Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.     

Extending Religion-Health Research to Secular Minorities: Issues and Concerns

Claims about religion’s beneficial effects on physical and psychological health have received substantial attention in popular media, but empirical support for these claims is mixed. Many of these claims are tenuous because they fail to address basic methodological issues relating to construct validity, sampling methods or analytical problems. A more conceptual problem has to do with the near universal lack of atheist control samples. While many studies include samples of individuals classified as “low spirituality” or religious “nones”, these groups are heterogeneous and contain only a fraction of members who would be considered truly secular. We illustrate the importance of including an atheist control group whenever possible in the religiosity/spirituality and health research and discuss areas for further investigation.     

The Implication of Popper’s Anti-inductive Theory for the Development of Indigenous Psychologies

Echoing Kumar’s advocacy that psychologists should not ignore Popper, this article received the methodological strategies for attaining the epistemological goal of universal psychology or global psychology as suggested by previous indigenous psychologists and argued that it is impossible for them to attain this goal by the inductive method. The significant implications of Popper’s anti-inductive theory for solving this dilemma are emphasized for future progress of indigenous psychologies.     

An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents’ preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables while the latter is used for identifying relatively homogeneous clusters of respondents. The proposed method offers an integrated graphical display that provides information on cluster-based structures inherent in multivariate categorical data as well as the interdependencies among the data. An empirical application is presented which demonstrates the usefulness of the proposed method and how it compares to several extant approaches. The work reported in this paper was supported by Grant 290439 and Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the first and third authors, respectively. We wish to thank Ulf B?ckenholt, Paul Green, and Marc Tomiuk for their insightful comments on an earlier version of this paper. We also wish to thank Byunghwa Yang for generously providing us with his data.