, also know as quantum key distribution.

Quantum computing

Use of quantum bits, also known as qubits, to process information by capitalizing on quantum mechanical effects that allow for a large amount of information, such as calculations, to be processed at the same time. A quantum computer that can harness qubits in a controlled quantum state may be able to compute and solve certain problems significantly faster than the most powerful supercomputers.

Quantum materials

Materials with unusual magnetic and electrical properties. Examples include: superconductors, graphene, topological insulators, Weyl semimetals, metal chalcogenides and others. While many of these materials are still being explored and studied, they are promising contenders that could enable energy-efficient electrical systems, better batteries and the development of new types of electronic devices.

Quantum sensing

Broad of range of devices, at various stages of technological readiness, that use quantum systems, properties, or phenomena to measure a physical quantity with increased precision, stability and accuracy. Recent developments in applications of quantum physics identified the possibility of exploiting quantum phenomena as means to develop quantum radar technology.

Quantum software

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Service robots

Robots that carry out tasks useful to humans that may be tedious, time-consuming, repetitive, dangerous or complement human behaviour when resources are not available, e.g. supporting elderly people. They are semi- or fully-autonomous, able to make decisions with some or no human interaction/intervention (depending on the degree of autonomy), and can be manually overridden by a human.

Space robotics

Devices, or ‘space robots’, that are able to perform various functions in orbit, such as assembling or servicing, to support astronauts, or replace human explorers in the exploration of remote planets.

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• v.37(16); 2022 Apr 25

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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