Despite the abundance of published material on conducting focus groups, scant specific information exists on how to analyze focus group data in social science research. Thus, the authors provide a new qualitative framework for collecting and analyzing focus group data. First, they identify types of data that can be collected during focus groups. Second, they identify the qualitative data analysis techniques best suited for analyzing these data. Third, they introduce what they term as a micro-interlocutor analysis, wherein meticulous information about which participant responds to each question, the order in which each participant responds, response characteristics, the nonverbal communication used, and the like is collected, analyzed, and interpreted. They conceptualize how conversation analysis offers great potential for analyzing focus group data. They believe that their framework goes far beyond analyzing only the verbal communication of focus group participants, thereby increasing the rigor of focus group analyses in social science research.

In this paper, we introduce various graphical methods that can be used to represent data in mixed research. First, we present a broad taxonomy of visual representation. Next, we use this taxonomy to provide an overview of visual techniques for quantitative data display and qualitative data display. Then, we propose what we call “crossover” visual extensions to summarize and integrate both qualitative and quantitative results within the same framework. We provide several examples of crossover (mixed research) graphical displays that illustrate this natural extension. In so doing, we contend that the use of crossover (mixed research) graphical displays enhances researchers’ understanding (i.e., increased Verstehen) of social and behavioral phenomena in general and the meaning that underlies these phenomena in particular.

The inflation of Type I error rates caused by the testing of multiple null hypotheses in factorial analyses of variance (ANOVAs) is a problem that is often not recognized in the behavioral sciences. Fletcher, Daw, and Young (1989) described the problem and conducted a limited simulation study to investigate the effectiveness of two strategies to correct the problem: use of an overall F test and use of a Bonferroni adjustment. Unfortunately, two limitations in the design of their simulation led these authors to conclusions about the overall F test that do not hold under all conditions. The present study was designed to overcome these limitations and to provide a more complete evaluation of such strategies. Our results indicated that the overall F test is effective only when all effects in the ANOVA are null. In contrast, the Bonferroni adjustment and recent modifications of the procedure control the Type I error rate regardless of the number of true null hypotheses in the ANOVA.

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The nested analysis of variance (ANOVA) model is often recommended for analysis of educational data in which students receive treatments within classrooms. The Type I error rate and statistical power of the F test for groups-within-treatments effects associated with such nested ANOVA designs were evaluated in a Monte Carlo study. Data were generated for ANOVA models comprising two and three levels of the treatment variable; two, three, and five groups nested within each treatment; and 3, 10, and 30 observations within each group. The intraclass correlation among scores within groups was controlled at levels ranging from zero (independent observations) to .50, and the F test for groups-within-treatments effects was conducted at nominal alpha levels ranging from .05 to .30. The results indicated that the test for groups within treatments did not indicate sufficient power, even at the most liberal alpha level examined, to detect intraclass correlation in the sample data. The procedure was not effective in supporting decisions to use individual observations rather than group means as the unit of analysis.