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Maxwell, Scott E.; Cole, David A.; Mitchell, Melissa A. – Multivariate Behavioral Research, 2011
Maxwell and Cole (2007) showed that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters in the special case of complete mediation. However, their results did not apply to the more typical case of partial mediation. We extend their previous work by showing that substantial bias can…
Descriptors: Psychological Studies, Mediation Theory, Bias, Research Methodology
Pek, Jolynn; MacCallum, Robert C. – Multivariate Behavioral Research, 2011
The detection of outliers and influential observations is routine practice in linear regression. Despite ongoing extensions and development of case diagnostics in structural equation models (SEM), their application has received limited attention and understanding in practice. The use of case diagnostics informs analysts of the uncertainty of model…
Descriptors: Structural Equation Models, Democracy, Regression (Statistics), Observation
Austin, Peter C. – Multivariate Behavioral Research, 2012
Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one…
Descriptors: Computation, Regression (Statistics), Statistical Bias, Error of Measurement
Austin, Peter C. – Multivariate Behavioral Research, 2011
Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being…
Descriptors: Smoking, Hospitals, Program Effectiveness, Probability
Preacher, Kristopher J. – Multivariate Behavioral Research, 2006
Fitting propensity (FP) is defined as a model's average ability to fit diverse data patterns, all else being equal. The relevance of FP to model selection is examined in the context of structural equation modeling (SEM). In SEM it is well known that the number of free model parameters influences FP, but other facets of FP are routinely excluded…
Descriptors: Structural Equation Models, Case Studies, Selection

Hoeksma, Jan B.; Knol, Dirk L. – Multivariate Behavioral Research, 2001
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…
Descriptors: Behavior Patterns, Case Studies, Development, Hypothesis Testing

McDonald, Roderick P. – Multivariate Behavioral Research, 1997
Structural equation modelling is becoming increasingly popular in education. This article examines and compares a number of alternative assumptions governing nondirected paths in structural equation models without latent variables vis-a-vis a data set on lung ventilation. Some problems with the conventional procedures in path analysis are pointed…
Descriptors: Case Studies, Path Analysis, Structural Equation Models
Hofer, Scott M.; Flaherty, Brian P.; Hoffman, Lesa – Multivariate Behavioral Research, 2006
The effect of time-related mean differences on estimates of association in cross-sectional studies has not been widely recognized in developmental and aging research. Cross-sectional studies of samples varying in age have found moderate to high levels of shared age-related variance among diverse age-related measures. These findings may be…
Descriptors: Time Perspective, Association (Psychology), Sampling, Case Studies