Coming Face to Face with Interview Bias

Throughout the Between a Graph and A Hard Place blog series, we’ve often spoken of bias and controlling bias. From keeping your own bias in check during qualitative data analysis to understanding social desirability bias during surveys and observations, managing bias is a constant challenge throughout all aspects of evaluation from planning to presenting. During interviews bias can be particularly difficult to avoid, taking many different forms and heavily impacting the quality of the data being collected. This post will take a look at what bias is and three different ways it can distort data during an interview. The next LRS post will tackle ways to mitigate bias, not only during interviews but throughout our lives. 

What is Bias

In order to mitigate bias, you need to be able to recognize it. In order to recognize it, you need to understand what bias is and the intricate ways it can manifest itself. In fact, just hearing the word bias could bring feelings or images into your head. Do you feel defensive because you generally try to ignore your bias? Or do you picture news media outlets covering political controversies? Either of these thoughts could influence how you read this article and the information that you take away from it, which is actually an effect of bias we will discuss further. Many slightly different definitions for bias float around the internet, but let’s try our best to start with the facts. Bias is defined on as “a particular tendency, trend, inclination, feeling, or opinion, especially one that is preconceived or unreasoned.” This broad definition shows how difficult it can be to avoid bias. I think we can all admit to having at least some arbitrary tendencies or opinions that are based on our preconceived notions rather than facts. Taking time to understand the prevalence of bias throughout our day to day life is important to realizing how much consideration and effort it takes to limit the many different types of biases from creeping into our research. 

A Biased Interviewer

There’s some pretty obvious, and some not so obvious, ways that bias may influence an interview. Since we’re library professionals who may find ourselves conducting interviews, let’s first examine how an interviewer’s personal bias can affect the data collected in an interview. If an interviewer sits down to conduct an interview but already has preconceived notions about who the interviewee is and what they are likely to say, their takeaways from the interview may be completely different than if they listened with an open mind. Even if you’re outwardly presenting all the correct active listening skills during an interview, if internally you aren’t managing your biases, it’s likely you won’t accurately hear what the interviewee is telling you.

“Cat People” versus “Dog People”

Let’s use a long standing divide between pet owners as an example – are you a “cat person” or a “dog person”? Maybe this is my own bias creeping in, but for this example let’s say the interviewer is a library staff member and a “cat person” and the interviewee is a community member who owns five dogs. The library staff want feedback on the library’s outreach efforts, so they have organized interviews with some regular patrons and these two characters are sitting down for a semi-structured interview. The interviewer is aware that this community member owns many dogs and therefore, has already labeled them as a “dog person.” During the interview the library staff asks, “How do you think the library can increase engagement throughout the community?” The dog owner responds, “Maybe the library could have a library mascot, such as a mascot dog, to be part of its branding and engage children and animal lovers.” However, the interviewer sat down for the interview stuck with the impression that this person is a “dog person” so when they hear “dog” included in the response they immediately stop listening and dismiss the idea of a mascot. This library staff member has assumed the interviewee just likes to make everything about dogs and also assumes that others won’t find this idea nearly as engaging. 

Is a library mascot a bad idea to engage the community? Probably not. Is the community member insisting that the mascot be a dog? Not at all. Does their reasoning behind the suggestion make sense? Yes. Nevertheless, the interviewer’s bias towards cats and their preconceived notions of the interviewee stops them from considering the positives of a library mascot or following up with the idea.

Obviously, this example only scratches the surface of how detrimental holding biases can be to data collection. Biases can make us blind to the ideas of people we have othered and this blindness may lead to poor interview takeaways and the dismissal of otherwise brilliant ideas.

The Interviewee’s Bias

Now remember that everyone may hold biases, including the person being interviewed. Not only does the interviewer’s bias influence what they hear, but how the interviewee perceives the interviewer will also influence what they say. Let’s rewind. Say these same two people, the “cat person” interviewer and the dog owning interviewee, sit down for the same interview, but this time the interviewer is wearing a sweater with kittens all over it. Because of the sweater the interviewee makes the correct assumption that the interviewer likes cats. The community member wants the library staff to like them and their ideas, so they respond to the same question by suggesting a library mascot “such as a cat” instead of a dog. In this case, the suggestion of a library mascot cat is only given because the interviewee feels that the library staff will respond positively if cats are brought up. When characteristics of the interviewer influence the response of the interviewee such as this, it is known as the interviewer effect

Displaying Bias Towards Responses

The interviewer may be surprised that the dog owner suggested the mascot be a cat instead of a dog, and this brings us back to interviewer bias and the third type of bias we will discuss. If the interviewer shows they are pleasantly surprised by sitting up, leaning forward, and smiling, this will likely encourage the interviewee to elaborate further on their mascot idea. The interviewer then goes off script to hear more about this idea of a library cat mascot and the interviewee, happy there is interest in their idea, responds enthusiastically. In this case, the library staff’s outward expressions are influencing the community member to continue talking on a subject they otherwise wouldn’t have. Suddenly the data collected in this interview is completely different than in our first example, and this interview may result in the library staff following up with their colleagues regarding the development of a mascot which is also a completely different outcome. 

Returning to Reality

These subtle shifts in interview responses and the consequentially different outcomes of these two interviews all took place because of how the interviewer and interviewee perceived each other. As you can imagine, when the biases we hold are more deeply ingrained than a preference towards dogs versus cats and are rooted in fear as biases often are, the effects can be even more significant. In the next post we will explore how to mitigate bias in research interviews and throughout our lives. In the meantime, here are five points to keep in mind. 

  1. In order to recognize and mitigate your own biases you must first understand what bias is.
  2. Bias can affect interviews when the interviewer has a preconceived idea of who the interviewee is and what they will say. This may cause the interviewer to only focus on certain parts of what is said and not the whole story.
  3. The interviewee can also be biased towards characteristics of the interviewer which may influence how they respond. 
  4. Both positive and negative reactions by the interviewer to responses can influence what the interviewee says if they are basing their responses on what they think the interviewer wants to hear.
  5. While bias during interviews can be subtle, it can still have a significant impact on the data collected if not left unchecked, and in many cases conducting a biased interview may harm or retraumatize the parties involved.  

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. Each post covers an aspect of evaluating. To receive posts via email, please complete this form.

Presenting Three Types of Interviews

How do you feel most comfortable speaking in front of an audience? Do you have the confidence to let your ideas flow freely, or do you prefer sticking to a plan? The Colorado Association of Libraries Conference (CALCON 2022) begins today, and there happens to be quite a connection between the styles in which we choose to present our ideas, such as at CALCON and how we conduct the next data collection method we are discussing–interviews. It’s a fitting time to take a closer look at interviews as another viable data collection method for evaluation in libraries since interviews fall into three categories (unstructured, semi-structured or structured), and much like understanding the most effective way to present information, you must understand how to structure, or not structure, your interview for it to produce quality data. Today, we will discuss the pros and cons of these three interview types. If you’re looking for some background reading, be sure to check out this post for an overview of interviews and when to use them. 

Unstructured and Ad-libbed

The first type of interview we’re going to cover, an unstructured interview, is the most conversational. An unstructured interview may be the most casual interview type, but the openness of what is discussed can lead to deep conversations and highly valuable qualitative data. In an unstructured interview you do not have set questions that you plan to ask your interviewee and instead, head into the conversation bringing only a topic that you would like to explore. This is similar to a presentation structured as an open discussion with the audience where no script exists. 

In order for an unstructured interview to provide you with quality data, generally two conditions need to be met. First, you must understand and work within the limitations unstructured interviews have. Data collected from unstructured interviews will not be easily replicable, so this is not the interview type to use if you are hoping to compare data across populations or through time. Unstructured interviews still produce valuable insights, but because each interview might cover slightly different material, the information is not consistent enough to generalize to a large population. This interview type is more effective for localized research and learning the interviewee’s story. 

The second condition is that it’s best to have had some previous experience as an interviewer. We don’t all jump into projects as seasoned researchers and of course, that is OK. However, how you word questions can have a large impact on the quality of the data you collect. As we’ve discussed previously, asking leading questions is going to give you skewed answers. You aren’t equipped with a full interview script during an unstructured interview, and you may find it surprisingly difficult to construct quality follow-up questions on the spot. The same applies to presentations: if you are not an experienced presenter, improvising on stage can be risky, but creating an outline and practicing your notes will set you up for success.  

The Semi-Structured Style

This leads us to the next way to conduct an interview, which is the semi-structured approach. This is the middle-ground, so conducting this type of interview can give you the best of both worlds. I would venture to say that the comparable presentation method, speaking freely while also following your notes, is the method preferred by many. An interview where you can stray from the script but do not scrap it all together allows you to ask follow up questions of your interviewee and explore interesting points further while also maintaining some consistency between interviews. Like unstructured interviews, semi-structured interviews should be conducted if you are still exploring your topic because your interviewee may be able to provide critical information you did not initially know to ask for directly. 

However, there are definitely cases where unstructured or structured interviews will be better options for you. The qualitative analysis of data from a semi-structured interview will still be more complex than that of a structured interview. On the other hand, if you are just beginning to explore a possible topic for evaluation and are interviewing an expert on the topic, allowing your interviewee to speak freely in a completely unstructured interview will likely be most beneficial.  

Structured or Scripted

Before a structured interview you plan every question you will ask and the specific order you will ask them. This is akin to writing a script and following it word for word during a presentation. By doing this during an interview, you will gather more consistent data that is easier to analyze. Because you are trying to work through a set number of questions, this interview type often contains less complex questions and therefore, receives shorter answers. Analyzing the data produced by a set of structured interviews is easier than a set of unstructured interviews. The study will also be more easily replicable. 

If you are conducting structured interviews and hoping to generalize your findings to a larger population, it is important to have a significant sample size. Luckily this is easier to complete with structured interviews than unstructured interviews because you are not asking follow up questions and will have a better idea of how long each interview will take. Consistency is key to avoiding bias, so you should not only try to keep your questions and question order the same but also the manner in which you ask each question.

One important factor to consider before conducting structured interviews is that your interviewees will not have as much opportunity to share the breadth of their experience. You must carefully develop your questions ahead of time to ensure that what you are asking will produce the data you are looking for. Unlike unstructured interviews, which usually take place to learn more about a topic, you should already have a solid understanding of the topic in order to know which questions you should ask.

Time for Questions

We hope this post has expanded your understanding of the possibilities and limitations for conducting interviews. Remember, if you still have questions, please reach out to LRS@LRS.ORG. In the meantime, here are five key takeaways from this post:

  1. You should carefully consider which interview type will best meet the needs of your evaluation as they all come with different benefits and challenges.
  2. It is best to have an experienced interviewer conduct unstructured interviews since they must come up with quality questions on the spot. 
  3. Unstructured or semi-structured interviews should be conducted if you are still exploring your topic and want the ability to ask follow up questions in response to what your interviewee shares. 
  4. Unstructured interviews are the least replicable and structured interviews will be the most replicable for future studies.
  5. You should stay consistent during structured interviews by sticking to your predetermined questions as well as the predetermined order of those questions.

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. Each post covers an aspect of evaluating. To receive posts via email, please complete this form.

A Trauma-Informed Lens: Communities, Libraries and Research

This post will delve into trauma-informed practices for research and everyday interactions for library workers. With that in mind, the key concepts from this piece are shared first, to preface the material and ensure you are aware of the content that will be covered up front.  

  • Trauma is defined by the Substance Abuse and Mental Health Service Administration (SAMHSA) as “an event, series of events, or set of circumstances that is experienced by an individual as physically or emotionally harmful or life threatening and that has lasting adverse effects on the individual’s functioning and mental, physical, social, emotional, or spiritual well-being.” 
  • Trauma can be experienced by anyone regardless of identities such as gender, age, race, socioeconomic status, and sexual orientation.
  • Communities can experience trauma collectively. This can happen through the perpetuation of systemic racism, for example.
  • Library workers regularly interact with those who have experienced trauma which can lead to secondary traumatic stress.
  • Anyone can heal from trauma, anyone can help somebody heal from trauma, and anybody can, knowingly or unknowingly, retraumatize somebody.

While trauma has always been pervasive in humans, it has become a common topic for discussion in the past few years, particularly since we found ourselves in the midst of a global pandemic and the focus on self care has increased. As with our individual experiences of the pandemic, our individual perceptions of traumatic experiences vary tremendously from person to person. Experiencing trauma can have lasting negative impacts on one’s mental, physical and emotional health. These impacts can then extend beyond the individual to the communities they exist within. Being trauma-informed will help you navigate your work with understanding and compassion for those around you as well as for yourself.

For Our Communities

The understanding of traumatic experiences has developed beyond a single event affecting a single person to also include an event or repeated acts that affect entire communities or generations. Dr. Laura Quiros gives an example of this in her post “How is trauma connected to diversity, equity, and inclusion work?” She writes, “Trauma is at the heart of diversity, equity, and inclusion work because repeated acts of marginalization, oppression and racism are wounds that overwhelm one’s ability to cope.” Therefore, oppression and inequity must be understood as intergenerational traumas for marginalized communities that are still being perpetuated today. 

With this understanding, having a trauma-informed approach to your library’s work becomes essential to creating a safe, equitable and inclusive environment. Trauma survivors enter your library regularly and you often can’t tell who has experienced trauma. In many circumstances, blatantly overlooking the trauma somebody has faced, or labeling it incorrectly and misunderstanding it can cause more harm and retraumatization.

For Our Libraries

SAMHSA has created frameworks for trauma-informed care and guides for implementing trauma-informed practices which can be found in their Concept of Trauma and Guidance for a Trauma-Informed Approach report. One of the standard approaches is the four R’s.

Source: Child Trends

Realize that trauma is widespread and has very real effects for individuals and communities. (Around 60% of adults have been found to have been exposed to at least one adverse childhood experience that was potentially traumatizing.) 

Recognize the symptoms of trauma and when someone’s actions may be affected by their past trauma.

Respond by implementing trauma-informed practices throughout your organization

Resist retraumatization through actions such as creating a welcoming environment, regulating emotions and offering connection and empathy.

While the four R’s are a great way to outline trauma-informed care, it’s important to have an understanding of how to implement this strategy, particularly for responding and resisting retraumatization. 

Even if you understand that your patrons may have experienced trauma it can be challenging to respond appropriately. Experiencing trauma affects one’s perception of the world around them, and may lead to reactive expressions, avoidance, or limited responsiveness, similar to fight, flight or freeze responses. As we move forward with suggestions for a trauma-informed approach to your work, remember that taking care of yourself is of the utmost importance. Setting boundaries and removing yourself from potentially harmful situations to protect yourself from trauma and secondary traumatic stress is always OK. 

For Our Research

Incorporating trauma-informed approaches into research is a crucial way to stop data collection from unintentionally harming research participants. While the rest of this post will focus on research through a trauma-informed lens, much of the information is immediately applicable to trauma-informed care in many scenarios including daily interactions with library patrons.

Though you may only have a brief interaction with an individual or group for the purpose of collecting data you can still have a significant effect, whether positive or negative, on their trauma recovery. In research, there is sometimes an attitude that the researcher must be impartial and removed from participant experiences, however, particularly while collecting qualitative data, this is not always the case.

Before interacting with research participants, it is important that you make efforts to properly understand the community you are working with. If you are aware that this community has experienced collective trauma, ensure that you understand the community’s framework, and don’t assign your own labels to what happened. Also, reflect on any social power dynamics that may be at play, and how you can increase the safety and comfort of your participants. As discussed in the LRS post Reading (and Recording) the Room: Focus Groups, creating a safe space is the number one priority for trauma-informed research. 

A key way to avoid retraumatization and help others heal from their trauma is through connection and relationship building. Even finding small ways to relate and start a friendly conversation can go a long way. While conducting research, make room for connections and show empathy for your fellow humans. This may involve sincerely acknowledging their hardships and/or offering resources to help somebody through their healing process. Additionally, making your research findings available to research participants whenever possible sustains a relationship of trust and transparency. 

Lastly, make sure you regard all participants with respect. If you find that this is not the case, you are probably not the right person to be conducting this research.

For a Survey

To show how these components of trauma-informed care can be directly applied to your research, let’s imagine that you just presented this information as a training session at your library. Now you want to evaluate this training on trauma-informed care by sending out a short survey to participants. Of course, it is possible some of your participants have experienced trauma and so, not only do you want to model a trauma-informed approach to your survey, it is crucial that you do so to actively avoid retraumatizing anyone.

You may create a survey welcome page that looks something like this:

Thank you for your interest in our survey. The purpose of this survey is to evaluate our library’s training on trauma-informed approaches. We will apply what we learn from your responses to our future training and continued improvement of our trauma-informed approach throughout the library. The goal is to help our patrons heal from traumas they may be coping with. 

As you move through this survey you can skip any questions you would like or stop the survey completely at any time. The questions will all pertain to your experience at our trauma-informed care training. This survey is completely anonymous, so no personally identifiable information will be asked for. It should take around 5 minutes to complete. If you have any questions or concerns please reach out to us at You can find additional resources on trauma-informed care and healing from trauma here. Thank you again for your time. 

This introduction shares the purpose of the survey, the impact the participants’ responses will have, the content of the survey and the ability survey takers have to skip questions. By explaining all of this, you are empowering participants to make an informed decision on whether they would like to complete the survey and creating a safe space for them to work within.

The survey introduction ends by sharing resources as well as contact information for further questions, comments or concerns. By opening up this line of communication you are offering a way to connect and providing support to anyone interested. Stating the length of the survey and thanking participants shows respect for the participants and their time. 

For Recovery

As demonstrated with this post, being transparent and sharing key information from the beginning empowers participants and builds trust. It also lessens the chance that retraumatization will occur if research participants are fully aware and agreeable to what will be discussed beforehand. 

Throughout your work, acting with consideration of the traumas your patrons may have faced, including structural inequities, will help to end the perpetuation of these traumas. As community hubs, libraries have the ability to foster connections and be impactful healing spaces. As our understanding of trauma and its effects evolve, our ability to effectively apply trauma-informed care should grow as well.  


LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. Each post covers an aspect of evaluating. To receive posts via email, please complete this form.

Planting Seeds for Success-FULL Focus Groups

Did last month’s post inspire you to incorporate a focus group into your project? Are you too busy at this moment? We understand! However, you never know when conducting a focus group may just turn into the best possible way to collect community input before moving forward with a big project, and we want to make sure that when that time comes you are fully prepared to host a focus group full of meaningful discussions from participants. So let’s dig in!

Laying the Groundwork

Our last post shared some ideas on how to create a safe space for a focus group, but there are three other pieces a focus group needs to run properly: purpose, participants, and questions. 

Planning With Purpose

We broadly discussed the purpose of focus groups previously, but today is all about getting our hands dirty. So let’s say you are faced with the decision of whether or not to conduct a focus group right now. What are some reasons you may choose to go through with the focus group? 

One of the most common reasons for conducting focus groups is when new services or programs are being implemented. Listening to people discuss their viewpoints and thoughts on these new developments can prevent unforeseen pitfalls in your planning before it is too late. Additionally, focus groups serve an important purpose when the information you are trying to gather cannot easily be answered on a written survey, possibly because the answer is abstract or lengthy. You may also want to conduct a focus group before creating a survey, in order to learn more about your survey audience and ensure you ask the right questions. Lastly, you should only proceed with a focus group if you have the time and resources to find willing participants! 

Selecting Participants 

You wouldn’t necessarily plant roses if your end goal is to grow a vegetable garden, so try to be just as intentional when picking participants for your focus group. Gathering your participants is possibly one of the most challenging aspects of designing a successful focus group, but having a clear purpose will direct you to the right participants. Select people who have a stake in what you’re researching or valuable knowledge you cannot easily access yourself. For example, if conducting a focus group before implementing a new early learning program at your library, a random selection of participants may give you participants who are not parents and do not intend to utilize this program. Recruiting participants who have young children for this study will give you more relevant information. 

Once you identify the group you would like to invite to participate, finding and contacting them is your next challenge. The more people you talk to the larger your candidate list will be and the more likely it is that you will have a full focus group. Libraries should consider utilizing the resources and partnerships they already have to find their preferred participants. Offering incentives is customary to thank your participants for their time. Funding for incentives can also be a challenge for libraries and a reason focus groups for libraries may be kept small. 

One way to build a candidate list is to create an interest form for patrons to fill out that gauges their willingness to participate and also checks whether they are part of your target group. Asking for volunteers for your focus groups can be problematic because those that will volunteer are likely more engaged with library activities and you may be hoping to hear from a group that is generally less engaged. Ask the people you seek out to participate by being open about the incentive offered and explaining the value they can bring to the discussion. 

Companions and Competitors 

Another piece to this puzzle is the participants’ group dynamics. The participants’ age, educational background, and socio-economic status, among many other factors, can have a large impact on who shares what during the focus group. Selecting participants that share common ground will increase the likelihood that conversation and ideas will flow naturally. After some common ground is established, diverse perspectives can also improve the conversation and limit groupthink. If you have multiple diverging groups that you want input from, conducting more than one focus group may be a good option to ensure that participants are comfortable speaking their mind within the group. Always plan for cancellations and no shows, and try to have at least three people participate in each focus group. Groups larger than eight may be challenging to direct.  

Cultivating Your Questions

Not only do you want a focus group full of participants but you also want the discussions to be full of relevant information. Always keep in mind the purpose of your project and the participants you are working with when creating the questions for your focus group. Each question should be directly tied to the information you are seeking. 

Participants will also be more comfortable if the questions are clearly worded without potentially confusing jargon. Don’t ask questions that make participants feel guilty or embarrassed and keep questions short. Multiple parts to one question may be overlooked or worse, lead to confusion and misunderstanding. 

Focus groups are your chance to ask open ended questions that spark lengthy responses and discussion. Questions should not be leading the participants towards a specific answer. Keep an open mind and don’t try to predict how participants will answer your questions. If your first focus group has a couple questions people seem reluctant to answer, put yourself in the participants’ shoes, learn from your mistakes and rework your questions for next time. 

Harvest Time!

We hope you can keep this food for thought safe until the time comes for you to begin your own focus group. Remember to create a safe space, understand what you are hoping to gain from your focus group and always keep that purpose in mind when selecting your participants and questions. Here are a few more takeaways from this post.

  1. Hold a focus group to find information that cannot easily be answered by a survey question or when you need to learn more from the community before moving forward with your plans.
  2. Don’t underestimate the work needed to find and recruit participants.
  3. Be conscious of the group you are putting together and how power structures may influence what people are willing to share within this group.
  4. Make sure that each of your questions has a purpose, but be willing to adjust your questions as you learn more about your participants.
  5. Keep questions simple and clear. Don’t ask manipulative questions. 

If you’re now daydreaming about starting a garden instead, check out Boulder Public Library’s Seed to Table program or Mesa County Libraries’ Discovery Garden.

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. To receive posts via email, please complete this form.

Reading (and Recording) the Room: Focus Groups

Ready to polish up your people skills? This month we are taking a step back from analysis and turning to data collection again. In previous posts we touched on different data collection methods, and in May we discussed the process of coding qualitative data. Understanding the basics of qualitative analysis opens up a world of possibilities for evaluation, and it definitely helps to know what you are taking on before beginning qualitative research since coding is an extensive process. With this background on coding qualitative data hopefully you feel more equipped to begin collecting it. Today we will focus our attention on a qualitative data collection method that we have mentioned several times but have yet to delve into—focus groups!


Focus groups consist of several selected participants that partake in an intentional conversation directed by a moderator in order to gather community input. They are used to answer open-ended questions and gain a deeper understanding from diverse viewpoints. In this post we will outline what a successful focus group looks like as well as acknowledge their limitations.

If you have not participated in or led a focus group before, here’s a general rundown of how they start. Once everyone is brought together in the same space (everyone includes a moderator, usually an assistant moderator or note taker, and the participants), the moderator facilitates introductions. Icebreakers and friendly chatter can help the participants feel more relaxed, which will lead to a more productive session. Ground rules that foster a respectful, safe environment should be clearly established by the moderator, and consent to participate is received from all participants. As the moderator, you want to be mindful of time during the introductions. Remember, the participants are spending their valuable time to be there, and you want to make sure the resources you spent to bring them together yield valuable insights. 

Moving Past the Small Talk

Jumping into your predetermined questions will begin the true discussion. Throughout the allotted time, moderators have the incredibly important job of fostering a safe environment for participants to speak candidly. The quality of data you collect hinges on the ability of the moderator to maintain control of the conversation while ensuring participants are at ease. A level of trust needs to be established to ensure that the participants are comfortable speaking their mind, which is essential to gather reliable data. Moderators can foster this environment through these ten actions:

  • Giving verbal and nonverbal listening cues
  • Asking follow up questions
  • Asking for clarification before assuming what someone means
  • Being flexible if important, unanticipated points arise 
  • Steering the conversation back on topic if it goes astray
  • Emanating confidence
  • Staying neutral 
  • Always being respectful of differing opinions
  • Ensuring everyone has an equal chance to share
  • Avoiding leading questions

If you find yourself moderating a focus group, remember that you are ultimately the leader of the group, which gives you the power to politely direct the conversation. This is particularly important if one participant is dominating the conversation and others are being left out. 

All in all, leading a focus group requires impeccable social and leadership skills. 

Active Listening 

Conducting a focus group can reveal surprising information and perspectives that may be crucial to your research but you would not have thought to include in an interview or survey on your own. Selecting a diverse group of participants can reveal aspects of your study you never knew you were missing. While conducting a single focus group still ranks low on the Community-based participatory research (CBPR) continuum, it can be one tool out of many to begin incorporating more CBPR practices into your library. 

Because you may learn something significant that steers your research, it is best to conduct a focus group early on, so it is easier to incorporate your findings into your research moving forward. The focus group questions should all directly relate to your purpose, but the unique and most advantageous aspect of a focus group is the social dynamic and dialogue that prompts complex idea sharing. 


Before you get too excited by all the thought provoking feedback that focus groups can produce, make sure you are also aware of some of their pitfalls as well. For starters, focus groups are often too small to be a statistically significant sample size for the population. If you are hoping to generalize your data to a large population or to make transformative planning decisions, a focus group should not be your only data source, but it can be a powerful source of data when combined with other methods. 

Although a single focus group may take less time and money to conduct than five separate interviews, focus groups also take a lot of planning to be well executed and should not be used as a rushed way to collect data. Similarly, focus groups require a skilled moderator in order to collect reliable data, and not having the right person available could cause your focus group to be less effective.

Possibly the most important thing to consider is that focus groups cannot guarantee anonymity due to the number of participants listening to each other. Sensitive topics that participants may not want to discuss in front of others are not well suited for focus groups since people are likely to hold back and not share their true internal reactions. In any focus group, regardless of topic, the moderator’s and participants’ expressed reactions can significantly influence what is subsequently shared. Therefore, focus groups can be susceptible to producing a consensus that may in fact be misleading, otherwise known as groupthink. This is another reason focus groups are often used in conjunction with other data collection methods. 


Clearly, focus groups have their strengths and weaknesses, but if well conducted they can bring invaluable, diverse perspectives to your research. Don’t forget to record the session so you have a complete transcript available to code! To wrap up, here are five key points to remember about focus groups. 

  1. It is essential to have a skilled moderator who will build a safe sharing space. 
  2. Remember to gain consent from each participant and lay ground rules to ensure everyone’s contributions are respected.
  3. Focus groups are generally not made up of a statistically significant sample size and therefore data from a single focus group should not be generalized to a large population. 
  4. Make sure participants are aware that confidentiality cannot be guaranteed. 
  5. Conduct focus groups early on in your study so that the unique insights they bring can inform your work moving forward. 

Thanks for reading! Next, we will cover some important logistical points such as how to select participants and ask the right questions. If you have any questions or comments we would love to hear from you. You can contact us at

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. To receive posts via email, please complete this form.

Mapping the Methods: Content Analysis Part 2

dog wearing glasses looking at books

Welcome back! I am excited to dive back into content analysis with you. It is no secret that content analysis can be far from a walk in the park and is possibly more comparable to following a treasure map across a remote island. Therefore, I will fill this post with a review of what we have already discussed, the final steps for analysis, obstacles to be aware of along the way, and a few helpful hints. 

Returning to Coding

We left off last week on coding, where short labels are applied to qualitative data which represent meaning within this data. Returning to this process throughout your analysis allows you to condense repetitive codes and make sure you are heading in a direction that is consistent with your data as a whole to avoid bias and better answer your research questions (or find the treasure, so to speak). 

Coding is actually the culmination of two steps. First, decoding data to find meaning and second, encoding it by applying a word or phrase that represents this meaning. Keeping these two steps in mind helps demystify the process of coding as a whole. The codes you create and how you apply them will depend solely on your data and the questions you are trying to answer. When creating codes it is helpful to think of them as not only labels but also links that piece your data together. 

Coding is a key step that organizes your data for further analysis. Once codes are applied to your data, you are ready to begin the more abstract part of analysis by categorizing the codes and searching for themes. 

Categorization of Codes

Categorizing codes is essentially synthesizing them into your analysis by identifying patterns in the coded data. Categories for codes are phrases that encompass an idea which multiple codes fall within. While searching for patterns of underlying meaning in your data, remember that patterns often develop by grouping similarities but can also develop through grouping data by outliers, frequency, order, causes, or other relationships. These possible pattern configurations are all helpful tools for categorizing your coded data. 

To continue with our previous example, in a response to the question “Do you feel that the library is an essential community resource, why or why not?” the code “family support” may fall into the category “highly valued early learning programs.” Other codes such as children’s programs, reading development, and storytime may also fall within this category.

Data outliers should not be viewed as problems but as points of interest and discovery. In this case, evidence that some patrons raising children do not use the library’s early learning resources does not necessarily make the previously mentioned category (“highly valued early learning programs”) wrong, but may lead to a new category entirely or reveal how these programs are more accessible to certain patrons than others. 

Developing Themes

Themes are a more abstract level of insight content analysis might reveal, meaning they are general and applicable beyond a single study. Themes may not always evolve from your coding and that is OK. Codes and categories can still be informative and point to paths for further research to answer your key questions accurately if themes do not develop.

Themes develop when you identify consistent patterns that stretch across the coded and categorized data and bring insight to your main research question. Once you have charted these patterns it is time to start digging for the treasure! Themes are not found by leaping to conclusions and away from your data, they develop through careful analysis of codes and categories to triangulate meaning based on evidence.

If multiple categories point to it, a theme developed from our example study could be “early learning programs bring people together from across the community” This is a concrete answer to the overarching research question “How can libraries increase a population’s sense of community?” and it could be used to inform decision making on future programming in your library.


Content analysis is a time intensive and sometimes frustrating process. You must be willing to dedicate time and effort to it for your conclusions to be accurate and limit bias. Also, it focuses solely on the content of your data without taking into consideration outside factors such as societal context. This limited focus, and the reduction of data to codes, categories and themes, may allow nuances of meaning to be lost. Condensing the data can be problematic if important aspects of it are ignored, or it can be exactly what you need to do to find the buried answers you are looking for. 

A Helpful Hint!

Coding does not have to be a lonely process. In fact, collaborating with a team can help you navigate this work and make the whole journey more enjoyable. As we discussed in the last post, each person will not apply the same codes to each excerpt and that is OK. Being open to a range of perspectives will bring insights to the data that you may never see alone. The possibilities for coding are enormous and narrowly focusing on one route can obscure key information and get you stuck.

Finally, make sure to take careful notes of your process and the codes you use. This will be helpful for you to refer back to throughout your analysis, and it will be helpful for those you share your study with to understand the work you put into it! 


Initially, qualitative data may feel overwhelming or ambiguous, but coding provides a map for condensing the data until you can categorize it and find meaning based within the text. It is rewarding when themes appear that were initially buried in the data. As you are putting time and effort into it, make sure to keep reminding yourself of the research question, the importance of your work, and your end goal. You may uncover key information when you least expect it! Coding reappears in other methods for qualitative analysis, so be sure to keep this information in mind as we continue this chapter.

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. To receive posts via email, please complete this form.

Mapping the Methods: Content Analysis Part 1

Hello data enthusiasts! Let’s return to our exploration of qualitative analysis. Last time we uncovered a few ways qualitative analysis can expand research findings by looking beyond number data for better insight on human experiences. Now I want to explore strategies for putting qualitative analysis into practice.

Concentrating on Content Analysis

As we discussed last time, qualitative analysis is flexible and adaptive to different types of data. As you may have already guessed, this means there are multiple methods for qualitative analysis depending on the kind of research you are conducting, the form of your data and the questions you are asking. Content analysis is one of many methods of qualitative analysis. It carefully filters, categorizes and condenses qualitative data sets (often text based) to discover hidden (or not-so-hidden) meanings! It is one of the most common methods of conducting qualitative analysis, and so it is a great place for us to start this chapter.

The key question that content analysis helps answer is, how do you categorize this textual data to best identify important patterns, anomalies, and relationships that answer your research question?

Planning for Success

You can visualize the content analysis process as following a treasure map where the treasure (buried in the data) is the insights your analysis will eventually reveal!

First, there’s quite a bit of preparation that needs to take place to ensure your analysis goes as smoothly as possible. For content analysis you should clearly identify the main question you want your data to answer. In other words, what is the treasure that you want to find? Content analysis is a long, strenuous process and having a specific goal will help direct you along the way. To build off our example from the last post, a survey that asks the open ended question, “Do you feel that the library is an essential community resource, why or why not?” may have a driving analysis question of, “How can libraries increase a population’s sense of community?”

However, as the analyst, you may now be staring at fifty lengthy responses, all of which have a person behind them with their own unique perceptions and experiences they want to share with you. You know there is useful information within the responses, and you want to make sure you are considering everyone’s responses by using correct research methods. 

This means it’s time to read your data, then read it again! While it takes both patience and time, this step can also streamline the rest of the process. You don’t need to read it with any specific goal in mind except to be open minded, consciously consider any biases you have, and take notes of your general impressions. Instead of fixating on specific responses try to take a step back and look at the data as a whole. You want to know your data thoroughly as you embark on the next step, just as you would want to know the map before setting off on an adventure!

Next Steps

Once you know your data backward and forward it is FINALLY time to start your content analysis with a method called coding. Coding is essentially categorizing the text with descriptive labels, or codes.

Coding has multiple steps, but the process is also repetitive and cyclical. Remember, you can always return to previous steps and adjust something so your analysis better encompasses the data. It’s unlikely you will find the treasure immediately, so always be willing to backtrack if necessary! 

Meaning Units

Before you create codes for your data, it may help to condense text into sections that hold meaning, called meaning units. Don’t let this step intimidate you. You still want these meaning units to be close to, if not literally, the text of the data. For example, perhaps someone’s response to our example question includes the sentence, “I’ve always enjoyed the library, but it was a particularly great resource for me while raising my children.” A condensed meaning unit you may take from this is “a great resource while raising my children.”

It is important that your meaning units relate as directly to the text as possible, and you are careful not to over-interpret or otherwise misrepresent the responses in the data set . 


You begin to interpret the data and take it to a slightly more abstract form with the next step, which is applying codes. An example of coding is taking the meaning unit “a great resource while raising my children” and assigning the label “family support.” If these are not the specific words you would use to describe this meaning unit that is OK. Codes will vary person to person and also change depending on the focus of your driving question.

As long as you work diligently to keep the coding faithful to the text, while acknowledging and limiting your bias from previous experiences on the subject, codes will not be right or wrong. Many codes will be used repetitively throughout your data analysis. You may assign the code “family support” to other meaning units from other survey participants if appropriate. There will likely be certain codes that are used often and other outlying codes that are not. There is also no right or wrong answer for the number of codes that you use. It depends on the size of your data set and the variations within it. 

It can be helpful to use your intuition while creating codes as long as you are still basing these labels in the text and staying aware of how your biases will affect their selection. Similarly, there may be aspects of a map you intuitively understand, but it wouldn’t be very smart to throw the map away entirely and assume you know the way yourself.   

While coding you may need to change meaning units that suddenly don’t make sense moving forward. Remember this backtracking is a normal part of the process to make sure the codes you are using reflect the whole of the data the best that they can. 


After creating codes and applying codes to your data your analysis is off to a good start! Stay tuned next week to learn where to go from here. We will explore categorizing the codes you create to find themes and finish your analysis! 

Here is a quick reflection on what this post covered:

  1. Content analysis is a common method for qualitative analysis that categorizes data to reveal key research findings.
  2. There is a lot of preparation involved in this long process. Take notes to track your work and know your research question and data thoroughly!
  3. Meaning units help you identify the meaningful parts of the text that you will code. 
  4. Codes are descriptive labels you apply to meaningful parts of your data to make sense of it all. 
  5. Your intuition can be helpful, but only if you are aware of how your biases may affect your analysis. Find a balance!

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. To receive posts via email, please complete this form.

New Season, New Chapter: Qualitative Analysis

Hello Everyone! I am honored to have the opportunity of continuing the LRS series staple Between a Graph and a Hard Place. Our last post began with “happy fall,” and now we are well on our way to Colorado’s mud season. Along with the change in seasons, a shift in topics feels like a great way to begin the series anew.

We left off discussing the use of observation as an important method for data collection and how to observe as unobtrusively as possible. To begin this new chapter, let’s take a step back to explore a type of research where observation is a crucial tool: qualitative analysis. 

The importance of numbers in research is impossible to overstate. While they can still be misleading, poorly displayed or simply inaccurate, we can all agree there is something reassuring about having the number data to back up your assertions. However, in a world filled with as many unique human experiences as ours, numbers alone (meaning quantitative research) can’t always give comprehensive and nuanced answers to every question, and that is where qualitative analysis shines.  


In previous posts we established that qualitative analysis delves into data derived from stories and answers questions such as “why” and “how.” Now, let’s dig a little deeper. Qualitative analysis is not only a tool for stories. It is used to examine survey responses, feedback from focus groups, narratives, anecdotes, social media posts, secondary and primary sources and even artwork! In fact, practically anything that includes human expressions, perceptions, emotions, assumptions and/or experiences can be analyzed qualitatively. As muddy as this may seem at first glance, understanding the potential for qualitative analysis through multiple mediums is crucial to identifying where and how you can incorporate it into your own research.

What you might have picked up on already is that, in its broadest and most simplified sense, qualitative analysis is used to make sense of data that is not numerical. Qualitative analysis is a tool for qualitative research where language and behavior are studied to find patterns and/or anomalies that convey information about a data set.

Helpful Aspects of Qualitative Analysis

The social sciences have applied qualitative analysis to their research for over a century, but until the middle to late 20th century, qualitative data collection and analysis were thought to conflict with quantitative research methods. One reason for this is that qualitative research, as opposed to quantitative research, accepts that the researcher is never fully objective and detached from the data being examined. When analyzing language and behavior, it is important for researchers to be aware of and limit their biases by understanding and reflecting upon how their own experiences and assumptions are a lens through which the data is viewed. The researcher will be an aspect of the study in a qualitative analysis. 

The fact that qualitative analysis plays by different rules than quantitative analysis does not lessen the value of the insights that qualitative analysis uncovers. Additionally, quantitative and qualitative research do not have to be at odds with each other. These methods can work together to provide a better picture of the phenomenon under investigation. Here is an example of how qualitative analysis can bring new insights to a study:  

Let’s say a Likert scale, a quantitative tool, is used in an attempt to assess the extent to which library patrons view their library as an essential community resource. Participants in the study are asked how they feel about the statement “libraries are an essential community resource,” and one of many patrons selects the answer “strongly agree.” This is considered quantifiable because their selection of “strongly agree” counts as a tally toward the total number of participants selecting this answer. However, when the same question is asked and the same participant has the opportunity to give a narrative response they write, “I agree with this statement, but the library is also where I found my community. I met my closest friends through library programs and afterwards became involved in community outreach through them.” 

Of course, this is only one response, and a detailed qualitative analysis would include an extensive data set. However, themes such as finding community and community outreach appear in this short answer and imply that the library builds community as much as it acts as a resource for it. If this is a consistent theme through multiple participant responses, it could be crucial enough to shift the focus of the study or open up avenues to find support for the library system in the future. 

Hopefully this sheds some light on how qualitative analysis can give you insights where quantitative analysis, when used alone, might fall short. Qualitative analysis can feel subjective and potentially problematic when compared to quantitative analysis, but the important thing to remember is that the two methods exist separately as apples and oranges. In fact, the entire order of the research can be turned upside down in qualitative analysis because it is not always necessary to start with a single hypothesis that you are attempting to prove or disprove. Qualitative analysis allows for more flexibility throughout a study because you analyze data as data collection is taking place, not only at the end as is done in quantitative analysis. 


If you’re feeling a bit lost, or are just swamped with things to do today and need something quick to skim. Here’s a recap of five points we just covered:

  1. There are times when your research questions will not be comprehensively answered by quantitative data, let qualitative data help! 
  2. Qualitative data takes many different forms. Widening your vision of what qualifies as “data” can reveal new opportunities for learning. 
  3. There are a variety of methods for analyzing qualitative data, but whether the researcher is using their intuition or a computer software, they will never be fully removed from the research findings.
  4. Qualitative and quantitative analysis are two completely different approaches to data, viewing one through the lens of the other will only lead to frustration.
  5. Qualitative analysis allows for more flexibility to shift focus throughout the study as the data is analyzed.

These parameters for qualitative analysis will be used throughout the rest of this chapter as we begin to answer questions such as, how do you analyze this data accurately? When is it appropriate to incorporate qualitative analysis into your research? And what limitations does qualitative analysis have? I hope to explore the answers with you in future posts, and in the meantime, happy spring!  

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. To receive posts via email, please complete this form.

How to observe without being totally awkward

Dog looks worried or confused in office

Happy fall to all you data nerds out there! We appreciate you being here with us. Last time we discussed how to get permission from your participants when you want to do an observation. You might be wondering how you can actually do the observation without it being completely awkward and perhaps even cringey. Today we are going to discuss just that!

First let’s review our goal for this project: We want to evaluate if caregivers are learning skills during storytime and using those skills with their children outside of storytime 

Based on this goal, we decided to do observations of caregivers and children in the library while they are not participating in storytime. Ideally from a research perspective, we would observe them at home, but that would not be practical or comfortable for anyone involved. Even in the library context, we are going to need to be careful to make sure that our participants feel as comfortable as possible. 

Being a participant observer

There are a variety of ways you can behave as an observer. For most library situations, I recommend a version of what researchers call “participant observation.” You’re observing while still interacting with the people you’re observing to a limited extent. This setup feels more comfortable while still giving you, as the observer, some distance from what you are observing. What would this look like for our example project? When the family you are observing tells the children’s desk that they are in the library, you would first introduce yourself to the family. Then during the observation you would talk with them only if it’s really important or necessary.

When is it really necessary to jump out of observer mode? A classic example I lived through with a team of librarian-observers was a child in the group we were observing getting a serious nosebleed. At the time there was only one library staff member who was teaching the group, but three of us were observing. One of us stopped observing and took the child to get medical attention. The instructor who actually knew the content that needed to be covered continued running the group. My best advice for when to break out of observing “mode” is to try to avoid it, but trust yourself when it feels like an appropriate time to spring into action. You are probably right!

Making people feel comfortable

When observing, we’re trying to balance getting quality data with making the participants feel comfortable. Every population, and every individual, has different needs to feel comfortable. It can help to start by thinking back to times you were in potentially awkward situations and someone made you feel more comfortable. What did they do? Remember in this case we want to go a step beyond that and treat people how they want to be treated, not just how we would want to be treated.

In a situation like this with caregivers, we should definitely reassure them that the library staff is not there to judge them. Parents feel judged a lot! It’s helpful to emphasize that you are evaluating storytime and not them. Nonetheless, don’t tell participants “We’re looking to see what early literacy skills you use outside of storytime.” Then they will inevitably show you every early literacy skill they have ever heard of! Instead, you might explain the project like this: “We want to make storytimes better. To do that, we need to understand how caregivers and children are interacting outside of storytime. We are watching so we can learn and make storytime as helpful and fun as possible. We are not evaluating you as a parent. Do you have any questions? Is there anything else I can do that would make you feel more comfortable?”

Working with children 

Children are going to want to interact with you while you’re observing them, especially if they know you. You should explain to them what you’re doing and why you are acting differently. For example: “Today, my job is to be very quiet and pay attention really carefully to the fun time you are having with your caregiver. You can look at me and I’m going to smile at you, but I’m not going to talk with you like I usually would. It doesn’t mean I’m mad at you. I’m just really focused on watching and listening today. I’ll tell you when we’re done and we can talk more then. Do you have any questions? ”


Having these kinds of conversations with participants before you start will help the observation go well. We observed teens for a project once, who are perhaps the most self-conscious creatures on the face of the earth. The staff observers introduced themselves to the teens at the beginning of their time together even though we already had informed consent. Although I can’t know for sure, I think we were able to collect valuable data on that project partly because the observers introduced themselves right before the observations and were very friendly and open. Remember, you set the tone for the whole interaction at the beginning.

Up next 

Next time we’ll talk about how to focus your observations and collect data that will be valuable to you. We’ll look forward to seeing you then!

LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. Each post covers an aspect of evaluating. To receive posts via email, please complete this form.

How to observe: Ask first!

A kitten peaks out from between books

Welcome back! We left off talking about why you would use observations to collect data. Observation can be a great data collection tool when you want to see how different people interact with each other, a space, or a passive program. Observation is also helpful when it is difficult for someone to answer a question accurately, like when you ask them to remember something they did or—particularly with children—if you ask them to give critical or written feedback, both of which can be developmentally inappropriate.

To review, our big research question is: “Does attending storytime help caregivers use new literacy skills at home?” Our small questions within that big question are:

  • Were caregivers already using literacy skills at home prior to attending a storytime? 
  • Are caregivers learning new literacy skills during storytime? 
  • Do caregivers use new literacy skills from storytime at home?

After thinking through these in our last post, we decided that it would be both helpful and realistic to observe caregivers and their children in the library. This won’t tell us about caregivers’ behavior at home, but it’s not an option for us to follow them around their homes. While observing caregivers in the library won’t tell us what they are doing at home, it still helps us see if they’re learning skills during storytime that they’re using outside of storytime.

Ok, so how would we actually do this? Here are the key elements of any observation:

  • Get permission from your participants
  • Decide how you will approach the observation
  • Focus in on some specific things you’re looking for
  • Take notes or videos of what you’re observing
  • Code the notes or video to identify patterns

For the rest of this blog post, we will focus on the first point. It’s important to get started on the right foot. 

Why is getting permission important? 

We’ve discussed informed consent before in this blog series, and it is still important with observations. If you came to your library and a staff member followed you around the whole time without an explanation, it would feel weird and even invasive. Now, you may be thinking, “Wait a minute, if people know we’re watching, they’re going to act differently!” You’re exactly right. But the reality is, people are going to know you’re observing them no matter what. Even if you think you could soundlessly skulk around the stacks, most people are going to sense something weird is going on. 

Remember–we don’t want to be creepy when we’re doing research! Watching people without asking their permission is 1) pretty creepy, 2) not good for building and maintaining trust with our users, and 3) violates one of our core library values of privacy. Ethically, we have to ask people if it’s ok to observe them while they’re in the library, even if it does change their behavior.

How do you ask permission to observe?

In this example, you could explain at storytime that you’re doing a research project to improve storytime. You are looking for some volunteers who spend time at the library outside of storytime to let you observe them interact with their child. If they are interested, you have a form they can sign, and the next time they’re hanging out at the library, you’d like them to come say “hi” at the desk in the children’s area. That way your staff know when they are in the library and the staff member observing them can introduce themselves too, which makes the whole thing less awkward.

As part of the informed consent process, remember that you need to address both that the participants can stop participating at any time, for any reason, and how you will protect their privacy. These elements are particularly important during an observation of caregivers and children. What identifying information do you absolutely need? The more anonymous the data can be, the better. Make sure you also establish a clear and easy way for the caregiver to end the observation while it is happening. 

In the next blog post in this series, we will explore the different approaches to observation. After you’ve gotten permission, how do you actually sit, watch, and collect meaningful data? Join us next time to find out!