RESUMEN
Survey research frequently involves the collection of data from multiple informants. Results, however, are usually analyzed by informant group, potentially ignoring important relationships across groups. When the same construct(s) are measured, integrative data analysis (IDA) allows pooling of data from multiple sources into one data set to examine information from multiple perspectives within the same analysis. Here, the IDA procedure is demonstrated via the examination of pooled data from student and teacher school climate surveys. This study contributes to the sparse literature regarding IDA applications in the social sciences, specifically in education. It also lays the groundwork for future educational researchers interested in the practical applications of the IDA framework to empirical data sets with complex model structures.
RESUMEN
A growing number of statistical analyses are being developed for single-case research. One important factor in evaluating these methods is the extent to which each corresponds to visual analysis. Few studies have compared statistical and visual analysis, and information about more recently developed statistics is scarce. Therefore, our purpose was to evaluate the agreement between visual analysis and four statistical analyses: improvement rate difference (IRD); Tau-U; Hedges, Pustejovsky, Shadish (HPS) effect size; and between-case standardized mean difference (BC-SMD). Results indicate that IRD and BC-SMD had the strongest overall agreement with visual analysis. Although Tau-U had strong agreement with visual analysis on raw values, it had poorer agreement when those values were dichotomized to represent the presence or absence of a functional relation. Overall, visual analysis appeared to be more conservative than statistical analysis, but further research is needed to evaluate the nature of these disagreements.
