Qualitative Data « Evaluation is an Everyday Activity

I had a topic all ready to write about then I got sick. I’m sitting here typing this trying to remember what that topic was, to no avail. That topic went the way of much of my recent memory; another day, perhaps.

I do remember the conversation with my daughter about correlation. She had a correlation of .3 something with a probability of 0.011 and didn’t understand what that meant. We had a long discussion of causation and attribution and correlation.

We had another long conversation about practical v. statistical significance, something her statistics professor isn’t teaching. She isn’t learning about data management in her statistics class either. Having dealt with both qualitative and quantitative data for a long time, I have come to realize that data management needs to be understood long before you memorize the formulas for the various statistical tests you wish to perform. What if the flood happens????

So today I’m telling you about data management as I understand it, because the did actually happen and, fortunately, I didn’t loose my data. I had a data dictionary.

Data dictionary. The first step in data management is a data dictionary. There are other names for this, which escape me right now…know that a hard copy of how and what you have coded is critical. Yes, make a back up copy on your hard drive…have a hard copy because the flood might happen. (It is raining right now and it is Oregon in November.)

Take a hard copy of your survey, evaluation form, qualitative data coding sheet and mark on it what every code notation you used means. I’d show you an example of what I do, only they are at the office and I am home sick without my files. So, I’ll show you a clip art instead… data management . No, I don’t use cards any more for my data (I did once…most of you won’t remember that time…), I do make a hard copy with clear notations. I find my self doing that with other things to make sure I code the response the same way. That is what a data dictionary allows you to do–check yourself.

Then I run a frequencies and percentages analysis. I use SPSS (because that is what I learned first). I look for outliers, variables that are miscoded, and system generated missing data that isn’t missing. I look for any anomaly in the data, any humon error (i. e. my error). Then I fix it. Then I run my analyses.

There are probably more steps than I’ve covered today. These are the first steps that absolutely must be done BEFORE you do any analyses. Then you have a good chance of keeping your data safe.

There has been quite a bit written about data visualization, a topic important to evaluators who want their findings used. Michael Patton talks about evaluation use in his 4th edition of utilization-focused evaluation. He doesn’t however list data visualization in the index; so he may talk about it somewhere–it isn’t obvious.

The current issue of New Directions for Evaluation is devoted to data visualization and it is the first part (implying, I hope, for at least a part 2). Tarek Azzam and Stephanie Evergreen are the guest editors. This volume (the first on this topic in 15 years) sets the stage (chapter 1) and talks about quantitative data visualization and quantitative data visualization. The last chapter talks about the tools that are available to the evaluator and there are many and they are various. I cannot do them justice in this space; read about them in the NDE volume. (If you are an AEA member, the volume is available on line.)

freshspectrum, a blog by Chris Lysy, talks about INTERACTIVE data visualization with illustrations.

Stephanie Evergreen, the co-guest editor of the above NDE, also blogs and in her October 2 post, talks about “Design for Federal Proposals (aka Design in a Black & White Environment)”. More on data visualization.

The data visualizer that made the largest impact on me was Hans Rosling in his TED talks. Certainly the software he uses makes the images engaging. If he didn’t understand his data the way he does, he wouldn’t be able to do what he does.

Data visualization is everywhere. There will be multiple sessions at the AEA conference next week. If you can, check them out–get there early as they will fill quickly.

When I did my dissertation, there were several soon-to-be-colleagues who were irate that I did a quantitative study on qualitative data. (I was looking at cognitive bias, actually.) I needed to reduce my qualitative data so that I could represent it quantitatively. This approach to coding is called magnitude coding. Magnitude coding is just one of the 25 first cycle coding methods that Johnny Saldaña (2013) talks about in his book, The coding manual for qualitative researchers (see pages 72-77). (I know you cannot read the cover title–this is just to give you a visual; if you want to order it, which I recommend, go to Sage Publishers, Inc.) Miles and Huberman (1994) also address this topic.

So what is magnitude coding? It is a form of coding that “consists of and adds a supplemental alphanumeric or symbolic code or sub-code to an existing coded datum…to indicate its intensity, frequency, direction, presence , or evaluative content” (Saldaña, 2013, p. 72-73). It could also indicate the absence of the characteristic of interest. Magnitude codes can be qualitative or quantitative and/or nominal. These codes enhance the description of your data.

Saldaña provides multiple examples that cover many different approaches. Magnitude codes can be words or abbreviations that suggest intensity or frequency or codes can be numbers which do the same thing. These codes can suggest direction (i.e., positive or negative, using arrows). They can also use symbols like a plus (+) or a minus (–), or other symbols indicating presence or absence of a characteristic. One important factor for evaluators to consider is that magnitude coding also suggests evaluative content, that is , did the content demonstrate merit, worth, value? (Saldaña also talks about evaluation coding; see page 119.)

Saldaña gives an example of analysis showing a summary table. Computer assisted qualitative data analysis software (CAQDAS) and Microsoft Excel can also provide summaries. He notes “that is very difficult to sidestep quantitative representation and suggestions of magnitude in any qualitative research” (Saldaña, 2013, p. 77). We use quantitative phrases all the time–most, often, extremely, frequently, seldom, few, etc. These words tend “to enhance the ‘approximate accuracy’ and texture of the prose” (Saldaña, 2013, p. 77).

Making your qualitative data quantitative is only one approach to coding, an approach that is sometimes very necessary.

Before you know it, Evaluation ’13 will be here and thousands of evaluators will converge on Washington DC, the venue for this year’s AEA annual meeting.

The Local Arrangements Working Group (LAWG) is blogging this week in AEA365. (You might want to check out all the posts this week.) There are A LOT of links in these posts (including related past posts) that are worth checking. For those who have not been to AEA before or for those who have recently embraced evaluation, reading their posts are a wealth of information.

What I want to focus on today is the role of the local arrangements working group. The Washington Evaluators group is working in tandem with AEA to organize the local part of the conference. These folks live locally and know the area. Often they include graduate students as well as seasoned evaluators. (David Bernstein and Valerie Caracelli are the co-chairs of this year’s LAWG .) They have a wealth of information in their committee. (Scroll down to the “Please Check Back for Periodic Updates” to see the large committee–it really does take a village!) They only serve for the current year and are truly local. Next year in Denver, there will be a whole new LAWG.

Some things that the committee do include identifying (and evaluating) local restaurants, things to do in DC, and getting around DC. Although these links provide valuable information, there are those of us (me… ) who are still technopeasants and do not travel with a smart phone, tablet, computer, or other electronic connectivity and would like hard copy of pertinent information. (I want to pay attention to real people in real time–I acknowledge that I am probably an artifact, certainly a technology immigrant–see previous blog about civility.)

Restaurants change quicker than I can keep track–although I’m sure that there are still some which existed when I was in DC last for business. I’m sure that today, most restaurants provide vegetarian, vegan, gluten-free options (it is, after all, the current trend). That is very different from when I was there for the last AEA in 2002. I did a quick search for vegetarian restaurants using the search options available at the LAWG/Washington Evaluators’ site–there were several…I also went to look at reviews…I wonder about the singular bad (very) review…was it just an off night or a true reflection?

There are so many things to do in DC…please take a day–the newer monuments are amazing–see them.

Getting around DC…use the Metro–it gets you to most places; it is inexpensive; it is SAFE! It has been expanded to reach beyond the DC boundaries. If nothing else, ride the Metro–you will be able to see a lot of DC. You can get from Reagan-Washington NationalAirport to the conference venue (yes, you will have to walk 4 blocks and there may be some problem with a receipt–put the fare plus $0.05 on the Metro card and turn in the card).

The LAWG has done a wonderful job providing information to evaluators…check out their site. See you in DC.

Wow! 25 First Cycle and 6 Second Cycle methods for coding qualitative data.

Who would have thought that there are so many methods of coding qualitative data. I’ve been coding qualitative data for a long time and only now am I aware that what I was doing was, according to Miles and Huberman (1994), my go-to book for coding, miles and huberman qualitative data is called “Descriptive Coding” although Johnny Saldana calls it “Attribute Coding”. (This is discussed at length in his volume The Coding Manual for Qualitative Researchers.) I just thought I was coding; I was, just not as systematically as suggested by Saldana.

Saldana talks about First Cycle coding methods, Second Cycle coding methods and a hybrid method that lies between them. He lists 25 First Cycle coding methods and spends over 120 pages discussing first cycle coding.

I’m quoting now. He says that “First Cycle methods are those processes that happen during the initial coding of data and are divided into seven subcategories: Grammatical, Elemental, Affective, Literary and Language, Exploratory, Procedural and a final profile entitled Themeing the Data. Second Cycle methods are a bit more challenging because they require such analytic skills as classifying, prioritizing, integrating, synthesizing, abstracting, conceptualizing, and theory building.”

He also insists that coding qualitative data is a iterative process; that data are coded and recoded. Not just a one pass through the data.

Somewhere I missed the boat. What occurs to me is that since I learned about coding qualitative data by hand because there were few CAQDAS (Computer Assisted Qualitative Data Analysis Software) available (something Saldana advocates for nascent qualitative researchers) is that the field has developed, refined, expanded, and become detailed. Much work has been done that went unobserved by me.

He also talks about the fact that the study’s qualitative data may need more than one coding method–Yikes! I thought there was only one. Boy was I mistaken. Reading the Coding Manual is enlightening (a good example of life long learning). All this will come in handy when I collect the qualitative data for the evaluation I’m now planning. Another take away point that is stressed in the coding manual and in the third edition of the Miles & Huberman book (with the co-author of Johnny Saldana) is to start coding/reading the data as soon as it is collected. Reading the data when you collect it allows you to remember what you observed/heard, allows/encourages analytic memo writing (more on that in a separate post), and allows you to start building your coding scheme.

If you do a lot of qualitative data collection, you need these two books on your shelf.

I’m about to start a large scale project, one that will be primarily qualitative (it may end up being a mixed methods study; time will tell); I’m in the planning stages with the PI now. I’ve done qualitative studies before–how could I not with all the time I’ve been an evaluator? My go to book for qualitative data analysis has always been Miles and Huberman (although my volume is black). This is their second edition published in 1994. I loved that book for a variety of reasons: 1) it provided me with a road map to process qualitative data; 2) it offered the reader an appendix for choosing a qualitative software program (now out of date); and 3) it was systematic and detailed in its description of display. I was very saddened to learn that both the authors had died and there would not be a third edition. Imaging my delight when I got the Sage flier of a third edition! Of course I ordered it. I also discovered that Saldana (the new third author on the third edition) has written another book on qualitative data that he sites a lot in this third edition (Coding manual for qualitative researchers ) and I ordered that volume as well.

Saldana, in the third edition, talks a lot about data display, one of the three factors that qualitative researchers must keep in mind. The other two are data condensation and conclusion drawing/verification. In their review, Sage Publications says, “The Third Edition’s presentation of the fundamentals of research design and data management is followed by five distinct methods of analysis: exploring, describing, ordering, explaining, and predicting.” These five chapters are the heart of the book (in my thinking); that is not to say that the rest of the book doesn’t have gems as well–it does. The chapter on “Writing About Qualitative Research” and the appendix are two. The appendix (this time) is an “An Annotated Bibliography of Qualitative Research Resources”, which lists at least 32 different classifications of references that would be helpful to all manner of qualitative researchers. Because it is annotated, the bibliography provides a one sentence summary of the substance of the book. A find, to be sure. Check out the third edition.

I will be attending a professional development session with Mr. Saldana next week. It will be a treat to meet him and hear what he has to say about qualitative data. I’m taking the two books with me…I’ll write more on this topic when I return. (I won’t be posting next week).

Miscellaneous thought 1.

Yesterday, I had a conversation with a long time friend of mine. When we stopped and calculated (which we don’t do very often), we realized that we have know each other since 1981. We met at the first AEA (only it wasn’t AEA then) conference in Austin, TX. I was a graduate student; my friend was a practicing professional/academic. Although we were initially talking about other things evaluation; I asked my friend to look at an evaluation form I was developing. I truly believe that having other eyes (a pilot if you will) view the document helps. It certainly did in this case. I feel really good about the form. In the course of the conversation, my friend advocated strongly for a odd numbered scales. My friend had good reasons, specifically

1) It tends to force more comparisons on the respondents; and

2) if you haven’t given me a neutral point I tend to mess up the scale on purpose because you are limiting my ability to tell you what I am thinking.

I, of course, had an opposing view (rule number 8–question authority). I said, ” My personal preference is an even number scale to avoid a mid-point. This is important because I want to know if the framework (of the program in question) I provided worked well with the group and a mid-point would provide the respondent with a neutral point of view, not a working or not working opinion. An even number (in my case four points) can be divided into working and not working halves. When I’m offered a middle point, I tend to circle that because folks really don’t want to know what I’m thinking. By giving me an opt out/neutral/neither for or against option they are not asking my opinion or view point.”

Recently, I came across an aea365 post on just this topic. Although this specific post was talking about Likert scales, it applies to all scaling that uses a range of numbers (as my friend pointed out). The authors sum up their views with this comment, “There isn’t a simple rule regarding when to use odd or even, ultimately that decision should be informed by (a) your survey topic, (b) what you know about your respondents, (c) how you plan to administer the survey, and (d) your purpose. Take time to consider these four elements coupled with the advantages and disadvantages of odd/even, and you will likely reach a decision that works best for you.” (Certainly knowing my friend like I do, I would be suspicious of responses that my friend submitted.) Although they list advantages and disadvantages for odd and even responses, I think there are other advantages and disadvantages that they did not mentioned yet are summed up in their concluding sentence.

Miscellaneous thought 2.

I’m reading the new edition of Qualitative Data Analysis (QDA). This has always been my go to book for QDA and I was very sad when I learned that both of the original authors had died. The new author, Johnny Saldana (who is also the author of The Coding Manual for Qualitative Researchers), talks (in the third person plural, active voice) about being a pragmatic realist. That is an interesting concept. They (because the new author includes the previous authors in his statement) say “that social phenomena exist not only in the mind but also in the world–and that some reasonably stable relationships can be found among the idiosyncratic messiness of life.” Although I had never used those exact words before, I agree. It is nice to know the label that applies to my world view. Life is full of idiosyncratic messiness; probably why I think systems thinking is so important. I’m reading this volume because I’ve been asked to write the review of one of my favorite books. We will see if I can get through it between now and July 1 when the draft of the review is due. Probably aught to pair it with Saldana’s other book; won’t happen between now and July 1.

I have a few thoughts about causation, which I will get to in a bit…first, though, I want to give my answers to the post last week.

I had listed the following and wondered if you thought they were a design, a method, or an approach. (I had also asked which of the 5Cs was being addressed–clarity or consistency.) Here is what I think about the other question.

Case study is a method used when gathering qualitative data, that is, words as opposed to numbers. Bob Stake, Robert Brinkerhoff, Robert Yin, and others have written extensively on this method.

Pretest-post test Control Group is (according to Campbell and Stanley, 1963) an example of a true experimental design if a control group is used (pg. 8 and 13). NOTE: if only one group is used (according to Campbell and Stanley, 1963), pretest-post test is considered a pre-experimental design (pg. 7 and 8); still it is a design.

Ethnography is a method used when gathering qualitative data often used in evaluation by those with training in anthropology. David Fetterman is one such person who has written on this topic.

Interpretive is an adjective use to describe the approach one uses in an inquiry (whether that inquiry is as an evaluator or a researcher) and can be traced back to the sociologists Max Weber and Wilhem Dilthey in the later part of the 19th century.

Naturalistic is an adjective use to describe an approach with a diversity of constructions and is a function of “…what the investigator does…” (Lincoln and Guba, 1985, pg.8).

Random Control Trials (RCT) is the “gold standard” of clinical trials, now being touted as the be all and end all of experimental design; its proponents advocate the use of RCT in all inquiry as it provides the investigator with evidence that X (not Y) caused Z.

Quasi-Experimental is a term used by Campbell and Stanley(1963) to denote a design where random assignment cannot be made for ethical or practical reasons be accomplished; this is often contrasted with random selection for survey purposes.

Qualitative is an adjective to describe an approach (as in qualitative inquiry), a type of data (as in qualitative data) or
methods (as in qualitative methods). I think of qualitative as an approach which includes many methods.

Focus Group is a method of gathering qualitative data through the use of specific, structured interviews in the form of questions; it is also an adjective for defining the type of interviews or the type of study being conducted (Krueger & Casey, 2009, pg. 2)

Needs Assessment is method for determining priorities for the allocation of resources and actions to reduce the gap between the existing and the desired.

I’m sure there are other answers to the terms listed above; these are mine. I’ve gotten one response (from Simon Hearn at BetterEvaluation). If I get others, I’ll aggregate them and share them with you. (Simon can check his answers against this post.

Now causation, and I pose another question: If evaluation (remember the root word here is value) is determining if a program (intervention, policy, product, etc. ) made a difference, and determined the merit or worth (i.e., value) of that program (intervention, policy, product, etc.), how certain are you that your program (intervention, policy, program, etc.) caused the outcome? Chris Lysy and Jane Davidson have developed several cartoons that address this topic. They are worth the time to read them.

I was reminded recently about the 1992 AEA meeting in Seattle, WA. That seems like so long ago. The hot topic of that meeting was whether qualitative data or quantitative data were best. At the time I was a nascent evaluator having been in the field less that 10 years and absorbed debates like this as a dry sponge does water. It was interesting; stimulating; exciting. It felt cutting edge.

Now 20+ years later, I wonder what all the hype was about. Now, there can be rigor in what ever data are collected, regardless of type (numbers or words); language has been developed to look at that rigor. (Rigor can also escape the investigator regardless of the data collected; another post, another day.) Words are important for telling stories (and there is a wealth of information on how story can be rigorous) and numbers are important for counting (and numbers have a long history of use–Thanks Don Campbell). Using both (that is, mixed methods) makes really good sense when conducting an evaluation in community environments, work that I’ve done for most of my career (community-based work).

I was reading another evaluation blog (ACET) and found the following bit of information that I thought I’d share as it is relevant to looking at data. This particular post (July, 2012) was a reflection of the author. (I quote from that blog).

§ Utilizing both quantitative and qualitative data. Many of ACET’s evaluations utilize both quantitative (e.g., numerical survey items) and qualitative (e.g., open-ended survey items or interviews) data to measure outcomes. Using both types of data helps triangulate evaluation findings. I learned that when close-ended survey findings are intertwined with open-ended responses, a clearer picture of program effectiveness occurs. Using both types of data also helps to further explain the findings. For example, if 80% of group A “Strongly agreed” to question 1, their open-ended responses to question 2 may explain why they “Strongly agreed” to question 1.

Triangulation was a new (to me at least) concept in 1981 when a whole chapter was devoted to the topic in a volume dedicated to Donald Campbell, titled Scientific Inquiry and the Social Sciences. I have no doubt that this concept was not new; Crano, the author of this chapter titled “Triangulation and Cross-Cultural Research”, has three and one half pages of references listed that support the premise put forth in the chapter. Mainly, that using data from multiple different sources may increase the understanding of the phenomena under investigation. That is what triangulation is all about–looking at a question from multiple points of view; bringing together the words and the numbers and then offering a defensible explanation.

I’m afraid that many beginning evaluators forget that words can support numbers and numbers can support words.

Ever wonder where the 0.05 probability level number was derived? Ever wonder if that is the best number? How many of you were taught in your introduction to statistics course that 0.05 is the probability level necessary for rejecting the null hypothesis of no difference? This confidence may be spurious. As Paul Bakker indicates in the AEA 365 blog post for March 28, “Before you analyze your data, discuss with your clients and the relevant decision makers the level of confidence they need to make a decision.” Do they really need to be 95% confident? Or would 90% confidence be sufficient? What about 75% or even 55%?

Think about it for a minute? If you were a brain surgeon, you wouldn’t want anything less than 99.99% confidence; if you were looking at level of risk for a stock market investment, 55% would probably make you a lot of money. The academic community has held to and used the probability level of 0.05 for years (the computation of the p value dating back to 1770). (Quoting Wikipedia, ” In the 1770s Laplace considered the statistics of almost half a million births. The statistics showed an excess of boys compared to girls. He concluded by calculation of a p-value that the excess was a real, but unexplained, effect.”) Fisher first proposed the 0.05 level in 1025 and established a one in 20 limit for statistical significance when considering a two tailed test. Sometimes the academic community makes the probability level even more restrictive by using 0.01 or 0.001 to demonstrate that the findings are significant. Scientific journals expect 95% confidence or a probability level of at least 0.05.

Although I have held to these levels, especially when I publish a manuscript, I have often wondered if this level makes sense. If I am only curious about a difference, do I need 0.05? Oor could I use 0.10 or 0.15 or even 0.20? I have often asked students if they are conducting confirmatory or exploratory research? I think confirmatory research expects a more stringent probability level. I think exploratory research requires a less stringent probability level. The 0.05 seems so arbitrary.

Then there is the grounded theory approach which doesn’t use a probability level. It generates theory from categories which are generated from concepts which are identified from data, usually qualitative in nature. It uses language like fit, relevance, workability, and modifiability. It does not report statistically significant probabilities as it doesn’t use inferential statistics. Instead, it uses a series of probability statements about the relationships between concepts.

So what do we do? What do you do? Let me know.

Evaluation is an Everyday Activity

Program Evaluation Discussions

Tag Archives: Qualitative Data

Quandry

Data visualization

Magnitude coding

Evaluation ’13

Qualitative data analysis

Miscellaneous Thoughts

Causation?

Triangulation (or another way to look at data)

Minimum rule?

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