Perspective on article by P. Isaac published in the Psychological Bulletin, 1970, 74, 213-18. (CB)

A statistical model for relating latent variables from manifest variables, called a linear structural equation model, is presented. The approach is illustrated by a test theory model and longitudianl study of intelligence. (Author/JKS)

The rationale behind analysis of variance (including analysis of covariance and multiple analyses of variance and covariance) methods is reviewed, and unplanned and planned methods of evaluating differences between means are briefly described. Two advantages of using planned or a priori tests over unplanned or post hoc tests are presented. In…

For Y. Yao's and G. S. James' tests, Type I error rates were estimated for various combinations of the number of variables, sample-size and sample-size-to-variables ratios, and heteroscedasticity. These tests are alternatives to Hotelling's T(sup 2) and are intended for use when variance-covariance matrices are unequal for two independent samples.…

This article focuses on steps in conducting empirical-analytic research and the problems of controlling for or estimating three sources of error: the amount of measurement error, research design error, and the amount of statistical or sampling error. (Author/CT)

A brief note presenting algebraically equivalent formulas for the variances of three error types. (Author)

Computer simulations indicate that the correlation between baseline and change, by itself, does not invalidate the use of gain scores to measure change, but when the negative correlation is accompanied by decrease in variance from pretest to posttest, covariance is a superior measure of change. (SLD)

For the conditions investigated in the study, the parametric ANCOVA was typically the procedure of choice both as a test of equality of conditional means and as a test of equality of conditional distributions. (Author/LMO)

Formulas are provided for estimating statistical power of a test of significance for the difference among means under a variety of conditions. A table for quick power estimates that require no computation for comparing two means in analysis of variance and analysis of covariance is included. (TJH)

The mathematical derivation of the statistics used for inference in some linear models assumes that the values of the independent variables are measured without error. This assumption is often disregarded when these models are utilized in research. This study is an investigation of the consequences of the violation of this assumption for one…

Functional and structural relationship alternatives to the standard "F"-test for analysis of covariance (ANCOVA) are discussed for cases when the covariate is measured with error. An approximate statistical test based on the functional relationship approach is preferred on the basis of Monte Carlo simulation results. (SLD)

Parametric analysis of covariance was compared to analysis of covariance with data transformed using ranks. Using a computer simulation approach the two strategies were compared in terms of the proportion of Type I errors made and statistical power when the conditional distribution of errors were: (1) normal and homoscedastic, (2) normal and…

Since measurement errors exist in panel surveys, LISREL (a procedure for controlling measurement error by the analysis of covariance structures) was used in this investigation to determine the stability of the social-psychological concept of locus of control expectancy change with the aquisition of post secondary education. The National…

An analysis of covariance (ANCOVA) is done to correct for chance differences that occur when subjects are assigned randomly to treatment groups. When properly used, this correction results in adjustment of the group means for pre-existing differences caused by sampling error and reduction of the size of the error variance of the analysis. The…

In this paper problems caused by the existence of errors of measurement are identified for factor analysis, regression analysis, ANOVA, and ANCOVA. At least one detrimental effect is shown to exist for each type of analysis. When a researcher's interest is with infallible variables, he runs the risk of biased results from all of the procedures…