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Levy, Roy – AERA Online Paper Repository, 2017
A conceptual distinction is drawn between indicators, which serve to define latent variables, and outcomes, which do not. However, commonly used frequentist and Bayesian estimation procedures do not honor this distinction. They allow the outcomes to influence the latent variables and the measurement model parameters for the indicators, rendering…
Descriptors: Bayesian Statistics, Structural Equation Models, Sampling, Goodness of Fit
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Kang, Yoonjeong; Hancock, Gregory R. – Journal of Experimental Education, 2017
Structured means analysis is a very useful approach for testing hypotheses about population means on latent constructs. In such models, a z test is most commonly used for testing the statistical significance of the relevant parameter estimates or of the differences between parameter estimates, where a z value is computed based on the asymptotic…
Descriptors: Models, Statistical Analysis, Hypothesis Testing, Statistical Significance
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Hansen, Kajsa Yang; Rosen, Monica; Gustafsson, Jan-Eric – Scandinavian Journal of Educational Research, 2011
This study examines the changes in educational inequality at the school- and individual-levels in 1991 and 2001. Comparisons are made between the IEA Reading Literacy Study 1991 and the so called 10-Year Trend Study in PIRLS 2001. The between-school differences in reading achievement variance and the size of the relationship between SES and…
Descriptors: Equal Education, Socioeconomic Status, Structural Equation Models, Reading Achievement
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Marsh, Herbert W.; Ludtke, Oliver; Nagengast, Benjamin; Trautwein, Ulrich; Morin, Alexandre J. S.; Abduljabbar, Adel S.; Koller, Olaf – Educational Psychologist, 2012
Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes--inappropriately--represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables;…
Descriptors: Foreign Countries, Classroom Environment, Educational Research, Research Design
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Peugh, James L.; Enders, Craig K. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Cluster sampling results in response variable variation both among respondents (i.e., within-cluster or Level 1) and among clusters (i.e., between-cluster or Level 2). Properly modeling within- and between-cluster variation could be of substantive interest in numerous settings, but applied researchers typically test only within-cluster (i.e.,…
Descriptors: Structural Equation Models, Monte Carlo Methods, Multivariate Analysis, Sampling
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Brown, Robert M.; Mazzarol, Timothy William – Higher Education: The International Journal of Higher Education and Educational Planning, 2009
This paper outlines the findings of a study employing a partial least squares (PLS) structural equation methodology to test a customer satisfaction model of the drivers of student satisfaction and loyalty in higher education settings. Drawing upon a moderately large sample of students enrolled in four "types" of Australian universities,…
Descriptors: Higher Education, Structural Equation Models, Least Squares Statistics, Student Attitudes
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Stapleton, Laura M. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This article discusses replication sampling variance estimation techniques that are often applied in analyses using data from complex sampling designs: jackknife repeated replication, balanced repeated replication, and bootstrapping. These techniques are used with traditional analyses such as regression, but are currently not used with structural…
Descriptors: Structural Equation Models, Simulation, Sampling, Longitudinal Studies
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Marsh, Herbert W.; Ludtke, Oliver; Robitzsch, Alexander; Trautwein, Ulrich; Asparouhov, Tihomir; Muthen, Bengt; Nagengast, Benjamin – Multivariate Behavioral Research, 2009
This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2)…
Descriptors: Educational Environment, Context Effect, Models, Structural Equation Models
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La Du, Terence J.; Tanaka, J. S. – Multivariate Behavioral Research, 1995
After reviewing the multiple fit indices in structural equation models, evidence on their behavior is presented through simulation studies in which sample size, estimation method, and model misspecification varied. Two sampling studies, with and without known populations, are presented, and implications for the use of fit indices are discussed.…
Descriptors: Estimation (Mathematics), Goodness of Fit, Sample Size, Sampling
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Bentler, Peter M.; Yuan, Ke-Hai – Multivariate Behavioral Research, 1999
Studied the small sample behavior of several test statistics based on the maximum-likelihood estimator but designed to perform better with nonnormal data. Monte Carlo results indicate the satisfactory performance of the "F" statistic recently proposed by K. Yuan and P. Bentler (1997). (SLD)
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Monte Carlo Methods, Sample Size
Jo, See-Heyon – 1995
The question of how to analyze unbalanced hierarchical data generated from structural equation models has been a common problem for researchers and analysts. Among difficulties plaguing statistical modeling are estimation bias due to measurement error and the estimation of the effects of the individual's hierarchical social milieu. This paper…
Descriptors: Algorithms, Bayesian Statistics, Equations (Mathematics), Error of Measurement
Nevitt, Johnathan; Hancock, Gregory R. – 1998
Though common structural equation modeling (SEM) methods are predicated upon the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to use distribution-free estimation methods. Fortunately, promising alternatives are being integrated into…
Descriptors: Chi Square, Computer Software, Error of Measurement, Estimation (Mathematics)