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Oud, Johan H. L.; Folmer, Henk – Multivariate Behavioral Research, 2011
This article addresses modeling oscillation in continuous time. It criticizes Steele and Ferrer's article "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011), particularly the approximate estimation procedure applied. This procedure is the latent version of the local linear approximation procedure…
Descriptors: Structural Equation Models, Computation, Calculus, Simulation
Steele, Joel S.; Ferrer, Emilio – Multivariate Behavioral Research, 2011
This article presents our response to Oud and Folmer's "Modeling Oscillation, Approximately or Exactly?" (2011), which criticizes aspects of our article, "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011). In this response, we present a conceptual explanation of the derivative-based estimation…
Descriptors: Calculus, Responses, Simulation, Models
Steele, Joel S.; Ferrer, Emilio – Multivariate Behavioral Research, 2011
We examine emotion self-regulation and coregulation in romantic couples using daily self-reports of positive and negative affect. We fit these data using a damped linear oscillator model specified as a latent differential equation to investigate affect dynamics at the individual level and coupled influences for the 2 partners in each couple.…
Descriptors: Affective Behavior, Calculus, Models, Females
Reichardt, Charles S. – Multivariate Behavioral Research, 2011
Maxwell, Cole, and Mitchell (2011) demonstrated that simple structural equation models, when used with cross-sectional data, generally produce biased estimates of meditated effects. I extend those results by showing how simple structural equation models can produce biased estimates of meditated effects when used even with longitudinal data. Even…
Descriptors: Structural Equation Models, Statistical Data, Longitudinal Studies, Error of Measurement
Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S. – Multivariate Behavioral Research, 2012
A Monte Carlo simulation was conducted to investigate the robustness of 4 latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of nonnormality of the observed…
Descriptors: Monte Carlo Methods, Computation, Robustness (Statistics), Structural Equation Models
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

Babyak, Michael A.; Green, Samuel B. – Multivariate Behavioral Research, 1997
Contrasting positions about the evaluation of multiple tests of constraints and control of Type I errors in structural equation modeling (SEM) are presented. It is argued that researchers should consider controlling for Type I errors in evaluating multiple tests of constraints for other than exploratory analyses. (SLD)
Descriptors: Error of Measurement, 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
Coffman, Donna L.; MacCallum, Robert C. – Multivariate Behavioral Research, 2005
The biasing effects of measurement error in path analysis models can be overcome by the use of latent variable models. In cases where path analysis is used in practice, it is often possible to use parcels as indicators of a latent variable. The purpose of the current study was to compare latent variable models in which parcels were used as…
Descriptors: Measurement, Error of Measurement, Path Analysis, Structural Equation Models
Song, Xin-Yuan; Lee, Sik-Yum – Multivariate Behavioral Research, 2006
In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…
Descriptors: Structural Equation Models, Bayesian Statistics, Markov Processes, Monte Carlo Methods
Song, Xin-Yuan; Lee, Sik-Yum – Multivariate Behavioral Research, 2005
In this article, a maximum likelihood approach is developed to analyze structural equation models with dichotomous variables that are common in behavioral, psychological and social research. To assess nonlinear causal effects among the latent variables, the structural equation in the model is defined by a nonlinear function. The basic idea of the…
Descriptors: Structural Equation Models, Simulation, Computation, Error of Measurement

Neale, Michael C.; And Others – Multivariate Behavioral Research, 1994
In studies of relatives, conventional multiple regression may not be appropriate because observations are not independent. Obtaining estimates of regression coefficients and correct standard errors from these populations through a structural equation modeling framework is discussed and illustrated with data from twins. (SLD)
Descriptors: Analysis of Covariance, Causal Models, Data Collection, Error of Measurement