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Peer reviewedVelicer, Wayne F.; McDonald, Roderick P. – Multivariate Behavioral Research, 1984
A new approach to time series analysis was developed. It employs a generalized transformation of the observed data to meet the assumptions of the general linear model, thus eliminating the need to identify a specific model. This approach permits alternative computational procedures, based on a generalized least squares algorithm. (Author/BW)
Descriptors: Goodness of Fit, Least Squares Statistics, Mathematical Models, Research Design
Peer reviewedHattie, John – Multivariate Behavioral Research, 1984
This paper describes a simulation that determines the adequacy of various indices as decision criteria for assessing unidimensionality. Using the sum of absolute residuals from the two-parameter latent trait model, indices were obtained that could discriminate between one latent trait and more than one latent trait. (Author/BW)
Descriptors: Achievement Tests, Latent Trait Theory, Mathematical Models, Monte Carlo Methods
Peer reviewedCollins, Linda M.; And Others – Multivariate Behavioral Research, 1986
The present study compares the performance of phi coefficients and tetrachorics along two dimensions of factor recovery in binary data. These dimensions are (1) accuracy of nontrivial factor identifications; and (2) factor structure recovery given a priori knowledge of the correct number of factors to rotate. (Author/LMO)
Descriptors: Computer Software, Factor Analysis, Factor Structure, Item Analysis
Peer reviewedBentler, Peter M.; Weeks, David G. – Multivariate Behavioral Research, 1979
Factor analysis in several populations, covariance structure models, three-mode factor analysis, structural equations systems with measurement model, and analysis of covariance with measurement model are all shown to be specializations of a general moment structure model. Some new structured linear models are also described. (Author/CTM)
Descriptors: Analysis of Covariance, Computer Programs, Critical Path Method, Factor 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 reviewedSchweizer, Karl – Multivariate Behavioral Research, 1992
Two versions of a decision rule for determining the most appropriate number of clusters on the basis of a correlation matrix are presented, applied, and compared with three other decision rules. The new rule is efficient for determining the number of clusters on the surface level for multilevel data. (SLD)
Descriptors: Cluster Analysis, Cluster Grouping, Comparative Analysis, Correlation
Peer reviewedPruzek, Robert M.; Lepak, Greg M. – Multivariate Behavioral Research, 1992
Adaptive forms of weighted structural regression are developed and discussed. Bootstrapping studies indicate that the new methods have potential to recover known population regression weights and predict criterion score values routinely better than do ordinary least squares methods. The new methods are scale free and simple to compute. (SLD)
Descriptors: Equations (Mathematics), Least Squares Statistics, Mathematical Models, Predictive Measurement
Peer reviewedVelicer, Wayne F.; McDonald, Roderick P. – Multivariate Behavioral Research, 1991
The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)
Descriptors: Cross Sectional Studies, Data Analysis, Generalizability Theory, Mathematical Models
Peer reviewedMendoza, Jorge L.; And Others – Multivariate Behavioral Research, 1991
Using a Monte Carlo simulation, a bootstrap procedure was evaluated for setting a confidence interval on the unrestricted population correlation (rho) assuming various degrees of incomplete truncation on the predictor. Sample size was the most important factor in determining accuracy and stability. Sample size should be at least 50. (SLD)
Descriptors: Computer Simulation, Correlation, Estimation (Mathematics), Mathematical Models
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
Peer reviewedSchonemann, Peter H.; And Others – Multivariate Behavioral Research, 1975
Descriptors: Algorithms, Data Analysis, Dimensional Preference, Individual Differences
Peer reviewedLevin, Joseph – Multivariate Behavioral Research, 1986
The relation between the power of a significance test in a block design with correlated measurements and the reliability of the measuring instrument is analyzed in terms of the components of variance entering the reliability coefficient and the noncentrality parameter. (Author/LMO)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Models, Power (Statistics)
Peer reviewedMurphy, Kevin R. – Multivariate Behavioral Research, 1982
When either regression models or subjectively-weighted models are used as aids in making placement decisions, the discriminant validity of these models is questioned. The validity of several regression models and of subjectively weighted models was investigated in two experiments. (Author/JKS)
Descriptors: College Admission, Discriminant Analysis, Higher Education, Mathematical Models
Peer reviewedWerts, C. E.; And Others – Multivariate Behavioral Research, 1980
This paper demonstrates how the problem of calibrating measures can be formulated in terms of confirmatory factor analysis. The relationships between traditional approaches and a confirmatory factor approach are specified. (Author/CTM)
Descriptors: Achievement Tests, Equated Scores, Factor Analysis, Intermediate Grades
Peer reviewedRoskam, Edward E.; And Others – Multivariate Behavioral Research, 1992
First- and second-round commentaries on an article by L. Guttman are presented. The following authors responded, with two articles each: (1) E. E. Roskam and J. Ellis; (2) P. H. Schonemann; (3) A. R. Jensen; (4) J. C. Loehlin; and (5) J.-E. Gustafsson. (SLD)
Descriptors: Factor Analysis, Groups, Intelligence, Mathematical Models


