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Preacher, Kristopher J.; Zyphur, Michael J.; Zhang, Zhen – Psychological Methods, 2010
Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover,…
Descriptors: Structural Equation Models, Hypothesis Testing, Statistical Analysis, Predictor Variables
Bickel, Robert – Guilford Publications, 2007
This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical…
Descriptors: Regression (Statistics), Social Sciences, Statistical Analysis, Structural Equation Models
Hartig, Johannes; Holzel, Britta; Moosbrugger, Helfried – Multivariate Behavioral Research, 2007
Numerous studies have shown increasing item reliabilities as an effect of the item position in personality scales. Traditionally, these context effects are analyzed based on item-total correlations. This approach neglects that trends in item reliabilities can be caused either by an increase in true score variance or by a decrease in error…
Descriptors: True Scores, Error of Measurement, Structural Equation Models, Simulation
Wang, Zhongmiao; Thompson, Bruce – Journal of Experimental Education, 2007
In this study the authors investigated the use of 5 (i.e., Claudy, Ezekiel, Olkin-Pratt, Pratt, and Smith) R[squared] correction formulas with the Pearson r[squared]. The authors estimated adjustment bias and precision under 6 x 3 x 6 conditions (i.e., population [rho] values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9; population shapes normal, skewness…
Descriptors: Effect Size, Correlation, Mathematical Formulas, Monte Carlo Methods
College Persistence: Structural Equations Modeling Test of an Integrated Model of Student Retention.
Peer reviewedCabrera, Alberto F.; And Others – Journal of Higher Education, 1993
A study integrated the major propositions of 2 theories of college persistence (Tinto's and Bean's) and used the resulting framework to survey a population of 466 first-year college students at 1 university. Findings supported most of the hypothesized links between the models and revealed a complex role for environmental factors in retention.…
Descriptors: Academic Persistence, College Environment, College Freshmen, College Students
Peer reviewedPike, Gary R. – Research in Higher Education, 1991
Analysis of data on freshman-to-senior developmental gains in 722 University of Tennessee-Knoxville students provides evidence of the advantages of structural equation modeling with latent variables and suggests that the group differences identified by traditional analysis of variance and covariance techniques may be an artifact of measurement…
Descriptors: Case Studies, College Freshmen, College Seniors, Error of Measurement

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