Finding your way: the difference between research and evaluation

Sign posts on the top of an alpine peak

Have you ever stayed up late, staring up at the night sky, wondering “What is the difference between evaluation and research?” No?! Well, even if you haven’t lost sleep pondering this, we think it’s an important topic. Why? In this blog series, we’ll be focused on how to do an evaluation: how to determine the value and impact of programs, services, and experiences. At the same time, we’ll be talking a lot about methods from social science research because those are our tools for collecting and analyzing data. 

Knowing how evaluation and research relate to each other gives you a better understanding of where you are now, where you’re going, and how to get there as you work on a project. It’s like having a map in your head with a little star that says “you are here!”

Let’s start with clarifying what we mean by research. We might say that we’re going to research some recipes for dinner, or some interesting STEM activities for kids. In that context, research means “go find more information about.” When we talk about research in this post, we mean original research: when a study is designed to answer a question by methodically collecting and analyzing data.

Often original research happens at a university, within a specific discipline like physics, psychology, or history. In general, original research

  • aims to answer a question
  • is based in a theory (a set of related ideas about how something works)
  • tests a hypothesis (an idea about what will happen this time)
  • comes to a conclusion that can be applied in a lot of situations (generalized)
  • increases our overall knowledge on a topic

Evaluation and research do have commonalities. They’re both processes of inquiry, or ways of finding out more information in order to answer a question. So what makes them different? The answer to that can depend a bit on who you ask (a recent survey of 522 researchers and evaluators found that they had several ways of thinking about how research and evaluation relate). 

For our purposes you just need to know which it is you are doing—evaluation or research? A broadly accepted way of thinking about how evaluation and research are different comes from Michael Scriven, an evaluation expert and professor. He defines evaluation this way in his Evaluation Thesaurus: “Evaluation determines the merit, worth, or value of things.” He goes on to explain that “Social science research, by contrast, does not aim for or achieve evaluative conclusions…Social science research does not establish standards or values and then integrate them with factual results to reach evaluative conclusions. In fact, the dominant social science doctrine for many decades prided itself on being value free.” This definition and more information are available at the Evaluation Exchange.

Put another way: evaluation and social science research use the same strategies to collect and analyze data, but the goals of each are different. A useful visualization of this concept, created by John LaVelle, is below.

An hourglass showing evaluation and research

Essentially evaluation aims to do exactly what it says—determine value. Did it work? Should we keep doing it or do something else instead? What was the value of what we did? Social science research, on the other hand, aims to maintain a more impartial stance—describe what is happening, as it is, and generally not judge or evaluate it as valuable or not.

As we move forward and learn more about the evaluation process, keep this idea in the back of your mind—that little “you are here!” star. We usually start an evaluation because we want to know if something is working and providing value in the way we hoped. Remembering that’s why you started and where you’re going can help you orient yourself throughout the project. We look forward to seeing you back here next time!


Between a Graph and a Hard Place Chapter Two: Do it yourself

Research can be a scary word that comes with a lot of fear about our own skills. We think of experts conducting field work, gathering data, and writing long, technical reports. Like reading a foreign language, it’s easy to feel ill-equipped for deciphering what it all means.

Chapter one of Between a Graph and a Hard Place gave you the lexicon for understanding existing data and research. We covered a myriad of topics from checking sources to reading data visualizations that you can find here in case you missed any. Now, we’re excited to introduce chapter two of our blog series in which you—the reader—will go out and conquer your own research and evaluation projects. You don’t need to be a researcher, just curious about how to gather insights about the work you’re doing in a library.

Every other week we’ll cover new topics that build your research “vocabulary,” starting with forming a research question or goal. On our journey we’ll talk about issues like researching vulnerable populations and other ethical considerations. Using real world examples from Library Land, chapter two will also cover the basics of collecting and analyzing data, including how to do it without ever leaving your desk—or home. We’ll talk about surveys, focus groups, and observations. You’ll learn how to code data and run simple analyses. It’s a lot to tackle, but it’s easier than you think!

No matter what position you have in a library and no matter what kind of library it is, having the skills to collect and interpret data and evaluate the work you’re doing is critical. Like I said earlier, you don’t have to be a researcher to conduct research. You work in libraries, which means you probably like discovering information and communicating it to others—the foundation is already there! So join us on the next chapter of our data journey. We’ll get out from between a graph and a hard place and onto a path toward research fluency.

Your Ruby Slippers: five key data takeaways

Hi there, readers! We have so enjoyed having you on this data journey with us. The posts we’ve shared since March are an introduction to data literacy, and we’re wrapping up that theme today. Fear not! This series—Between a Graph & a Hard Place—isn’t going anywhere. We’re just starting a new theme, like the next chapter in a book. (We’re data people, but who can resist a book metaphor?)  

We hope that you’ve learned something—preferably lots of things—and will join us on the next leg of our journey. Based on surveying you, our readers, the new direction we’re taking is to share how you can actually do research and evaluation in the library. After today’s post, we’re going to post every other week. We love writing these, and they take time to write well. If you’re worried you’ll forget when we’re posting again, it’s easy to sign up here to get notified when we have a new post.

We’d like to give a good send off to this chapter and show you how all the posts tie together. As we review each post today, I want you to keep five big themes in mind. These key ideas apply to every area of data literacy and each post from the series connects to them. 

Five themes in data literacy:

  • The quality of research varies. Details matter, so take the time to think about them. 
  • Your common sense will take you far. Does what you’re reading make sense?
  • Our human brain has feelings, biases, and preferences. Stay aware of yours.
  • Researchers are also human. They have feelings, biases, and preferences too.
  • When considering what data mean, err on the cautious side. What do we know from these data? What is more of a guess?

These themes are your data literacy ruby slippers. You have them with you all the time, and if you start to feel lost or confused, they can show you the way home. You just have to remember you have them! With these big themes in mind, we’re ready to review data literacy.

How to compare apples to oranges

  • When data are compared, think carefully about what two things are being compared and if they are truly similar to each other.
  • One way to make things more comparable is to use per capita, or per person, data.
  • Comparisons can be messy. Keep your thinking cap on.

Habits of mind for working with data

  • Give yourself permission to struggle and get help.
  • Acknowledge your feelings about the topic.
  • Whether you like the data or not, that information gives you an opportunity to learn.

Do the data have an alibi?

  • The quality of the data matters.
  • Where were the data published and when were they collected?
  • Who the authors are is also important. What is their area of expertise? Why did they publish this?

What’s typical and why does it matter?

  • Means and medians are measures of what’s typical.
  • Knowing what’s typical can be very helpful for comparisons.
  • The mean (average of a data set) is impacted by extreme values, so sometimes the median (middle value in a data set) is more representative. 

Correlation doesn’t equal causation

  • Correlation is one way that two variables relate to each other.
  • A strong correlation is when we can predict with a high level of accuracy the values of one variable based on the values of the other. They co-occur. 
  • Causation is different because it’s a cause and effect relationship: we know that A leads to B. 

The right data for the job – part 1

  • Do the data collected make sense based on the research question?
  • What data were collected and how they were collected are both important.

The right data for the job – part 2

  • Definitions impact what data are collected and how they are interpreted. 
  • The data collected for research are usually a sample of a larger population.
  • To be representative, the sample needs to reflect the population in key ways.

Visualizing Data: a misleading y-axis

  • The y-axis (vertical axis) does not always begin at zero on a chart.
  • The y-axis may be shown on a larger or smaller scale (zoomed in or out).
  • Depending on how the y-axis is displayed, the data will look different—which can highlight or obscure differences between groups or changes over time.

Visualizing Data: the logarithmic scale

  • Logarithmic (or log) scales are another way to display the y-axis. 
  • On a log scale, the distances between intervals increase by a percentage: multiplying by x each time.
  • Log scales are useful because they show rates of change—the percent something increases or decreases.

Visualizing Data: color

  • Color can help you understand visual information, but it can also confuse or mislead you.
  • We have feelings about colors and their meanings, which are not always conscious.
  • Red holds a special place in our brain. It says “pay attention.” 

Visualizing Data: choosing the right chart

  • The best chart for showing change over time is a line or bar chart. 
  • The best chart for showing multiple variables is a bar chart.
  • The best chart for comparing something to the total is a pie chart.

Here we are, at the end of this chapter! We are delighted to have come this far. Knowing that these blog posts have been useful for you all makes us so happy. Please join us on July 29th to continue the journey. We look forward to seeing you then!

Visualizing Data: choosing the right chart

If you walk into a hardware store, you might see an entire aisle of screws—short ones, long ones, phillips head, flat head, ones with weird little anchors on the ends. They might all be screws, but they each serve a specific purpose—for wood or cement, for different screwdrivers, for thick or thin materials. It’s the same with data visualizations. They might all be charts, but pie charts, bar charts, and line charts all serve a different purpose. When data visualizers use the wrong one (often unintentionally), you’re left with a chart that doesn’t really make sense. 

Below are charts using the same data—the number of reference questions, by topic, asked each month from January through April. Let’s take a look at what information we can gather based on how those data are displayed in the visualization.

Line ChartsLine charts are commonly used to track changes over a period of time. They have a y-axis (up and down) and an x-axis (left to right) to plot two different variables. While a bar chart can also be used for this purpose, a line chart is particularly helpful when smaller changes exist or when you’re comparing changes over the same period of time for more than one group, like in the chart above. 

Here we can see that something might have happened in February to cause healthcare, business, and employment to all increase. Homework questions dropped off a bit though. Did schools give kids time off before online learning started? We know to investigate those questions because the line chart helps us identify trends. 

Pie/Donut ChartsPie/donut charts should only be used to compare parts to a whole. Each category is associated with a slice of the pie which corresponds to that category’s proportion (or percentage) of the total.  We can see that the majority of questions asked during this time period were about employment because it’s the largest slice. The least amount of questions were about genealogy. However, there’s a lot we can’t see. For instance, we have no idea how many reference questions in each category were asked in each month. We can’t see if there was a spike in healthcare questions in February when flu season hit its peak.

If you added up the values of each slice, they would equal 100 percent because each slice of the chart is determined by dividing the whole (total number of reference questions) by the part (question topic). As a reader, a huge red flag should go off if they don’t (unless the chart states it’s due to rounding). Sometimes pie charts will only have a legend that tells you what each slice represents, rather than data labels. In these cases, it’s even harder to discern how slices compare to one another because our brains are terrible at making spatial comparisons between circular areas. In general, pie charts should not contain more than five slices. When they do, it becomes difficult to read and some slices might be so small that you can’t interpret them anyways, rendering the data visualization pretty much useless. 

Bar ChartsBar charts are used to compare things between different groups or to track changes over time. They can also be used to present data that sum to more/less than 100 percent because, unlike pie charts, they aren’t limited to presenting parts to a whole. Like a line chart, they have an x-axis and y-axis, but bar charts aren’t confined to using a unit of time across the x-axis. For instance, a bar chart could use a demographic variable like age group. They can also be stacked, like in the example below. Conclusion

When looking at charts, think about whether the one the creator chose makes sense for the data story they’re trying to tell. Are they talking about changes over time, comparisons between multiple groups, or how much something makes up of the total? If the story doesn’t match the visual, be careful to draw any conclusions based on the chart. In addition, 3D renderings of any of these charts are likely to cause distortion and be visually inaccurate, even if it’s the right type of chart for the job. Here’s a nifty cheat sheet that always helps me recall when each chart should be used, and some important notes to remember: 

  • If it’s talking about something changing over time, it should be a line or bar chart 
  • If it’s talking about multiple variables, it should be a bar chart
  • If it’s talking about comparing something to the total, it should be a pie chart.

LRS’s Between a Graph and a Hard Place blog series provides strategies for looking at data with a critical eye. Every week we’ll cover a different topic. You can use these strategies with any kind of data, so while the series may be inspired by the many COVID-19 statistics being reported, the examples we’ll share will focus on other topics. To receive posts via email, please complete this form.

Let us know what you think!

When the COVID-19 pandemic began a couple of months ago, we at LRS began thinking about how we could help. What skills could we share that might be useful to library staff and our communities?  So many different sources were releasing charts and graphs to help us all understand what was happening, and we were all trying to process a lot of data every day. LRS created the Between a Graph and a Hard Place blog series to provide strategies for looking at all kinds of data with a critical eye—strategies that could be used in a library or in our everyday lives. 

We are wrapping up the first part of that series and we would love to get your feedback about what worked, what didn’t, and what you think we should do next. Don’t worry—we’re going to keep writing these posts for you! However, in lieu of publishing a post this week, we have created a survey to collect your thoughts to help guide our future posts. If you have ten minutes, we would greatly appreciate it if you’re able to fill it out. 

Thank you so much and see you next week! 

Visualizing Data: Color

I love color. As long as I can remember, I have kept my crayons organized in rainbow order. It makes me happy to see them that way! It’s a little tedious with the magical 64 pack of crayons, but totally worth it. I am an extreme example, but humans in general are visual creatures. Color impacts how we perceive and understand visual information—including graphs, charts, and infographics. 

A good data visualization combines a thoughtful display of the data with strong art and design principles, including color. Our brains are wired to pay attention to color, even if some of us perceive it differently (read more here). While color can help you understand visual information, it can also confuse or mislead you. Understanding the principles that data visualization designers use can give you insight into the role that color plays when you process visual information. 

When we make charts at LRS, we try to use several different shades of one color or one main color and a highlight color. Why just one or two colors? Believe me, if it worked, I would make all of our charts look like rainbows. The problem is that for each color you use, a viewer has to process how they personally feel about it and what that color symbolizes in our culture. Then they have to sort out what that color means in the chart. 

Our emotional reactions to color are not always conscious. If I went to the dentist and found myself sitting in a neon yellow waiting room, I would become incredibly anxious, but I may not know why. Designers spend a lot of time studying color and use it strategically, which is both good and bad for you, the viewer. The power of color can help you understand and it can emotionally manipulate you. 

What’s your favorite color? Do you know why? What about your least favorite color? Why? You carry around those preferences in your brain all the time. We’re going to look at some examples now, and I want you to keep track of how you feel about the colors.

Look at that beautiful rainbow! These pale shades of basic colors makes me think about spring and a happy version of childhood where nothing ever goes wrong—like a fanciful children’s book about talking animals. As a designer, I would use these colors to evoke viewers’ sense of nostalgia about childhood before I talk about children’s programming at the library. 

As a viewer, I’m distracted by the colors even though I like them. I really like that shade of green, so I just want to think about that column. Is the green column the most important data in this chart? I have no idea. My eye also keeps getting drawn to the red color—is that where I’m supposed to focus? While these colors are all different, they still have a similar level of saturation or brightness. What happens when that is not consistent?

This chart is really hard for me to interpret as a viewer. I think I’m supposed to focus on 2010—I can barely pull my eyes away from it. The data from 1980 is bright too. I don’t know why the data for 1980 and 2010 are shown in brighter, more saturated colors. I’m losing track of 2020, even though it has the largest value. 

Two color choices are creating a lot of confusion here. One is the use of red. Red holds a special place in our brain (read more here). It’s one of our brain’s priority colors—meaning that we are particularly skilled at perceiving it and its different shades. The cultural symbolism of red is also important. Think about the places red shows up in our world: stop signs, stop lights, warning symbols, and sports cars. Red says to us: pay attention. And sometimes also “bad” or “danger.” We can’t help but stare at the saturated red color and assume it’s important. 

The second confusing choice is the saturation of the colors. The green column is as saturated as the red column, which makes me assume it is the second most important data here. My intuition thinks more color = more attendance, but in this chart the two most saturated columns are not the ones with the highest values. Overall, color is not helping here.

Ah, ok. I’m still not sure what the takeaway message from this chart is, but at least I don’t want to run away from it. It’s easier for me to think about the data now that I’m not distracted by the colors. I can focus and develop some questions. The one thing that is missing is a visual cue about where I should focus or what is most important.

Ah, there’s my cue! This chart provides both a cohesive experience and a good indication of where the viewer is supposed to focus. I don’t need to spend a lot of energy deciphering it. I still don’t know what happened in 2010, but I feel curious and ready to find out more. The use of color augments my understanding of the data.

Out there in the wilds of the internet, there are some data visualizations where color is a barrier to understanding the data or used to elicit an emotional response. As a viewer, you don’t get to change the colors to be less distracting or add in a helpful cue about where to focus. If only we could! Instead, notice if the colors are distracting you or producing a strong emotional reaction and do your best to work around it. Often that means focusing on the data in spite of the colors. I have also printed data visualizations in grayscale to strip the color out myself. 

I could go on about color, but I want you to get back out there, using these skills! If you want to learn more about color, I recommend this episode of the podcast Radiolab.


Visualizing Data: the logarithmic scale

Welcome to part 2 on data visualizations. If you’re just joining us, we talked last week about how the y-axis can be altered to mislead a reader about the data. You can find that post here. Now, let’s jump right back into another big data visualization misunderstanding. 

The goal of data visualizations is to allow readers to easily understand complex data, but sometimes it’s the data visualization that we don’t understand. Certain techniques are utilized because they are the best fit for the data—not the best fit for the reader—and that can cause quite a bit of confusion if we don’t know what we’re looking at! Such is the case with logarithmic scales, which most people are unfamiliar with, but encounter all the time. Let’s break it down together.

That scale is growing out of control! 

Logarithmic (or log) scales are simply another way to display your y-axis. Unlike linear scales, where the distance between each interval increases by the same amount (adding x each time), the distance between each interval in a log scale increases by the same percentage (multiplying by x each time). Log scales are useful because they show rates of change—the percent something increases or decreases.

Imagine your library grows its print collection yearly by 100 percent. That means every year you double the number of books on your shelves. The first year you have only 192, the next year 384, then 768…1,536…Fifteen years later you’d have more than 3 million books! Good luck using a linear scale to show that kind of growth in your annual report. A better option would be to use a log scale where you can show your collection has grown annually by 100 percent. Take a look at the same data using a linear scale versus a log scale. Can you tell which one is which?

That’s right, the one on top uses a log scale (x10) and the one on the bottom uses a linear scale (+1 million). As you can see, the linear scale makes these data look like you didn’t get any books until eight years after you opened! However, if you weren’t familiar with log scales you might also think you increased your collection by the same number of books every year, instead of at the same rate

Let’s say instead of expanding your book collection by 100% annually, that growth rate begins to slow down after eight years. You still increased your collection by 27,000 books in the last year, but the log scale might make you assume you got less books than you did the first couple of years. This flattening effect is often misleading, but it simply shows a decrease in the rate, not in absolute numbers. 

Log scales have their advantages and are often used to display data that cover a wide range of values or numbers that are growing exponentially. For epidemiologists who study disease spread, log scales allow them to chart the first outbreak (often a couple of people) up to community or global spread. The volcanic explosivity index and the Richter scale, which measures earthquakes, are other common uses of a log scale.


Like we mentioned last week, data visualizations are all about conveying the data’s story. When you see a log scale, remember that the story is about the rate of change, not the absolute numbers. Understanding how and why certain data visualization tactics are used will help you read any data story. Next week we’ll cover some new tactics so be sure to join us! 

LRS’s Between a Graph and a Hard Place blog series provides strategies for looking at data with a critical eye. Every week we’ll cover a different topic. You can use these strategies with any kind of data, so while the series may be inspired by the many COVID-19 statistics being reported, the examples we’ll share will focus on other topics. To receive posts via email, please complete this form.

Visualizing Data: a misleading y-axis

With great power comes great responsibility—that’s how I feel about data visualizations. Good ones help readers quickly understand the data and can convey an important message to a lot of people. However, bad data visualizations can intentionally or unintentionally mislead, causing us to come to the wrong conclusions. In this multi-part post, we’ll unpack some of the most common mistakes and give you the tools to spot them. 

Omitting the baseline

Imagine you’re telling someone the plot of a novel, but you start in the middle. All of the sudden the protagonist slays the dragon, which is a huge jump from introduction to climax. The character arc would feel pretty extreme, right? This is the literary equivalent to omitting the baseline in data visualizations. 

Omitting the baseline means the y-axis (height of the graph) doesn’t start at zero, resulting in a truncated graph. Truncated graphs might be unintentionally used to save space or intentionally done to cause one group to look better than it should. Take a look at the truncated graph below. The creator of this visualization (me) made a design choice to start the y-axis at 3,000 instead of zero because all of the data were around 3,000. Then it’s easier to see the differences right? 

 Yes, but now Library A appears to have circulated more than twice as many books as Library B or C this month and that’s just not true. Below are the same data with the baseline. In comparison, all three libraries circulated about the same number of books. The difference between Library A and C appears much less significant. 

Some graphs might leave off the y-axis entirely for a cleaner look making it harder to tell if the data are truncated. Ask yourself if the different columns look proportional. For instance, if 3,200 should look like half the size of 3,500. If not, then your baseline isn’t zero. 

Manipulating the y-axis

Manipulating the y-axis can be thought of as the exact opposite of truncating data. This visualization tactic is used to blow out the scale of a graph to minimize or maximize a change. For instance, this graph shows average annual global temperature from 1880 to 2015. 

Source: National Review

The scale goes from -10 to 110, which I suppose is the range of possible temperatures in fahrenheit. However, this scale doesn’t make sense for these data. Instead, it serves to flatten the line and convey the idea that average annual global temperatures really haven’t changed in the last 135 years. Here’s the graph again with a more meaningful y-axis. Now we can see the upward trend more clearly.

Source: Quartz

You may have noticed this graph has a truncated scale (missing baseline)! So why is ok in this situation and not others? When talking about scales and axes, one way isn’t necessarily wrong or right, it’s about the message the visualization is designed to convey. You want to make sure that the data are not visualized to be intentionally misleading—making you think something is more or less important than it really is.


None of these data visualization tactics that we covered today are inherently wrong. Remember that data visualizations are all about conveying the data’s story and like any story, people can take creative license. It’s important to be able to spot these scale manipulations to avoid getting the wrong idea about what the data are really telling you.

Next week we’ll cover some more common tactics, so stay tuned! 

The right data for the job – part II

Hello, again! Are you ready to learn more about the right data for the job? We are reviewing the  qualifications of various data to answer different kinds of research questions, just like we would review job candidates’ qualifications for a job. Last week we talked about the importance of what data were collected and how they were collected. This week we’re going to consider the importance of definitions and what it means for data to be representative. I know you have been waiting anxiously to figure out what we were going to do with those hats, so let’s jump back in! 

1) Can you define that for me?

In research, definitions matter a lot. How researchers define important concepts impacts both what data are collected and how they are interpreted. For instance, last week we talked about collecting data by looking at something people created – hats in a knitting class – and whether these hats could be defined as a “success.” Those hats can be used as our data, but we need to specify how we are defining success.

So how does one go about measuring if a hat was “successful”? Is a hat successful simply if it is completed? Or, does it need to be round and fit on someone’s head? What if it’s too itchy for any human to wear, but a cat decides it’s an amazing toy? To come to a conclusion about the success of these particular hats, and use that to evaluate the success of the program, researchers need to make decisions about these types of questions and how they relate to the research question. 

As a reader of research, look for a clear connection between the research question, how the concepts being studied are defined, and the conclusions that were drawn. They should all align. When you’re researching a new topic, be aware that there can be wide variety in how a concept is defined in different fields and by different researchers.

2) Representative

Do the data actually represent the thing that is being studied? Let’s say you want to know how many people in your service area read a book last month. You could call every single person to ask, but this is unrealistic because of the resources it would require. An alternative approach is to collect data from a sample of the population. In this scenario, everyone in your service area is the population and your sample is the people you actually collect data from. 

Creating a truly representative sample is difficult because it must meet these l criteria:

  1. Your sample should equal a certain percentage of your population. There are tools, like this one, to easily calculate what your sample size should be.  In general, if your population is smaller than 100, you should be surveying everyone. 
  2. Every member of the population needs to have an equal chance of being included in the study – meaning that the sample is randomly selected. This reduces bias and the potential for certain groups to be over-represented and their opinions magnified while others are under-represented. 

Results from a sample can be generalized to the population if it meets these criteria. 

What if the sample doesn’t meet these criteria? Then, check for another criterion – whether the sample otherwise mirrors the characteristics of the population.

Let’s say your sample size is 250, so you ask the first 250 people who walk into the library if they read a book last month. These data are going to be skewed because not everyone in your service area visits the library and those individuals that don’t haven’t had a chance to participate. Those that walk in also might not be representative of your population. For instance, if 50 percent of your population has a college education, 20 percent are African American, and 10 percent are above the age of 65, your sample should also reflect that.  

When reading research, check to see whether the sample meets the three criteria above. If it doesn’t meet the first two, you can be more confident that the results are still somewhat representative of the population if the demographics of the sample are similar to the population’s.

Getting a representative sample can be challenging, and researchers may acknowledge that some groups were over- or under-represented in their study. That doesn’t mean that research can’t provide valuable information. It does mean that this particular research may not be able to draw accurate conclusions beyond those individuals who participated in the study, or about the groups that were under-represented in their study. Be cautious about research that does not acknowledge or discuss significant differences between the population and their sample. 


You made it! You are ready to go interview some data! Let’s review: research results are based on data, and the quality of those data matters. Do the data collected actually answer the question that was asked? What data were collected, how they were collected, what definitions were used, and whether the data are representative all impact the quality and interpretation of the data. You don’t need to be an expert to consider whether the data used are really answering the research question. Use your common sense and these tips to think critically about the right data for the job.

The right data for the job

Imagine that different types of data are different people that you are interviewing for a job. The job is to answer a specific research question. You want to know what their qualifications are—will they do a good job? Are their strengths a good match for the task? Like we’ve mentioned before, if there are issues with the underlying data, then there will be issues with the results. It’s important to consider if the data collected make sense for the question that was asked. 

Today, the data “qualifications” we’ll be looking at are what kind of data were collected and how the data were collected. Next week, in part two of this post, we’ll cover definitions and what it means for data to be representative. Are you ready to get in there and interview some data? Come on, it’ll be fun!

1) What data were collected? 

The data collected need to be appropriate for the research question. One step of deciding what data are most appropriate is selecting quantitative or qualitative data. Quantitative research is concerned with things we can count. How many books were checked out last year? Of all the people who have a library card, how many of them checked out at least one item last year? Qualitative research is concerned with capturing people’s experiences and perspectives. Why did someone check out an item? How did having that item make them feel? Qualitative research often aims to give us insight into the thinking or feeling behind an action. 

Which type of data is most appropriate depends on the research question. If the researchers want to know how many children completed a summer learning program, the quantitative data from registration and completion work well. If researchers want to know what children got out of their experience, it would be more appropriate to collect qualitative data on a question like, “Please tell us more about your experience with our summer programming. What did you enjoy? What could be improved?”

What doesn’t work is if the researcher wants to know about the thinking behind an action, but asks for a number. Or if the researcher wants to know a number, but asks participants how they felt about their experience. The key is for the research question and the data collected to be a good match. 

2) How were the data collected?

Once researchers figure out what they want to know, they have to decide how to collect the data. There are many ways to collect data—too many for this post. A few common ways that data are collected in libraries are to ask people (such as with surveys or interviews), observe people, or evaluate something they created. Each of these strategies is more appropriate in some situations than others.


Asking is great when you want to understand someone’s experience. People understand what’s going on internally for them. An example is asking participants what they wanted to get out of an experience and what they felt like they did get out of it. They are the experts on that. Asking, in particular using surveys, is also an effective strategy for collecting a large amount of information relatively quickly.

There are a few challenges with asking people. One issue is that we all want to feel good about ourselves and look good to others. Even if I receive an anonymous survey in the mail, I may put down an inaccurate answer if it fulfills these desires. We call this social desirability bias.

For example, if you want to know how many books I checked out last year, I would be happy to tell you. The problem is that I don’t remember. Maybe 50? Maybe 100? Or maybe I do remember, but I want to tell you it was 500 because that makes me feel good.

The second issue is that sometimes we simply don’t know, but we’ll guess if you ask us to. Our memory is unreliable. When asking people to report on something, it needs to be something that they can report relatively easily and accurately. A final issue is that sometimes people don’t understand the question as the researchers intended it.

Researchers keep these challenges in mind so they can take steps to mitigate them. Some common practices are to avoid asking questions that are highly sensitive, asking about things that are easy to remember, and testing surveys and interview questions in advance for understanding.


Observation enables researchers to directly witness people’s behaviors and interactions, rather than relying on their self-report. This can be very useful in situations where people don’t know or struggle to articulate the information researchers are trying to collect.

For example, let’s say a researcher wants to know more about children’s experiences at a storytime. Children are still learning words to express how they feel. If children who attended storytime were asked what they got out of the experience, they might say it was fun. They are probably not going to say they worked on fine motor skills and social skills. An observer, however, can collect data about the activities during storytime, watch children’s interactions with each other, and their facial expressions. 

The challenge with any observation is that the information is captured or analyzed by a person. We are all biased and subjective in unique ways, and notice some things and miss others. To mitigate this challenge, researchers use a structure or guide while observing and, when possible, more than one observer. 

Create something

Creating something is a good way to collect data in situations where researchers are evaluating skills. Let’s say there was a knitting class at the library. The main goal was for participants to knit a hat. At the end of the class, every participant selects their best work and those hats all go on display in the library. Those hats are the data. To determine if the class’s goal was achieved, you would look at all the hats and decide if they were  “successful” hats. 

As you read research, take note of the reason why researchers chose a particular method of collecting data. Does their reasoning make sense? Do the strengths of that method match well with the research question? What’s important here again is the match between the research question and how the data were collected. 


Wow! That was a lot. How does your brain feel? A little gooey? Very understandable. Remember, you need the right data for the job. What data were collected and how they were collected are two ways to see if the data are a good fit for the research question. We’ll cover the other data qualifications next week: definitions and what it means for data to be representative. And we’ll talk more about those hats!