Cited by 192Statistical Methods and Bayesian InferenceAdvanced Causal Inference TechniquesStatistical Methods in Clinical Trials

Modeling individual differences in randomized experiments using growth models: recommendations for design, statistical analysis and reporting of results of internet interventions

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Hesser, Hugo·2015·Internet Interventions

Abstract

Growth models (also known as linear mixed effects models, multilevel models, and random coefficients models) have the capability of studying change at the group as well as the individual level. In addition, these methods have documented advantages over traditional data analytic approaches in the analysis of repeated-measures data. These advantages include, but are not limited to, the ability to incorporate time-varying predictors, handle dependence among repeated observations in a very flexible manner, and to provide accurate estimates with missing data under fairly unrestrictive missing data assumptions. The flexibility of the growth curve modeling approach to the analysis of change makes it the preferred choice in the evaluation of direct, indirect and moderated intervention effects. Although offering many benefits, growth models present challenges in terms of design, analysis and reporting of results. This paper provides a nontechnical overview of growth models in the analysis of change in randomized experiments and advocates for their use in the field of internet interventions. Practical recommendations for design, analysis and reporting of results from growth models are provided.

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Authors
Hesser, H.
Affiliations
Linköping University (SE)
Corresponding author
Hesser, Hugo
Venue
Internet Interventions
Publisher
Elsevier BV
Published
2015-05
Volume
2
Issue
2
Pages
110-120
ISSN
2214-7829 (ISSN-L)
OpenAlex
W2143313266
Cited by
192
References
88
Citations by year
2026: 7 · 2025: 14 · 2024: 24 · 2023: 14 · 2022: 15 · 2021: 12 · 2020: 36 · 2019: 23 · 2018: 17 · 2017: 19 · 2016: 5 · 2015: 6
FWCI
13.94
Type
journal article
Language
en
Open access
Locations
  • Internet Interventions (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
growth models, individual differences, randomized experiments, longitudinal analysis, repeated measures, missing data, intervention effects, design guidelines, result reporting, multilevel models

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