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W. Holmes Finch – Educational and Psychological Measurement, 2024
Dominance analysis (DA) is a very useful tool for ordering independent variables in a regression model based on their relative importance in explaining variance in the dependent variable. This approach, which was originally described by Budescu, has recently been extended to use with structural equation models examining relationships among latent…
Descriptors: Models, Regression (Statistics), Structural Equation Models, Predictor Variables
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Sim, Mikyung; Kim, Su-Young; Suh, Youngsuk – Educational and Psychological Measurement, 2022
Mediation models have been widely used in many disciplines to better understand the underlying processes between independent and dependent variables. Despite their popularity and importance, the appropriate sample sizes for estimating those models are not well known. Although several approaches (such as Monte Carlo methods) exist, applied…
Descriptors: Sample Size, Statistical Analysis, Predictor Variables, Path Analysis
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Magnus, Brooke E.; Liu, Yang – Educational and Psychological Measurement, 2022
Questionnaires inquiring about psychopathology symptoms often produce data with excess zeros or the equivalent (e.g., none, never, and not at all). This type of zero inflation is especially common in nonclinical samples in which many people do not exhibit psychopathology, and if unaccounted for, can result in biased parameter estimates when…
Descriptors: Symptoms (Individual Disorders), Psychopathology, Research Methodology, Probability
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Jana Welling; Timo Gnambs; Claus H. Carstensen – Educational and Psychological Measurement, 2024
Disengaged responding poses a severe threat to the validity of educational large-scale assessments, because item responses from unmotivated test-takers do not reflect their actual ability. Existing identification approaches rely primarily on item response times, which bears the risk of misclassifying fast engaged or slow disengaged responses.…
Descriptors: Foreign Countries, College Students, Guessing (Tests), Multiple Choice Tests
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Kim, Eunsook; von der Embse, Nathaniel – Educational and Psychological Measurement, 2021
Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the…
Descriptors: Probability, Models, Statistical Analysis, Congruence (Psychology)
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Cain, Meghan K.; Zhang, Zhiyong; Bergeman, C. S. – Educational and Psychological Measurement, 2018
This article serves as a practical guide to mediation design and analysis by evaluating the ability of mediation models to detect a significant mediation effect using limited data. The cross-sectional mediation model, which has been shown to be biased when the mediation is happening over time, is compared with longitudinal mediation models:…
Descriptors: Mediation Theory, Case Studies, Longitudinal Studies, Measurement Techniques
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Raykov, Tenko; Lee, Chun-Lung; Marcoulides, George A.; Chang, Chi – Educational and Psychological Measurement, 2013
The relationship between saturated path-analysis models and their fit to data is revisited. It is demonstrated that a saturated model need not fit perfectly or even well a given data set when fit to the raw data is examined, a criterion currently frequently overlooked by researchers utilizing path analysis modeling techniques. The potential of…
Descriptors: Structural Equation Models, Goodness of Fit, Path Analysis, Correlation
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Aydin, Burak; Leite, Walter L.; Algina, James – Educational and Psychological Measurement, 2016
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…
Descriptors: Error of Measurement, Predictor Variables, Randomized Controlled Trials, Experimental Groups
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Hirschfeld, Robert R.; Thomas, Christopher H.; McNatt, D. Brian – Educational and Psychological Measurement, 2008
The authors explored implications of individuals' self-deception (a trait) for their self-reported intrinsic and extrinsic motivational dispositions and their actual learning performance. In doing so, a higher order structural model was developed and tested in which intrinsic and extrinsic motivational dispositions were underlying factors that…
Descriptors: Deception, Predictor Variables, Motivation, Incentives
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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
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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
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Friedman, Sally; Weisberg, Herbert F. – Educational and Psychological Measurement, 1981
The first eigenvalue of a correlation matrix indicates the maximum amount of the variance of the variables which can be accounted for with a linear model by a single underlying factor. The first eigenvalue measures the primary cluster in the matrix, its number of variables and average correlation. (Author/RL)
Descriptors: Correlation, Mathematical Models, Matrices, Predictor Variables
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Gocka, Edward F. – Educational and Psychological Measurement, 1973
The proposed method has the advantage of being a rational procedure which reduces the larger set of variables'' down to a desired subset of predictor variables. The selected subset, then, can be coded for a full regression run if it contains multiple level category variables among those selected. (Author)
Descriptors: Mathematical Models, Measurement Techniques, Multiple Regression Analysis, Predictor Variables
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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
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Rogosa, David – Educational and Psychological Measurement, 1981
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)
Descriptors: Hypothesis Testing, Mathematical Models, Predictor Variables, Regression (Statistics)
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