A Hierarchical Map and Application to Traverse and Unify Analyses Subsumed by Canonical Correlation.

This article presents a hierarchical map of analyses subsumed by canonical correlation and a shiny application to facilitate the connections between said analyses. Building on the work of other researchers who used canonical correlation analyses to unify analyses in the general linear model, we demonstrate that the hierarchy is not as flat as some have portrayed. While a simpler hierarchy may seem to be more accessible, it implies a lack of relationship between analyses that may cause confusion when learning the vast majority of univariate and multivariate analyses in the general linear model. Because it is not always intuitive how all the relevant analyses for a given data type can be conducted, we developed the Shiny application canCORRgam to demonstrate the hierarchical path of analyses subsumed by canonical correlation for 15 different models. The canCORRgam application provides emerging researchers evidence of the transitive properties implied in the map. Our work also promotes meta-analytic thinking and practice as we provide the tools, formula, and software to relate test statistics to effect sizes in addition to transforming relevant test statistics and effect sizes to equivalent test statistics and effect sizes.

Investigating bias in squared regression structure coefficients.

The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients.

Students' Attitudes Toward Science as Predictors of Gains on Student Content Knowledge: Benefits of an After-School Program.

High-quality after-school programs devoted to science have the potential to enhance students' science knowledge and attitudes, which may impact their decisions about pursuing science-related careers. Due to the unique nature of these informal learning environments, an understanding of the relationships among aspects of students' content knowledge acquisition and attitudes toward science may aid in the development of effective science-related interventions. We investigated the impact of a semester-long after-school intervention utilizing an inquiry-based infectious diseases curriculum (designed for use after-school) on 63 urban students' content knowledge and aspects of their attitudes towards science. Content knowledge increased 24.6% from pre- to posttest. Multiple regression analyses indicated suggested that the "self-directed effort" subscale of the Simpson-Troost Attitude Questionnaire - Revised best predicted increases in students' science content knowledge. The construct "science is fun for me" served as a suppressor effect. These findings suggest that future after-school programs focusing on aspects of attitudes toward science most closely associated with gains in content knowledge might improve students' enthusiasm and academic preparedness for additional science coursework by improving student attitudes towards their perceptions of their self-directed effort.