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Gottschall, Amanda C.; West, Stephen G.; Enders, Craig K. – Multivariate Behavioral Research, 2012
Behavioral science researchers routinely use scale scores that sum or average a set of questionnaire items to address their substantive questions. A researcher applying multiple imputation to incomplete questionnaire data can either impute the incomplete items prior to computing scale scores or impute the scale scores directly from other scale…
Descriptors: Questionnaires, Data Analysis, Computation, Monte Carlo Methods
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Vallejo, G.; Fernandez, M. P.; Livacic-Rojas, P. E.; Tuero-Herrero, E. – Multivariate Behavioral Research, 2011
Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured…
Descriptors: Personality Problems, Psychosis, Prevention, Patients
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Zijlstra, Wobbe P.; Van Der Ark, L. Andries; Sijtsma, Klaas – Multivariate Behavioral Research, 2007
Classical methods for detecting outliers deal with continuous variables. These methods are not readily applicable to categorical data, such as incorrect/correct scores (0/1) and ordered rating scale scores (e.g., 0,..., 4) typical of multi-item tests and questionnaires. This study proposes two definitions of outlier scores suited for categorical…
Descriptors: Rating Scales, Scores, Regression (Statistics), Statistical Analysis
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Maraun, Michael D.; Slaney, Kathleen – Multivariate Behavioral Research, 2005
MAXCOV-HITMAX was invented by Paul Meehl as a tool for the detection of latent taxonic structures (i.e., structures in which the latent variable, u, is not continuously, but rather Bernoulli, distributed). It involves the examination of the shape of a certain conditional covariance function and is based on Meehl's claims that (R1) Taxonic…
Descriptors: Multivariate Analysis, Hypothesis Testing, Monte Carlo Methods, Behavioral Science Research
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Snyder, Conrad W., Jr. – Multivariate Behavioral Research, 1976
Examines intrinsic individual differences in conceptual behavior with a multivariate model, three mode factor analysis. The analyses yielded five individual difference performance factors, three stage factors, and four response components indicating the importance of a multivariate representation of complex behavior. (Author/DEP)
Descriptors: Behavioral Science Research, College Students, Concept Formation, Factor Analysis
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Song, Xin-Yuan; Lee, Sik-Yum – Multivariate Behavioral Research, 2005
In this article, a maximum likelihood approach is developed to analyze structural equation models with dichotomous variables that are common in behavioral, psychological and social research. To assess nonlinear causal effects among the latent variables, the structural equation in the model is defined by a nonlinear function. The basic idea of the…
Descriptors: Structural Equation Models, Simulation, Computation, Error of Measurement