In the context of hybrid instruction, this study was designed to explore whether gender has an influence on learners' preferences for synchronous or asynchronous modes of computer-mediated communication, and whether this decision impacts learners' self-efficacy (SE) towards knowledge acquisition. The participants were 180 teacher-education…

Although an analysis of covariance (ANCOVA) allows for the removal of an uncontrolled source of variation that is represented by the covariates, this "correction," which occurs with the dependent variable scores is unfortunately seen by some as a blanket adjustment device that can be used with an inadequate amount of consideration for…

A high-breakdown estimator is a robust statistic that can withstand a large amount of contaminated data. In linear regression, high-breakdown estimators can detect outliers and distinguish between good and bad leverage points. This paper summarizes the case for high-breakdown regression and emphasizes the least quartile difference estimator (LQD)…

This presentation is based on a method for analyzing the verbal flow in text-building behavior. The method is called Perspective Text Analysis (PTA). It locates and makes use of the discontinuities in produced text. Its task is to characterize the mechanism that governs language production and to foster an understanding of the actual processes of…

The analysis of covariance as a procedure for statistical correction of the effects for an extraneous variable, called a "covariate," is presented. An heuristic data set is used to make the discussion of the calculation of ANCOVA partitions easier to follow. A discussion of homogeneity of regression as an essential condition to be met…

Previous research has demonstrated particular inadequacies in conventional methods used to identify outlier cases in bivariate regression models. Only through a combination of methods can one detect all of the deviant points potentially overinfluencing a regression model's parameters. This paper investigates whether a range of data points might…

Commonality analysis is a procedure for decomposing the coefficient of determination (R superscript 2) in multiple regression analyses into the percent of variance in the dependent variable associated with each independent variable uniquely, and the proportion of explained variance associated with the common effects of predictors in various…

The purpose of this paper is to illustrate the use of tobit analysis and tobit decomposition in educational research. Tobit estimates of growth rate in the presence of a ceiling effect were compared with ordinary least squares (OLS) and weighted least squares (WLS) estimates. The tobit estimates had the smallest standard error, the smallest bias,…

The question of regression to the mean is discussed in the contexts of the Title I Evaluation and Reporting System (TIERS) and the Bilingual Education Evaluation System (BEES), both of which involve assessing the achievement growth of project students from pre- to post-test. The correction formula recommended for use with TIERS was designed to…

The predicted scores produced by regression with (1) single best predictor, (2) two best predictors, (3) all predictors with observed values, and (4) all predictors with or without observed values were compared with variable means as estimates of missing values. The study was conducted in a simulation mode on nx8 data matrices using various levels…

Researchers sometimes use stepwise methods to eliminate variables from analyses when the variables do not appreciably improve the ability to explain or predict inferences about the importance of various predictor variables. It is argued that stepwise methods are usually not appropriate for these purposes for three reasons. First, some researchers…

The parallel analysis method for determining the number of components to retain in a principal components analysis has received a recent resurgence of support and interest. However, researchers and practitioners desiring to use this criterion have been hampered by the required Monte Carlo analyses needed to develop the criteria. Two recent…

This study applies a procedure which yields estimates of true score change on the Scholastic Aptitude Test (SAT) adjusted for regression effects and student self-selection. It is shown that student self-selection in deciding to repeat an admissions test probably involves factors in addition to the measurement error attributable to variations in…

Correlation-based approaches to causal analysis contain too much irrelevant information that masks and modulates the true nature of causal processes in the world. Both causal modeling and path analysis/structural equations give the wrong answers for certain conceptions of causation, given certain assumptions about the "error" variables.…

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…