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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
Open accessAbstract
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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Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2075823 · Cited by 18,773
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Hierarchical Linear Models: Applications and Data Analysis Methods
Davidian, M. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s288 · Cited by 13,820
Causality : Models , Reasoning , and Inference
Bentler, P., Heslenfeld, D., Molenaar, P., & Mcdonald, R. (2002) · Choice Reviews Online · doi:10.5860/choice.47-3771 · Cited by 13,039
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Singer, J. D., & Willett, J. B. (2003) · doi:10.1093/acprof:oso/9780195152968.001.0001 · Cited by 10,647
Random effects structure for confirmatory hypothesis testing: Keep it maximal
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013) · Journal of Memory and Language · doi:10.1016/j.jml.2012.11.001 · Cited by 10,328
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
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Nguefack, H. L. N., Pagé, M. G., Katz, J. D., Choinière, M., Vanasse, A., Dorais, M., Samb, O. M., & Lacasse, A. (2020) · Clinical Epidemiology · doi:10.2147/clep.s265287 · Cited by 533
Ferreira, M. R., Andreyev, J. N., Mohammed, K., Truelove, L., Gowan, S. M., Li, J., Gulliford, S., Marchesi, J. R., & Dearnaley, D. P. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-0960 · Cited by 205
Lindner, P., Miloff, A., Fagernäs, S., Andersen, J., Sigeman, M., Andersson, G., Furmark, T., & Carlbring, P. (2019) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2018.07.003 · Cited by 175
Ivanova, E., Lindner, P., Ly, K. H., Dahlin, M., Vernmark, K., Andersson, G., & Carlbring, P. (2016) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2016.09.012 · Cited by 165
Miloff, A., Lindner, P., Dafgård, P., Deak, S., Garke, M., Hamilton, W., Heinsoo, J., Kristoffersson, G., Rafi, J., Sindemark, K., et al. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.04.004 · Cited by 155
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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