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Olvera Astivia, Oscar L.; Kroc, Edward – Educational and Psychological Measurement, 2019
Within the context of moderated multiple regression, mean centering is recommended both to simplify the interpretation of the coefficients and to reduce the problem of multicollinearity. For almost 30 years, theoreticians and applied researchers have advocated for centering as an effective way to reduce the correlation between variables and thus…
Descriptors: Multiple Regression Analysis, Computation, Correlation, Statistical Distributions
Shear, Benjamin R.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2013
Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…
Descriptors: Error of Measurement, Multiple Regression Analysis, Data Analysis, Computer Simulation
Reckase, Mark D.; Xu, Jing-Ru – Educational and Psychological Measurement, 2015
How to compute and report subscores for a test that was originally designed for reporting scores on a unidimensional scale has been a topic of interest in recent years. In the research reported here, we describe an application of multidimensional item response theory to identify a subscore structure in a test designed for reporting results using a…
Descriptors: English, Language Skills, English Language Learners, Scores
Kobrin, Jennifer L.; Kim, YoungKoung; Sackett, Paul R. – Educational and Psychological Measurement, 2012
There is much debate on the merits and pitfalls of standardized tests for college admission, with questions regarding the format (multiple-choice vs. constructed response), cognitive complexity, and content of these assessments (achievement vs. aptitude) at the forefront of the discussion. This study addressed these questions by investigating the…
Descriptors: Grade Point Average, Standardized Tests, Predictive Validity, Predictor Variables
Chan, Wai – Educational and Psychological Measurement, 2009
A typical question in multiple regression analysis is to determine if a set of predictors gives the same degree of predictor power in two different populations. Olkin and Finn (1995) proposed two asymptotic-based methods for testing the equality of two population squared multiple correlations, [rho][superscript 2][subscript 1] and…
Descriptors: Multiple Regression Analysis, Intervals, Correlation, Computation
Algina, James; Keselman, Harvey J.; Penfield, Randall J. – Educational and Psychological Measurement, 2008
A squared semipartial correlation coefficient ([Delta]R[superscript 2]) is the increase in the squared multiple correlation coefficient that occurs when a predictor is added to a multiple regression model. Prior research has shown that coverage probability for a confidence interval constructed by using a modified percentile bootstrap method with…
Descriptors: Intervals, Correlation, Probability, Multiple Regression Analysis
Knofczynski, Gregory T.; Mundfrom, Daniel – Educational and Psychological Measurement, 2008
When using multiple regression for prediction purposes, the issue of minimum required sample size often needs to be addressed. Using a Monte Carlo simulation, models with varying numbers of independent variables were examined and minimum sample sizes were determined for multiple scenarios at each number of independent variables. The scenarios…
Descriptors: Sample Size, Monte Carlo Methods, Predictor Variables, Prediction
Algina, James; Keselman, H. J.; Penfield, Randall D. – Educational and Psychological Measurement, 2007
The increase in the squared multiple correlation coefficient ([Delta]R[squared]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. The coverage probability that an asymptotic and percentile bootstrap confidence interval includes [Delta][rho][squared] was investigated. As expected,…
Descriptors: Probability, Intervals, Multiple Regression Analysis, Correlation

Aiken, Lewis R., Jr. – Educational and Psychological Measurement, 1974
Short-cut formulas are presented for direct computation of the beta weights, the standard errors of the beta weights, and the multiple correlation coefficient for multiple regression problems involving three independent variables and one dependent variable. (Author)
Descriptors: Correlation, Multiple Regression Analysis, Statistical Analysis

Huberty, Carl J.; Mourad, Salah A. – Educational and Psychological Measurement, 1980
Real data are used to illustrate the comparison of two estimators for the square of a population correlation coefficient and the true validity of a sample prediction equation. Interpretive approaches to, and problems in, multiple correlation/prediction estimation are discussed. (Author/BW)
Descriptors: Correlation, Multiple Regression Analysis, Predictive Measurement, Validity
Shieh, Gwowen – Educational and Psychological Measurement, 2006
This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…
Descriptors: Multiple Regression Analysis, Modeling (Psychology), Predictor Variables, Correlation
Tables for Determining the Minimum Incremental Significance of the Multiple Correlation Coefficient.

Dutoit, Eugene F.; Penfield, Douglas A. – Educational and Psychological Measurement, 1979
Assuming a multiple linear regression model with q independent variables, a procedure is developed for determining the minimum statistically significant increase in the multiple correlation coefficient when an additional independent variable is considered for regression. The procedure is presented analytically and in table form. Examples are…
Descriptors: Correlation, Multiple Regression Analysis, Predictor Variables, Tables (Data)

Tzelgov, Joseph; Stern, Iris – Educational and Psychological Measurement, 1978
Following Conger's revised definition of suppressor variables, the universe relationships among two predictors and a criterion is analyzed. A simple mapping of relationships, based on the correlation between two predictors and the ratio of their validities, is provided. The relation between suppressor and part correlation is also discussed.…
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Predictor Variables

Williams, John D.; Lindem, Alfred C. – Educational and Psychological Measurement, 1971
Setwise regression analysis is a new technique developed to allow a stepwise solution when the interest is in sets of variables rather than in single variables. (CK)
Descriptors: Computer Programs, Correlation, Multiple Regression Analysis, Predictor Variables

Elshout, Jan; And Others – Educational and Psychological Measurement, 1979
It has been shown that the degree of restriction of range taken into account in testing the hypothesis that rho equals zero, entails risks of incorrect inferences. It is argued that an alternative is to disregard the restriction of range and to use the common t-statistics proposed by regression theory. (Author/JKS)
Descriptors: Correlation, Data Analysis, Hypothesis Testing, Multiple Regression Analysis