Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…

A new framework is offered that uses multilevel logistic regression (MLR) to analyze data from "visual world" eyetracking experiments used in psycholinguistic research. The MLR framework overcomes some of the problems with conventional analyses, making it possible to incorporate time as a continuous variable and gaze location as a categorical…

The logic and procedures underlying a disturbance term regression test of logical consistency for structural models are reviewed for recursive and nonrecursive designs. It is shown that in a simple three-variable, complete mediational case the test procedure is mathematically equivalent to a part correlation. (Author/LMO)

The form of the Johnson-Neyman region of significance is shown to be determined by the statistic for testing the null hypothesis that the population within-group regressions are parallel. Results are obtained for both simultaneous and nonsimultaneous regions of significance. (Author)

If heterogeneous regression slopes are present in analysis of covariance (ANCOVA), the likelihood of committing a Type I error is greater than what had been prespecified. The power of the ANCOVA test of hypothesis for all possible differences of treatment effects is not maximized. (Author/RL)

Consider the nonparametric regression model Y = m(X)+ [tau](X)[epsilon], where X and [epsilon] are independent random variables, [epsilon] has a median of zero and variance [sigma][squared], [tau] is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated…

The present study compares the robustness of two different one way fixed-effects analysis of covariance (ANCOVA) models to investigate whether the model which uses a test statistic incorporating estimates of separate unequal regression slopes is more robust than the conventional model which assumes the slopes are equal. (Author/BW)

Combining information to estimate standardized partial regression coefficients in a linear model is discussed. A combined estimate obtained from the pooled correlation matrix is proposed, and its large sample distribution is obtained. The method is generalized to handle a random effects model in which correlation parameters vary across studies.…

Three linear models (Fixed, Random, and Provisional) and their inferential counterparts are presented and discussed. The three linear models are considered in relation to the analysis of nonlinearity, and limitations in the use of polynominal and product variables are discussed. Several exemplary research papers are examined and reconsidered in…

Practical formulas for several analysis of variance (ANOVA) designs and models are presented which make it possible for readers to compute strength of association measures without the use of complete ANOVA tables. (Author/PN)

Numerous designs using analysis of variance (ANOVA) to test ordinal hypotheses were assessed using a Monte Carlo simulation. Each statistic was computed on each of over 10,000 random samples drawn from a variety of population conditions. The number of groups, population variance, and patterns of population means were varied. In the non-null…

Problems associated with the analysis of data collected using the Solomon Four Group Design are discussed. The design includes an experimental group and a control group that have been pretested and posttested, and an experimental and a control group that have been posttested only. A sample problem is approached in three different ways. First, the…

A modification index is presented to aid in reformulating hypothetical models rejected after analysis of empirical data. This index is an improvement over the one in the LISREL V computer program in that it takes into account changes in all parameters of the model when one parameter is freed. (SLD)

P. O. Johnson and J. Neyman (1936) proposed a general linear hypothesis testing procedure for testing the null hypothesis of no treatment difference in the presence of some covariates. This is generally known as the Johnson-Neyman (JN) technique. The need for the hypothesis testing step (often omitted) as originally presented and the…

Analysis of hypothetical data with dependent responses demonstrates how information loss caused by the overt response scale has an unknown influence on effect sizes in moderated regression analysis. The number of scale steps measuring the dependent variable results in a form of systematic error that alters interaction effect sizes. (SLD)