scholarly journals Using Structural Equation Modeling to Study Traits and States in Intensive Longitudinal Data

2020 ◽  
Author(s):  
Sebastian Castro-Alvarez ◽  
Jorge Tendeiro ◽  
Rob Meijer ◽  
Laura Francina Bringmann
Keyword(s):  
Longitudinal Data ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Longitudinal Research ◽  
Equation Modeling ◽  
Intensive Longitudinal Data ◽  
The Common ◽  
The Stability

Traditionally, researchers have used time series and multilevel models to analyze intensive longitudinal data. However, these models do not directly address traits and states which conceptualize the stability and variability implicit in longitudinal research, and they do not explicitly take into account measurement error. An alternative to overcome these drawbacks is to consider structural equation models (state-trait SEMs) for longitudinal data that represent traits and states as latent variables. Most of these models are encompassed in the Latent State-Trait (LST) theory. These state-trait SEMs can be problematic when the number of measurement occasions increases. As they require the data to be in wide format, these models quickly become overparameterized and lead to non-convergence issues. For these reasons, multilevel versions of state-trait SEMs have been proposed, which require the data in long format. To study how suitable state-trait SEMs are for intensive longitudinal data, we carried out a simulation study. We compared the traditional single level to the multilevel version of three state-trait SEMs. The selected models were the multistate-singletrait (MSST) model, the common and unique trait-state (CUTS) model, and the trait-state-occasion (TSO) model. Furthermore, we also included an empirical application. Our results indicated that the TSO model performed best in both the simulated and the empirical data. To conclude, we highlight the usefulness of state-trait SEMs to study the psychometric properties of the questionnaires used in intensive longitudinal data. Yet, these models still have multiple limitations, some of which might be overcome by extending them to more general frameworks.

Multilevel Structural Equation Modeling for Intensive Longitudinal Data: A Practical Guide for Personality Researchers

2019 ◽  
Author(s):  
Gentiana Sadikaj ◽  
Aidan G.C. Wright ◽  
David Dunkley ◽  
David Zuroff ◽  
D.S. Moskowitz
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variable ◽  
Structural Equation ◽  
Longitudinal Research ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Intensive Longitudinal Data ◽  
Mixed Effects Modeling ◽  
Multilevel Structural Equation

Intensive longitudinal research designs are increasingly used to study personality processes. The resulting data can be highly informative in ways that other data cannot, but these data also pose statistical challenges. Most often a multilevel or mixed effects modeling approach is adopted which is appropriate but may not be optimal. Surprisingly little attention is given to reliability of measurement, and the models often lack adequate complexity to test theoretical questions of interest. These limitations can be addressed with multilevel structural equation modeling (MSEM), which weds the ability to deal with nested data structures with the strengths of structural equation modeling (e.g., latent variable models, multiple outcomes and mediators). This article provides a gentle introduction to MSEM for personality researchers. Following an initial review of the relevant challenges facing researchers interested in studying personality using intensive longitudinal data, basic issues in MSEM are summarized, and a series of example models are presented. The online supplementary material provides Mplus syntax for the models presented.

New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

scholarly journals Analyzing factorial survey data with structural equation models

2021 ◽  
pp. 004912412110431
Author(s):  
Bert Weijters ◽  
Eldad Davidov ◽  
Hans Baumgartner
Keyword(s):  
Structural Equation Modeling ◽  
Survey Data ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Social Science Research ◽  
Science Research ◽  
Equation Modeling ◽  
Factorial Survey ◽  
Modeling Framework

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different attributes by participants to arrive at an overall response to the vignettes. In the current paper, we explain how data from factorial surveys can be analyzed in a structural equation modeling framework using an approach called structural equation modeling for within-subject experiments. We review the use of factorial surveys in social science research, discuss typically used methods to analyze factorial survey data, introduce the structural equation modeling for within-subject experiments approach, and present an empirical illustration of the proposed method. We conclude by describing several extensions, providing some practical recommendations, and discussing potential limitations.

A Tutorial in Bayesian Mediation Analysis With Latent Variables

Methodology ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 137-146 ◽  
Author(s):  
Milica Miočević
Keyword(s):  
Mediation Analysis ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Estimation Method ◽  
Model Specification ◽  
Equation Modeling ◽  
Ml Estimation ◽  
Definition Of ◽  
Bayesian Mediation

Abstract. Maximum Likelihood (ML) estimation is a common estimation method in Structural Equation Modeling (SEM), and parameters in such analyses are interpreted using frequentist terms and definition of probability. It is also possible, and sometimes more advantageous ( Lee & Song, 2004 ; Rindskopf, 2012 ), to fit structural equation models in the Bayesian framework ( Kaplan & Depaoli, 2012 ; Levy & Choi, 2013 ; Scheines, Hoijtink, & Boomsma, 1999 ). Bayesian mediation analysis has been described for manifest variable models ( Enders, Fairchild, & MacKinnon, 2013 ; Yuan & MacKinnon, 2009 ). This tutorial outlines considerations in the analysis and interpretation of results for the single mediator model with latent variables. The reader is guided through model specification, estimation, and the interpretations of results obtained using two kinds of diffuse priors and one set of informative priors. Recommendations are made for applied researchers and annotated syntax is provided in R2OpenBUGS and Mplus. The target audience for this article are researchers wanting to learn how to fit the single mediator model as a Bayesian SEM.

scholarly journals On the Interpretation of Parameters in Multivariate Multilevel Models Across Different Combinations of Model Specification and Estimation

2019 ◽  
Vol 2 (3) ◽  
pp. 288-311 ◽  
Author(s):  
Lesa Hoffman
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Recent Change ◽  
Model Specification ◽  
Predictor Variables ◽  
Random Slopes ◽  
Multivariate Multilevel Models

The increasing availability of software with which to estimate multivariate multilevel models (also called multilevel structural equation models) makes it easier than ever before to leverage these powerful techniques to answer research questions at multiple levels of analysis simultaneously. However, interpretation can be tricky given that different choices for centering model predictors can lead to different versions of what appear to be the same parameters; this is especially the case when the predictors are latent variables created through model-estimated variance components. A further complication is a recent change to Mplus (Version 8.1), a popular software program for estimating multivariate multilevel models, in which the selection of Bayesian estimation instead of maximum likelihood results in different lower-level predictors when random slopes are requested. This article provides a detailed explication of how the parameters of multilevel models differ as a function of the analyst’s decisions regarding centering and the form of lower-level predictors (i.e., observed or latent), the method of estimation, and the variant of program syntax used. After explaining how different methods of centering lower-level observed predictor variables result in different higher-level effects within univariate multilevel models, this article uses simulated data to demonstrate how these same concepts apply in specifying multivariate multilevel models with latent lower-level predictor variables. Complete data, input, and output files for all of the example models have been made available online to further aid readers in accurately translating these central tenets of multivariate multilevel modeling into practice.

scholarly journals Measurement in Intensive Longitudinal Data

2021 ◽  
Author(s):  
Daniel McNeish
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Self Regulation ◽  
Measurement Model ◽  
Equation Modeling ◽  
Longitudinal Methods ◽  
Intensive Longitudinal Data ◽  
Measurement Models ◽  
Technological Advances ◽  
Intensive Longitudinal Methods

Technological advances have increased the prevalence of intensive longitudinal data as well as statistical techniques appropriate for these data, such as dynamic structural equation modeling (DSEM). Intensive longitudinal designs often investigate constructs related to affect or mood and do so with multiple item scales. However, applications of intensive longitudinal methods often rely on simple sums or averages of the administered items rather than considering a proper measurement model. This paper demonstrates how to incorporate measurement models into DSEM to (1) provide more rigorous measurement of constructs used in intensive longitudinal studies and (2) assess whether scales are invariant across time and across people, which is not possible when item responses are summed or averaged. We provide an example from an ecological momentary assessment study on self-regulation in adults with binge eating disorder and walkthrough how to fit the model in Mplus and how to interpret the results.

scholarly journals Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial

2020 ◽  
Author(s):  
Yilin Andre Wang ◽  
Mijke Rhemtulla
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Latent Variables ◽  
Power Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Regression Coefficient ◽  
Equation Modeling ◽  
Shiny App ◽  
Target Effect

Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample size rules of thumb that are not backed up by research, and power analyses for detecting model misfit. Missing from most current practices is power analysis to detect a target effect (e.g., a regression coefficient between latent variables). In this paper we (a) distinguish power to detect model misspecification from power to detect a target effect, (b) report the results of a simulation study on power to detect a target regression coefficient in a 3-predictor latent regression model, and (c) introduce a Shiny app, pwrSEM, for user-friendly power analysis for detecting target effects in structural equation models.

scholarly journals Causality on longitudinal data: Stable specification search in constrained structural equation modeling

2017 ◽  
Vol 27 (12) ◽  
pp. 3814-3834 ◽  
Author(s):  
Ridho Rahmadi ◽  
Perry Groot ◽  
Marieke HC van Rijn ◽  
Jan AJG van den Brand ◽  
Marianne Heins ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Chronic Fatigue ◽  
Causal Modeling ◽  
Model Complexity ◽  
Equation Modeling ◽  
Data Set ◽  
Finite Data

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

Sign in / Sign up

Export Citation Format

Share Document