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Hancock, Gregory R.; Schoonen, Rob – Language Learning, 2015
Although classical statistical techniques have been a valuable tool in second language (L2) research, L2 research questions have started to grow beyond those techniques' capabilities, and indeed are often limited by them. Questions about how complex constructs relate to each other or to constituent subskills, about longitudinal development in…
Descriptors: Structural Equation Models, Language Research, Second Language Learning, Statistical Analysis
Ockey, Gary J.; Choi, Ikkyu – Language Assessment Quarterly, 2015
Studies that use structural equation modeling (SEM) techniques are increasingly encountered in the language assessment literature. This popularity has created the need for a set of guidelines that can indicate what should be included in a research report and make it possible for research consumers to judge the appropriateness of the…
Descriptors: Second Language Learning, Structural Equation Models, Language Tests, Guidelines
Cheung, Mike
W.-L. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects…
Descriptors: Structural Equation Models, Maximum Likelihood Statistics, Guidelines, Multivariate Analysis
Blozis, Shelley A.; Harring, Jeffrey R.; Mels, Gerhard – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Latent curve models offer a flexible approach to the study of longitudinal data when the form of change in a response is nonlinear. This article considers such models that are conditionally linear with regard to the random coefficients at the 2nd level. This framework allows fixed parameters to enter a model linearly or nonlinearly, and random…
Descriptors: Structural Equation Models, Longitudinal Studies, Guidelines, Computer Software
Lei, Pui-Wa; Wu, Qiong – Educational Measurement: Issues and Practice, 2007
Structural equation modeling (SEM) is a versatile statistical modeling tool. Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies. General steps of SEM are discussed along with important considerations in each step. Simple examples are provided to…
Descriptors: Structural Equation Models, Guidelines, Definitions, Computer Software
Geiser, Christian; Eid, Michael; Nussbeck, Fridtjof W. – Psychological Methods, 2008
In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006, see EJ751121) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an…
Descriptors: Factor Structure, Factor Analysis, Structural Equation Models, Questionnaires
Schreiber, James B.; Nora, Amaury; Stage, Frances K.; Barlow, Elizabeth A.; King, Jamie – Journal of Educational Research, 2006
The authors provide a basic set of guidelines and recommendations for information that should be included in any manuscript that has confirmatory factor analysis or structural equation modeling as the primary statistical analysis technique. The authors provide an introduction to both techniques, along with sample analyses, recommendations for…
Descriptors: Structural Equation Models, Guidelines, Factor Analysis, Statistical Analysis