Statistics in Epidemiology

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 5031

Special Issue Editor


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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
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Special Issue Information

Dear Colleagues,

I am pleased to announce a Special Issue on the use of latent variable modeling in epidemiology. I am soliciting manuscripts using all possible latent variable modeling methods, including but not limited to structural equation modeling (SEM), latent growth curve modeling (LGCM/LGM), confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and cross-sectional or longitudinal mixture modeling (e.g., growth mixture modeling, latent class analysis). Suitable manuscripts could include but are not limited to an assessment of trajectories of a public health issue, such as electronic cigarette smoking, testing of the construct validity of an epidemiologic measure of a known construct (e.g., depression, suicidal ideation), comparing ideal measurement level (e.g., ordinal versus binary or count) of substance use behaviors such as cigarette smoking, or testing the validity of common theoretical models (e.g., health beliefs model) to public health issues such as type II diabetes, using structural equation modeling. Manuscripts applying latent variable modeling to the COVID-19 epidemic are especially welcome. In addition, manuscripts introducing specific latent variable modeling methods to epidemiology practitioners are especially welcome, including those discussing controversies in SEM such as the assessment of model fit, inappropriate use of chi-square to assess model fit, and post hoc model adjustment to improve model fit to the data.

I am looking forward to receiving your submissions. I hope you and your loved ones are staying safe.

Sincerely,

Prof. Dr. Daniel Rodriguez
Guest Editor

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Keywords

  • latent variable modeling
  • structural equation modeling (SEM)
  • confirmatory factor analysis (CFA)
  • latent class analysis (LCA)
  • latent growth curve modeling (LGCM)
  • growth mixture modeling (GMM)
  • exploratory structural equation modeling (ESEM)
  • epidemiology
  • public health

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Published Papers (1 paper)

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Research

10 pages, 230 KiB  
Article
Psychometric Properties of the Adult Self-Report: Data from over 11,000 American Adults
by Michelle Guerrero, Matt Hoffmann and Laura Pulkki-Råback
Stats 2020, 3(4), 465-474; https://doi.org/10.3390/stats3040029 - 29 Oct 2020
Cited by 13 | Viewed by 4839
Abstract
The first purpose of this study was to examine the factor structure of the Adult Self-Report (ASR) via traditional confirmatory factor analysis (CFA) and contemporary exploratory structural equation modeling (ESEM). The second purpose was to examine the measurement invariance of the ASR subscales [...] Read more.
The first purpose of this study was to examine the factor structure of the Adult Self-Report (ASR) via traditional confirmatory factor analysis (CFA) and contemporary exploratory structural equation modeling (ESEM). The second purpose was to examine the measurement invariance of the ASR subscales across age groups. We used baseline data from the Adolescent Brain Cognitive Development study. ASR data from 11,773 participants were used to conduct the CFA and ESEM analyses and data from 11,678 participants were used to conduct measurement invariance testing. Fit indices supported both the CFA and ESEM solutions, with the ESEM solution yielding better fit indices. However, several items in the ESEM solution did not sufficiently load on their intended factors and/or cross-loaded on unintended factors. Results from the measurement invariance analysis suggested that the ASR subscales are robust and fully invariant across subgroups of adults formed on the basis of age (18–35 years vs. 36–59 years). Future research should seek to both CFA and ESEM to provide a more comprehensive assessment of the ASR. Full article
(This article belongs to the Special Issue Statistics in Epidemiology)
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