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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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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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“An advantage of LMM is that the method minimizes the loss of information due to missing values (Hesser, 2015).”
From Efficacy of a web-based psychoeducational intervention, Fex-can sex, for young adult childhood cancer survivors with sexual dysfunction: A randomized controlled trial (2024) · cites this paper as Hesser, 2015
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“This statistical approach is considered to present advantages over traditional data analytic approaches for the analysis of repeated-measures data such as, the ability to incorporate time-varying predictors, handle dependence among repeated observations in a flexible manner, and to provide accurate estimates with missing data under unrestrictive missing data assumptions (Hesser, 2015).”
From A guided internet-delivered individually-tailored ACT-influenced cognitive behavioural intervention to improve psychosocial outcomes in breast cancer survivors (iNNOVBC): Study protocol (2019) · cites this paper as Hesser, 2015
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Metadata
- Authors
- Hesser, H.
- Affiliations
- Linköping University (SE)
- Corresponding author
- Hesser, Hugo
- ORCID
- 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
- gold
- doi.org
- Licence
- 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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