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Julian F. Lohmann; Steffen Zitzmann; Martin Hecht – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The recently proposed "continuous-time latent curve model with structured residuals" (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle…
Descriptors: Time Management, Behavioral Science Research, Predictive Validity, Predictor Variables
Bang Quan Zheng; Peter M. Bentler – Structural Equation Modeling: A Multidisciplinary Journal, 2025
This paper aims to advocate for a balanced approach to model fit evaluation in structural equation modeling (SEM). The ongoing debate surrounding chi-square test statistics and fit indices has been characterized by ambiguity and controversy. Despite the acknowledged limitations of relying solely on the chi-square test, its careful application can…
Descriptors: Monte Carlo Methods, Structural Equation Models, Goodness of Fit, Robustness (Statistics)
Dandan Tang; Steven M. Boker; Xin Tong – Structural Equation Modeling: A Multidisciplinary Journal, 2025
The replication crisis in social and behavioral sciences has raised concerns about the reliability and validity of empirical studies. While research in the literature has explored contributing factors to this crisis, the issues related to analytical tools have received less attention. This study focuses on a widely used analytical tool -…
Descriptors: Test Validity, Factor Analysis, Replication (Evaluation), Social Science Research
Hilley, Chanler D.; O'Rourke, Holly P. – International Journal of Behavioral Development, 2022
Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent…
Descriptors: Behavioral Science Research, Research Methodology, Longitudinal Studies, Behavior Change
Ke-Hai Yuan; Zhiyong Zhang – Grantee Submission, 2024
Data in social and behavioral sciences typically contain measurement errors and also do not have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze such data. This article discuss issues in latent-variable modeling as compared to regression analysis with composite-scores. Via logical reasoning and analytical results…
Descriptors: Error of Measurement, Measurement Techniques, Social Science Research, Behavioral Science Research
Ke-Hai Yuan; Zhiyong Zhang; Lijuan Wang – Grantee Submission, 2024
Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models…
Descriptors: Structural Equation Models, Path Analysis, Weighted Scores, Comparative Testing
Runyon, Katie; Stevens, Tara; Roberts, Brook; Whittaker, Richelle; Clark, Ashley; Chapman, Christy K.; Boggs-Lopez, Misty – Contemporary School Psychology, 2018
The most recent version of the Individuals with Disabilities Education Improvement Act (IDEIA) emphasizes research-based intervention in the school setting. Administrators expect school psychologists to lead initiatives introducing interventions and techniques derived from scientific approaches, such as applied behavior analysis (ABA). However, in…
Descriptors: Self Efficacy, School Psychologists, School Psychology, Structural Equation Models
Markus, Keith A. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…
Descriptors: Structural Equation Models, Behavioral Science Research, Research Methodology, Influences
Pek, Jolynn; MacCallum, Robert C. – Multivariate Behavioral Research, 2011
The detection of outliers and influential observations is routine practice in linear regression. Despite ongoing extensions and development of case diagnostics in structural equation models (SEM), their application has received limited attention and understanding in practice. The use of case diagnostics informs analysts of the uncertainty of model…
Descriptors: Structural Equation Models, Democracy, Regression (Statistics), Observation
Zhong, Xiaoling; Yuan, Ke-Hai – Multivariate Behavioral Research, 2011
In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data,…
Descriptors: Structural Equation Models, Simulation, Racial Identification, Computation
Cheung, Mike W. -L. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Confidence intervals (CIs) for parameters are usually constructed based on the estimated standard errors. These are known as Wald CIs. This article argues that likelihood-based CIs (CIs based on likelihood ratio statistics) are often preferred to Wald CIs. It shows how the likelihood-based CIs and the Wald CIs for many statistics and psychometric…
Descriptors: Intervals, Structural Equation Models, Simulation, Correlation
Peter, Jochen; Valkenburg, Patti M. – Human Communication Research, 2009
The aim of this study was to investigate, within a social comparison framework, the causal relationship between adolescents' use of sexually explicit Internet material (SEIM) and their sexual satisfaction. In addition, we tested which adolescents were most susceptible to a potential influence of SEIM on sexual satisfaction. Between May 2006 and…
Descriptors: Structural Equation Models, Adolescents, Sexuality, Internet
Scott, Felicity; Rhodes, Ryan E.; Downs, Danielle Symons – Journal of American College Health, 2009
Objective: Public health messaging about physical activity (PA) sometimes combines moderate and vigorous intensity, but the variance/invariance of the motives for PA by intensity has received scant attention. Thus, the purpose of this study was to examine the beliefs and motivations associated with regular moderate- and vigorous-intensity PA in a…
Descriptors: Physical Activities, Physical Activity Level, Public Health, Social Cognition
Williams, Jason; MacKinnon, David P. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Recent advances in testing mediation have found that certain resampling methods and tests based on the mathematical distribution of 2 normal random variables substantially outperform the traditional "z" test. However, these studies have primarily focused only on models with a single mediator and 2 component paths. To address this limitation, a…
Descriptors: Intervals, Testing, Predictor Variables, Effect Size
Sun, Jun; Willson, Victor L. – Educational and Psychological Measurement, 2008
This article proposes a multilevel modeling approach to study the general and specific attitudes formed in human learning behavior. Based on the premises of activity theory, it conceptualizes the unit of analysis for attitude measurement as a scalable and evolving activity system rather than a single action. Measurement issues related to this…
Descriptors: Structural Equation Models, Psychometrics, Multitrait Multimethod Techniques, Attitude Measures
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