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Murrah, William M. – Educational and Psychological Measurement, 2020
Multiple regression is often used to compare the importance of two or more predictors. When the predictors being compared are measured with error, the estimated coefficients can be biased and Type I error rates can be inflated. This study explores the impact of measurement error on comparing predictors when one is measured with error, followed by…
Descriptors: Error of Measurement, Statistical Bias, Multiple Regression Analysis, Predictor Variables
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
Le, Huy; Marcus, Justin – Educational and Psychological Measurement, 2012
This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…
Descriptors: Monte Carlo Methods, Probability, Mathematical Concepts, Effect Size
Williams, Matt N.; Gomez Grajales, Carlos Alberto; Kurkiewicz, Dason – Practical Assessment, Research & Evaluation, 2013
In 2002, an article entitled "Four assumptions of multiple regression that researchers should always test" by Osborne and Waters was published in "PARE." This article has gone on to be viewed more than 275,000 times (as of August 2013), and it is one of the first results displayed in a Google search for "regression…
Descriptors: Multiple Regression Analysis, Misconceptions, Reader Response, Predictor Variables
Aloe, Ariel M.; Becker, Betsy Jane – Journal of Educational and Behavioral Statistics, 2012
A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…
Descriptors: Meta Analysis, Effect Size, Multiple Regression Analysis, Models
Kim, Rae Seon – ProQuest LLC, 2011
When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…
Descriptors: Meta Analysis, Effect Size, Multiple Regression Analysis, Error of Measurement
Brown, Jane D. – Developmental Psychology, 2011
Steinberg and Monahan's (2011) reanalysis of the Teen Media longitudinal survey of adolescents does not meet prevailing standards for propensity score analysis and therefore does not undermine the original conclusions of the Brown, L'Engle, Pardun, Guo, Kenneavy, and Jackson (2006) analysis. The media do matter in the sexual socialization of…
Descriptors: Socialization, Adolescents, Scores, Sexuality
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
Pustjens, Heidi; Van de gaer, Eva; Van Damme, Jan; Onghena, Patrick – School Effectiveness and School Improvement, 2008
The major aim of educational effectiveness research is to examine and explain school, class, and teacher differences with respect to relevant educational criteria. Until now, in the large majority of studies, language and mathematics scores were used as a criterion. In the present study, the educational track students choose at the start of…
Descriptors: Catholic Schools, Academic Achievement, Secondary Education, Instructional Effectiveness
Aguinis, Herman; Pierce, Charles A. – Applied Psychological Measurement, 2006
The computation and reporting of effect size estimates is becoming the norm in many journals in psychology and related disciplines. Despite the increased importance of effect sizes, researchers may not report them or may report inaccurate values because of a lack of appropriate computational tools. For instance, Pierce, Block, and Aguinis (2004)…
Descriptors: Effect Size, Multiple Regression Analysis, Predictor Variables, Error of Measurement
Strand, Kenneth H. – Online Submission, 2000
This paper contains information concerning the following: 1. An overview of multivariate analysis of variance, and discriminant (DA) and canonical (CA) analyses. 2. An introduction to specification and measurement errors, and collinearity. 3. The sparsity of information concerning specification and measurement errors and collinearity as they…
Descriptors: Multivariate Analysis, Multiple Regression Analysis, Discriminant Analysis, Error of Measurement
Vehrs, Pat R.; George, James D.; Fellingham, Gilbert W.; Plowman, Sharon A.; Dustman-Allen, Kymberli – Measurement in Physical Education and Exercise Science, 2007
This study was designed to develop a single-stage submaximal treadmill jogging (TMJ) test to predict VO[subscript 2]max in fit adults. Participants (N = 400; men = 250 and women = 150), ages 18 to 40 years, successfully completed a maximal graded exercise test (GXT) at 1 of 3 laboratories to determine VO[subscript 2]max. The TMJ test was completed…
Descriptors: Metabolism, Body Composition, Physical Activities, Physical Fitness
Peer reviewedWerts, Charles E.; Linn, Robert L. – Educational and Psychological Measurement, 1972
The general problem of using group status to estimate true scores given multiple measures is considered in this paper. (Authors)
Descriptors: Error of Measurement, Group Status, Mathematical Applications, Multiple Regression Analysis
Peer reviewedRothstein, Hannah R.; And Others – Educational and Psychological Measurement, 1990
A microcomputer program that computes statistical power for analyses performed by multiple regression/correlation is described. The program features a spreadsheet-like interface, outputting the effect size and value of power corresponding to the input parameters, including predictor variables, sample size, alpha, and error type. (TJH)
Descriptors: Computer Software, Correlation, Effect Size, Error of Measurement
Cummings, Corenna C. – 1982
The accuracy and variability of 4 cross-validation procedures and 18 formulas were compared concerning their ability to estimate the population multiple correlation and the validity of the sample regression equation in the population. The investigation included two types of regression, multiple and stepwise; three sample sizes, N = 30, 60, 120;…
Descriptors: Correlation, Error of Measurement, Mathematical Formulas, Multiple Regression Analysis
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