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Direct linkEnders, Craig K. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
The Bollen-Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to nonnormal data. The purpose of this article is to demonstrate the use of a custom SAS macro program that can be used to implement the Bollen-Stine bootstrap with existing SEM software.…
Descriptors: Computer Software, Structural Equation Models, Statistical Analysis, Goodness of Fit
Little, Todd D.; Slegers, David W.; Card, Noel A. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
A non-arbitrary method for the identification and scale setting of latent variables in general structural equation modeling is introduced. This particular technique provides identical model fit as traditional methods (e.g., the marker variable method), but it allows one to estimate the latent parameters in a nonarbitrary metric that reflects the…
Descriptors: Structural Equation Models, Identification, Scaling, Metric System
Lu, Irene R. R.; Thomas, D. Roland; Zumbo, Bruno D. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This article reviews the problems associated with using item response theory (IRT)-based latent variable scores for analytical modeling, discusses the connection between IRT and structural equation modeling (SEM)-based latent regression modeling for discrete data, and compares regression parameter estimates obtained using predicted IRT scores and…
Descriptors: Least Squares Statistics, Item Response Theory, Structural Equation Models, Comparative Analysis
Ximenez, Carmen – Structural Equation Modeling: A Multidisciplinary Journal, 2006
The recovery of weak factors has been extensively studied in the context of exploratory factor analysis. This article presents the results of a Monte Carlo simulation study of recovery of weak factor loadings in confirmatory factor analysis under conditions of estimation method (maximum likelihood vs. unweighted least squares), sample size,…
Descriptors: Monte Carlo Methods, Factor Analysis, Least Squares Statistics, Sample Size
Fox, John – Structural Equation Modeling: A Multidisciplinary Journal, 2006
R is free, open-source, cooperatively developed software that implements the S statistical programming language and computing environment. The current capabilities of R are extensive, and it is in wide use, especially among statisticians. The sem package provides basic structural equation modeling facilities in R, including the ability to fit…
Descriptors: Structural Equation Models, Computer Software, Least Squares Statistics, Programming Languages
Lei, Ming; Lomax, Richard G. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This simulation study investigated the robustness of structural equation modeling to different degrees of nonnormality under 2 estimation methods, generalized least squares and maximum likelihood, and 4 sample sizes, 100, 250, 500, and 1,000. Each of the slight and severe nonnormality degrees was comprised of pure skewness, pure kurtosis, and both…
Descriptors: Structural Equation Models, Simulation, Sample Size, Least Squares Statistics
Kim, Sooyeon; Hagtvet, Knut A. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
This study focused on misspecifications in composing parcels to represent a latent construct. Two measurement design factors, item reliability and intercorrelations among parcels, defined 12 true unidimensional parcel models. Deviations from the true model were examined via a 2-facet measurement model in which items and parcels represented the 2…
Descriptors: Simulation, Factor Structure, Measurement Techniques, Goodness of Fit
Hipp, John R.; Bauer, Daniel J.; Bollen, Kenneth A. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This article describes a SAS macro to assess model fit of structural equation models by employing a test of the model-implied vanishing tetrads. Use of this test has been limited in the past, in part due to the lack of software that fully automates the test in a user-friendly way. The current SAS macro provides a straightforward method for…
Descriptors: Alternative Assessment, Structural Equation Models, Computer Software, Evaluation Methods
Stapleton, Laura M.; Leite, Walter L. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
With increases in the use of structural equation modeling (SEM) in the social sciences, graduate course offerings in this statistical technique can be expected to increase. Knowledge of the content of current SEM course offerings may provide ideas to instructors developing new courses or enhancing current courses. This article discusses results…
Descriptors: Course Descriptions, Course Content, Social Sciences, Structural Equation Models
Glockner-Rist, Angelika; Hoijtink, Herbert – Structural Equation Modeling: A Multidisciplinary Journal, 2003
Both structural equation modeling (SEM) and item response theory (IRT) can be used for factor analysis of dichotomous item responses. In this case, the measurement models of both approaches are formally equivalent. They were refined within and across different disciplines, and make complementary contributions to central measurement problems…
Descriptors: Social Science Research, Measurement Techniques, Social Sciences, Item Response Theory
Gold, Michael S.; Bentler, Peter M.; Kim, Kevin H. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
This article describes a Monte Carlo study of 2 methods for treating incomplete nonnormal data. Skewed, kurtotic data sets conforming to a single structured model, but varying in sample size, percentage of data missing, and missing-data mechanism, were produced. An asymptotically distribution-free available-case (ADFAC) method and structured-model…
Descriptors: Monte Carlo Methods, Computation, Sample Size, Comparative Analysis
Huba, G. J.; Panter, A. T.; Melchior, Lisa A.; Trevithick, Lee; Woods, Elizabeth R.; Wright, Eric; Feudo, Rudy; Tierney, Steven; Schneir, Arlene; Tenner, Adam; Remafedi, Gary; Greenberg, Brian; Sturdevant, Marsha; Goodman, Elizabeth; Hodgins, Antigone; Wallace, Michael; Brady, Russell E.; Singer, Barney; Marconi, Katherine – Structural Equation Modeling: A Multidisciplinary Journal, 2003
This article examines the structure of several HIV risk behaviors in an ethnically and geographically diverse sample of 8,251 clients from 10 innovative demonstration projects intended for adolescents living with, or at risk for, HIV. Exploratory and confirmatory factor analyses identified 2 risk factors for men (sexual intercourse with men and a…
Descriptors: Health Programs, Substance Abuse, Structural Equation Models, Risk
Bai, Yun; Poon, Wai-Yin; Cheung, Gordon Wai Hung – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Two-level data sets are frequently encountered in social and behavioral science research. They arise when observations are drawn from a known hierarchical structure, as when individuals are randomly drawn from groups that are randomly drawn from a target population. When the covariance structures in the group level and the individual level are the…
Descriptors: Evaluation Methods, Predictor Variables, Social Science Research, Behavioral Science Research
Noar, Seth M. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
Across a variety of disciplines and areas of inquiry, reliable and valid measures are a cornerstone of quality research. This is the case because to have confidence in the findings of our studies, we must first have confidence in the quality of our measures. This article briefly reviews the literature on scale development and provides an empirical…
Descriptors: Measures (Individuals), Factor Analysis, Structural Equation Models, Test Validity
Leung, Doris Y. P.; Kember, David – Structural Equation Modeling: A Multidisciplinary Journal, 2006
The expansion in the number of people entering higher education has resulted in a substantial increase in the proportion of students enrolling in nontraditional modes, such as part-time study. This study examined the question of whether part-time study curtails the development of the types of intellectual capabilities needed for a knowledge-based…
Descriptors: Full Time Students, Teaching Methods, Teacher Student Relationship, Part Time Students
