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Wolfle, Lee M.; Ethington, Corinna A. – 1985
The purpose of this paper is to examine the validity of regression estimates when skewed dichotomous scales are used as independent variables. When Pearson product-moment correlations are used to measure zero-order associations involving dichotomous variables, the resulting coefficients underestimate the true associations. As a result, using…
Descriptors: Correlation, Estimation (Mathematics), Matrices, Multiple Regression Analysis
Phillips, Gary W. – 1982
The usefulness of path analysis as a means of better understanding various linear models is demonstrated. First, two linear models are presented in matrix form using linear structural relations (LISREL) notation. The two models, regression and factor analysis, are shown to be identical although the research question and data matrix to which these…
Descriptors: Estimation (Mathematics), Factor Analysis, Mathematical Models, Matrices
McMurray, Mary Anne – 1987
This paper illustrates the transformation of a raw data matrix into a matrix of associations, and then into a factor matrix. Factor analysis attempts to distill the most important relationships among a set of variables, thereby permitting some theoretical simplification. In this heuristic data, a correlation matrix was derived to display…
Descriptors: Correlation, Factor Analysis, Factor Structure, Goodness of Fit
Ethington, Corinna A. – 1986
This study examined the effect of type of correlation matrix on the robustness of LISREL maximum likelihood and unweighted least squares structural parameter estimates for models with categorical manifest variables. Two types of correlation matrices were analyzed; one containing Pearson product-moment correlations and one containing tetrachoric,…
Descriptors: Computer Software, Correlation, Estimation (Mathematics), Goodness of Fit
Tucker, Ledyard R.; And Others – 1986
A Monte Carlo study of five indices of dimensionality of binary items used a computer model that allowed sampling of both items and people. Five parameters were systematically varied in a factorial design: (1) number of common factors from one to five; (2) number of items, including 20, 30, 40, and 60; (3) sample sizes of 125 and 500; (4) nearly…
Descriptors: Correlation, Difficulty Level, Educational Research, Expectancy Tables