Module 05 - Home Page

Cross-Case Displays   --  What are They and Why are They Valuable in Qualitative Research?  

Analyzing the Data and Drawing Study Conclusions and Findings  

Module Objectives

When you have completed this module, you should be able to:

1. Explain and assess the strengths and weaknesses in  Cross-Case analysis  in qualitative research (Case Assignment);

2. Understand the importance to a study of carefully redefining the research problem when appropriate information is developed in a study. 

3. Explain how the definition of a problem affects the appropriateness of the methods and procedures used to analyze data and drawing study conclusions and findings in qualitative research. (Case Assignment and SLP)

4. Understand and explain how to analyze and describe the variance in the variables in a qualitative study. (Case Assignment)

All sciences are now under the obligation to prepare the ground for the future task of the philosopher, which is to solve the problem of value, to determine the true hierarchy of values.

Friedrich Nietzsche (1844–1900), German philosopher. The Genealogy of Morals, "First Essay," sct. 17 (1887).

What Are Cross-Case Displays?

Many qualitative researchers prefer to focus on multiple cases instead of a single, bounded case or site.  Cross-case displays are the arrays or displays used to present the study data in cross-case analysis.  It could be a matrix which allows the researcher to compare and contrast the various "cases".   However, in cross-case displays data in one case is compared to data in the other cases. (Remember, for both In-Case and Cross-Case Displays, the displays allow the researcher to analyze the data in a systematic or organized manner, usually through the use of "thin quantitative measures and text.) 

Why Are Cross-Case Analysis and Displays Valuable?

Cross-Case analysis is important because it allows the researcher to "increase generalizability."  It also deepens the understanding and explanation of the cross-case analysis.  Furthermore, it allows the researcher to understand under what sets of conditions the study's hypotheses are minimized and maximized.  This advantage of cross-case analysis is the result of an increased sample size and a greater potential for exploring and comparing a greater range of cases.

Multiple cases allow the researcher to find the negative cases, which in turn, fosters the development of stronger theories.  They also help the researcher to form more general categories of how specific conditions observed in the study may be related.  Thus, the researcher may be more able to "refine" his or her theories.

Another distinction between Cross-Case and Within-Case analysis is found in their different approaches to inquiry.  The difference being the manner in which "Cases" and "Variables" are considered in analysis.  

In Within-Case analysis the focus is entirely on the case variables. Within-Case analysis (displays) is variable-oriented.  In these studies the researcher studies the strength of the relationships between variables.  Therefore, the stronger the relationship the greater meaning is given to that relationship in the analysis.

On the other hand, in Cross-Case analysis the researcher examines the total picture of each case, identifying reoccurring patterns.

Another difference between Within-Case and Cross-Case analysis is that Within-Case analysis (variable-oriented) is conceptual and theory-centered in the beginning and good at finding probabilistic relationship, whereas, Cross-case analysis is more valuable in finding specific, concrete patterns.  It is also much better in handling the complexities of assessing causal relationships.

 The Skill of the Researcher in Analyzing the Data and Drawing Study Conclusions and Findings   

The man of science is a poor philosopher.

Albert Einstein (1879–1955), German-born U.S. theoretical physicist. Out of My Later Years, ch. 12 (1950).
 

Analyzing Data and Drawing Conclusions

As stated several times already in the course the most important factor in qualitative research is the skill of the researcher. If you haven't fully grasped the true meaning of that statement you certainly will as we discuss analyzing data and drawing conclusions for qualitative studies.

There is no one prescribed manner in which to analyze data so we must describe the steps in a general way as follows:

1. The researcher should perform a "squint analysis" by quickly scanning the data displays to determine if any data is remarkable.  The researcher should then verify or revise their opinion.
2. The researcher should note patterns, themes; do comparisons between data; cluster data appropriately; and use counting when valuable.
3. As patterns and opinions lead the researcher to conclusions, a written  explanation for each one should be prepared.  the use of summary tables may help the researcher.
4. The researcher should verify that all conclusions are supported by written field notes or other documentation.  in other words, all conclusions should be confirmed, rechecked and verified.
5. The researcher should document their conclusion-drawing procedures.  they should be prepared to explain their logic used when drawing conclusions.

Now do you understand how sensitive qualitative studies are to the skill of the researcher?  Another important point that is critical in qualitative studies is the control of researcher/observer bias.

In Module 6, we will examine the process of assessing causality, the standards for the quality of conclusions, and the testing and/or confirming study findings.