A Guide To The Methods, Benefits & Problems of The Interpretation of Data

Data interpretation blog post by datapine

Table of Contents

1) What Is Data Interpretation?

2) How To Interpret Data?

3) Why Data Interpretation Is Important?

4) Data Interpretation Skills

5) Data Analysis & Interpretation Problems

6) Data Interpretation Techniques & Methods

7) The Use of Dashboards For Data Interpretation

8) Business Data Interpretation Examples

Data analysis and interpretation have now taken center stage with the advent of the digital age… and the sheer amount of data can be frightening. In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 trillion gigabytes! Based on that amount of data alone, it is clear the calling card of any successful enterprise in today’s global world will be the ability to analyze complex data, produce actionable insights, and adapt to new market needs… all at the speed of thought.

Business dashboards are the digital age tools for big data. Capable of displaying key performance indicators (KPIs) for both quantitative and qualitative data analyses, they are ideal for making the fast-paced and data-driven market decisions that push today’s industry leaders to sustainable success. Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision-making and are key instruments in data interpretation. First of all, let’s find a definition to understand what lies behind this practice.

What Is Data Interpretation?

Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.

The importance of data interpretation is evident, and this is why it needs to be done properly. Data is very likely to arrive from multiple sources and has a tendency to enter the analysis process with haphazard ordering. Data analysis tends to be extremely subjective. That is to say, the nature and goal of interpretation will vary from business to business, likely correlating to the type of data being analyzed. While there are several types of processes that are implemented based on the nature of individual data, the two broadest and most common categories are “quantitative and qualitative analysis.”

Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding measurement scales. Before any serious data analysis can begin, the measurement scale must be decided for the data as this will have a long-term impact on data interpretation ROI. The varying scales include:

  • Nominal Scale: non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
  • Ordinal Scale: exclusive categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales (i.e., good, very good, fair, etc., OR agree, strongly agree, disagree, etc.).
  • Interval: a measurement scale where data is grouped into categories with orderly and equal distances between the categories. There is always an arbitrary zero point.
  • Ratio: contains features of all three.

For a more in-depth review of scales of measurement, read our article on data analysis questions . Once measurement scales have been selected, it is time to select which of the two broad interpretation processes will best suit your data needs. Let’s take a closer look at those specific methods and possible data interpretation problems.

How To Interpret Data? Top Methods & Techniques

Illustration of data interpretation on blackboard

When interpreting data, an analyst must try to discern the differences between correlation, causation, and coincidences, as well as many other biases – but he also has to consider all the factors involved that may have led to a result. There are various data interpretation types and methods one can use to achieve this.

The interpretation of data is designed to help people make sense of numerical data that has been collected, analyzed, and presented. Having a baseline method for interpreting data will provide your analyst teams with a structure and consistent foundation. Indeed, if several departments have different approaches to interpreting the same data while sharing the same goals, some mismatched objectives can result. Disparate methods will lead to duplicated efforts, inconsistent solutions, wasted energy, and inevitably – time and money. In this part, we will look at the two main methods of interpretation of data: qualitative and quantitative analysis.

Qualitative Data Interpretation

Qualitative data analysis can be summed up in one word – categorical. With this type of analysis, data is not described through numerical values or patterns but through the use of descriptive context (i.e., text). Typically, narrative data is gathered by employing a wide variety of person-to-person techniques. These techniques include:

  • Observations: detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity, and the method of communication employed.
  • Focus groups: Group people and ask them relevant questions to generate a collaborative discussion about a research topic.
  • Secondary Research: much like how patterns of behavior can be observed, various types of documentation resources can be coded and divided based on the type of material they contain.
  • Interviews: one of the best collection methods for narrative data. Inquiry responses can be grouped by theme, topic, or category. The interview approach allows for highly focused data segmentation.

A key difference between qualitative and quantitative analysis is clearly noticeable in the interpretation stage. The first one is widely open to interpretation and must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. As person-to-person data collection techniques can often result in disputes pertaining to proper analysis, qualitative data analysis is often summarized through three basic principles: notice things, collect things, and think about things.

After qualitative data has been collected through transcripts, questionnaires, audio and video recordings, or the researcher’s notes, it is time to interpret it. For that purpose, there are some common methods used by researchers and analysts.

  • Content analysis : As its name suggests, this is a research method used to identify frequencies and recurring words, subjects, and concepts in image, video, or audio content. It transforms qualitative information into quantitative data to help discover trends and conclusions that will later support important research or business decisions. This method is often used by marketers to understand brand sentiment from the mouths of customers themselves. Through that, they can extract valuable information to improve their products and services. It is recommended to use content analytics tools for this method as manually performing it is very time-consuming and can lead to human error or subjectivity issues. Having a clear goal in mind before diving into it is another great practice for avoiding getting lost in the fog.  
  • Thematic analysis: This method focuses on analyzing qualitative data, such as interview transcripts, survey questions, and others, to identify common patterns and separate the data into different groups according to found similarities or themes. For example, imagine you want to analyze what customers think about your restaurant. For this purpose, you do a thematic analysis on 1000 reviews and find common themes such as “fresh food”, “cold food”, “small portions”, “friendly staff”, etc. With those recurring themes in hand, you can extract conclusions about what could be improved or enhanced based on your customer’s experiences. Since this technique is more exploratory, be open to changing your research questions or goals as you go. 
  • Narrative analysis: A bit more specific and complicated than the two previous methods, it is used to analyze stories and discover their meaning. These stories can be extracted from testimonials, case studies, and interviews, as these formats give people more space to tell their experiences. Given that collecting this kind of data is harder and more time-consuming, sample sizes for narrative analysis are usually smaller, which makes it harder to reproduce its findings. However, it is still a valuable technique for understanding customers' preferences and mindsets.  
  • Discourse analysis : This method is used to draw the meaning of any type of visual, written, or symbolic language in relation to a social, political, cultural, or historical context. It is used to understand how context can affect how language is carried out and understood. For example, if you are doing research on power dynamics, using discourse analysis to analyze a conversation between a janitor and a CEO and draw conclusions about their responses based on the context and your research questions is a great use case for this technique. That said, like all methods in this section, discourse analytics is time-consuming as the data needs to be analyzed until no new insights emerge.  
  • Grounded theory analysis : The grounded theory approach aims to create or discover a new theory by carefully testing and evaluating the data available. Unlike all other qualitative approaches on this list, grounded theory helps extract conclusions and hypotheses from the data instead of going into the analysis with a defined hypothesis. This method is very popular amongst researchers, analysts, and marketers as the results are completely data-backed, providing a factual explanation of any scenario. It is often used when researching a completely new topic or with little knowledge as this space to start from the ground up. 

Quantitative Data Interpretation

If quantitative data interpretation could be summed up in one word (and it really can’t), that word would be “numerical.” There are few certainties when it comes to data analysis, but you can be sure that if the research you are engaging in has no numbers involved, it is not quantitative research, as this analysis refers to a set of processes by which numerical data is analyzed. More often than not, it involves the use of statistical modeling such as standard deviation, mean, and median. Let’s quickly review the most common statistical terms:

  • Mean: A mean represents a numerical average for a set of responses. When dealing with a data set (or multiple data sets), a mean will represent the central value of a specific set of numbers. It is the sum of the values divided by the number of values within the data set. Other terms that can be used to describe the concept are arithmetic mean, average, and mathematical expectation.
  • Standard deviation: This is another statistical term commonly used in quantitative analysis. Standard deviation reveals the distribution of the responses around the mean. It describes the degree of consistency within the responses; together with the mean, it provides insight into data sets.
  • Frequency distribution: This is a measurement gauging the rate of a response appearance within a data set. When using a survey, for example, frequency distribution, it can determine the number of times a specific ordinal scale response appears (i.e., agree, strongly agree, disagree, etc.). Frequency distribution is extremely keen in determining the degree of consensus among data points.

Typically, quantitative data is measured by visually presenting correlation tests between two or more variables of significance. Different processes can be used together or separately, and comparisons can be made to ultimately arrive at a conclusion. Other signature interpretation processes of quantitative data include:

  • Regression analysis: Essentially, it uses historical data to understand the relationship between a dependent variable and one or more independent variables. Knowing which variables are related and how they developed in the past allows you to anticipate possible outcomes and make better decisions going forward. For example, if you want to predict your sales for next month, you can use regression to understand what factors will affect them, such as products on sale and the launch of a new campaign, among many others. 
  • Cohort analysis: This method identifies groups of users who share common characteristics during a particular time period. In a business scenario, cohort analysis is commonly used to understand customer behaviors. For example, a cohort could be all users who have signed up for a free trial on a given day. An analysis would be carried out to see how these users behave, what actions they carry out, and how their behavior differs from other user groups.
  • Predictive analysis: As its name suggests, the predictive method aims to predict future developments by analyzing historical and current data. Powered by technologies such as artificial intelligence and machine learning, predictive analytics practices enable businesses to identify patterns or potential issues and plan informed strategies in advance.
  • Prescriptive analysis: Also powered by predictions, the prescriptive method uses techniques such as graph analysis, complex event processing, and neural networks, among others, to try to unravel the effect that future decisions will have in order to adjust them before they are actually made. This helps businesses to develop responsive, practical business strategies.
  • Conjoint analysis: Typically applied to survey analysis, the conjoint approach is used to analyze how individuals value different attributes of a product or service. This helps researchers and businesses to define pricing, product features, packaging, and many other attributes. A common use is menu-based conjoint analysis, in which individuals are given a “menu” of options from which they can build their ideal concept or product. Through this, analysts can understand which attributes they would pick above others and drive conclusions.
  • Cluster analysis: Last but not least, the cluster is a method used to group objects into categories. Since there is no target variable when using cluster analysis, it is a useful method to find hidden trends and patterns in the data. In a business context, clustering is used for audience segmentation to create targeted experiences. In market research, it is often used to identify age groups, geographical information, and earnings, among others.

Now that we have seen how to interpret data, let's move on and ask ourselves some questions: What are some of the benefits of data interpretation? Why do all industries engage in data research and analysis? These are basic questions, but they often don’t receive adequate attention.

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Why Data Interpretation Is Important

illustrating quantitative data interpretation with charts & graphs

The purpose of collection and interpretation is to acquire useful and usable information and to make the most informed decisions possible. From businesses to newlyweds researching their first home, data collection and interpretation provide limitless benefits for a wide range of institutions and individuals.

Data analysis and interpretation, regardless of the method and qualitative/quantitative status, may include the following characteristics:

  • Data identification and explanation
  • Comparing and contrasting data
  • Identification of data outliers
  • Future predictions

Data analysis and interpretation, in the end, help improve processes and identify problems. It is difficult to grow and make dependable improvements without, at the very least, minimal data collection and interpretation. What is the keyword? Dependable. Vague ideas regarding performance enhancement exist within all institutions and industries. Yet, without proper research and analysis, an idea is likely to remain in a stagnant state forever (i.e., minimal growth). So… what are a few of the business benefits of digital age data analysis and interpretation? Let’s take a look!

1) Informed decision-making: A decision is only as good as the knowledge that formed it. Informed data decision-making can potentially set industry leaders apart from the rest of the market pack. Studies have shown that companies in the top third of their industries are, on average, 5% more productive and 6% more profitable when implementing informed data decision-making processes. Most decisive actions will arise only after a problem has been identified or a goal defined. Data analysis should include identification, thesis development, and data collection, followed by data communication.

If institutions only follow that simple order, one that we should all be familiar with from grade school science fairs, then they will be able to solve issues as they emerge in real-time. Informed decision-making has a tendency to be cyclical. This means there is really no end, and eventually, new questions and conditions arise within the process that need to be studied further. The monitoring of data results will inevitably return the process to the start with new data and sights.

2) Anticipating needs with trends identification: data insights provide knowledge, and knowledge is power. The insights obtained from market and consumer data analyses have the ability to set trends for peers within similar market segments. A perfect example of how data analytics can impact trend prediction is evidenced in the music identification application Shazam . The application allows users to upload an audio clip of a song they like but can’t seem to identify. Users make 15 million song identifications a day. With this data, Shazam has been instrumental in predicting future popular artists.

When industry trends are identified, they can then serve a greater industry purpose. For example, the insights from Shazam’s monitoring benefits not only Shazam in understanding how to meet consumer needs but also grant music executives and record label companies an insight into the pop-culture scene of the day. Data gathering and interpretation processes can allow for industry-wide climate prediction and result in greater revenue streams across the market. For this reason, all institutions should follow the basic data cycle of collection, interpretation, decision-making, and monitoring.

3) Cost efficiency: Proper implementation of analytics processes can provide businesses with profound cost advantages within their industries. A recent data study performed by Deloitte vividly demonstrates this in finding that data analysis ROI is driven by efficient cost reductions. Often, this benefit is overlooked because making money is typically viewed as “sexier” than saving money. Yet, sound data analyses have the ability to alert management to cost-reduction opportunities without any significant exertion of effort on the part of human capital.

A great example of the potential for cost efficiency through data analysis is Intel. Prior to 2012, Intel would conduct over 19,000 manufacturing function tests on their chips before they could be deemed acceptable for release. To cut costs and reduce test time, Intel implemented predictive data analyses. By using historical and current data, Intel now avoids testing each chip 19,000 times by focusing on specific and individual chip tests. After its implementation in 2012, Intel saved over $3 million in manufacturing costs. Cost reduction may not be as “sexy” as data profit, but as Intel proves, it is a benefit of data analysis that should not be neglected.

4) Clear foresight: companies that collect and analyze their data gain better knowledge about themselves, their processes, and their performance. They can identify performance challenges when they arise and take action to overcome them. Data interpretation through visual representations lets them process their findings faster and make better-informed decisions on the company's future.

Key Data Interpretation Skills You Should Have

Just like any other process, data interpretation and analysis require researchers or analysts to have some key skills to be able to perform successfully. It is not enough just to apply some methods and tools to the data; the person who is managing it needs to be objective and have a data-driven mind, among other skills. 

It is a common misconception to think that the required skills are mostly number-related. While data interpretation is heavily analytically driven, it also requires communication and narrative skills, as the results of the analysis need to be presented in a way that is easy to understand for all types of audiences. 

Luckily, with the rise of self-service tools and AI-driven technologies, data interpretation is no longer segregated for analysts only. However, the topic still remains a big challenge for businesses that make big investments in data and tools to support it, as the interpretation skills required are still lacking. It is worthless to put massive amounts of money into extracting information if you are not going to be able to interpret what that information is telling you. For that reason, below we list the top 5 data interpretation skills your employees or researchers should have to extract the maximum potential from the data. 

  • Data Literacy: The first and most important skill to have is data literacy. This means having the ability to understand, work, and communicate with data. It involves knowing the types of data sources, methods, and ethical implications of using them. In research, this skill is often a given. However, in a business context, there might be many employees who are not comfortable with data. The issue is the interpretation of data can not be solely responsible for the data team, as it is not sustainable in the long run. Experts advise business leaders to carefully assess the literacy level across their workforce and implement training instances to ensure everyone can interpret their data. 
  • Data Tools: The data interpretation and analysis process involves using various tools to collect, clean, store, and analyze the data. The complexity of the tools varies depending on the type of data and the analysis goals. Going from simple ones like Excel to more complex ones like databases, such as SQL, or programming languages, such as R or Python. It also involves visual analytics tools to bring the data to life through the use of graphs and charts. Managing these tools is a fundamental skill as they make the process faster and more efficient. As mentioned before, most modern solutions are now self-service, enabling less technical users to use them without problem.
  • Critical Thinking: Another very important skill is to have critical thinking. Data hides a range of conclusions, trends, and patterns that must be discovered. It is not just about comparing numbers; it is about putting a story together based on multiple factors that will lead to a conclusion. Therefore, having the ability to look further from what is right in front of you is an invaluable skill for data interpretation. 
  • Data Ethics: In the information age, being aware of the legal and ethical responsibilities that come with the use of data is of utmost importance. In short, data ethics involves respecting the privacy and confidentiality of data subjects, as well as ensuring accuracy and transparency for data usage. It requires the analyzer or researcher to be completely objective with its interpretation to avoid any biases or discrimination. Many countries have already implemented regulations regarding the use of data, including the GDPR or the ACM Code Of Ethics. Awareness of these regulations and responsibilities is a fundamental skill that anyone working in data interpretation should have. 
  • Domain Knowledge: Another skill that is considered important when interpreting data is to have domain knowledge. As mentioned before, data hides valuable insights that need to be uncovered. To do so, the analyst needs to know about the industry or domain from which the information is coming and use that knowledge to explore it and put it into a broader context. This is especially valuable in a business context, where most departments are now analyzing data independently with the help of a live dashboard instead of relying on the IT department, which can often overlook some aspects due to a lack of expertise in the topic. 

Common Data Analysis And Interpretation Problems

Man running away from common data interpretation problems

The oft-repeated mantra of those who fear data advancements in the digital age is “big data equals big trouble.” While that statement is not accurate, it is safe to say that certain data interpretation problems or “pitfalls” exist and can occur when analyzing data, especially at the speed of thought. Let’s identify some of the most common data misinterpretation risks and shed some light on how they can be avoided:

1) Correlation mistaken for causation: our first misinterpretation of data refers to the tendency of data analysts to mix the cause of a phenomenon with correlation. It is the assumption that because two actions occurred together, one caused the other. This is inaccurate, as actions can occur together, absent a cause-and-effect relationship.

  • Digital age example: assuming that increased revenue results from increased social media followers… there might be a definitive correlation between the two, especially with today’s multi-channel purchasing experiences. But that does not mean an increase in followers is the direct cause of increased revenue. There could be both a common cause and an indirect causality.
  • Remedy: attempt to eliminate the variable you believe to be causing the phenomenon.

2) Confirmation bias: our second problem is data interpretation bias. It occurs when you have a theory or hypothesis in mind but are intent on only discovering data patterns that support it while rejecting those that do not.

  • Digital age example: your boss asks you to analyze the success of a recent multi-platform social media marketing campaign. While analyzing the potential data variables from the campaign (one that you ran and believe performed well), you see that the share rate for Facebook posts was great, while the share rate for Twitter Tweets was not. Using only Facebook posts to prove your hypothesis that the campaign was successful would be a perfect manifestation of confirmation bias.
  • Remedy: as this pitfall is often based on subjective desires, one remedy would be to analyze data with a team of objective individuals. If this is not possible, another solution is to resist the urge to make a conclusion before data exploration has been completed. Remember to always try to disprove a hypothesis, not prove it.

3) Irrelevant data: the third data misinterpretation pitfall is especially important in the digital age. As large data is no longer centrally stored and as it continues to be analyzed at the speed of thought, it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to correct.

  • Digital age example: in attempting to gauge the success of an email lead generation campaign, you notice that the number of homepage views directly resulting from the campaign increased, but the number of monthly newsletter subscribers did not. Based on the number of homepage views, you decide the campaign was a success when really it generated zero leads.
  • Remedy: proactively and clearly frame any data analysis variables and KPIs prior to engaging in a data review. If the metric you use to measure the success of a lead generation campaign is newsletter subscribers, there is no need to review the number of homepage visits. Be sure to focus on the data variable that answers your question or solves your problem and not on irrelevant data.

4) Truncating an Axes: When creating a graph to start interpreting the results of your analysis, it is important to keep the axes truthful and avoid generating misleading visualizations. Starting the axes in a value that doesn’t portray the actual truth about the data can lead to false conclusions. 

  • Digital age example: In the image below, we can see a graph from Fox News in which the Y-axes start at 34%, making it seem that the difference between 35% and 39.6% is way higher than it actually is. This could lead to a misinterpretation of the tax rate changes. 

Fox news graph truncating an axes

* Source : www.venngage.com *

  • Remedy: Be careful with how your data is visualized. Be respectful and realistic with axes to avoid misinterpretation of your data. See below how the Fox News chart looks when using the correct axis values. This chart was created with datapine's modern online data visualization tool.

Fox news graph with the correct axes values

5) (Small) sample size: Another common problem is using a small sample size. Logically, the bigger the sample size, the more accurate and reliable the results. However, this also depends on the size of the effect of the study. For example, the sample size in a survey about the quality of education will not be the same as for one about people doing outdoor sports in a specific area. 

  • Digital age example: Imagine you ask 30 people a question, and 29 answer “yes,” resulting in 95% of the total. Now imagine you ask the same question to 1000, and 950 of them answer “yes,” which is again 95%. While these percentages might look the same, they certainly do not mean the same thing, as a 30-person sample size is not a significant number to establish a truthful conclusion. 
  • Remedy: Researchers say that in order to determine the correct sample size to get truthful and meaningful results, it is necessary to define a margin of error that will represent the maximum amount they want the results to deviate from the statistical mean. Paired with this, they need to define a confidence level that should be between 90 and 99%. With these two values in hand, researchers can calculate an accurate sample size for their studies.

6) Reliability, subjectivity, and generalizability : When performing qualitative analysis, researchers must consider practical and theoretical limitations when interpreting the data. In some cases, this type of research can be considered unreliable because of uncontrolled factors that might or might not affect the results. This is paired with the fact that the researcher has a primary role in the interpretation process, meaning he or she decides what is relevant and what is not, and as we know, interpretations can be very subjective.

Generalizability is also an issue that researchers face when dealing with qualitative analysis. As mentioned in the point about having a small sample size, it is difficult to draw conclusions that are 100% representative because the results might be biased or unrepresentative of a wider population. 

While these factors are mostly present in qualitative research, they can also affect the quantitative analysis. For example, when choosing which KPIs to portray and how to portray them, analysts can also be biased and represent them in a way that benefits their analysis.

  • Digital age example: Biased questions in a survey are a great example of reliability and subjectivity issues. Imagine you are sending a survey to your clients to see how satisfied they are with your customer service with this question: “How amazing was your experience with our customer service team?”. Here, we can see that this question clearly influences the response of the individual by putting the word “amazing” on it. 
  • Remedy: A solution to avoid these issues is to keep your research honest and neutral. Keep the wording of the questions as objective as possible. For example: “On a scale of 1-10, how satisfied were you with our customer service team?”. This does not lead the respondent to any specific answer, meaning the results of your survey will be reliable. 

Data Interpretation Best Practices & Tips

Data interpretation methods and techniques by datapine

Data analysis and interpretation are critical to developing sound conclusions and making better-informed decisions. As we have seen with this article, there is an art and science to the interpretation of data. To help you with this purpose, we will list a few relevant techniques, methods, and tricks you can implement for a successful data management process. 

As mentioned at the beginning of this post, the first step to interpreting data in a successful way is to identify the type of analysis you will perform and apply the methods respectively. Clearly differentiate between qualitative (observe, document, and interview notice, collect and think about things) and quantitative analysis (you lead research with a lot of numerical data to be analyzed through various statistical methods). 

1) Ask the right data interpretation questions

The first data interpretation technique is to define a clear baseline for your work. This can be done by answering some critical questions that will serve as a useful guideline to start. Some of them include: what are the goals and objectives of my analysis? What type of data interpretation method will I use? Who will use this data in the future? And most importantly, what general question am I trying to answer?

Once all this information has been defined, you will be ready for the next step: collecting your data. 

2) Collect and assimilate your data

Now that a clear baseline has been established, it is time to collect the information you will use. Always remember that your methods for data collection will vary depending on what type of analysis method you use, which can be qualitative or quantitative. Based on that, relying on professional online data analysis tools to facilitate the process is a great practice in this regard, as manually collecting and assessing raw data is not only very time-consuming and expensive but is also at risk of errors and subjectivity. 

Once your data is collected, you need to carefully assess it to understand if the quality is appropriate to be used during a study. This means, is the sample size big enough? Were the procedures used to collect the data implemented correctly? Is the date range from the data correct? If coming from an external source, is it a trusted and objective one? 

With all the needed information in hand, you are ready to start the interpretation process, but first, you need to visualize your data. 

3) Use the right data visualization type 

Data visualizations such as business graphs , charts, and tables are fundamental to successfully interpreting data. This is because data visualization via interactive charts and graphs makes the information more understandable and accessible. As you might be aware, there are different types of visualizations you can use, but not all of them are suitable for any analysis purpose. Using the wrong graph can lead to misinterpretation of your data, so it’s very important to carefully pick the right visual for it. Let’s look at some use cases of common data visualizations. 

  • Bar chart: One of the most used chart types, the bar chart uses rectangular bars to show the relationship between 2 or more variables. There are different types of bar charts for different interpretations, including the horizontal bar chart, column bar chart, and stacked bar chart. 
  • Line chart: Most commonly used to show trends, acceleration or decelerations, and volatility, the line chart aims to show how data changes over a period of time, for example, sales over a year. A few tips to keep this chart ready for interpretation are not using many variables that can overcrowd the graph and keeping your axis scale close to the highest data point to avoid making the information hard to read. 
  • Pie chart: Although it doesn’t do a lot in terms of analysis due to its uncomplex nature, pie charts are widely used to show the proportional composition of a variable. Visually speaking, showing a percentage in a bar chart is way more complicated than showing it in a pie chart. However, this also depends on the number of variables you are comparing. If your pie chart needs to be divided into 10 portions, then it is better to use a bar chart instead. 
  • Tables: While they are not a specific type of chart, tables are widely used when interpreting data. Tables are especially useful when you want to portray data in its raw format. They give you the freedom to easily look up or compare individual values while also displaying grand totals. 

With the use of data visualizations becoming more and more critical for businesses’ analytical success, many tools have emerged to help users visualize their data in a cohesive and interactive way. One of the most popular ones is the use of BI dashboards . These visual tools provide a centralized view of various graphs and charts that paint a bigger picture of a topic. We will discuss the power of dashboards for an efficient data interpretation practice in the next portion of this post. If you want to learn more about different types of graphs and charts , take a look at our complete guide on the topic. 

4) Start interpreting 

After the tedious preparation part, you can start extracting conclusions from your data. As mentioned many times throughout the post, the way you decide to interpret the data will solely depend on the methods you initially decided to use. If you had initial research questions or hypotheses, then you should look for ways to prove their validity. If you are going into the data with no defined hypothesis, then start looking for relationships and patterns that will allow you to extract valuable conclusions from the information. 

During the process of interpretation, stay curious and creative, dig into the data, and determine if there are any other critical questions that should be asked. If any new questions arise, you need to assess if you have the necessary information to answer them. Being able to identify if you need to dedicate more time and resources to the research is a very important step. No matter if you are studying customer behaviors or a new cancer treatment, the findings from your analysis may dictate important decisions in the future. Therefore, taking the time to really assess the information is key. For that purpose, data interpretation software proves to be very useful.

5) Keep your interpretation objective

As mentioned above, objectivity is one of the most important data interpretation skills but also one of the hardest. Being the person closest to the investigation, it is easy to become subjective when looking for answers in the data. A good way to stay objective is to show the information related to the study to other people, for example, research partners or even the people who will use your findings once they are done. This can help avoid confirmation bias and any reliability issues with your interpretation. 

Remember, using a visualization tool such as a modern dashboard will make the interpretation process way easier and more efficient as the data can be navigated and manipulated in an easy and organized way. And not just that, using a dashboard tool to present your findings to a specific audience will make the information easier to understand and the presentation way more engaging thanks to the visual nature of these tools. 

6) Mark your findings and draw conclusions

Findings are the observations you extracted from your data. They are the facts that will help you drive deeper conclusions about your research. For example, findings can be trends and patterns you found during your interpretation process. To put your findings into perspective, you can compare them with other resources that use similar methods and use them as benchmarks.

Reflect on your own thinking and reasoning and be aware of the many pitfalls data analysis and interpretation carry—correlation versus causation, subjective bias, false information, inaccurate data, etc. Once you are comfortable with interpreting the data, you will be ready to develop conclusions, see if your initial questions were answered, and suggest recommendations based on them.

Interpretation of Data: The Use of Dashboards Bridging The Gap

As we have seen, quantitative and qualitative methods are distinct types of data interpretation and analysis. Both offer a varying degree of return on investment (ROI) regarding data investigation, testing, and decision-making. But how do you mix the two and prevent a data disconnect? The answer is professional data dashboards. 

For a few years now, dashboards have become invaluable tools to visualize and interpret data. These tools offer a centralized and interactive view of data and provide the perfect environment for exploration and extracting valuable conclusions. They bridge the quantitative and qualitative information gap by unifying all the data in one place with the help of stunning visuals. 

Not only that, but these powerful tools offer a large list of benefits, and we will discuss some of them below. 

1) Connecting and blending data. With today’s pace of innovation, it is no longer feasible (nor desirable) to have bulk data centrally located. As businesses continue to globalize and borders continue to dissolve, it will become increasingly important for businesses to possess the capability to run diverse data analyses absent the limitations of location. Data dashboards decentralize data without compromising on the necessary speed of thought while blending both quantitative and qualitative data. Whether you want to measure customer trends or organizational performance, you now have the capability to do both without the need for a singular selection.

2) Mobile Data. Related to the notion of “connected and blended data” is that of mobile data. In today’s digital world, employees are spending less time at their desks and simultaneously increasing production. This is made possible because mobile solutions for analytical tools are no longer standalone. Today, mobile analysis applications seamlessly integrate with everyday business tools. In turn, both quantitative and qualitative data are now available on-demand where they’re needed, when they’re needed, and how they’re needed via interactive online dashboards .

3) Visualization. Data dashboards merge the data gap between qualitative and quantitative data interpretation methods through the science of visualization. Dashboard solutions come “out of the box” and are well-equipped to create easy-to-understand data demonstrations. Modern online data visualization tools provide a variety of color and filter patterns, encourage user interaction, and are engineered to help enhance future trend predictability. All of these visual characteristics make for an easy transition among data methods – you only need to find the right types of data visualization to tell your data story the best way possible.

4) Collaboration. Whether in a business environment or a research project, collaboration is key in data interpretation and analysis. Dashboards are online tools that can be easily shared through a password-protected URL or automated email. Through them, users can collaborate and communicate through the data in an efficient way. Eliminating the need for infinite files with lost updates. Tools such as datapine offer real-time updates, meaning your dashboards will update on their own as soon as new information is available.  

Examples Of Data Interpretation In Business

To give you an idea of how a dashboard can fulfill the need to bridge quantitative and qualitative analysis and help in understanding how to interpret data in research thanks to visualization, below, we will discuss three valuable examples to put their value into perspective.

1. Customer Satisfaction Dashboard 

This market research dashboard brings together both qualitative and quantitative data that are knowledgeably analyzed and visualized in a meaningful way that everyone can understand, thus empowering any viewer to interpret it. Let’s explore it below. 

Data interpretation example on customers' satisfaction with a brand

**click to enlarge**

The value of this template lies in its highly visual nature. As mentioned earlier, visuals make the interpretation process way easier and more efficient. Having critical pieces of data represented with colorful and interactive icons and graphs makes it possible to uncover insights at a glance. For example, the colors green, yellow, and red on the charts for the NPS and the customer effort score allow us to conclude that most respondents are satisfied with this brand with a short glance. A further dive into the line chart below can help us dive deeper into this conclusion, as we can see both metrics developed positively in the past 6 months. 

The bottom part of the template provides visually stunning representations of different satisfaction scores for quality, pricing, design, and service. By looking at these, we can conclude that, overall, customers are satisfied with this company in most areas. 

2. Brand Analysis Dashboard

Next, in our list of data interpretation examples, we have a template that shows the answers to a survey on awareness for Brand D. The sample size is listed on top to get a perspective of the data, which is represented using interactive charts and graphs. 

Data interpretation example using a market research dashboard for brand awareness analysis

When interpreting information, context is key to understanding it correctly. For that reason, the dashboard starts by offering insights into the demographics of the surveyed audience. In general, we can see ages and gender are diverse. Therefore, we can conclude these brands are not targeting customers from a specified demographic, an important aspect to put the surveyed answers into perspective. 

Looking at the awareness portion, we can see that brand B is the most popular one, with brand D coming second on both questions. This means brand D is not doing wrong, but there is still room for improvement compared to brand B. To see where brand D could improve, the researcher could go into the bottom part of the dashboard and consult the answers for branding themes and celebrity analysis. These are important as they give clear insight into what people and messages the audience associates with brand D. This is an opportunity to exploit these topics in different ways and achieve growth and success. 

3. Product Innovation Dashboard 

Our third and last dashboard example shows the answers to a survey on product innovation for a technology company. Just like the previous templates, the interactive and visual nature of the dashboard makes it the perfect tool to interpret data efficiently and effectively. 

Market research results on product innovation, useful for product development and pricing decisions as an example of data interpretation using dashboards

Starting from right to left, we first get a list of the top 5 products by purchase intention. This information lets us understand if the product being evaluated resembles what the audience already intends to purchase. It is a great starting point to see how customers would respond to the new product. This information can be complemented with other key metrics displayed in the dashboard. For example, the usage and purchase intention track how the market would receive the product and if they would purchase it, respectively. Interpreting these values as positive or negative will depend on the company and its expectations regarding the survey. 

Complementing these metrics, we have the willingness to pay. Arguably, one of the most important metrics to define pricing strategies. Here, we can see that most respondents think the suggested price is a good value for money. Therefore, we can interpret that the product would sell for that price. 

To see more data analysis and interpretation examples for different industries and functions, visit our library of business dashboards .

To Conclude…

As we reach the end of this insightful post about data interpretation and analysis, we hope you have a clear understanding of the topic. We've covered the definition and given some examples and methods to perform a successful interpretation process.

The importance of data interpretation is undeniable. Dashboards not only bridge the information gap between traditional data interpretation methods and technology, but they can help remedy and prevent the major pitfalls of the process. As a digital age solution, they combine the best of the past and the present to allow for informed decision-making with maximum data interpretation ROI.

To start visualizing your insights in a meaningful and actionable way, test our online reporting software for free with our 14-day trial !

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sample thesis data analysis and interpretation

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Setting research questions/ hypotheses

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Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

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Writing the Data Analysis Chapter(s): Results and Evidence

Posted by Rene Tetzner | Oct 19, 2021 | PhD Success | 0 |

Writing the Data Analysis Chapter(s): Results and Evidence

4.4 Writing the Data Analysis Chapter(s): Results and Evidence

Unlike the introduction, literature review and methodology chapter(s), your results chapter(s) will need to be written for the first time as you draft your thesis even if you submitted a proposal, though this part of your thesis will certainly build upon the preceding chapters. You should have carefully recorded and collected the data (test results, participant responses, computer print outs, observations, transcriptions, notes of various kinds etc.) from your research as you conducted it, so now is the time to review, organise and analyse the data. If your study is quantitative in nature, make sure that you know what all the numbers mean and that you consider them in direct relation to the topic, problem or phenomenon you are investigating, and especially in relation to your research questions and hypotheses. You may find that you require the services of a statistician to help make sense of the data, in which case, obtaining that help sooner rather than later is advisable, because you need to understand your results thoroughly before you can write about them. If, on the other hand, your study is qualitative, you will need to read through the data you have collected several times to become familiar with them both as a whole and in detail so that you can establish important themes, patterns and categories. Remember that ‘qualitative analysis is a creative process and requires thoughtful judgments about what is significant and meaningful in the data’ (Roberts, 2010, p.174; see also Miles & Huberman, 1994) – judgements that often need to be made before the findings can be effectively analysed and presented. If you are combining methodologies in your research, you will also need to consider relationships between the results obtained from the different methods, integrating all the data you have obtained and discovering how the results of one approach support or correlate with the results of another. Ideally, you will have taken careful notes recording your initial thoughts and analyses about the sources you consulted and the results and evidence provided by particular methods and instruments as you put them into practice (as suggested in Sections 2.1.2 and 2.1.4), as these will prove helpful while you consider how best to present your results in your thesis.

Although the ways in which to present and organise the results of doctoral research differ markedly depending on the nature of the study and its findings, as on author and committee preferences and university and department guidelines, there are several basic principles that apply to virtually all theses. First and foremost is the need to present the results of your research both clearly and concisely, and in as objective and factual a manner as possible. There will be time and space to elaborate and interpret your results and speculate on their significance and implications in the final discussion chapter(s) of your thesis, but, generally speaking, such reflection on the meaning of the results should be entirely separate from the factual report of your research findings. There are exceptions, of course, and some candidates, supervisors and departments may prefer the factual presentation and interpretive discussion of results to be blended, just as some thesis topics may demand such treatment, but this is rare and best avoided unless there are persuasive reasons to avoid separating the facts from your thoughts about them. If you do find that you need to blend facts and interpretation in reporting your results, make sure that your language leaves no doubt about the line between the two: words such as ‘seems,’ ‘appears,’ ‘may,’ ‘might,’ probably’ and the like will effectively distinguish analytical speculation from more factual reporting (see also Section 4.5).

You need not dedicate much space in this part of the thesis to the methods you used to arrive at your results because these have already been described in your methodology chapter(s), but they can certainly be revisited briefly to clarify or lend structure to your report. Results are most often presented in a straightforward narrative form which is often supplemented by tables and perhaps by figures such as graphs, charts and maps. An effective approach is to decide immediately which information would be best included in tables and figures, and then to prepare those tables and figures before you begin writing the text for the chapter (see Section 4.4.1 on designing effective tables and figures). Arranging your data into the visually immediate formats provided by tables and figures can, for one, produce interesting surprises by enabling you to see trends and details that you may not have noticed previously, and writing the report of your results will prove easier when you have the tables and figures to work with just as your readers ultimately will. In addition, while the text of the results chapter(s) should certainly highlight the most notable data included in tables and figures, it is essential not to repeat information unnecessarily, so writing with the tables and figures already constructed will help you keep repetition to a minimum. Finally, writing about the tables and figures you create will help you test their clarity and effectiveness for your readers, and you can make any necessary adjustments to the tables and figures as you work. Be sure to refer to each table and figure by number in your text and to make it absolutely clear what you want your readers to see or understand in the table or figure (e.g., ‘see Table 1 for the scores’ and ‘Figure 2 shows this relationship’).

Beyond combining textual narration with the data presented in tables and figures, you will need to organise your report of the results in a manner best suited to the material. You may choose to arrange the presentation of your results chronologically or in a hierarchical order that represents their importance; you might subdivide your results into sections (or separate chapters if there is a great deal of information to accommodate) focussing on the findings of different kinds of methodology (quantitative versus qualitative, for instance) or of different tests, trials, surveys, reviews, case studies and so on; or you may want to create sections (or chapters) focussing on specific themes, patterns or categories or on your research questions and/or hypotheses. The last approach allows you to cluster results that relate to a particular question or hypothesis into a single section and can be particularly useful because it provides cohesion for the thesis as a whole and forces you to focus closely on the issues central to the topic, problem or phenomenon you are investigating. You will, for instance, be able to refer back to the questions and hypotheses presented in your introduction (see Section 3.1), to answer the questions and confirm or dismiss the hypotheses and to anticipate in relation to those questions and hypotheses the discussion and interpretation of your findings that will appear in the next part of the thesis (see Section 4.5). Less effective is an approach that organises the presentation of results according to the items of a survey or questionnaire, because these lend the structure of the instrument used to the results instead of connecting those results directly to the aims, themes and argument of your thesis, but such an organisation can certainly be an important early step in your analysis of the findings and might even be valid for the final thesis if, for instance, your work focuses on developing the instrument involved.

The results generated by doctoral research are unique, and this book cannot hope to outline all the possible approaches for presenting the data and analyses that constitute research results, but it is essential that you devote considerable thought and special care to the way in which you structure the report of your results (Section 6.1 on headings may prove helpful). Whatever structure you choose should accurately reflect the nature of your results and highlight their most important and interesting trends, and it should also effectively allow you (in the next part of the thesis) to discuss and speculate upon your findings in ways that will test the premises of your study, work well in the overall argument of your thesis and lead to significant implications for your research. Regardless of how you organise the main body of your results chapter(s), however, you should include a final paragraph (or more than one paragraph if necessary) that briefly summarises and explains the key results and also guides the reader on to the discussion and interpretation of those results in the following chapter(s).

Why PhD Success?

To Graduate Successfully

This article is part of a book called "PhD Success" which focuses on the writing process of a phd thesis, with its aim being to provide sound practices and principles for reporting and formatting in text the methods, results and discussion of even the most innovative and unique research in ways that are clear, correct, professional and persuasive.

The assumption of the book is that the doctoral candidate reading it is both eager to write and more than capable of doing so, but nonetheless requires information and guidance on exactly what he or she should be writing and how best to approach the task. The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples.

The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples. PhD Success provides guidance for students familiar with English and the procedures of English universities, but it also acknowledges that many theses in the English language are now written by candidates whose first language is not English, so it carefully explains the scholarly styles, conventions and standards expected of a successful doctoral thesis in the English language.

Individual chapters of this book address reflective and critical writing early in the thesis process; working successfully with thesis supervisors and benefiting from commentary and criticism; drafting and revising effective thesis chapters and developing an academic or scientific argument; writing and formatting a thesis in clear and correct scholarly English; citing, quoting and documenting sources thoroughly and accurately; and preparing for and excelling in thesis meetings and examinations. 

Completing a doctoral thesis successfully requires long and penetrating thought, intellectual rigour and creativity, original research and sound methods (whether established or innovative), precision in recording detail and a wide-ranging thoroughness, as much perseverance and mental toughness as insight and brilliance, and, no matter how many helpful writing guides are consulted, a great deal of hard work over a significant period of time. Writing a thesis can be an enjoyable as well as a challenging experience, however, and even if it is not always so, the personal and professional rewards of achieving such an enormous goal are considerable, as all doctoral candidates no doubt realise, and will last a great deal longer than any problems that may be encountered during the process.

Interested in Proofreading your PhD Thesis? Get in Touch with us

If you are interested in proofreading your PhD thesis or dissertation, please explore our expert dissertation proofreading services.

sample thesis data analysis and interpretation

Revising the Proposal Draft

October 16, 2021

Accuracy in Quoted Material and Possible Source Alterations in Theses

Accuracy in Quoted Material and Possible Source Alterations in Theses

November 11, 2021

Writing the Introduction for the Proposal

Writing the Introduction for the Proposal

October 10, 2021

Bibliographical References – Volume & Page Numbers & Source in Book

Bibliographical References – Volume & Page Numbers & Source in Book

November 9, 2021

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The Oxford Handbook of Qualitative Research (2nd edn)

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31 Interpretation In Qualitative Research: What, Why, How

Allen Trent, College of Education, University of Wyoming

Jeasik Cho, Department of Educational Studies, University of Wyoming

  • Published: 02 September 2020
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This chapter addresses a wide range of concepts related to interpretation in qualitative research, examines the meaning and importance of interpretation in qualitative inquiry, and explores the ways methodology, data, and the self/researcher as instrument interact and impact interpretive processes. Additionally, the chapter presents a series of strategies for qualitative researchers engaged in the process of interpretation and closes by presenting a framework for qualitative researchers designed to inform their interpretations. The framework includes attention to the key qualitative research concepts transparency, reflexivity, analysis, validity, evidence, and literature. Four questions frame the chapter: What is interpretation, and why are interpretive strategies important in qualitative research? How do methodology, data, and the researcher/self impact interpretation in qualitative research? How do qualitative researchers engage in the process of interpretation? And, in what ways can a framework for interpretation strategies support qualitative researchers across multiple methodologies and paradigms?

“ All human knowledge takes the form of interpretation.” In this seemingly simple statement, the late German philosopher Walter Benjamin asserted that all knowledge is mediated and constructed. In doing so, he situates himself as an interpretivist, one who believes that human subjectivity, individuals’ characteristics, feelings, opinions, and experiential backgrounds impact observations, analysis of these observations, and resultant knowledge/truth constructions. Hammersley ( 2013 ) noted,

People—unlike atoms … actively interpret or make sense of their environment and of themselves; the ways in which they do this are shaped by the particular cultures in which they live; and these distinctive cultural orientations will strongly influence not only what they believe but also what they do. (p. 26)

Contrast this perspective with positivist claims that knowledge is based exclusively on external facts, objectively observed and recorded. Interpretivists, then, acknowledge that if positivistic notions of knowledge and truth are inadequate to explain social phenomena, then positivist, hard science approaches to research (i.e., the scientific method and its variants) are also inadequate and can even have a detrimental impact. According to Polyani (1967), “The ideal of exact science would turn out to be fundamentally misleading and possibly a source of devastating fallacies” (as cited in Packer, 2018 , p. 71). So, although the literature often contrasts quantitative and qualitative research as largely a difference in kinds of data employed (numerical vs. linguistic), instead, the primary differentiation is in the foundational, paradigmatic assumptions about truth, knowledge, and objectivity.

This chapter is about interpretation and the strategies that qualitative researchers use to interpret a wide variety of “texts.” Knowledge, we assert, is constructed, both individually (constructivism) and socially (constructionism). We accept this as our starting point. Our aim here is to share our perspective on a broad set of concepts associated with the interpretive, or meaning-making, process. Although it may happen at different times and in different ways, interpretation is part of almost all qualitative research.

Qualitative research is an umbrella term that encompasses a wide array of paradigmatic views, goals, and methods. Still, there are key unifying elements that include a generally constructionist epistemological standpoint, attention to primarily linguistic data, and generally accepted protocols or syntax for conducting research. Typically, qualitative researchers begin with a starting point—a curiosity, a problem in need of solutions, a research question, and/or a desire to better understand a situation from the “native” perspectives of the individuals who inhabit that context. This is what anthropologists call the emic , or insider’s, perspective. Olivier de Sardan ( 2015 ) wrote, “It evokes the meaning that social facts have for the actors concerned. It is opposed to the term etic , which, at times, designates more external or ‘objective’ data, and, at others, the researcher’s interpretive analysis” (p. 65).

From this starting point, researchers determine the appropriate kinds of data to collect, engage in fieldwork as participant observers to gather these data, organize the data, look for patterns, and attempt to understand the emic perspectives while integrating their own emergent interpretations. Researchers construct meaning from data by synthesizing research “findings,” “assertions,” or “theories” that can be shared so that others may also gain insights from the conducted inquiry. This interpretive process has a long history; hermeneutics, the theory of interpretation, blossomed in the 17th century in the form of biblical exegesis (Packer, 2018 ).

Although there are commonalities that cut across most forms of qualitative research, this is not to say that there is an accepted, linear, standardized approach. To be sure, there are an infinite number of variations and nuances in the qualitative research process. For example, some forms of inquiry begin with a firm research question; others start without even a clear focus for study. Grounded theorists begin data analysis and interpretation very early in the research process, whereas some case study researchers, for example, may collect data in the field for a period of time before seriously considering the data and its implications. Some ethnographers may be a part of the context (e.g., observing in classrooms), but they may assume more observer-like roles, as opposed to actively participating in the context. Alternatively, action researchers, in studying issues related to their own practice, are necessarily situated toward the participant end of the participant–observer continuum.

Our focus here is on one integrated part of the qualitative research process, interpretation, the hermeneutic process of collective and individual “meaning making.” Like Willig ( 2017 ), we believe “interpretation is at the heart of qualitative research because qualitative research is concerned with meaning and the process of meaning-making … qualitative data … needs to be given meaning by the researcher” (p. 276). As we discuss throughout this chapter, researchers take a variety of approaches to interpretation in qualitative work. Four general questions guide our explorations:

What is interpretation, and why are interpretive strategies important in qualitative research?

How do methodology, data, and the researcher/self impact interpretation in qualitative research?

How do qualitative researchers engage in the process of interpretation?

In what ways can a framework for interpretation strategies support qualitative researchers across multiple methodological and paradigmatic views?

We address each of these guiding questions in our attempt to explicate our interpretation of “interpretation” and, as educational researchers, we include examples from our own work to illustrate some key concepts.

What Is Interpretation, and Why Are Interpretive Strategies Important in Qualitative Research?

Qualitative researchers and those writing about qualitative methods often intertwine the terms analysis and interpretation . For example, Hubbard and Power ( 2003 ) described data analysis as “bringing order, structure, and meaning to the data” (p. 88). To us, this description combines analysis with interpretation. Although there is nothing wrong with this construction, our understanding aligns more closely with Mills’s ( 2018 ) claim that, “put simply, analysis involves summarizing what’s in the data, whereas interpretation involves making sense of—finding meaning in—that data” (p. 176). Hesse-Biber ( 2017 ) also separated out the essential process of interpretation. She described the steps in qualitative analysis and interpretation as data preparation, data exploration, and data reduction (all part of Mills’s “analysis” processes), followed by interpretation (pp. 307–328). Willig ( 2017 ) elaborated: analysis, she claims, is “sober and systematic,” whereas interpretation is associated with “creativity and the imagination … interpretation is seen as stimulating, it is interesting and it can be illuminating” (p. 276). For the purpose of this chapter, we will adhere to Mills’s distinction, understanding analysis as summarizing and organizing and interpretation as meaning making. Unavoidably, these closely related processes overlap and interact, but our focus will be primarily on the more complex of these endeavors, interpretation. Interpretation, in this sense, is in part translation, but translation is not an objective act. Instead, translation necessarily involves selectivity and the ascribing of meaning. Qualitative researchers “aim beneath manifest behavior to the meaning events have for those who experience them” (Eisner, 1991 , p. 35). The presentation of these insider/emic perspectives, coupled with researchers’ own interpretations, is a hallmark of qualitative research.

Qualitative researchers have long borrowed from extant models for fieldwork and interpretation. Approaches from anthropology and the arts have become especially prominent. For example, Eisner’s ( 1991 ) form of qualitative inquiry, educational criticism , draws heavily on accepted models of art criticism. T. Barrett ( 2011 ), an authority on art criticism, described interpretation as a complex set of processes based on a set of principles. We believe many of these principles apply as readily to qualitative research as they do to critique. The following principles, adapted from T. Barrett’s principles of interpretation (2011), inform our examination:

Qualitative phenomena have “aboutness” : All social phenomena have meaning, but meanings in this context can be multiple, even contradictory.

Interpretations are persuasive arguments : All interpretations are arguments, and qualitative researchers, like critics, strive to build strong arguments grounded in the information, or data, available.

  Some interpretations are better than others : Barrett noted that “some interpretations are better argued, better grounded with evidence, and therefore more reasonable, more certain, and more acceptable than others.” This contradicts the argument that “all interpretations are equal,” heard in the common refrain, “Well, that’s just your interpretation.”

There can be different, competing, and contradictory interpretations of the same phenomena : As noted at the beginning of this chapter, we acknowledge that subjectivity matters, and, unavoidably, it impacts one’s interpretations. As Barrett noted, “Interpretations are often based on a worldview.”

Interpretations are not (and cannot be) “right,” but instead, they can be more or less reasonable, convincing, and informative : There is never one “true” interpretation, but some interpretations are more compelling than others.

Interpretations can be judged by coherence, correspondence, and inclusiveness : Does the argument/interpretation make sense (coherence)? Does the interpretation fit the data (correspondence)? Have all data been attended to, including outlier data that do not necessarily support identified themes (inclusiveness)?

Interpretation is ultimately a communal endeavor : Initial interpretations may be incomplete, nearsighted, and/or narrow, but eventually these interpretations become richer, broader, and more inclusive. Feminist revisionist history projects are an exemplary case. Over time, the writing, art, and cultural contributions of countless women, previously ignored, diminished, or distorted, have come to be accepted as prominent contributions given serious consideration.

So, meaning is conferred; interpretations are socially constructed arguments; multiple interpretations are to be expected; and some interpretations are better than others. As we discuss later in this chapter, what makes an interpretation “better” often hinges on the purpose/goals of the research in question. Interpretations designed to generate theory, or generalizable rules, will be better for responding to research questions aligned with the aims of more traditional quantitative/positivist research, whereas interpretations designed to construct meanings through social interaction, to generate multiple perspectives, and to represent the context-specific perspectives of the research participants are better for researchers constructing thick, contextually rich descriptions, stories, or narratives. The former relies on more atomistic interpretive strategies, whereas the latter adheres to a more holistic approach (Willis, 2007 ). Both approaches to analysis/interpretation are addressed in more detail later in this chapter.

At this point, readers might ask, Why does interpretation matter, anyway? Our response to this question involves the distinctive nature of interpretation and the ability of the interpretive process to put unique fingerprints on an otherwise relatively static set of data. Once interview data are collected and transcribed (and we realize that even the process of transcription is, in part, interpretive), documents are collected, and observations are recorded, qualitative researchers could just, in good faith and with fidelity, represent the data in as straightforward ways as possible, allowing readers to “see for themselves” by sharing as much actual data (e.g., the transcribed words of the research participants) as possible. This approach, however, includes analysis, what we have defined as summarizing and organizing data for presentation, but it falls short of what we reference and define as interpretation—attempting to explain the meaning of others’ words and actions. According to Lichtman ( 2013 ),

While early efforts at qualitative research might have stopped at description, it is now more generally accepted that a qualitative researcher goes beyond pure description.… Many believe that it is the role of the researcher to bring understanding, interpretation, and meaning. (p. 17)

Because we are fond of the arts and arts-based approaches to qualitative research, an example from the late jazz drummer, Buddy Rich, seems fitting. Rich explains the importance of having the flexibility to interpret: “I don’t think any arranger should ever write a drum part for a drummer, because if a drummer can’t create his own interpretation of the chart, and he plays everything that’s written, he becomes mechanical; he has no freedom.” The same is true for qualitative researchers: without the freedom to interpret, the researcher merely regurgitates, attempting to share with readers/reviewers exactly what the research subjects shared with him or her. It is only through interpretation that the researcher, as collaborator with unavoidable subjectivities, is able to construct unique, contextualized meaning. Interpretation, then, in this sense, is knowledge construction.

In closing this section, we will illustrate the analysis-versus-interpretation distinction with the following transcript excerpt. In this study, the authors (Trent & Zorko, 2006 ) were studying student teaching from the perspective of K–12 students. This quote comes from a high school student in a focus group interview. She is describing a student teacher she had:

The right-hand column contains codes or labels applied to parts of the transcript text. Coding will be discussed in more depth later in this chapter, but for now, note that the codes are mostly summarizing the main ideas of the text, sometimes using the exact words of the research participant. This type of coding is a part of what we have called analysis—organizing and summarizing the data. It is a way of beginning to say “what is” there. As noted, though, most qualitative researchers go deeper. They want to know more than what is; they also ask, What does it mean? This is a question of interpretation.

Specific to the transcript excerpt, researchers might next begin to cluster the early codes into like groups. For example, the teacher “felt targeted,” “assumed kids were going to behave inappropriately,” and appeared to be “overwhelmed.” A researcher might cluster this group of codes in a category called “teacher feelings and perceptions” and may then cluster the codes “could not control class” and “students off task” into a category called “classroom management.” The researcher then, in taking a fresh look at these categories and the included codes, may begin to conclude that what is going on in this situation is that the student teacher does not have sufficient training in classroom management models and strategies and may also be lacking the skills she needs to build relationships with her students. These then would be interpretations, persuasive arguments connected to the study’s data. In this specific example, the researchers might proceed to write a memo about these emerging interpretations. In this memo, they might more clearly define their early categories and may also look through other data to see if there are other codes or categories that align with or overlap this initial analysis. They may write further about their emergent interpretations and, in doing so, may inform future data collection in ways that will allow them to either support or refute their early interpretations. These researchers will also likely find that the processes of analysis and interpretation are inextricably intertwined. Good interpretations very often depend on thorough and thoughtful analyses.

How Do Methodology, Data, and the Researcher/Self Impact Interpretation in Qualitative Research?

Methodological conventions guide interpretation and the use of interpretive strategies. For example, in grounded theory and in similar methodological traditions, “formal analysis begins early in the study and is nearly completed by the end of data collection” (Bogdan & Biklen, 2007 , p. 73). Alternatively, for researchers from other traditions, for example, case study researchers, “formal analysis and theory development [interpretation] do not occur until after the data collection is near complete” (p. 73).

Researchers subscribing to methodologies that prescribe early data analysis and interpretation may employ methods like analytic induction or the constant comparison method. In using analytic induction, researchers develop a rough definition of the phenomena under study; collect data to compare to this rough definition; modify the definition as needed, based on cases that both fit and do not fit the definition; and, finally, establish a clear, universal definition (theory) of the phenomena (Robinson, 1951, cited in Bogdan & Biklen, 2007 , p. 73). Generally, those using a constant comparison approach begin data collection immediately; identify key issues, events, and activities related to the study that then become categories of focus; collect data that provide incidents of these categories; write about and describe the categories, accounting for specific incidents and seeking others; discover basic processes and relationships; and, finally, code and write about the categories as theory, “grounded” in the data (Glaser, 1965 ). Although processes like analytic induction and constant comparison can be listed as steps to follow, in actuality, these are more typically recursive processes in which the researcher repeatedly goes back and forth between the data and emerging analyses and interpretations.

In addition to methodological conventions that prescribe data analysis early (e.g., grounded theory) or later (e.g., case study) in the inquiry process, methodological approaches also impact the general approach to analysis and interpretation. Ellingson ( 2011 ) situated qualitative research methodologies on a continuum spanning “science”-like approaches on one end juxtaposed with “art”-like approaches on the other.

Researchers pursuing a more science-oriented approach seek valid, reliable, generalizable knowledge; believe in neutral, objective researchers; and ultimately claim single, authoritative interpretations. Researchers adhering to these science-focused, postpositivistic approaches may count frequencies, emphasize the validity of the employed coding system, and point to intercoder reliability and random sampling as criteria that bolster the research credibility. Researchers at or near the science end of the continuum might employ analysis and interpretation strategies that include “paired comparisons,” “pile sorts,” “word counts,” identifying “key words in context,” and “triad tests” (Bernard, Wutich, & Ryan, 2017 , pp. 112, 381, 113, 170). These researchers may ultimately seek to develop taxonomies or other authoritative final products that organize and explain the collected data.

For example, in a study we conducted about preservice teachers’ experiences learning to teach second-language learners, the researchers collected larger data sets and used a statistical analysis package to analyze survey data, and the resultant findings included descriptive statistics. These survey results were supported with open-ended, qualitative data. For example, one of the study’s findings was that “a strong majority of candidates (96%) agreed that an immersion approach alone will not guarantee academic or linguistic success for second language learners.” In narrative explanations, one preservice teacher, representative of many others, remarked, “There has to be extra instructional efforts to help their students learn English … they won’t learn English by merely sitting in the classrooms” (Cho, Rios, Trent, & Mayfield, 2012 , p. 75).

Methodologies on the art side of Ellingson’s ( 2011 ) continuum, alternatively, “value humanistic, openly subjective knowledge, such as that embodied in stories, poetry, photography, and painting” (p. 599). Analysis and interpretation in these (often more contemporary) methodological approaches do not strive for “social scientific truth,” but instead are formulated to “enable us to learn about ourselves, each other, and the world through encountering the unique lens of a person’s (or a group’s) passionate rendering of a reality into a moving, aesthetic expression of meaning” (p. 599). For these “artistic/interpretivists, truths are multiple, fluctuating and ambiguous” (p. 599). Methodologies taking more subjective approaches to analysis and interpretation include autoethnography, testimonio, performance studies, feminist theorists/researchers, and others from related critical methodological forms of qualitative practice. More specifically arts-based approaches include poetic inquiry, fiction-based research, music as method, and dance and movement as inquiry (Leavy, 2017 ). Interpretation in these approaches is inherent. For example, “ interpretive poetry is understood as a method of merging the participant’s words with the researcher’s perspective” (Leavy, 2017 , p. 82).

As an example, one of us engaged in an artistic inquiry with a group of students in an art class for elementary teachers. We called it “Dreams as Data” and, among the project aims, we wanted to gather participants’ “dreams for education in the future” and display these dreams in an accessible, interactive, artistic display (see Trent, 2002 ). The intent was not to statistically analyze the dreams/data; instead, it was more universal. We wanted, as Ellingson ( 2011 , p. 599) noted, to use participant responses in ways that “enable us to learn about ourselves, each other, and the world.” The decision was made to leave responses intact and to share the whole/raw data set in the artistic display in ways that allowed the viewers to holistically analyze and interpret for themselves. Additionally, the researcher (Trent, 2002 ) collaborated with his students to construct their own contextually situated interpretations of the data. The following text is an excerpt from one participant’s response:

Almost a century ago, John Dewey eloquently wrote about the need to imagine and create the education that ALL children deserve, not just the richest, the Whitest, or the easiest to teach. At the dawn of this new century, on some mornings, I wake up fearful that we are further away from this ideal than ever.… Collective action, in a critical, hopeful, joyful, anti-racist and pro-justice spirit, is foremost in my mind as I reflect on and act in my daily work.… Although I realize the constraints on teachers and schools in the current political arena, I do believe in the power of teachers to stand next to, encourage, and believe in the students they teach—in short, to change lives. (Trent, 2002 , p. 49)

In sum, researchers whom Ellingson ( 2011 ) characterized as being on the science end of the continuum typically use more detailed or atomistic strategies to analyze and interpret qualitative data, whereas those toward the artistic end most often employ more holistic strategies. Both general approaches to qualitative data analysis and interpretation, atomistic and holistic, will be addressed later in this chapter.

As noted, qualitative researchers attend to data in a wide variety of ways depending on paradigmatic and epistemological beliefs, methodological conventions, and the purpose/aims of the research. These factors impact the kinds of data collected and the ways these data are ultimately analyzed and interpreted. For example, life history or testimonio researchers conduct extensive individual interviews, ethnographers record detailed observational notes, critical theorists may examine documents from pop culture, and ethnomethodologists may collect videotapes of interaction for analysis and interpretation.

In addition to the wide range of data types that are collected by qualitative researchers (and most qualitative researchers collect multiple forms of data), qualitative researchers, again influenced by the factors noted earlier, employ a variety of approaches to analyzing and interpreting data. As mentioned earlier in this chapter, some advocate for a detailed/atomistic, fine-grained approach to data (see, e.g., Bernard et al., 2017 ); others prefer a more broad-based, holistic, “eyeballing” of the data. According to Willis ( 2007 ), “Eyeballers reject the more structured approaches to analysis that break down the data into small units and, from the perspective of the eyeballers, destroy the wholeness and some of the meaningfulness of the data” (p. 298).

Regardless, we assert, as illustrated in Figure 31.1 , that as the process evolves, data collection becomes less prominent later in the process, as interpretation and making sense/meaning of the data becomes more prominent. It is through this emphasis on interpretation that qualitative researchers put their individual imprints on the data, allowing for the emergence of multiple, rich perspectives. This space for interpretation allows researchers the freedom Buddy Rich alluded to in his quote about interpreting musical charts. Without this freedom, Rich noted that the process would simply be “mechanical.” Furthermore, allowing space for multiple interpretations nourishes the perspectives of many others in the community. Writer and theorist Meg Wheatley explained, “Everyone in a complex system has a slightly different interpretation. The more interpretations we gather, the easier it becomes to gain a sense of the whole.” In qualitative research, “there is no ‘getting it right’ because there could be many ‘rights’ ” (as cited in Lichtman, 2013 ).

Increasing Role of Interpretation in Data Analysis

In addition to the roles methodology and data play in the interpretive process, perhaps the most important is the role of the self/the researcher in the interpretive process. According to Lichtman ( 2013 ), “Data are collected, information is gathered, settings are viewed, and realities are constructed through his or her eyes and ears … the qualitative researcher interprets and makes sense of the data” (p. 21). Eisner ( 1991 ) supported the notion of the researcher “self as instrument,” noting that expert researchers know not simply what to attend to, but also what to neglect. He describes the researcher’s role in the interpretive process as combining sensibility , the ability to observe and ascertain nuances, with schema , a deep understanding or cognitive framework of the phenomena under study.

J. Barrett ( 2007 ) described self/researcher roles as “transformations” (p. 418) at multiple points throughout the inquiry process: early in the process, researchers create representations through data generation, conducting observations and interviews and collecting documents and artifacts. Then,

transformation occurs when the “raw” data generated in the field are shaped into data records by the researcher. These data records are produced through organizing and reconstructing the researcher’s notes and transcribing audio and video recordings in the form of permanent records that serve as the “evidentiary warrants” of the generated data. The researcher strives to capture aspects of the phenomenal world with fidelity by selecting salient aspects to incorporate into the data record. (J. Barrett, 2007 , p. 418)

Transformation continues when the researcher codes, categorizes, and explores patterns in the data (the process we call analysis).

Transformations also involve interpreting what the data mean and relating these interpretations to other sources of insight about the phenomena, including findings from related research, conceptual literature, and common experience.… Data analysis and interpretation are often intertwined and rely upon the researcher’s logic, artistry, imagination, clarity, and knowledge of the field under study. (J. Barrett, 2007 , p. 418)

We mentioned the often-blended roles of participation and observation earlier in this chapter. The role(s) of the self/researcher are often described as points along a participant–observer continuum (see, e.g., Bogdan & Biklen, 2007 ). On the far observer end of this continuum, the researcher situates as detached, tries to be inconspicuous (so as not to impact/disrupt the phenomena under study), and approaches the studied context as if viewing it from behind a one-way mirror. On the opposite, participant end, the researcher is completely immersed and involved in the context. It would be difficult for an outsider to distinguish between researcher and subjects. For example, “some feminist researchers and postmodernists take a political stance and have an agenda that places the researcher in an activist posture. These researchers often become quite involved with the individuals they study and try to improve their human condition” (Lichtman, 2013 , p. 17).

We assert that most researchers fall somewhere between these poles. We believe that complete detachment is both impossible and misguided. In doing so, we, along with many others, acknowledge (and honor) the role of subjectivity, the researcher’s beliefs, opinions, biases, and predispositions. Positivist researchers seeking objective data and accounts either ignore the impact of subjectivity or attempt to drastically diminish/eliminate its impact. Even qualitative researchers have developed methods to avoid researcher subjectivity affecting research data collection, analysis, and interpretation. For example, foundational phenomenologist Husserl ( 1913/1962 ) developed the concept of bracketing , what Lichtman describes as “trying to identify your views on the topic and then putting them aside” (2013, p. 22). Like Slotnick and Janesick ( 2011 ), we ultimately claim “it is impossible to bracket yourself” (p. 1358). Instead, we take a balanced approach, like Eisner, understanding that subjectivity allows researchers to produce the rich, idiosyncratic, insightful, and yet data-based interpretations and accounts of lived experience that accomplish the primary purposes of qualitative inquiry. Eisner ( 1991 ) wrote, “Rather than regarding uniformity and standardization as the summum bonum, educational criticism [Eisner’s form of qualitative research] views unique insight as the higher good” (p. 35). That said, we also claim that, just because we acknowledge and value the role of researcher subjectivity, researchers are still obligated to ground their findings in reasonable interpretations of the data. Eisner ( 1991 ) explained:

This appreciation for personal insight as a source of meaning does not provide a license for freedom. Educational critics must provide evidence and reasons. But they reject the assumption that unique interpretation is a conceptual liability in understanding, and they see the insights secured from multiple views as more attractive than the comforts provided by a single right one. (p. 35)

Connected to this participant–observer continuum is the way the researcher positions him- or herself in relation to the “subjects” of the study. Traditionally, researchers, including early qualitative researchers, anthropologists, and ethnographers, referenced those studied as subjects . More recently, qualitative researchers better understand that research should be a reciprocal process in which both researcher and the foci of the research should derive meaningful benefit. Researchers aligned with this thinking frequently use the term participants to describe those groups and individuals included in a study. Going a step further, some researchers view research participants as experts on the studied topic and as equal collaborators in the meaning-making process. In these instances, researchers often use the terms co-researchers or co-investigators .

The qualitative researcher, then, plays significant roles throughout the inquiry process. These roles include transforming data, collaborating with research participants or co-researchers, determining appropriate points to situate along the participant–observer continuum, and ascribing personal insights, meanings, and interpretations that are both unique and justified with data exemplars. Performing these roles unavoidably impacts and changes the researcher. Slotnick and Janesick ( 2011 ) noted, “Since, in qualitative research the individual is the research instrument through which all data are passed, interpreted, and reported, the scholar’s role is constantly evolving as self evolves” (p. 1358).

As we note later, key in all this is for researchers to be transparent about the topics discussed in the preceding section: What methodological conventions have been employed and why? How have data been treated throughout the inquiry to arrive at assertions and findings that may or may not be transferable to other idiosyncratic contexts? And, finally, in what ways has the researcher/self been situated in and impacted the inquiry? Unavoidably, we assert, the self lies at the critical intersection of data and theory, and, as such, two legs of this stool, data and researcher, interact to create the third, theory.

How Do Qualitative Researchers Engage in the Process of Interpretation?

Theorists seem to have a propensity to dichotomize concepts, pulling them apart and placing binary opposites on the far ends of conceptual continuums. Qualitative research theorists are no different, and we have already mentioned some of these continua in this chapter. For example, in the previous section, we discussed the participant–observer continuum. Earlier, we referenced both Willis’s ( 2007 ) conceptualization of atomistic versus holistic approaches to qualitative analysis and interpretation and Ellingson’s ( 2011 ) science–art continuum. Each of these latter two conceptualizations inform how qualitative researchers engage in the process of interpretation.

Willis ( 2007 ) shared that the purpose of a qualitative project might be explained as “what we expect to gain from research” (p. 288). The purpose, or what we expect to gain, then guides and informs the approaches researchers might take to interpretation. Some researchers, typically positivist/postpositivist, conduct studies that aim to test theories about how the world works and/or how people behave. These researchers attempt to discover general laws, truths, or relationships that can be generalized. Others, less confident in the ability of research to attain a single, generalizable law or truth, might seek “local theory.” These researchers still seek truths, but “instead of generalizable laws or rules, they search for truths about the local context … to understand what is really happening and then to communicate the essence of this to others” (Willis, 2007 , p. 291). In both these purposes, researchers employ atomistic strategies in an inductive process in which researchers “break the data down into small units and then build broader and broader generalizations as the data analysis proceeds” (p. 317). The earlier mentioned processes of analytic induction, constant comparison, and grounded theory fit within this conceptualization of atomistic approaches to interpretation. For example, a line-by-line coding of a transcript might begin an atomistic approach to data analysis.

Alternatively, other researchers pursue distinctly different aims. Researchers with an objective description purpose focus on accurately describing the people and context under study. These researchers adhere to standards and practices designed to achieve objectivity, and their approach to interpretation falls within the binary atomistic/holistic distinction.

The purpose of hermeneutic approaches to research is to “understand the perspectives of humans. And because understanding is situational, hermeneutic research tends to look at the details of the context in which the study occurred. The result is generally rich data reports that include multiple perspectives” (Willis, 2007 , p. 293).

Still other researchers see their purpose as the creation of stories or narratives that utilize “a social process that constructs meaning through interaction … it is an effort to represent in detail the perspectives of participants … whereas description produces one truth about the topic of study, storytelling may generate multiple perspectives, interpretations, and analyses by the researcher and participants” (Willis, 2007 , p. 295).

In these latter purposes (hermeneutic, storytelling, narrative production), researchers typically employ more holistic strategies. According to Willis ( 2007 ), “Holistic approaches tend to leave the data intact and to emphasize that meaning must be derived for a contextual reading of the data rather than the extraction of data segments for detailed analysis” (p. 297). This was the case with the Dreams as Data project mentioned earlier.

We understand the propensity to dichotomize, situate concepts as binary opposites, and create neat continua between these polar descriptors. These sorts of reduction and deconstruction support our understandings and, hopefully, enable us to eventually reconstruct these ideas in meaningful ways. Still, in reality, we realize most of us will, and should, work in the middle of these conceptualizations in fluid ways that allow us to pursue strategies, processes, and theories most appropriate for the research task at hand. As noted, Ellingson ( 2011 ) set up another conceptual continuum, but, like ours, her advice was to “straddle multiple points across the field of qualitative methods” (p. 595). She explained, “I make the case for qualitative methods to be conceptualized as a continuum anchored by art and science, with vast middle spaces that embody infinite possibilities for blending artistic, expository, and social scientific ways of analysis and representation” (p. 595).

We explained at the beginning of this chapter that we view analysis as organizing and summarizing qualitative data and interpretation as constructing meaning. In this sense, analysis allows us to describe the phenomena under study. It enables us to succinctly answer what and how questions and ensures that our descriptions are grounded in the data collected. Descriptions, however, rarely respond to questions of why . Why questions are the domain of interpretation, and, as noted throughout this text, interpretation is complex. Gubrium and Holstein ( 2000 ) noted, “Traditionally, qualitative inquiry has concerned itself with what and how questions … qualitative researchers typically approach why questions cautiously, explanation is tricky business” (p. 502). Eisner ( 1991 ) described this distinctive nature of interpretation: “It means that inquirers try to account for [interpretation] what they have given account of ” (p. 35).

Our focus here is on interpretation, but interpretation requires analysis, because without clear understandings of the data and its characteristics, derived through systematic examination and organization (e.g., coding, memoing, categorizing), “interpretations” resulting from inquiry will likely be incomplete, uninformed, and inconsistent with the constructed perspectives of the study participants. Fortunately for qualitative researchers, we have many sources that lead us through analytic processes. We earlier mentioned the accepted processes of analytic induction and the constant comparison method. These detailed processes (see, e.g., Bogdan & Biklen, 2007 ) combine the inextricably linked activities of analysis and interpretation, with analysis more typically appearing as earlier steps in the process and meaning construction—interpretation—happening later.

A wide variety of resources support researchers engaged in the processes of analysis and interpretation. Saldaña ( 2011 ), for example, provided a detailed description of coding types and processes. He showed researchers how to use process coding (uses gerunds, “-ing” words to capture action), in vivo coding (uses the actual words of the research participants/ subjects), descriptive coding (uses nouns to summarize the data topics), versus coding (uses “vs” to identify conflicts and power issues), and values coding (identifies participants’ values, attitudes, and/or beliefs). To exemplify some of these coding strategies, we include an excerpt from a transcript of a meeting of a school improvement committee. In this study, the collaborators were focused on building “school community.” This excerpt illustrates the application of a variety of codes described by Saldaña to this text:

To connect and elaborate the ideas developed in coding, Saldaña ( 2011 ) suggested researchers categorize the applied codes, write memos to deepen understandings and illuminate additional questions, and identify emergent themes. To begin the categorization process, Saldaña recommended all codes be “classified into similar clusters … once the codes have been classified, a category label is applied to them” (p. 97). So, in continuing with the study of school community example coded here, the researcher might create a cluster/category called “Value of Collaboration” and in this category might include the codes “relationships,” “building community,” and “effective strategies.”

Having coded and categorized a study’s various data forms, a typical next step for researchers is to write memos or analytic memos . Writing analytic memos allows the researcher(s) to

set in words your interpretation of the data … an analytic memo further articulates your … thinking processes on what things may mean … as the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the report itself. (Saldaña, 2011 , p. 98)

In the study of student teaching from K–12 students’ perspectives (Trent & Zorko, 2006 ), we noticed throughout our analysis a series of focus group interview quotes coded “names.” The following quote from a high school student is representative of many others:

I think that, ah, they [student teachers] should like know your face and your name because, uh, I don’t like it if they don’t and they’ll just like … cause they’ll blow you off a lot easier if they don’t know, like our new principal is here … he is, like, he always, like, tries to make sure to say hi even to the, like, not popular people if you can call it that, you know, and I mean, yah, and the people that don’t usually socialize a lot, I mean he makes an effort to know them and know their name like so they will cooperate better with him.

Although we did not ask the focus groups a specific question about whether student teachers knew the K–12 students’ names, the topic came up in every focus group interview. We coded the above excerpt and the others “knowing names,” and these data were grouped with others under the category “relationships.” In an initial analytic memo about this, the researchers wrote,

STUDENT TEACHING STUDY—MEMO #3 “Knowing Names as Relationship Building” Most groups made unsolicited mentions of student teachers knowing, or not knowing, their names. We haven’t asked students about this, but it must be important to them because it always seems to come up. Students expected student teachers to know their names. When they did, students noticed and seemed pleased. When they didn’t, students seemed disappointed, even annoyed. An elementary student told us that early in the semester, “she knew our names … cause when we rose [sic] our hands, she didn’t have to come and look at our name tags … it made me feel very happy.” A high schooler, expressing displeasure that his student teacher didn’t know students’ names, told us, “They should like know your name because it shows they care about you as a person. I mean, we know their names, so they should take the time to learn ours too.” Another high school student said that even after 3 months, she wasn’t sure the student teacher knew her name. Another student echoed, “Same here.” Each of these students asserted that this (knowing students’ names) had impacted their relationship with the student teacher. This high school student focus group stressed that a good relationship, built early, directly impacts classroom interaction and student learning. A student explained it like this: “If you get to know each other, you can have fun with them … they seem to understand you more, you’re more relaxed, and learning seems easier.”

As noted in these brief examples, coding, categorizing, and writing memos about a study’s data are all accepted processes for data analysis and allow researchers to begin constructing new understandings and forming interpretations of the studied phenomena. We find the qualitative research literature to be particularly strong in offering support and guidance for researchers engaged in these analytic practices. In addition to those already noted in this chapter, we have found the following resources provide practical, yet theoretically grounded approaches to qualitative data analysis. For more detailed, procedural, or atomistic approaches to data analysis, we direct researchers to Miles and Huberman’s classic 1994 text, Qualitative Data Analysis , and Bernard et al.’s 2017 book Analyzing Qualitative Data: Systematic Approaches. For analysis and interpretation strategies falling somewhere between the atomistic and holistic poles, we suggest Hesse-Biber and Leavy’s ( 2011 ) chapter, “Analysis and Interpretation of Qualitative Data,” in their book, The Practice of Qualitative Research (second edition); Lichtman’s chapter, “Making Meaning From Your Data,” in her 2013 book Qualitative Research in Education: A User’s Guide (third edition); and “Processing Fieldnotes: Coding and Memoing,” a chapter in Emerson, Fretz, and Shaw’s ( 1995 ) book, Writing Ethnographic Fieldwork . Each of these sources succinctly describes the processes of data preparation, data reduction, coding and categorizing data, and writing memos about emergent ideas and findings. For more holistic approaches, we have found Denzin and Lincoln’s ( 2007 ) Collecting and Interpreting Qualitative Materials and Ellis and Bochner’s ( 2000 ) chapter “Autoethnography, Personal Narrative, Reflexivity” to both be very informative. Finally, Leavy’s 2017 book, Method Meets Art: Arts-Based Research Practice , provides support and guidance to researchers engaged in arts-based research.

Even after reviewing the multiple resources for treating data included here, qualitative researchers might still be wondering, But exactly how do we interpret? In the remainder of this section and in the concluding section of this chapter, we more concretely provide responses to this question and, in closing, we propose a framework for researchers to utilize as they engage in the complex, ambiguous, and yet exciting process of constructing meanings and new understandings from qualitative sources.

These meanings and understandings are often presented as theory, but theories in this sense should be viewed more as “guides to perception” as opposed to “devices that lead to the tight control or precise prediction of events” (Eisner, 1991 , p. 95). Perhaps Erickson’s ( 1986 ) concept of assertions is a more appropriate aim for qualitative researchers. He claimed that assertions are declarative statements; they include a summary of the new understandings, and they are supported by evidence/data. These assertions are open to revision and are revised when disconfirming evidence requires modification. Assertions, theories, or other explanations resulting from interpretation in research are typically presented as “findings” in written research reports. Belgrave and Smith ( 2002 ) emphasized the importance of these interpretations (as opposed to descriptions): “The core of the report is not the events reported by the respondent, but rather the subjective meaning of the reported events for the respondent” (p. 248).

Mills ( 2018 ) viewed interpretation as responding to the question, So what? He provided researchers a series of concrete strategies for both analysis and interpretation. Specific to interpretation, Mills (pp. 204–207) suggested a variety of techniques, including the following:

“ Extend the analysis ”: In doing so, researchers ask additional questions about the research. The data appear to say X , but could it be otherwise? In what ways do the data support emergent finding X ? And, in what ways do they not?

“ Connect findings with personal experience ”: Using this technique, researchers share interpretations based on their intimate knowledge of the context, the observed actions of the individuals in the studied context, and the data points that support emerging interpretations, as well as their awareness of discrepant events or outlier data. In a sense, the researcher is saying, “Based on my experiences in conducting this study, this is what I make of it all.”

“ Seek the advice of ‘critical’ friends ”: In doing so, researchers utilize trusted colleagues, fellow researchers, experts in the field of study, and others to offer insights, alternative interpretations, and the application of their own unique lenses to a researcher’s initial findings. We especially like this strategy because we acknowledge that, too often, qualitative interpretation is a “solo” affair.

“ Contextualize findings in the literature ”: This allows researchers to compare their interpretations to those of others writing about and studying the same/similar phenomena. The results of this contextualization may be that the current study’s findings correspond with the findings of other researchers. The results might, alternatively, differ from the findings of other researchers. In either instance, the researcher can highlight his or her unique contributions to our understanding of the topic under study.

“ Turn to theory ”: Mills defined theory as “an analytical and interpretive framework that helps the researcher make sense of ‘what is going on’ in the social setting being studied.” In turning to theory, researchers search for increasing levels of abstraction and move beyond purely descriptive accounts. Connecting to extant or generating new theory enables researchers to link their work to the broader contemporary issues in the field.

Other theorists offer additional advice for researchers engaged in the act of interpretation. Richardson ( 1995 ) reminded us to account for the power dynamics in the researcher–researched relationship and notes that, in doing so, we can allow for oppressed and marginalized voices to be heard in context. Bogdan and Biklen ( 2007 ) suggested that researchers engaged in interpretation revisit foundational writing about qualitative research, read studies related to the current research, ask evaluative questions (e.g., Is what I’m seeing here good or bad?), ask about implications of particular findings/interpretations, think about the audience for interpretations, look for stories and incidents that illustrate a specific finding/interpretation, and attempt to summarize key interpretations in a succinct paragraph. All these suggestions can be pertinent in certain situations and with particular methodological approaches. In the next and closing section of this chapter, we present a framework for interpretive strategies we believe will support, guide, and be applicable to qualitative researchers across multiple methodologies and paradigms.

In What Ways Can a Framework for Interpretation Strategies Support Qualitative Researchers across Multiple Methodological and Paradigmatic Views?

The process of qualitative research is often compared to a journey, one without a detailed itinerary and ending, but with general direction and aims and yet an open-endedness that adds excitement and thrives on curiosity. Qualitative researchers are travelers. They travel physically to field sites; they travel mentally through various epistemological, theoretical, and methodological grounds; they travel through a series of problem-finding, access, data collection, and data analysis processes; and, finally—the topic of this chapter—they travel through the process of making meaning of all this physical and cognitive travel via interpretation.

Although travel is an appropriate metaphor to describe the journey of qualitative researchers, we will also use “travel” to symbolize a framework for qualitative research interpretation strategies. By design, this framework applies across multiple paradigmatic, epistemological, and methodological traditions. The application of this framework is not formulaic or highly prescriptive; it is also not an anything-goes approach. It falls, and is applicable, between these poles, giving concrete (suggested) direction to qualitative researchers wanting to make the most of the interpretations that result from their research and yet allowing the necessary flexibility for researchers to employ the methods, theories, and approaches they deem most appropriate to the research problem(s) under study.

TRAVEL, a Comprehensive Approach to Qualitative Interpretation

In using the word TRAVEL as a mnemonic device, our aim is to highlight six essential concepts we argue all qualitative researchers should attend to in the interpretive process: transparency, reflexivity, analysis, validity, evidence, and literature. The importance of each is addressed here.

Transparency , as a research concept seems, well, transparent. But, too often, we read qualitative research reports and are left with many questions: How were research participants and the topic of study selected/excluded? How were the data collected, when, and for how long? Who analyzed and interpreted these data? A single researcher? Multiple? What interpretive strategies were employed? Are there data points that substantiate these interpretations/findings? What analytic procedures were used to organize the data prior to making the presented interpretations? In being transparent about data collection, analysis, and interpretation processes, researchers allow reviewers/readers insight into the research endeavor, and this transparency leads to credibility for both researcher and researcher’s claims. Altheide and Johnson ( 2011 ) explained,

There is great diversity of qualitative research.… While these approaches differ, they also share an ethical obligation to make public their claims, to show the reader, audience, or consumer why they should be trusted as faithful accounts of some phenomenon. (p. 584)

This includes, they noted, articulating

what the different sources of data were, how they were interwoven, and … how subsequent interpretations and conclusions are more or less closely tied to the various data … the main concern is that the connection be apparent, and to the extent possible, transparent. (p. 590)

In the Dreams as Data art and research project noted earlier, transparency was addressed in multiple ways. Readers of the project write-up were informed that interpretations resulting from the study, framed as themes , were a result of collaborative analysis that included insights from both students and instructor. Viewers of the art installation/data display had the rare opportunity to see all participant responses. In other words, viewers had access to the entire raw data set (see Trent, 2002 ). More frequently, we encounter only research “findings” already distilled, analyzed, and interpreted in research accounts, often by a single researcher. Allowing research consumers access to the data to interpret for themselves in the Dreams project was an intentional attempt at transparency.

Reflexivity , the second of our concepts for interpretive researcher consideration, has garnered a great deal of attention in qualitative research literature. Some have called this increased attention the reflexive turn (see, e.g., Denzin & Lincoln, 2004 ).

Although you can find many meanings for the term reflexivity, it is usually associated with a critical reflection on the practice and process of research and the role of the researcher. It concerns itself with the impact of the researcher on the system and the system on the researcher. It acknowledges the mutual relationships between the researcher and who and what is studied … by acknowledging the role of the self in qualitative research, the researcher is able to sort through biases and think about how they affect various aspects of the research, especially interpretation of meanings. (Lichtman, 2013 , p. 165)

As with transparency, attending to reflexivity allows researchers to attach credibility to presented findings. Providing a reflexive account of researcher subjectivity and the interactions of this subjectivity within the research process is a way for researchers to communicate openly with their audience. Instead of trying to exhume inherent bias from the process, qualitative researchers share with readers the value of having a specific, idiosyncratic positionality. As a result, situated, contextualized interpretations are viewed as an asset, as opposed to a liability.

LaBanca ( 2011 ), acknowledging the often solitary nature of qualitative research, called for researchers to engage others in the reflexive process. Like many other researchers, LaBanca utilized a researcher journal to chronicle reflexive thoughts, explorations, and understandings, but he took it a step farther. Realizing the value of others’ input, LaBanca posts his reflexive journal entries on a blog (what he calls an online reflexivity blog ) and invites critical friends, other researchers, and interested members of the community to audit his reflexive moves, providing insights, questions, and critique that inform his research and study interpretations.

We agree this is a novel approach worth considering. We, too, understand that multiple interpreters will undoubtedly produce multiple interpretations, a richness of qualitative research. So, we suggest researchers consider bringing others in before the production of the report. This could be fruitful in multiple stages of the inquiry process, but especially in the complex, idiosyncratic processes of reflexivity and interpretation. We are both educators and educational researchers. Historically, each of these roles has tended to be constructed as an isolated endeavor, the solitary teacher, the solo researcher/fieldworker. As noted earlier and in the analysis section that follows, introducing collaborative processes to what has often been a solitary activity offers much promise for generating rich interpretations that benefit from multiple perspectives.

Being consciously reflexive throughout our practice as researchers has benefitted us in many ways. In a study of teacher education curricula designed to prepare preservice teachers to support second-language learners, we realized hard truths that caused us to reflect on and adapt our own practices as teacher educators. Reflexivity can inform a researcher at all parts of the inquiry, even in early stages. For example, one of us was beginning a study of instructional practices in an elementary school. The communicated methods of the study indicated that the researcher would be largely an observer. Early fieldwork revealed that the researcher became much more involved as a participant than anticipated. Deep reflection and writing about the classroom interactions allowed the researcher to realize that the initial purpose of the research was not being accomplished, and the researcher believed he was having a negative impact on the classroom culture. Reflexivity in this instance prompted the researcher to leave the field and abandon the project as it was just beginning. Researchers should plan to openly engage in reflexive activities, including writing about their ongoing reflections and subjectivities. Including excerpts of this writing in research account supports our earlier recommendation of transparency.

Early in this chapter, for the purposes of discussion and examination, we defined analysis as “summarizing and organizing” data in a qualitative study and interpretation as “meaning making.” Although our focus has been on interpretation as the primary topic, the importance of good analysis cannot be underestimated, because without it, resultant interpretations are likely incomplete and potentially uninformed. Comprehensive analysis puts researchers in a position to be deeply familiar with collected data and to organize these data into forms that lead to rich, unique interpretations, and yet interpretations that are clearly connected to data exemplars. Although we find it advantageous to examine analysis and interpretation as different but related practices, in reality, the lines blur as qualitative researchers engage in these recursive processes.

We earlier noted our affinity for a variety of approaches to analysis (see, e.g., Hesse-Biber & Leavy, 2011 ; Lichtman, 2013 ; or Saldaña, 2011 ). Emerson et al. ( 1995 ) presented a grounded approach to qualitative data analysis: In early stages, researchers engage in a close, line-by-line reading of data/collected text and accompany this reading with open coding , a process of categorizing and labeling the inquiry data. Next, researchers write initial memos to describe and organize the data under analysis. These analytic phases allow the researcher(s) to prepare, organize, summarize, and understand the data, in preparation for the more interpretive processes of focused coding and the writing up of interpretations and themes in the form of integrative memos .

Similarly, Mills ( 2018 ) provided guidance on the process of analysis for qualitative action researchers. His suggestions for organizing and summarizing data include coding (labeling data and looking for patterns); identifying themes by considering the big picture while looking for recurrent phrases, descriptions, or topics; asking key questions about the study data (who, what, where, when, why, and how); developing concept maps (graphic organizers that show initial organization and relationships in the data); and stating what’s missing by articulating what data are not present (pp. 179–189).

Many theorists, like Emerson et al. ( 1995 ) and Mills ( 2018 ) noted here, provide guidance for individual researchers engaged in individual data collection, analysis, and interpretation; others, however, invite us to consider the benefits of collaboratively engaging in these processes through the use of collaborative research and analysis teams. Paulus, Woodside, and Ziegler ( 2008 ) wrote about their experiences in collaborative qualitative research: “Collaborative research often refers to collaboration among the researcher and the participants. Few studies investigate the collaborative process among researchers themselves” (p. 226).

Paulus et al. ( 2008 ) claimed that the collaborative process “challenged and transformed our assumptions about qualitative research” (p. 226). Engaging in reflexivity, analysis, and interpretation as a collaborative enabled these researchers to reframe their views about the research process, finding that the process was much more recursive, as opposed to following a linear progression. They also found that cooperatively analyzing and interpreting data yielded “collaboratively constructed meanings” as opposed to “individual discoveries.” And finally, instead of the traditional “individual products” resulting from solo research, collaborative interpretation allowed researchers to participate in an “ongoing conversation” (p. 226).

These researchers explained that engaging in collaborative analysis and interpretation of qualitative data challenged their previously held assumptions. They noted,

through collaboration, procedures are likely to be transparent to the group and can, therefore, be made public. Data analysis benefits from an iterative, dialogic, and collaborative process because thinking is made explicit in a way that is difficult to replicate as a single researcher. (Paulus et al., 2008 , p. 236)

They shared that, during the collaborative process, “we constantly checked our interpretation against the text, the context, prior interpretations, and each other’s interpretations” (p. 234).

We, too, have engaged in analysis similar to these described processes, including working on research teams. We encourage other researchers to find processes that fit with the methodology and data of a particular study, use the techniques and strategies most appropriate, and then cite the utilized authority to justify the selected path. We urge traditionally solo researchers to consider trying a collaborative approach. Generally, we suggest researchers be familiar with a wide repertoire of practices. In doing so, they will be in better positions to select and use strategies most appropriate for their studies and data. Succinctly preparing, organizing, categorizing, and summarizing data sets the researcher(s) up to construct meaningful interpretations in the forms of assertions, findings, themes, and theories.

Researchers want their findings to be sound, backed by evidence, and justifiable and to accurately represent the phenomena under study. In short, researchers seek validity for their work. We assert that qualitative researchers should attend to validity concepts as a part of their interpretive practices. We have previously written and theorized about validity, and, in doing so, we have highlighted and labeled what we consider two distinctly different approaches, transactional and transformational (Cho & Trent, 2006 ). We define transactional validity in qualitative research as an interactive process occurring among the researcher, the researched, and the collected data, one that is aimed at achieving a relatively higher level of accuracy. Techniques, methods, and/or strategies are employed during the conduct of the inquiry. These techniques, such as member checking and triangulation, are seen as a medium with which to ensure an accurate reflection of reality (or, at least, participants’ constructions of reality). Lincoln and Guba’s ( 1985 ) widely known notion of trustworthiness in “naturalistic inquiry” is grounded in this approach. In seeking trustworthiness, researchers attend to research credibility, transferability, dependability, and confirmability. Validity approaches described by Maxwell ( 1992 ) as “descriptive” and “interpretive” also proceed in the usage of transactional processes.

For example, in the write-up of a study on the facilitation of teacher research, one of us (Trent, 2012 ) wrote about the use of transactional processes:

“Member checking is asking the members of the population being studied for their reaction to the findings” (Sagor, 2000 , p. 136). Interpretations and findings of this research, in draft form, were shared with teachers (for member checking) on multiple occasions throughout the study. Additionally, teachers reviewed and provided feedback on the final draft of this article. (p. 44)

This member checking led to changes in some resultant interpretations (called findings in this particular study) and to adaptations of others that shaped these findings in ways that made them both richer and more contextualized.

Alternatively, in transformational approaches, validity is not so much something that can be achieved solely by employing certain techniques. Transformationalists assert that because traditional or positivist inquiry is no longer seen as an absolute means to truth in the realm of human science, alternative notions of validity should be considered to achieve social justice, deeper understandings, broader visions, and other legitimate aims of qualitative research. In this sense, it is the ameliorative aspects of the research that achieve (or do not achieve) its validity. Validity is determined by the resultant actions prompted by the research endeavor.

Lather ( 1993 ), Richardson ( 1997 ), and others (e.g., Lenzo, 1995 ; Scheurich, 1996 ) proposed a transgressive approach to validity that emphasized a higher degree of self-reflexivity. For example, Lather proposed a “catalytic validity” described as “the degree to which the research empowers and emancipates the research subjects” (Scheurich, 1996 , p. 4). Beverley ( 2000 , p. 556) proposed testimonio as a qualitative research strategy. These first-person narratives find their validity in their ability to raise consciousness and thus provoke political action to remedy problems of oppressed peoples (e.g., poverty, marginality, exploitation).

We, too, have pursued research with transformational aims. In the earlier mentioned study of preservice teachers’ experiences learning to teach second-language learners (Cho et al., 2012 ), our aims were to empower faculty members, evolve the curriculum, and, ultimately, better serve preservice teachers so that they might better serve English-language learners in their classrooms. As program curricula and activities have changed as a result, we claim a degree of transformational validity for this research.

Important, then, for qualitative researchers throughout the inquiry, but especially when engaged in the process of interpretation, is to determine the type(s) of validity applicable to the study. What are the aims of the study? Providing an “accurate” account of studied phenomena? Empowering participants to take action for themselves and others? The determination of this purpose will, in turn, inform researchers’ analysis and interpretation of data. Understanding and attending to the appropriate validity criteria will bolster researcher claims to meaningful findings and assertions.

Regardless of purpose or chosen validity considerations, qualitative research depends on evidence . Researchers in different qualitative methodologies rely on different types of evidence to support their claims. Qualitative researchers typically utilize a variety of forms of evidence including texts (written notes, transcripts, images, etc.), audio and video recordings, cultural artifacts, documents related to the inquiry, journal entries, and field notes taken during observations of social contexts and interactions. Schwandt ( 2001 ) wrote,

Evidence is essential to justification, and justification takes the form of an argument about the merit(s) of a given claim. It is generally accepted that no evidence is conclusive or unassailable (and hence, no argument is foolproof). Thus, evidence must often be judged for its credibility, and that typically means examining its source and the procedures by which it was produced [thus the need for transparency discussed earlier]. (p. 82)

Altheide and Johnson ( 2011 ) drew a distinction between evidence and facts:

Qualitative researchers distinguish evidence from facts. Evidence and facts are similar but not identical. We can often agree on facts, e.g., there is a rock, it is harder than cotton candy. Evidence involves an assertion that some facts are relevant to an argument or claim about a relationship. Since a position in an argument is likely tied to an ideological or even epistemological position, evidence is not completely bound by facts, but it is more problematic and subject to disagreement. (p. 586)

Inquirers should make every attempt to link evidence to claims (or findings, interpretations, assertions, conclusions, etc.). There are many strategies for making these connections. Induction involves accumulating multiple data points to infer a general conclusion. Confirmation entails directly linking evidence to resultant interpretations. Testability/falsifiability means illustrating that evidence does not necessarily contradict the claim/interpretation and so increases the credibility of the claim (Schwandt, 2001 ). In the study about learning to teach second-language learners, for example, a study finding (Cho et al., 2012 ) was that “as a moral claim , candidates increasingly [in higher levels of the teacher education program] feel more responsible and committed to … [English language learners]” (p. 77). We supported this finding with a series of data points that included the following preservice teacher response: “It is as much the responsibility of the teacher to help teach second-language learners the English language as it is our responsibility to teach traditional English speakers to read or correctly perform math functions.” Claims supported by evidence allow readers to see for themselves and to both examine researcher assertions in tandem with evidence and form further interpretations of their own.

Some postmodernists reject the notion that qualitative interpretations are arguments based on evidence. Instead, they argue that qualitative accounts are not intended to faithfully represent that experience, but instead are designed to evoke some feelings or reactions in the reader of the account (Schwandt, 2001 ). We argue that, even in these instances where transformational validity concerns take priority over transactional processes, evidence still matters. Did the assertions accomplish the evocative aims? What evidence/arguments were used to evoke these reactions? Does the presented claim correspond with the study’s evidence? Is the account inclusive? In other words, does it attend to all evidence or selectively compartmentalize some data while capitalizing on other evidentiary forms?

Researchers, we argue, should be both transparent and reflexive about these questions and, regardless of research methodology or purpose, should share with readers of the account their evidentiary moves and aims. Altheide and Johnson ( 2011 ) called this an evidentiary narrative and explain:

Ultimately, evidence is bound up with our identity in a situation.… An “evidentiary narrative” emerges from a reconsideration of how knowledge and belief systems in everyday life are tied to epistemic communities that provide perspectives, scenarios, and scripts that reflect symbolic and social moral orders. An “evidentiary narrative” symbolically joins an actor, an audience, a point of view (definition of a situation), assumptions, and a claim about a relationship between two or more phenomena. If any of these factors are not part of the context of meaning for a claim, it will not be honored, and thus, not seen as evidence. (p. 686)

In sum, readers/consumers of a research account deserve to know how evidence was treated and viewed in an inquiry. They want and should be aware of accounts that aim to evoke versus represent, and then they can apply their own criteria (including the potential transferability to their situated context). Renowned ethnographer and qualitative research theorist Harry Wolcott ( 1990 ) urged researchers to “let readers ‘see’ for themselves” by providing more detail rather than less and by sharing primary data/evidence to support interpretations. In the end, readers do not expect perfection. Writer Eric Liu ( 2010 ) explained, “We don’t expect flawless interpretation. We expect good faith. We demand honesty.”

Last, in this journey through concepts we assert are pertinent to researchers engaged in interpretive processes, we include attention to the literature . In discussing literature, qualitative researchers typically mean publications about the prior research conducted on topics aligned with or related to a study. Most often, this research/literature is reviewed and compiled by researchers in a section of the research report titled “Literature Review.” It is here we find others’ studies, methods, and theories related to our topics of study, and it is here we hope the assertions and theories that result from our studies will someday reside.

We acknowledge the value of being familiar with research related to topics of study. This familiarity can inform multiple phases of the inquiry process. Understanding the extant knowledge base can inform research questions and topic selection, data collection and analysis plans, and the interpretive process. In what ways do the interpretations from this study correspond with other research conducted on this topic? Do findings/interpretations corroborate, expand, or contradict other researchers’ interpretations of similar phenomena? In any of these scenarios (correspondence, expansion, contradiction), new findings and interpretations from a study add to and deepen the knowledge base, or literature, on a topic of investigation.

For example, in our literature review for the study of student teaching, we quickly determined that the knowledge base and extant theories related to the student teaching experience were immense, but also quickly realized that few, if any, studies had examined student teaching from the perspective of the K–12 students who had the student teachers. This focus on the literature related to our topic of student teaching prompted us to embark on a study that would fill a gap in this literature: Most of the knowledge base focused on the experiences and learning of the student teachers themselves. Our study, then, by focusing on the K–12 students’ perspectives, added literature/theories/assertions to a previously untapped area. The “literature” in this area (at least we would like to think) is now more robust as a result.

In another example, a research team (Trent et al., 2003 ) focused on institutional diversity efforts, mined the literature, found an appropriate existing (a priori) set of theories/assertions, and then used the existing theoretical framework from the literature as a framework to analyze data, in this case, a variety of institutional activities related to diversity.

Conducting a literature review to explore extant theories on a topic of study can serve a variety of purposes. As evidenced in these examples, consulting the literature/extant theory can reveal gaps in the literature. A literature review might also lead researchers to existing theoretical frameworks that support analysis and interpretation of their data (as in the use of the a priori framework example). Finally, a review of current theories related to a topic of inquiry might confirm that much theory already exists, but that further study may add to, bolster, and/or elaborate on the current knowledge base.

Guidance for researchers conducting literature reviews is plentiful. Lichtman ( 2013 ) suggested researchers conduct a brief literature review, begin research, and then update and modify the literature review as the inquiry unfolds. She suggested reviewing a wide range of related materials (not just scholarly journals) and additionally suggested that researchers attend to literature on methodology, not just the topic of study. She also encouraged researchers to bracket and write down thoughts on the research topic as they review the literature, and, important for this chapter, that researchers “integrate your literature review throughout your writing rather than using a traditional approach of placing it in a separate chapter” (p. 173).

We agree that the power of a literature review to provide context for a study can be maximized when this information is not compartmentalized apart from a study’s findings. Integrating (or at least revisiting) reviewed literature juxtaposed alongside findings can illustrate how new interpretations add to an evolving story. Eisenhart ( 1998 ) expanded the traditional conception of the literature review and discussed the concept of an interpretive review . By taking this interpretive approach, Eisenhart claimed that reviews, alongside related interpretations/findings on a specific topic, have the potential to allow readers to see the studied phenomena in entirely new ways, through new lenses, revealing heretofore unconsidered perspectives. Reviews that offer surprising and enriching perspectives on meanings and circumstances “shake things up, break down boundaries, and cause things (or thinking) to expand” (p. 394). Coupling reviews of this sort with current interpretations will “give us stories that startle us with what we have failed to notice” (p. 395).

In reviews of research studies, it can certainly be important to evaluate the findings in light of established theories and methods [the sorts of things typically included in literature reviews]. However, it also seems important to ask how well the studies disrupt conventional assumptions and help us to reconfigure new, more inclusive, and more promising perspectives on human views and actions. From an interpretivist perspective, it would be most important to review how well methods and findings permit readers to grasp the sense of unfamiliar perspectives and actions. (Eisenhart, 1998 , p. 397)

Though our interpretation-related journey in this chapter nears an end, we are hopeful it is just the beginning of multiple new conversations among ourselves and in concert with other qualitative researchers. Our aims have been to circumscribe interpretation in qualitative research; emphasize the importance of interpretation in achieving the aims of the qualitative project; discuss the interactions of methodology, data, and the researcher/self as these concepts and theories intertwine with interpretive processes; describe some concrete ways that qualitative inquirers engage the process of interpretation; and, finally, provide a framework of interpretive strategies that may serve as a guide for ourselves and other researchers.

In closing, we note that the TRAVEL framework, construed as a journey to be undertaken by researchers engaged in interpretive processes, is not designed to be rigid or prescriptive, but instead is designed to be a flexible set of concepts that will inform researchers across multiple epistemological, methodological, and theoretical paradigms. We chose the concepts of transparency, reflexivity, analysis, validity, evidence, and literature (TRAVEL) because they are applicable to the infinite journeys undertaken by qualitative researchers who have come before and to those who will come after us. As we journeyed through our interpretations of interpretation, we have discovered new things about ourselves and our work. We hope readers also garner insights that enrich their interpretive excursions. Happy travels!

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Research Method

Home » Data Interpretation – Process, Methods and Questions

Data Interpretation – Process, Methods and Questions

Table of Contents

Data Interpretation

Data Interpretation

Definition :

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

Data Interpretation Process

Here are the steps involved in the data interpretation process:

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

Types of Data Interpretation

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

Descriptive Interpretation

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

Inferential Interpretation

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

Predictive Interpretation

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

Exploratory Interpretation

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

Causal Interpretation

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

Data Interpretation Methods

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

Statistical Analysis

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

Data Visualization

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

Text Analysis

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

Machine Learning

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

Qualitative Analysis

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

Geospatial Analysis

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

Applications of Data Interpretation

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

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

When to use Data Interpretation

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

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

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

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

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

Data Interpretation Examples

Here are some real-time examples of data interpretation:

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

Data Interpretation Questions

Data Interpretation Questions samples:

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

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

Purpose of Data Interpretation

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

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

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

Characteristics of Data Interpretation

Here are some characteristics of data interpretation:

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

Advantages of Data Interpretation

Data interpretation has several advantages, including:

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

Limitations of Data Interpretation

Data interpretation has some limitations, including:

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

Difference between Data Interpretation and Data Analysis

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

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

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

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

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Hoopes and henderson prizes honor undergraduates for outstanding thesis work.

  • May 21, 2024

Five seniors in MCB , CPB , and Neuro have been awarded prestigious prizes for their thesis work. Neuroscience concentrator Daniel Kwon, MCB and Computer Science concentrator Michelle Lu, and MCB concentrator Tomi Siyanbade were recognized by the university-wide Hoopes Prize . The Hoopes celebrates excellence in undergraduate research and thesis projects across all disciplines. Meanwhile, CPB concentrator Jorge Guerra and MCB concentrator Brandon Kwon received the Henderson Prize from the Board of Tutors in Biochemical Sciences . Founded in 1926, the Board of Tutors organizes tutorials and mentoring opportunities for students in MCB and CPB. The Board of Tutors’ membership includes several MCB faculty and prominent researchers in cellular biology and biochemistry. 

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Jorge Guerra ( CPB with Secondary in Statistics)

Jorge Guerra conducted his thesis research in the Gaudet Lab . He looked into the structures of metal-transporting proteins called Nramps in the bacteria Eggerthella lenta . “Natural resistance-associated macrophage proteins (Nramps) help cells maintain homeostasis by transporting essential transition metal cations, such as iron and manganese, into the cell,” Guerra explains. “These proteins achieve this using a highly-conserved metal-binding-site motif consisting of aspartate, asparagine, and methionine residues.” Eggerthella lenta ’s Nramp-like proteins contain an evolutionary divergent binding site motif, which led Guerra to wonder if these sites changed how the protein interacts with metals. “To answer this question, I used X-ray crystallography to determine the structure of an Nramp-like protein in E. lenta and compare it to the structure of canonical Nramps,” he says. “I also conducted in vitro proteoliposome-based transport assays to test the metal selectivity of this Nramp-like protein.” Guerra found that mutations in the binding sites did, in fact, alter metal transport by the Nramp-like protein. These results could shed light on how the bacteria uses its Nramp-like proteins to maintain a healthy state. 

 Guerra is motivated by science’s potential to save lives and bolster human health. “During my freshman year at Harvard, I became interested in learning how scientists leverage their understanding of protein structure and function to discover and design therapeutics that save lives,” he says. “I saw this project as a perfect opportunity to explore interesting questions and begin building a strong foundation in structural biology.”

Receiving the Henderson Prize is an honor, Guerra says. “I have no doubt that this will motivate me to continue putting my heart into my work as a scientist.” 

He adds that he is grateful to his support system. “First, I would like to thank Dr. Rachelle Gaudet and Dr. Shamayeeta Ray . I could not have asked for better mentors,” he says. “Their mentorship has been instrumental to my development as a scientist, and I am grateful I had the opportunity to work alongside them. I would also like to thank the rest of the Gaudet Lab for creating such an enjoyable work environment, and for lending a helping hand when I needed one. Lastly, I would like to thank my family for their unwavering love and support.” 

sample thesis data analysis and interpretation

Brandon Kwon ( MCB with a Secondary in Global Health & Health Policy)

MCB concentrator Brandon Kwon says that receiving the Henderson Prize was a “huge, unexpected surprise.” 

Kwon conducted his research in Alessandro Alessandrini’s lab at MGH, which specializes in immune response to organ transplants. “My initial interest in transplant immunology began with a conversation I had with an elderly Spanish-speaking couple while volunteering at St. Jude Hospital in high school,” Kwon says. “I was helping at the rehabilitation facility when a simple request for water turned into a conversation about their son’s struggle to save his failing kidney. They expressed their dismay over the long transplant waitlist, and I could sense their growing hopelessness. The husband lamented, “¿Por qué es tan difícil? La vida es más importante.” (Why is it so difficult? Life is the most important.) This encounter made me reflect on the organ shortage crisis and inspired me to contribute to research aimed at improving the transplant process so that these limited resources aren’t wasted and patients don’t continue to struggle post-surgery.”

In his thesis work, Kwon studied B cells, which are known for producing antibodies that neutralize pathogens and for playing a regulatory role in reining in the immune system. He wanted to find out if the B cells go through a transition from contributing to transplant rejection to a regulatory state that stops the attacking immune cells. To find out, he performed single cell RNA-sequencing and flow cytometric analyses to catalog cells expressing particular genes. “From these experiments, I observed a temporal shift toward B cells expressing Siglec-G and FcγR2b, both markers indicative of a regulatory phenotype,” Kwon explains. In further experiments, he knocked out the gene FcγR2b, and all of the mice died after their transplant surgeries. Kwon concludes that the gene plays an important role in shifting the immune system from an attacking state to a regulatory tolerance state. 

Kwon expressed appreciation for a number of academics who have helped him during his undergraduate journey. “First and foremost, I’m deeply grateful to my principal investigator, Dr. Alessandro Alessandrini, who took me under his wing as an inexperienced freshman and patiently guided me in my academic endeavors,” he says. “I also want to thank Ed Szuter for his mentorship, from training me in lab techniques to supporting my summer projects. Additionally, I extend my gratitude to Dr. Takahiro Yokose for his assistance with my projects, ranging from performing mouse surgeries to teaching me RStudio data analysis. On campus, my thanks go to Dr. Dominic Mao and Dr. Monique Brewster for their support within the Molecular and Cellular Biology department, making sure I was always on track and providing help whenever needed.”

sample thesis data analysis and interpretation

Daniel Kwon ( Neuro , MBB) Neuroscience concentrator Daniel Kwon was honored with a Hoopes Prize for research he conducted in Evan Macosko’s lab at the Broad Institute. He focused on a gene called XPO7. Previous studies have suggested that XPO7 is a risk factor for schizophrenia. Kwon wanted to know whether mutations in XPO7 alter stress response. “To investigate, I studied mouse models with XPO7 mutations, focusing on how they respond to stress, a known environmental factor that exacerbates schizophrenia symptoms,” Kwon explains. “To observe biological differences, I measured their corticosterone, a stress hormone, levels. For behavioral differences, I captured depth recordings of their behavior and analyzed them with unsupervised machine learning algorithms to identify sub-second behaviors that differ between genotype and condition.” 

He found that mice with broken XPO7 proteins had slower response times and exhibited fewer adaptations to stress. Their corticosterone response also appeared blunted. These findings indicate that XPO7 may regulate stress responses through the hypothalamic-pituitary-adrenal (HPA) axis.

Doing behavioral experiments with mice proved to be a challenge, as mice are nocturnal. Kwon had to work around the animals’ schedules, sometimes spending all night in the lab and only leaving at dawn. “Grappling with the interpretation of my data posed an additional hurdle,” he adds. “Notably, the absence of prior literature linking the cellular and molecular functions of XPO7 with schizophrenia compounded the challenge. This lack of established groundwork necessitated a comprehensive exploration and analysis of the data, requiring innovative approaches to discern meaningful patterns and correlations within the findings.”

“I feel immensely grateful to receive the Hoopes Prize and be recognized for the culmination of my undergraduate thesis work,” Kwon adds. “This recognition serves as a reminder of the invaluable support and guidance I received along the way.” He expressed gratitude to his PI Evan Macosko, his postdoctoral mentor Alyssa Lawler , and his family and friends. 

sample thesis data analysis and interpretation

Michelle Lu ( MCB -Computer Science) 

For her Hoopes-winning thesis, MCB concentrator Michelle Lu developed a computational platform that opens up new applications for nanopore sequencing. “Nanopore sequencing is a fourth-generation sequencing technology that has only become practically feasible for tRNAs in the past year,” Lu explains. “The cost and time efficiency of tRNA nanopore sequencing would be improved by the ability to sequence multiple samples simultaneously, and then computationally separate the data from each sample.” Her project focused on the separation of data or “demultiplexing.”

She applied her computational tool to the sequencing of tRNAs, or transfer RNAs, which are shorter RNAs that serve as links between the messenger RNA and the growing chain of amino acids during protein synthesis. Using a barcode system of unique genetic sequences, she was able to analyze modifications to tRNAs. 

When Lu’s “demultiplexer” is released, it will be the first such computer program for tRNA nanopore sequencing. 

Lu sees her thesis as a capstone to her joint concentration in MCB and computer science. “This was certainly the first time in my life that I have tackled a project of this scope and timescale,” she says. “Any long scientific project is bound to be riddled with unexpected roadblocks – experimental equipment that breaks and needs to be replaced, a computational platform that takes over a month of fiddling with package dependencies to be able to install, etc. These hurdles challenged me to be agile in my long-term planning, one of the most valuable skills I’ve improved during my thesis.” 

Lu adds, “I truly have so many people I’d like to thank. My research advisors, Prof. George Church and Dr. Russel Vincent , as well as all members of the Church Lab. My MCB advisors and tutor – Monique, Dominic, and Prof. Léger-Abraham . My advisors in the CS department. And of course, my friends and family. Thank you all for making this thesis possible!”

sample thesis data analysis and interpretation

Tomi Siyanbade ( MCB with a Secondary in Global Health and Health Policy and a Language Citation in French)

MCB concentrator Tomi Siyanbade teamed with the Sabeti Lab to pursue a project with real-world ramifications. “My thesis focuses on developing accessible (which I defined as low-cost, user-friendly, and implementable in low-resource settings) and accurate diagnostic tests to detect Lassa Fever, a serious viral disease found across West Africa,” she says. “I was primarily concerned with accuracy and accessibility to the target end-users: the community health professionals at the front lines of outbreaks.” 

“I wanted to work on something with a tangible impact in the next 1-2 years rather than 10-20,” she adds. “The impact-driven nature of the Sabeti lab was one of its key draws to me, and I wanted to participate in a thesis project that could ultimately benefit real people and wouldn’t just end once I left. Personally, I also realized that diagnostics are one of the most impactful tools for improving human health, and as a Nigerian, I was aware of the immense burden that Lassa posed to health.” To address the Lassa Fever diagnostic gap, Siyanbade developed a CRISPR-based test that could recognize key sequences from the viral genome. The final test reads out on a strip of paper, similar to how over-the-counter COVID tests do. “It was ultimately challenging to assess my test’s accuracy and clinical significance,” Siyanbade says. “The initial literary review, as well as the final test validation, was difficult with the relative deficit incomparable Lassa diagnostics that are approved in the world today, coupled with my lack of access to sequences for such a potentially dangerous pathogen. Overall, this made it more difficult not to “move the goalpost” and collect data that could convince me of the test’s true potential impact in the field.”

Siyanbade adds that she is glad to receive the Hoopes Prize and grateful to everyone who has helped her throughout her thesis journey. 

Congratulations to these Hoopes and Henderson Prize winners!

sample thesis data analysis and interpretation

(top l to bottom r) Daniel Kwon, Michelle Lu, Tomi Siyanbade, Jorge Guerra, and Brandon Kwon 


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  1. Demographic Analysis in SPSS

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  23. Hoopes and Henderson Prizes Honor Undergraduates for Outstanding Thesis

    This lack of established groundwork necessitated a comprehensive exploration and analysis of the data, requiring innovative approaches to discern meaningful patterns and correlations within the findings." "I feel immensely grateful to receive the Hoopes Prize and be recognized for the culmination of my undergraduate thesis work," Kwon adds.

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