Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple times and the resulting estimates of the parameter(s) of interest are combined across the completed datasets, is becoming increasingly popular for handling missing data. However, MI can result in biased inference if not carried out appropriately or if the…

In recent years the educational community has increasingly embraced digital technologies for the purposes of developing alternative learning environments, providing diagnostic feedback, and fostering the development of so-called 21st-century skills. This special issue is dedicated to bridging recent work from the disciplines of educational and…

Salthouse (2011) critically reviewed cross-sectional and longitudinal relations among adult age, brain structure, and cognition (ABC) and identified problems in interpretation of the extant literature. His review, however, missed several important points. First, there is enough disparity among the measures of brain structure and cognitive…

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

This paper presents an argument against the wider adoption of complex forms of data analysis, using multi-level modeling (MLM) as an extended case study. MLM was devised to overcome some deficiencies in existing datasets, such as the bias caused by clustering. The paper suggests that MLM has an unclear theoretical and empirical basis, has not led…

In this study, we examine the role of academic preparation in the transition from community colleges to four-year institutions. We address two specific questions: To what extent do academically unprepared students transfer to four-year institutions? And, can positive experiences in community colleges diminish the role of inadequate academic…

Makes the case that hierarchical linear models or longitudinal multilevel models are a better alternative than standard regression models for empirical tests of predictive developmental hypotheses. Describes a multivariate longitudinal model linking developmental data to a criterion and presents an example from a study of the prediction of infant…