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Peer reviewedHarrop, John W.; Velicer, Wayne F. – Multivariate Behavioral Research, 1985
Computer generated data representative of 16 Auto Regressive Integrated Moving Averages (ARIMA) models were used to compare the results of interrupted time-series analysis using: (1) the known model identification, (2) an assumed (l,0,0) model, and (3) an assumed (3,0,0) model as an approximation to the General Transformation approach. (Author/BW)
Descriptors: Computer Simulation, Data Analysis, Mathematical Models, Matrices
Peer reviewedCliff, Norman – Multivariate Behavioral Research, 1983
The dangers of overlooking time-honored cautions in the making causal interpretations of data analyses from correlational studies when using highly sophisticated computer programs and their associated techniques are discussed. (JKS)
Descriptors: Computer Programs, Goodness of Fit, Mathematical Models, Multivariate Analysis
Peer reviewedShapira, Zur; Zevulun, Eli – Multivariate Behavioral Research, 1989
Data from four studies were used to analyze the hypothesis that performance evaluation variables can be constructed with a rater's facet and a trait's facet. The regularity of the pattern of intercorrelations across different rater groups was remarkable. The use of facet analysis before confirmatory analysis is discussed. (SLD)
Descriptors: Evaluation Criteria, Evaluation Methods, Job Performance, Multitrait Multimethod Techniques
Peer reviewedWood, Phillip Karl; Games, Paul – Multivariate Behavioral Research, 1990
Conceptual rationales from five research contexts are presented, which all posit unmeasured variables that interact with observed independent variables to produce a complete model of the dependent variable. Strategies for overcoming related difficulties are outlined, including increased longitudinal assessment, oversampling of levels of…
Descriptors: Estimation (Mathematics), Longitudinal Studies, Mathematical Models, Multivariate Analysis
Peer reviewedCohen, Jacob; Nee, John C. M. – Multivariate Behavioral Research, 1990
The analysis of contingency tables via set correlation allows the assessment of subhypotheses involving contrast functions of the categories of the nominal scales. The robustness of such methods with regard to Type I error and statistical power was studied via a Monte Carlo experiment. (TJH)
Descriptors: Computer Simulation, Monte Carlo Methods, Multivariate Analysis, Power (Statistics)
Peer reviewedKaplan, David; Wenger, R. Neill – Multivariate Behavioral Research, 1993
This article presents a didactic discussion on the role of asymptotically independent test statistics and separable hypotheses as they pertain to issues of specification error, power, and model misspecification in the covariance structure modeling framework. A small population study supports the major findings. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Hypothesis Testing, Models
Peer reviewedTimm, Neil H. – Multivariate Behavioral Research, 1999
Investigates the equality of "p" correlated effect sizes for "k" independent studies in which treatment and control groups are compared using Hotelling's "T" statistic. Illustrates the procedure and discusses the importance of sample size. (SLD)
Descriptors: Comparative Analysis, Control Groups, Correlation, Effect Size
Gonzalez-Roma, Vicente; Hernandez, Ana; Gomez-Benito, Juana – Multivariate Behavioral Research, 2006
In this simulation study, we investigate the power and Type I error rate of a procedure based on the mean and covariance structure analysis (MACS) model in detecting differential item functioning (DIF) of graded response items with five response categories. The following factors were manipulated: type of DIF (uniform and non-uniform), DIF…
Descriptors: Multivariate Analysis, Item Response Theory, Test Bias, Sample Size
Paulhus, Delroy L.; Robins, Richard W.; Trzesniewski, Kali H.; Tracy, Jessica L. – Multivariate Behavioral Research, 2004
Suppressor situations occur when the simultaneous inclusion of two predictors improves one or both validities. A common allegation is that suppressor effects rarely replicate and have little substantive import. We present substantive examples from two established research domains to counter this skepticism. In the first domain, we show how…
Descriptors: Anxiety, Antisocial Behavior, Predictor Variables, Personality Traits
Yuan, Ke-Hai; Lambert, Paul L.; Fouladi, Rachel T. – Multivariate Behavioral Research, 2004
Mardia's measure of multivariate kurtosis has been implemented in many statistical packages commonly used by social scientists. It provides important information on whether a commonly used multivariate procedure is appropriate for inference. Many statistical packages also have options for missing data. However, there is no procedure for applying…
Descriptors: Social Science Research, Research Methodology, Statistical Distributions, Statistical Analysis
Peer reviewedEveritt, B. S. – Multivariate Behavioral Research, 1981
Results show that the proposed sampling distribution of the test appears to be appropriate only for sample sizes above 50, and for data where the sample size is 10 times the number of variables. For such cases the power of the test is found to be fairly low. (Author/RL)
Descriptors: Mathematical Formulas, Maximum Likelihood Statistics, Monte Carlo Methods, Multivariate Analysis
Peer reviewedDelaney, Harold D.; Maxwell, Scott E. – Multivariate Behavioral Research, 1981
The use of analysis of covariance in conjunction with the multivariate approach to analyzing repeated measures designs is considered for designs involving between- and within-subject factors, one dependent variable, and one observation per subject on the covariate. (Author/RL)
Descriptors: Analysis of Covariance, Correlation, Mathematical Models, Measurement Techniques
Peer reviewedMcArdle, John J. – Multivariate Behavioral Research, 1994
Benefits and limitations of structural equation models for multivariate experiments with incomplete data are presented. Examples from studies of latent variable path models of cognitive performance illustrate analyses with latent variables, omitted variables, randomly missing data, and nonrandomly missing data. (SLD)
Descriptors: Cost Effectiveness, Experiments, Factor Analysis, Longitudinal Studies
Peer reviewedMacKinnon, David P.; And Others – Multivariate Behavioral Research, 1995
Analytical solutions for point and variance estimators of the mediated effect, the ratio of mediated to direct effect, and the proportion of the total effect mediated were determined through simulation for different samples. The sample sizes needed for accuracy and stability are discussed with implications for mediated effects estimates. (SLD)
Descriptors: Equations (Mathematics), Error of Measurement, Estimation (Mathematics), Multivariate Analysis
Peer reviewedWidaman, Keith F. – Multivariate Behavioral Research, 1993
Across conditions, differences between population parameters defined by common factor analysis and component analysis are demonstrated. Implications for data analytic and theoretical issues related to choice of analytic model are discussed. Results suggest that principal components analysis should not be used to obtain parameters reflecting latent…
Descriptors: Comparative Analysis, Equations (Mathematics), Estimation (Mathematics), Factor Analysis

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