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Maraun, Michael D.; And Others – Multivariate Behavioral Research, 1996
The issue of indeterminacy in factor analysis and the debate between the proposed alternative solution and posterior moment position are explored in an article and 14 commentaries and rebuttals in two rounds. Implications for applied work involving factor analysis are discussed. (SLD)
Descriptors: Factor Analysis, Factor Structure, Mathematical Models, Metaphors

Silvia, E. Suyapa M.; MacCallum, Robert C. – Multivariate Behavioral Research, 1988
The effects of several specification search strategies used with Covariance Structure Modeling to obtain more parsimonious models are examined. The initial models vary in their degree of "correctness." Restricting modifications to those justified by prior theoretical knowledge improves the success of a specification search. (TJH)
Descriptors: Analysis of Covariance, Mathematical Models, Research Methodology, Search Strategies

Reardon, Sean F.; Brennan, Robert T.; Buka, Stephen L. – Multivariate Behavioral Research, 2002
Developed procedures for constructing a retrospective person-period data set from cross-sectional data and discusses modeling strategies for estimating multilevel discrete-time event history models. Applied the methods to the analysis of cigarette use by 1,979 urban adolescents. Results show the influence of the racial composition of the…
Descriptors: Adolescents, Mathematical Models, Neighborhoods, Racial Composition

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

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

Cliff, 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

Velicer, 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

Velicer, 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

Schonemann, Peter H.; And Others – Multivariate Behavioral Research, 1975
Descriptors: Algorithms, Data Analysis, Dimensional Preference, Individual Differences

Roskam, 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

Hackett, Paul M. W.; And Others – Multivariate Behavioral Research, 1993
Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…
Descriptors: Dentistry, Health Promotion, Interviews, Mathematical Models

Spiegel, Douglas K. – Multivariate Behavioral Research, 1986
Tau, Lambda, and Kappa are measures developed for the analysis of discrete multivariate data of the type represented by stimulus response confusion matrices. The accuracy with which they may be estimated from small sample confusion matrices is investigated by Monte Carlo methods. (Author/LMO)
Descriptors: Mathematical Models, Matrices, Monte Carlo Methods, Multivariate Analysis
Component Analysis versus Common Factor Analysis: Some Issues in Selecting an Appropriate Procedure.

Velicer, Wayne F.; Jackson, Douglas N. – Multivariate Behavioral Research, 1990
Situations for which the researcher should use component analysis versus common factor analysis are discussed. Topics addressed include key algebraic similarities and differences, theoretical and practical issues, the factor indeterminacy issue, latent versus manifest variables, and differences between exploratory and confirmatory analysis…
Descriptors: Algebra, Comparative Analysis, Factor Analysis, Literature Reviews

Chou, Chih-Ping; Bentler, P. M. – Multivariate Behavioral Research, 1990
The empirical performance under null/alternative hypotheses of the likelihood ratio difference test (LRDT); Lagrange Multiplier test (evaluating the impact of model modification with a specific model); and Wald test (using a general model) were compared. The new tests for covariance structure analysis performed as well as did the LRDT. (RLC)
Descriptors: Analysis of Covariance, Comparative Analysis, Hypothesis Testing, Mathematical Models

Lambert, Zarrel V.; And Others – Multivariate Behavioral Research, 1991
A method is presented that eliminates some interpretational limitations arising from assumptions implicit in the use of arbitrary rules of thumb to interpret exploratory factor analytic results. The bootstrap method is presented as a way of approximating sampling distributions of estimated factor loadings. Simulated datasets illustrate the…
Descriptors: Behavioral Science Research, Computer Simulation, Estimation (Mathematics), Factor Structure
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