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Zheng, Yao; Wiebe, Richard P.; Cleveland, H. Harrington; Molenaar, Peter C. M.; Harris, Kitty S. – Multivariate Behavioral Research, 2013
Psychological constructs, such as negative affect and substance use cravings that closely predict relapse, show substantial intraindividual day-to-day variability. This intraindividual variability of relevant psychological states combined with the "one day at a time" nature of sustained abstinence warrant a day-to-day investigation of substance…
Descriptors: Substance Abuse, Smoking, Psychological Patterns, Young Adults
Kim, Rae-Seon; Becker, Betsy Jane – Multivariate Behavioral Research, 2010
We examined the degree of dependence between standardized-mean-difference effect sizes in multiple-treatment studies in meta-analysis in terms of the correlation formula provided by Gleser and Olkin (1994). To explore the impact of group size and the values of the true multiple-treatment effect sizes, we simplified the formula for the correlation…
Descriptors: Effect Size, Meta Analysis, Correlation, Control Groups
Huo, Yan; Budescu, David V. – Multivariate Behavioral Research, 2009
Dominance analysis (Budescu, 1993) offers a general framework for determination of relative importance of predictors in univariate and multivariate multiple regression models. This approach relies on pairwise comparisons of the contribution of predictors in all relevant subset models. In this article we extend dominance analysis to canonical…
Descriptors: Multivariate Analysis, Correlation, Regression (Statistics), Models
Molenaar, Peter C. M.; Nesselroade, John R. – Multivariate Behavioral Research, 2009
It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even…
Descriptors: Factor Analysis, Multivariate Analysis, Simulation, Affective Behavior
Ruscio, John; Kaczetow, Walter – Multivariate Behavioral Research, 2008
Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…
Descriptors: Monte Carlo Methods, Correlation, Sampling, Multivariate Analysis
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim – Multivariate Behavioral Research, 2008
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Descriptors: Models, Identification, Multivariate Analysis, Correlation
Steinley, Douglas; Brusco, Michael J. – Multivariate Behavioral Research, 2008
A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these…
Descriptors: Test Items, Simulation, Multivariate Analysis, Data Analysis

Tyler, David E. – Multivariate Behavioral Research, 1982
Miller and Farr's algorithm for the index of redundancy is shown to be incorrect by means of a counterexample. The consequences of this error for other conclusions drawn by the authors are discussed. (Author/JKS)
Descriptors: Algorithms, Correlation, Data Analysis, Multivariate Analysis

Cramer, Elliot M. – Multivariate Behavioral Research, 1974
Descriptors: Correlation, Matrices, Multiple Regression Analysis, Multivariate Analysis

Liang, Kun-Hsia; And Others – Multivariate Behavioral Research, 1995
A computer-assisted, K-fold cross-validation technique is discussed in the framework of canonical correlation analysis of randomly generated data sets. Analysis results suggest that this technique can effectively reduce the contamination of canonical variates and canonical correlations by sample-specific variance components. (Author/SLD)
Descriptors: Computer Simulation, Computer Software, Correlation, Multivariate Analysis

ten Berge, Jos M. F.; Knol, Dirk L. – Multivariate Behavioral Research, 1985
Constructing scales on the basis of components analysis by assigning weights 1 to variables with high positive loadings on the components and -1 to variables with high negative loadings was compared with other strategies of scale construction, which assign weights 1 or -1 to variables with high weights for the components. (Author/BW)
Descriptors: Correlation, Factor Analysis, Multivariate Analysis, Scaling

Thorndike, Robert M.; Weiss, David J. – Multivariate Behavioral Research, 1983
Three potential applications of stepwise procedures in canonical analysis and several alternative stepping decision rules are described. Results of an empirical investigation of the procedures indicated that more parsimonious approaches to maintaining variables held up better under cross-validation. (Author/JKS)
Descriptors: Correlation, Data Analysis, Multivariate Analysis, Regression (Statistics)

Cramer, Elliot M. – Multivariate Behavioral Research, 1974
Descriptors: Correlation, Matrices, Multiple Regression Analysis, Multivariate Analysis

Barcikowski, Robert S.; Stevens, James P. – Multivariate Behavioral Research, 1975
Results showed that the canonical correlations are very stable upon replication. The results also indicated that there is no solid evidence for concluding that components are superior to the coefficients, at least not in terms of being more reliable. (Author/BJG)
Descriptors: Correlation, Factor Analysis, Matrices, Monte Carlo Methods

Mendoza, Jorge L.; And Others – Multivariate Behavioral Research, 1978
Four testing procedures for establishing the number of non-zero population roots in canonical analysis are investigated. Results of a Monte Carlo study indicate that three well-established procedures were effective, and a new procedure designed to correct a supposed flaw in the other procedures was ineffective. (JKS)
Descriptors: Correlation, Hypothesis Testing, Monte Carlo Methods, Multivariate Analysis