Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures.

Kwok, Oi-Man; Underhill, Andrea T.; Berry, Jack W.; Luo, Wen; Elliott, Timothy R.; Yoon, Myeongsun

doi:10.1037/a0012765

Cited by 388Statistical Methods and Bayesian InferenceSpatial and Panel Data AnalysisMental Health Research Topics

Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures.

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Kwok, Oi-Man, Underhill, Andrea T., Berry, Jack W., Luo, Wen, Elliott, Timothy R., Yoon, Myeongsun·2008·Rehabilitation Psychology

DOI 10.1037/a0012765PMC PMC2613314

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Abstract

The use and quality of longitudinal research designs has increased over the past two decades, and new approaches for analyzing longitudinal data, including multi-level modeling (MLM) and latent growth modeling (LGM), have been developed. The purpose of this paper is to demonstrate the use of MLM and its advantages in analyzing longitudinal data. Data from a sample of individuals with intra-articular fractures of the lower extremity from the University of Alabama at Birmingham's Injury Control Research Center is analyzed using both SAS PROC MIXED and SPSS MIXED. We start our presentation with a discussion of data preparation for MLM analyses. We then provide example analyses of different growth models, including a simple linear growth model and a model with a time-invariant covariate, with interpretation for all the parameters in the models. More complicated growth models with different between- and within-individual covariance structures and nonlinear models are discussed. Finally, information related to MLM analysis such as online resources is provided at the end of the paper.

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Multilevel models are highly flexible in handling daily-level missing data.
“Multilevel models are highly flexible in handling daily-level missing data (Kwok et al., 2008)”
From Daily Communication Between First-Year College Students and Their Parents: Associations With Perceived Relationship Positivity (2024) · cites this paper as Kwok et al., 2008
The ‘Comparison of MLM with Repeated-Measures Univariate ANOVA’ section says MLM handles missing observations flexibly by incorporating all available data. The ‘Handling Missing Data’ section demonstrates this with four measurement occasions, but the paper does not specifically address daily-level data. (LLM, conf=0.84)
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Authors: Kwok, O.-M., Underhill, A. T., Berry, J. W., Luo, W., Elliott, T. R., & Yoon, M.
Affiliations: Texas A&M University (US)
University of Alabama at Birmingham (US)
University of Wisconsin–Milwaukee (US)
Corresponding author: Kwok, Oi-Man
ORCID: Kwok, Oi-Man
Underhill, Andrea T.
Berry, Jack W.
Luo, Wen
Elliott, Timothy R.
Yoon, Myeongsun
Venue: Rehabilitation Psychology
Publisher: American Psychological Association (APA)
Published: 2008-08
Volume: 53
Issue: 3
Pages: 370-386
ISSN: 0090-5550 (ISSN-L)
1939-1544
DOI: 10.1037/a0012765
PMC: PMC2613314
OpenAlex: W2159072822
Cited by: 388
References: 100
Citations by year: 2026: 9 · 2025: 16 · 2024: 10 · 2023: 21 · 2022: 28 · 2021: 52 · 2020: 30 · 2019: 33 · 2018: 26 · 2017: 25 · 2016: 30 · 2015: 31 · 2014: 22 · 2013: 16 · 2012: 12
FWCI: 8.52
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Rehabilitation Psychology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD039367)
US Department of Health and Human Services (R49-CE000191)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords: multilevel modeling, longitudinal analysis, intra-articular fractures, lower limb fractures, latent growth curve models, linear growth model, covariance structure, nonlinear growth models, SAS PROC MIXED, data preparation

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Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures.

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