Research Design and Methods by Bordens Kenneth;Abbott Bruce Barrington; & Bruce B. Abbott

Author:Bordens, Kenneth;Abbott, Bruce Barrington; & Bruce B. Abbott [Kenneth S. Bordens & Bruce B. Abbott]
Language: eng
Format: epub
Publisher: McGraw-Hill US Higher Ed ISE
Published: 2021-03-03T20:00:00+00:00

Step 1: Identifying Relevant Variables

Before you can hope to conduct a meta-analysis, you must identify the variables to be analyzed. This may sound easy enough. However, you will find that in practice it is somewhat difficult, especially in a research area in which there is a wide body of research. Generally, the rules that apply to developing testable research questions (see Chapter 4) also apply to meta-analysis. It is not enough to say, “I want to perform a meta-analysis of the memory literature.” Such a broad, general analysis would be extremely difficult to do. The same is true even in less extensive research areas.

Your research question must be sufficiently focused to allow for a reasonable meta-analysis. The unit of analysis in a meta-analysis should be the impact of variable X on variable Y (Rosenthal, 1984). Therefore, focus only on those variables that relate to your page 282specific question. For example, you might choose to meta-analyze the impact of imagery on memory. Here you are limiting yourself to a small segment of the memory literature.

After you have narrowed the scope of your analysis, you must decide what variables to record (such as sex of subject, independent variables) as you review each study. Your decision will be driven by your research question. Table 8-3 provides a list of information that might be included in a meta-analysis. For each study to be included in your meta-analysis, you should record the relevant variables, the full reference citation, and the nature of the subject sample and procedure (Rosenthal, 1984).

TABLE 8-3 Sample of Factors to Include When Meta-Analyzing Literature

Full reference citation

Names and addresses of authors

Sex of experimenter

Sex of subjects used in each experiment

Characteristics of subject sample (such as how obtained, number)

Task required of subjects and other details about the dependent variable

Design of the study (including any unusual features)

Control groups and procedures included to reduce confounding

Results from statistical tests that bear directly on the issue being considered in the meta-analysis (effect sizes, values of inferential statistics, p values)

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