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"Making Sense of It All:" A Second Look at Qualitative Data Compilation & Analysis

Once again, dear qualitative superstar scholars: we are "back to the future!" In our Module # 2 & the related supplementary materials, we began to take a look at some ways to summarize and report qualitative data. Let us now revisit the issue & continue to 'creatively brainstorm' regarding how to summarize and display the qualitative findings and results! This excellent material comes to us courtesy of superstar evaluation guru, Michael Quinn Patton, and his excellent handbook entitled, How to Use Qualitative Methods in Evaluation (1987, Sage Publications, Inc.). As others have found, Patton's perspective on qualitative research is so immensely valuable that it actually transcends evaluation studies per se. Simply put: it's a goldmine for all sorts of qualitative research needs!

We'll start off by taking a look at some 'procedural, housekeeping issues:' namely, how to get started and organize the masses of 'rich, thick' qualitative data in order to be ready to condense these data into a concise analysis report. Next, we'll share some clues on what Miles and Huberman and others have referred to as "making meaning:" that is, applying creative qualitative-type labels to the summarized data that we collect. Then, in a hypothesis-testing sense, we will discuss the issue of "negative cases," or "disconfirming evidence:" how to really subject our findings and conclusions to the test of 'competing evidence,' to see if our results manage to hold up.

I would urge you, at this point, to review and have handy the EDR 725 Qualitative Research Module # 2 materials. These 'advanced issues' are intended to build upon the two basic frameworks or paths for deciding how to summarize and report qualitative data. To remind you of these methods, they are:

1. The summary narrative method (condensed write-up of key findings & results, along with judicious 'sprinkling-in' of key illustrative quotes and similar 'raw qualitative data'); and

2. The matrix/table shell method (broadly speaking, any sorts of 'vivid visual displays' of the qualitative findings and results - not just the tables themselves, but any innovative type of graphic, chart, etc. to 'tell the story to the reader at a glance').

Ah, the process of sequentially closing in on your desired target outcome - of condensing and making meaning out of all of your qualitative data .. ! We are up to this challenge...!

I. "Getting Your House in Order:" Organizing Your Qualitative Data for Analysis

Patton & other qualitative researchers once again warn us to expect an avalanche here! Qualitative data, by their very nature, tend to be voluminous. I have often scripted focus group interview sessions on my PowerBook notebook computer. It is typical for me to come out of a single one-hour session with 15 pages or so of word-processed notes!

Patton suggests the following plan of action to help get organized & get ready for the analysis:

  1. Ensure that it is 'all there' and that you have made backup copies. This is certainly important with any kind of database. But it may be especially critical in the case of qualitative data, for if you are planning to do the coding, annotation and summary by hand, you will probably want several 'working copies' of the interview transcripts, written open-ended survey responses and such. Even if you are planning to use one of the newly emerging computerized packages for your qualitative data analysis, the same rule of thumb applies as to any kind of computer documents. Back up your files faithfully!

    In the case of hand-coding, as briefly mentioned in Module #2, different qualitative researchers have different preferences in this regard. You might choose any or all of the following methods to "reread, distill, and summarize:"

    1. Marking up working copies of interview transcripts with different colored translucent markers, as well as your own comments in the margins, to reflect the general categories into which you are placing the responses;
    2. Making notes of the comments that fall under these categories on different-colored stacks of index cards - with each 'rubber-banded' colored stack representing a different concept or category;
    3. The "butcher-paper-on-the-basement-wall" technique that Denise Ehlerman used, as described in Lesson Packet #3, with each sheet representing a different concept or category;
    4. Literally scissoring up a working copy of the original transcripts, quotes, etc., and then pile-sorting the pieces and rubber-banding them to reflect the 'summary and sorting' of the raw qualitative data under general 'umbrella-type' concepts or categories.

    Again, there is no single procedure that can be said to be superior to any other! The same, by the way, is true when comparing the relatively recent proliferation of computerized packages for summarizing and sorting qualitative data.

    But - this may itself be a plus! Just as we are all different & unique as individuals, it's kind of neat that qualitative data compilation and reporting allows us to take our own preferences and working styles into account this way!!!

    SIDE NOTE at this point: As with all computer hardware and software, changes happen virtually overnight. Just keeping up with 'what's out there right now' can be a challenge! But there are two outstanding sources where you can at least, in my opinion, get a quick 'grounding' in the various types of qualitative software and how the packages compare:

    1. Id like to share a valuable related resource with you entitled, A Software Sourcebook: Computer Programs for Qualitative Data Analysis, by Eben A. Weitzman and Matthew B. Miles, 1995, Sage Publications, Inc.
    2. If you don't need or want quite this much detail, i.e., an entire book, there is an excellent comparative chart and brief discussion of the types (i.e., Macintosh, Windows, MS-DOS based) and relative 'tradeoffs' of the more popular and established qualitative software packages. This readable material appears in an appendix entitled, "Choosing Computer Programs for Qualitative Data Analysis," in Qualitative Data Analysis: An Expanded Sourcebook, by Matthew B. Miles and A. Michael Huberman, 2nd ed., 1994, Sage Publications, Inc.

  2. Next, Patton and others (i.e., most notably, case study authority Robert K. Yin) recommends that you also go on to establish and write a case record. This is an artifact that will contain, literally, the essential elements or 'traces' of the steps of your study. You might picture it, for instance, as a series of file folders, organized by, say, type of document; time period; topic area; etc. (with the same to hold true if you are computerizing it - i.e., for the Macintosh, you can literally make 'electronic folders' containing this information!). It might have things in it such as your letters of permission to enter a site & do your study; training manuals; pre- and post-pilot test copies of instrumentation such as interview protocols; memos and meetings of minutes pertinent to your study; and working copies and final drafts of each successive stage of your summarization, down to the eventual final qualitative data analysis report.

    There are at least two distinct benefits of establishing such a case record:

    1. You will greatly expedite your own compilation and organization of your data analysis by having such key information organized and handy - you just never know when you might need to pull up and re-examine original source documents, early drafts, etc; and

    2. In terms of providing a documented, step-by-step archive of what you did, what source documents you used, and how you gathered and summarized the data, you are greatly enhancing reliability in a 'replicability' sense. Again, as we stated at the outset of the course, the concept of 'replication' is interpreted a bit more loosely in the case of situation- and context-specific qualitative research, than it is for the more tightly controlled, quantifiable, experimental-type studies. Nonetheless, it is important, in a 'scholarly community, research life cycle' sense, for you to have as complete and verifiable a record of what and how (with, of course, anonymity of respondents protected), in case another researcher who is intending to replicate/extend your study should request to see your 'road map.'

Now that your raw data are compiled and organized, the real challenge is at hand: "making meaning," concisely yet completely summarizing, in order to (as with all research!!! that we've been saying since our EDR 610, Intro to Research, days) "answer our research questions!" We've talked about ways to do this in Module #2 and also in our related discussion above regarding "annotating, placing data under umbrella concepts or labels." This is probably the most generally established way to 'condense and make meaning.' In that regard, I want to revisit the issue of 'label-making' and introduce two general families of such labels that Patton explicitly and colorfully defines for us!

II. "Telling It Like It Is:" The Concepts of "Indigenous Typologies" vs. "Analyst-Constructed Typologies"

In your quest to identify the over-arching "umbrella-type" concepts, labels, or categories under which the raw qualitative data seem to fit, there are two general ways of going about identifying this "framework of labels!" I like Patton's two-way division because it really seems to make practical sense! Here is my own 'take' on how they are interpreted:

  1. Indigenous typologies - are categories that come directly out of the jargon or everyday popular talk of the field in which you are researching! This is akin to the famous 12-step "talking their talk," with "they" being the subjects in the field. That is why the framework, or typology, is "indigenous:" it already exists for you.

    For this one, then, your job is to learn the underlying jargon of that field - whether it comes from an existing theoretical/conceptual model, or whether instead from popular practice, as labelled and identified by the subjects (i.e., target population and sample) themselves. You then use the existing terms and labels to try and compile your qualitative data. Furthermore, you make the judgment if the 'fit' is good between your data and that existing framework.

    As just one example, we have a number of EDL doctoral candidates researching various applications and extensions of the "12 Skill Dimensions of Leadership." They would then attempt to sort their data according to the 12 dimensions and using the particular terminology - in whole or in part - of each one.

    As another example, suppose that you are replicating and extending a study along the lines of the Packard/Dereshiwsky model of the "Factors of Organizational Effectiveness." This one consists of a rocket-type model, in honor of Christa McAuliffe. It has two 'layers:' support and focus factors, both of which, we feel, are essential to consider in terms of 'reaching one's ultimate goal(s),' the tip or pinnacle of the rocket. For a school, this would certainly include "academic achievement". We identify and label two specific families of particular support and focus factors, both of which, we feel, are sometimes overlooked but can actually 'make or break' our chances of reaching those ultimate 'pinnacle' goals.

    Again, in each of the above scenarios, as well as with indigenous typologies generally, your goal in starting out is to in some way "road test" an existing model or a way that subjects 'naturally talk about' the phenomenon you are studying. Thus, you make a first pass in summarizing your qualitative data at "talking their talk."

    To be more specific, you use the parts of the model, labels, terminology, etc., that they use, and you see if indeed, your qualitative raw data does seem to 'fit' under those pre-existing labels.

    *** If it does not, this is important information too! Namely, it is akin to 'rejecting a hypothesis:' you appear to be finding that 'the model needs modifying!' Maybe some categories don't quite fit anymore. Or you appear to be discovering other, emergent, new labels or categories under which a good proportion of your qualitative raw data appears to fit lots better than with the pre-imposed model or framework of terminology.

    *** This is why, even with a pre-existing framework of concepts or terms - an indigenous typology - it is still vitally important to stay open to new ways of compiling your data! To new categories, or even entire new models or frameworks, in a classic 'grounded theory,' emergent sense! Be sure that you use the existing indigenous typology as a guide or starting point - and then you see if it appears to 'fit' with your data - or not!!!! Either way - you do have a set of findings!!!

  2. Analyst-constructed typologies - ah, what a golden opportunity for the creative "True Colors Orange," free spirits and innovative thinkers among you!!! Yes, you: the one who enjoyed writing poetry and creative short stories! Who's got the makings of "The Great American Novel" in his/her 'mental ROM!' Here's your big chance!!! For this one, you, the analyst, try your hand at making up 'vivid, creative, visual labels' under which to compile and report your actual raw qualitative data!

    Whether you do this because you find the existing indigenous typology lacking, or whether there simply isn't a generally accepted framework - the end result could well be a major, memorable contribution to how we think about this phenomenon - if you get 'colorfully creative' in how you do it and if it appears to 'fit' your data!!!

    Michael Quinn Patton provides a neat example from a study originally done in 1977 by Robert L. Wolf and Barbara Tymitz. They were doing visual observations of visitors to a museum exhibit entitled, "Ice Age Mammals and Emergence of Man." From documenting and comparing their observational field notes of museum visitors' choices, body language and behavior, Wolf and Tymitz came up with the following creative labels to summarize and characterize the subjects' behaviors:

    The Commuter

    This is the person who merely uses the hall as a vehicle to get from the entry point to the exit point...

    The Nomad

    This is a casual visitor - a person who is wandering through the hall, apparently open to become interesting in something. The Nomad is not really sure why he or she is in the hall and not really sure that s/he is going to find anything interesting in this particular exhibit hall. Occasionally, the Nomad stope, but it does not appear that the nomadic visitor finds any one thing in the hall more interesting than any other thing.

    The Cafeteria Type

    This is the interested visitor who wants to get interested in something, and so the entire museum and the hall itself is treated as a cafeteria. Thus, the person walks along, hoping to find something of interest, hoping to "put something on his or her tray" and stopping from time to time in the hall. While it appears that there is something in the hall that spontaneously sparks the person's interest, we perceive this visitor has a predilection to becoming interested, and the exhibit provides the many things from which to choose.

    The V.I.P. - Very Interested Person

    This visitor comes into the hall with some prior interest in the content area. This person may not have come specifically to the hall, but once there, the hall serves to remind the V.I.P.'s that they were, in fact, interested in something in that hall beforehand. The V.I.P. goes through the hall much more carefully, much slower, much more critically - that is, they move from point to point, they stop, they examine aspects of the hall with a greater degree of scrutiny and care. (Wolf & Tymitz, 1977, pp. 10-11; all emphasis - italicized - in original text)

    Aren't the preceding labels vivid?! I like to think of them as "narrative metaphors and similies!" They convey, in summary fashion and 'at a glance,' an entire expanded image of the full range of the subjects' behavior, attitudes, choices, etc.!

    The Miles and Huberman Qualitative Data Analysis Sourcebook also has additional examples of such 'creative label-making' in Chapter 10, "Drawing and Verifying Conclusions."

    Perhaps, then, in contrast to "indigenous typologies" and 'road-testing existing models,' as explained above, the most common scenario for the "analyst-constructed typologies" is one where no single widely accepted model or framework exists. Thus, the qualitative researcher 'goes with the flow,' soaks in his/her data and lets the creative juices take over regarding how to compile and sort the responses. But I would again urge even the 'existing/indigenous' researchers to stay open to the fact that the existing model may not fit the current data, in whole or in part. So they, too, may need to switch gears and get 'similarly creative' regarding newly added, better-fitting labels, terms, or even entire models!

    So ... now you are in the thick of things as far as not only organizing and compiling, but also beginning to "make meaning of," your raw qualitative data. As you do this, however, a brief comparative analogy to the quantitative side of the fence should serve as a gentle warning!!! Remember the concept of "Type I error," or p-values, in analytic statistics? In a nutshell, this refers to the fact that you can 'do everything right:' i.e., carefully select a random sample from a well-defined population, compile your data without coding error, pick the 'best' statistic(s) and intperpret it/them - and still you cannot guarantee "with 100% confidence" that the results you find within that sample will apply for certain to the entire population! Fact is: you have to live with a certain level of risk - i.e., 5% or whatever level you can set - that your sample was simply 'flukey' and despite your best efforts, the comparable population results would in fact be different! You could, for instance, have randomly drawn "lots of extreme (high/low) scorers" on your particular phenomenon, where in fact, in the population at large, there is more of a 'mix.' As you may recall from your Intro to Statistics class: with analytic statistics you can't guarantee 'how right' you'll be - i.e., 100% certainty from sample to population would be a wonderful goal, but it is simply impractical due to the vagaries/possibility of an 'unlucky sample draw' and associated sampling error! But you can 'bound' 'how wrong' you'll be by pre-selecting that 'risk you can live with:' i.e., 5% or 1% most commonly - and then interpreting your calculated test statistic in light of that 'risk of being wrong.'

    Well -- in qualitative research, as indeed in life itself (!!!) - we cannot escape the possibility that 'we may be wrong' in our own interpretation of things. On top of that, in qualitative research we do not have the 'comfort and security' of p-values, Type I errors, etc., to guide us in 'how wrong we may or may not be.' That is: we can't compute a test statistic and then look it up in a table to be able to say, "My calculated value is greater than the critical value, so I can be at least 95% confident that my results will hold up!"

    Given this inherent 'fuzziness' and the greater role of human/researcher judgment and interpretation in analyzing our qualitative data, what can we do to help protect vs. bias and/or error?

    One remedy may be summed up in the following popular phrase: "Be ready to play 'Devil's Advocate' with yourself and your findings!" In other words: make certain, in your heart, mind and conscience, that if "contradictory cases or data" exist - in opposition to your own findings and results - you really have made an 'honest attempt' to find them and account for them!!!! And if necessary, modify your original findings and results in light of these "don't-fit-the-pattern" cases! That is the topic of the following discussion!


Once you have finished you should:

Go back to A Second Look at Data Compilation and Analysis

E-mail M. Dereshiwsky at statcatmd@aol.com
Call M. Dereshiwsky at (520) 523-1892


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