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Challenges in the Use of Structural Equation Modeling (SEM) for...
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Challenges in the Use of Structural Equation Modeling (SEM) for Longitudinal Data Analysis in Medicine

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 2333

Special Issue Editor

Prof. Dr. Daniel Rodriguez

E-Mail Website
Guest Editor
Urban Public Health and Nutrition, La Salle University, 1900 West Olney Avenue, Philadelphia, PA 19141, USA
Interests: structural equation modeling; substance use; physical activity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to explore the many challenges of employing structural equation modeling (SEM) methods in the analysis of repeated-measures data in medical research. SEM is a multivariate method that permits researchers to assess complex models using a mix of latent and observed variables. The proliferation of specialized software permits researchers to assess a variety of complicated research questions, sometimes by simply drawing pictures of relations without much comprehension of the complexities associated with the modeling. As such, researchers often apply SEM inappropriately, ignoring issues such as the selection of appropriate estimators, sample size, and model fit indexes. As such, we invite authors to submit manuscripts that address the many challenges of applying SEM to practical problems in medicine, with special emphasis on longitudinal data analysis. Appropriate questions include the estimation of sample size and power, the appropriate use of chi-square and other indexes of fit, dealing with non-normal data and post hoc model fitting.

Prof. Dr. Daniel Rodriguez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural equation modeling
  • longitudinal data analysis
  • power analysis
  • sample size estimation
  • parameter estimation

Published Papers (1 paper)

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Research

25 pages, 796 KiB  
Article
Determining Dimensionality with Dichotomous Variables: A Monte Carlo Simulation Study and Applications to Missing Data in Longitudinal Research
by Ting Dai and Adam Davey
Mathematics 2023, 11(6), 1411; https://doi.org/10.3390/math11061411 - 15 Mar 2023
Viewed by 2043
Abstract
Dichotomous data correspond with various types of commonly encountered data, e.g., positive/negative, case/control, missing/observed, in many fields, including medicine, health, and social sciences. Despite their ubiquity, criteria for determining dimensionality from dichotomous variables are not yet established. We conducted a large-scale simulation (Study [...] Read more.
Dichotomous data correspond with various types of commonly encountered data, e.g., positive/negative, case/control, missing/observed, in many fields, including medicine, health, and social sciences. Despite their ubiquity, criteria for determining dimensionality from dichotomous variables are not yet established. We conducted a large-scale simulation (Study 1) to evaluate four criteria—Kaiser, empirical Kaiser, parallel analysis, and profile likelihood—to determine the dimensionality of dichotomous data across combinations of correlation matrices (Pearson r or tetrachoric ρ) and analysis methods (principal component analysis or exploratory factor analysis), and combinations of study characteristics: sample sizes (100, 250, and 1000), variable splits (10%/90%, 25%/75%, and 50%/50%), dimensions (1, 3, 5, and 10), and items per dimension (3, 5, and 10) with 1000 replications per condition. Parallel analysis performed best, recovering dimensionality in 87.9% of replications when using principal component analysis with Pearson correlations. Guidance for selecting criteria is provided. In Study 2, we applied this dimensionality reduction approach to two different longitudinal data sets where missing data posed difficulty for multivariate data analysis. The applications of this approach to longitudinal data suggest that the exploration of resulting missing data meta-patterns is useful in practice. Full article
(This article belongs to the Special Issue Challenges in the Use of Structural Equation Modeling (SEM) for Longitudinal Data Analysis in Medicine)
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