Chapter 4 Analyses: Qualitative

You have completed your interviews and maintained your field notes, the next step is to do the transcriptions. If you can do the transcriptions as you go, all the better! You should include every utterance of the interviewee, including umms, laughter, etc. Incorporate your field notes and observations into the transcription (e.g., subject head down, begins to cry). The transcription is the most time consuming aspect, in my opinion.

You now have a ton of information from all of your interviews- what do you do with it? First, I suggest reading through all of your interviews several times. Think about them as you read them, what are the people saying? What are they NOT saying? Sometimes, what they avoid discussing is as interesting as what they do say. Try to put yourself in their place; you want to understand what their life is like.

Once you have a good feel for the data, the next step is to begin the analysis. There are a number of software programs available, such as Nvivo and Qualrus, but I am going to recommend a low tech method that I generally use. In this method a matrix or table is used in word. Picture a table as shown below for each interview question. Each person’s response is copied into the table. You then carefully read each response and develop the coding for each one. As you do this, questions to ask yourself: What are the key points that are mentioned? What is the underlying emotions associated with the response? You will want to go through this process several times. The first time, use the participants' words in the codes. Then go through again and think about larger concepts that are present in different people's responses- can you find an overarching code?

Sub#	What is your earliest memory?	coding
101	I remember my grandmother being sick, she had cancer. She passed away. I remember crying. It was summer time. I remember seeing her in her bed, not moving, with her eyes closed. I was with my mom.
102	I remember my grandmother holding me in her arms with a polka dot cap. I remember her holding me in her arms, rocking me, trying to get me to go to sleep. The look in her eyes of happiness and content, of having me there as she rocked me to sleep with her polka dot night cap. I remember vaguely maybe the TV being on, maybe the news. I remember an air conditioner in the wall going.
103	I was sitting at a table eating a sandwich and my mom was singing a song to me. It was a song I enjoyed her singing to me – I enjoyed the song and it made me feel comforted. I was focused on my mom’s voice and hearing the story in the song.

What coding would you give for each of the examples above? Try it and then see if yours are similar to mine (at the end of this post). It is always a good idea to have someone else independently code the responses and compare yours to theirs. For any that you differ on, try to reach a compromise. You might see if anyone in your dissertation is at similar place in his or her dissertation process and would be willing to trade coding checks. If not, check with your committee member- perhaps they know another student who might work.

Once the coding is complete, the next step is to look for similarities across your interviews based up on the coding; these similarities are called themes. Were there any themes in the examples presented above (of course your data will have more people, 3 people do not make up a theme)? I have listed two possible ones at the end of this post.

Once themes are determined, the next step is to begin writing up c. 4, data analysis. You will want to give your themes and provide examples of quotes illustrating them. Follow the qualitative checklist for how the chapter is to be arranged.

My coding and themes

101- grandmother, death, visual imagery, with mother, crying (negative emotion)

102- grandmother, visual imagery, auditory, empathy (recognizing other's emotions)

103- mom, eating, auditory imagery, feelings of comfort

Themes: female relative, visual and auditory imagery

Next time I will post an updated blog index. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Looking back from the finish line

Today, we have a guest post from new Walden graduate, Dr. Meredith Baker-Rush.

After five and a half years, I finally completed all my studies and the dissertation.  During my time at Walden, I followed the faculty suggestions, read all the references they highlighted, made reference books, and maintained constant communication with my faculty.  Every week, I had a schedule related to class work or dissertation activities and I balanced family and friends around the schoolwork demands.  My world centered on school and the big “D” (a.k.a. the dissertation). Now, I am done and my world has completely shifted!

So what does it look like after the dissertation is done?  Well, I must admit for the first few weeks, I was lost.  I went to my computer and did “little” things on my scheduled writing days, as that was all I knew.  I felt disconnected and at times almost uncomfortable with the “free time.”  After all, it was 5 ½ years since I even knew what “free time” was.  I did things I was supposed to do like complete the Proquest publication application, all pre-graduation requirements, and began the process for my loan repayment to help fill my “school scheduled time.”  I still search for new literature and read various articles but now it is for leisure and keeping up on the current literature.  I even began reading books for fun and even watch TV!  It has been a weird and surreal transition.

I think as I look back from the “finish line” the most obvious thing I see is how important and instrumental the chair, committee, and peers at Walden are to your success.  While you are the one writing and working out all the kinks, your chair, committee, URR, CAO, F&S, and so on are all taking a role in ensuring your success.  Everyone provides you input and helps shape you so that you will reach the level of quality you dream about.  They will bring words of wisdom, however in the moment, you may not be aware of the significance of their words or sometimes silence.  As I look back, I thought I appreciated it, but I was not truly aware of how impactful their words were.  I can still hear certain sayings or recall emails telling me to redo something (e.g., “run the stats again”).  In the moment I cried.  Now, I sit back and appreciate the tough love and direction in all aspects of the dissertation.  Earning a PhD is an honor, but is also a humbling achievement.  I could have never done it without my chair, committee member, peers, and all the people behind the scenes at Walden.

I was asked to write something about “looking back from the finish line” and I hope I addressed some of your questions.  I smile as I look forward knowing that I will continue to be successful because of the support I had along the way.  I am honored and humbled by all who taught me how to be a scholar. I am eternally thankful for all of their efforts.  So here’s to all of our success and the beginning of our journeys as scholars.   

Respectfully submitted,

Meredith Baker-Rush, PhD CCC-SLP/L

Doctor of Philosophy in Psychology, specialization in Health Psychology

Walden Class of 2016

Chapter 4 Analyses quant

You have collected all of your quantitative data, entered it into SPSS (or downloaded it), and double checked your data entry. Great… now what?? It can feel intimidating to view your first real data set and know you need to figure out what to do with it. I like to start with getting an overall feel for what is going on by calculating means and frequencies. Let’s take a moment and review what to use when.

If your variable is continuous, meaning there are no categories that you set up previous to the study you can calculate means and standard deviations. An example of a continuous variable would be where you asked people to enter their age today (e.g., 32, 56, etc.). Examples of categories would be gender: 1 = female, 2 = male or age: 1 = 20-30, 2 = 30-40, etc.). Hopefully, these terms are sounding familiar, if not go back to your stats book and review. A good reference for SPSS is

Pallant, J. (2013). SPSS survival guide, 4th ed. Berkshire England: McGraw Hill

For categorical variables (e.g., gender, education level) you can do a frequency table and get a feel for how your data looks. Make sure you don’t have any data entry errors- they will show up as a weird number, e.g., you have gender coded as 1 & 2 and a 21 shows up in the frequency table. Go back to the original data and double check it.

One issue you may need to consider is what to do with missing data, for example from people skipping questions. There are a number of ways to deal with this issue. Check with your methodologist to see what they prefer. They will probably ask you how many cells (individual data points) are missing, for which variables are they missing, and what is the largest number missing per individual, so be prepared for those questions.

Once missing data issues are resolved, my usual next step is run correlations between my variables just to get a feel for what is going on. I then do scatterplots for any that show up as significant. Again, I am just trying to get a feel for the data. My old undergrad stats professor used to say that you need to “take a bath in your data.” I like that idea, you need to understand the relationships before getting immersed in the formal data analysis.

You should have developed an analysis plan in your proposal, so now is the time to go to that. What happens if you just can’t figure things out? Contact your committee members and ask for help. As a committee member I sometimes have students send me their data set and I play with it a little, then I can talk them through issues.

Sometimes students decide to hire statistical consultants. Personally, I am not a fan of this. I prefer that the student figure out the stats with the help of his or her committee. The problem with a consultant is that you don’t really understand what they did and why. Even if they explain it, you really don’t have the level of understanding that you should. It misrepresents your knowledge level. People reading your dissertation will assume that you did the analyses and are capable of doing it (and perhaps teaching it!) again. If you must use a stats consultant, my advice is to rerun all of the analyses that they do, so you understand them too.

Another aspect to consider, is keeping track of all the analyses that you run and what they show. There are several ways to do this. You can simply save all of your SPSS outputs in a separate file on your computer (my least favorite, because then you have to reopen each to see it). Another way is to print out all of your data outputs and save them in a file or binder. My own favorite way to keep my data output is to copy it or rewrite it as I go into a word file. The advantage of this is that I am keeping everything together that is relevant (you will generate a lot of irrelevant info as you go). Do keep in mind that SPSS tables are not in APA format, so any that you want to use in your paper will need to rewritten.

I also find it helpful to think through what I am finding with each analysis (even though this technically goes in c. 5, I find it helpful to think about it at the analysis stage). Let’s work through an example, I find that my variable education level is correlated with my dependent variable, emotional intelligence (EI) total score. So my first question is which education level has a higher EI score? I could do a scatterplot or could just calculate the means for each education level (use analyze/ descriptive stats/explore). I then find that people with a graduate degree have a higher emotional intelligence score than people with a high school diploma in the sample. Is that what previous research has found? What if this is not the relationship other researchers have reported? I need to consider why my sample may be different. I check what else is correlated with education- perhaps I find that for this sample, gender is highly related to emotional intelligence. Do another scatterplot between gender and education. Whoa- all of the graduate level participants are female. Could that be the cause of the education and emotional intelligence score correlation?

Remember your methodologist can help with any issues you may find. There are also statistical consultants available for tricky issues, a faculty member can ask questions on your behalf. 

Next time we will have a guest author for Christmas. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Chapter 4: Evidence of Trustworthiness: Qualitative and Mixed Methods

An important element of qualitative studies is trustworthiness. In this section of c. 4, you will describe how you went about implementing the strategies and plans that you laid out in c. 3. Let's review the commonly used methods.

Credibility, which is comparable to internal validity. This is getting at the credibility of your data, common methods used are triangulation, prolonged contact, member checks, and saturation. You want to show that your data are as accurate as possible.

Transferability, which is comparable to external validity. This is getting at the generabilizability of your data to other groups. Common methods used are thick description and a variation in participant selection.

Dependability, comparable to reliability. You want to show the accuracy of your data methods, common methods are audit trails and triangulation. Triangulation is accomplished by asking the same research questions of different study participants and by collecting data from different sources and by using different methods to answer those research questions. Member checks occur when the researcher asks participants to review both the data collected by the interviewer and the researchers' interpretation of that interview data. Participants are generally appreciative of the member check process, and knowing that they will have a chance to verify their statements tends to cause study participants to fill in any gaps from earlier interviews.

Confirmability, comparable to objectivity. This is the degree to which the findings are the product of the focus of the study and not of the biases of the researcher One way to do this is through an audit trail. An adequate trail (or records) should be left to enable the auditor to determine if the conclusions, interpretations, and recommendations can be traced to their sources and if they are supported by the inquiry.

Next time we will look at Chapter 4 quantitative analysis. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Chapter 4: Data Analyses: Qualitative and Mixed Methods

Today we will examine the section of c.4 in qualitative studies and mixed methods: Data Analyses. This section asks that you clearly describe how you went about analyzing your qualitative data. To begin, you will outline the overall process that you used to move inductively from coded units to larger representations including categories and themes. If you followed a specific methodologist's methods (e.g., Creswell) cite him or her.

Next, you move into the specifics of your data by describing the specific codes, categories, and themes that emerged from the data using quotations as needed to emphasize their importance. Keep in mind that you are walking the reader through the process of your data analysis, so share specifics- how did you make decisions as to what were themes?

Finally, in most studies you will have a person or two who discussed experiences or ideas that were outside the normal experience of the others in your sample, these are called discrepant cases. Describe how these discrepant cases differed from the rest of the sample and how they were factored into the analysis. You should also consider whether there are obvious reasons for their differences, are these individuals older, younger, or in some other way different from others in the sample?

Next time we will talk about Chapter 4: Trustworthines. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu 

c.4: Data Analyses: Quantitative

Today we will take a look at the results section for quantitative studies. Start out by reporting descriptive statistics "that appropriately characterize the sample." What does this mean? Look at frequencies for your demographics, such as gender, marital status, etc. For continuous variables (not in categories) you will need to compute the means and standard deviations or standard errors (check with your committee as to which they prefer). An example of such a variable is age, the convention is to give these stats like this (M = 43 yr., SD = 5.2). You will also discuss any total scores or subscores that you may have calculated and their distribution.

The next step is to discuss and evaluate statistical assumptions as appropriate to the study. All statistical tests have specific assumptions that must be considered (see Pallant, 2013, for an in-depth discussion of them).  Let's take as an example, the assumptions for parametric tests (e.g., t-tests, analysis of variance): using an interval or ratio scale of measurement, random sampling, independence of observations (no measurement is influenced by another), a normal distribution, and homogeneity of variance (samples have similar variances). There are techniques to check these assumptions, and you would discuss in this section which ones you used and the results.

Next, you report your findings, organized by research questions and/or hypotheses. Include the exact statistics and associated probability values (some examples: t(32)=3.1, p < .01; r(N=45)= .16, p > .05). A reminder- if the probability is < .05 (less than), it is considered significant; if it is > than.05 (greater than) it is not significant. You should include confidence intervals around the statistics, as appropriate (check with your committee). Include effect sizes, as appropriate (e.g., R²,^; check with your committee as to what they prefer).

If you had multiple conditions, you may need to do post-hoc tests. Report the type and results of post-hoc analyses.  You may have additional statistical tests of hypotheses that emerged from the analysis of main hypotheses and you will need to report those. Finally, you may wish to clarify your results with tables and figures, include those as specified in APA manual. There is very specific formatting for these- so check it out in the manual.

Next time we will talk about c.4: Data Analyses: Qualitative. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu

Pallant, J. (2013). The SPSS Survival Manual, 5^th edition. Open University Press.

c.4: Demographics and Data Collection

The mixed methods and qualitative checklists have section called Demographics, in which you should discuss the relevant demographic characteristics of your participants. Typical items include gender, race, and age, as well as any characteristics specific to your study. For example, if you interviewed homeless teen mothers, it would be important to know how long they have been on their own and the age of their children.

For all methods, the next section is Data Collection. You need to describe when the study was done (for example, months and year). Describe how you recruited your participants, and how many participated in all phases of the study. If you had to change any of your data collection procedures from what was listed in c. 3, indicate how and why it was changed (and that you went through IRB to do so).

For Qualitative and Mixed Methods Studies. Describe the location of your study, how often you met with participants and the length of time both for individual interviews/surveys and for the total study. Next, describe how you recorded your interviews and how they were transcribed. If you encountered any unusual circumstances during your data collection describe it and how it affected your data collection (e.g., equipment failure, a participant died between interviews, etc.).

For Quantitative Studies. Describe your demographics as discussed above. Describe how representative your sample is to the population of interest or how proportional it is to the larger population if non-probability sampling is used (external validity). Provide results of basic univariate analyses that justify inclusion of covariates in your model, if applicable.  

For this section, keep in mind that your reader should have a good picture of how you did your study, and would be able to replicate it based upon your description.

Next time we will talk about c.4: Data Analyses: Quantitative. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu