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Showing 76 to 90 of 132 results Save | Export
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Keeling, Kellie B. – Multivariate Behavioral Research, 2000
Developed a new regression equation to estimate the mean value of eigenvalues in parallel analysis and studied the performance of the equation in comparison with previously published regression equations through simulation. Performance of the new equation was comparable to that of the LCHF equation of G. Lautenschlager and others (1989). (SLD)
Descriptors: Comparative Analysis, Equations (Mathematics), Estimation (Mathematics), Regression (Statistics)
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MacKinnon, David P.; Lockwood, Chondra M.; Williams, Jason – Multivariate Behavioral Research, 2004
The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal…
Descriptors: Simulation, Regression (Statistics), Data Analysis, Evaluation Methods
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Kim, Chulwan; Rangaswamy, Arvind; DeSarbo, Wayne S. – Multivariate Behavioral Research, 1999
Presents an approach to multidimensional unfolding that reduces the occurrence of degenerate solutions and conducts a Monte Carlo study to demonstrate the superiority of the new method to the ALSCAL and KYST nonmetric procedures for student preference data. (SLD)
Descriptors: Monte Carlo Methods, Multidimensional Scaling, Problem Solving, Simulation
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Bernaards A., Coen; Sijtsma, Klaas – Multivariate Behavioral Research, 1999
Used simulation to study the problem of missing item responses in tests and questionnaires when factor analysis is used to study the structure of the items. Factor loadings based on the EM algorithm best approximated the loading structure, with imputation of the mean per person across the scores for that person being the best alternative. (SLD)
Descriptors: Factor Analysis, Factor Structure, Item Response Theory, Simulation
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Lee, Sik-Yum; Song, Xin Yuan; Poon, Wai-Yin – Multivariate Behavioral Research, 2004
Various approaches using the maximum likelihood (ML) option of the LISREL program and products of indicators have been proposed to analyze structural equation models with non-linear latent effects on the basis of Kenny and Judd's formulation. Recently, some methods based on the Bayesian approach and the exact ML approaches have been developed.…
Descriptors: Comparative Analysis, Structural Equation Models, Statistical Analysis, Evaluation Methods
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Dolan, Conor V.; Molenaar, Peter C. M. – Multivariate Behavioral Research, 1994
In multigroup covariance structure analysis with structured means, the traditional latent selection model is formulated as a special case of phenotypic selection. Illustrations with real and simulated data demonstrate how one can test specific hypotheses concerning selection on latent variables. (SLD)
Descriptors: Analysis of Covariance, Group Membership, Hypothesis Testing, Selection
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Lee, Sik-Yum; Lu, Bin – Multivariate Behavioral Research, 2003
In this article, a case-deletion procedure is proposed to detect influential observations in a nonlinear structural equation model. The key idea is to develop the diagnostic measures based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. An one-step pseudo approximation is proposed to reduce the…
Descriptors: Structural Equation Models, Computation, Mathematics, Simulation
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Suziedelis, Antanas; And Others – Multivariate Behavioral Research, 1976
A method of typological analysis was applied to computer-generated 96-item questionnaire data for 100 cases, under a variety of conditions to analyze both the item-level and score-level. The results showed a considerable advantage of score-level approach in the number, size, and replicability of clusters recovered. (DEP)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Comparative Analysis
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Ford, David L.; And Others – Multivariate Behavioral Research, 1978
Econometric techniques for estimating the parameters of individual and group multi-attribute utility models are discussed. These techniques permit measurement of intra-and inter-individual heterogeneity with regard to the importance ascribed to the model attributes. (Author/JKS)
Descriptors: Economic Research, Higher Education, Individual Characteristics, Mathematical Models
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Revelle, William; Rocklin, Thomas – Multivariate Behavioral Research, 1979
A new procedure for determining the optimal number of interpretable factors to extract from a correlation matrix is introduced and compared to more conventional procedures. The new method evaluates the magnitude of the very simple structure index of goodness of fit for factor solutions of increasing rank. (Author/CTM)
Descriptors: Factor Analysis, Goodness of Fit, Hypothesis Testing, Research Design
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Pavur, Robert; Nath, Ravinder – Multivariate Behavioral Research, 1989
A Monte Carlo simulation study compared the power and Type I errors of the Wilks lambda statistic and the statistic of M. L. Puri and P. K. Sen (1971) on transformed data in a one-way multivariate analysis of variance. Preferred test procedures, based on robustness and power, are discussed. (SLD)
Descriptors: Comparative Analysis, Mathematical Models, Monte Carlo Methods, Multivariate Analysis
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Nandakumar, Ratna – Multivariate Behavioral Research, 1993
The methodology of P. E. Holland and P. R. Rosenbaum (1986) to assess unidimensionality of binary data is outlined and illustrated through a simulation with 36 items for 2,000 examinees. How to interpret the results is discussed. (SLD)
Descriptors: Computer Simulation, Educational Assessment, Equations (Mathematics), Mathematical Models
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Zhang, Zhiyong; Nesselroade, John R. – Multivariate Behavioral Research, 2007
Dynamic factor models have been used to analyze continuous time series behavioral data. We extend 2 main dynamic factor model variations--the direct autoregressive factor score (DAFS) model and the white noise factor score (WNFS) model--to categorical DAFS and WNFS models in the framework of the underlying variable method and illustrate them with…
Descriptors: Bayesian Statistics, Computation, Simulation, Behavioral Science Research
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Rasmussen, Jeffrey Lee – Multivariate Behavioral Research, 1988
A Monte Carlo simulation was used to compare the Mahalanobis "D" Squared and the Comrey "Dk" methods of detecting outliers in data sets. Under the conditions investigated, the "D" Squared technique was preferable as an outlier removal statistic. (SLD)
Descriptors: Comparative Analysis, Computer Simulation, Data Analysis, Monte Carlo Methods
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Harrop, 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
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