The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Growth Modeling Using Random Coefficient Models: Model Building, Testing, and Illustrations
Abstract
In this article, the authors illustrate how random coefficient modeling can be used to develop growth models for the analysis of longitudinal data. In contrast to previous discussions of random coefficient models, this article provides step-by-step guidance using a model comparison framework. By approaching the modeling this way, the authors are able to build off a regression foundation and progressively estimate and evaluate more complex models. In the model comparison framework, the article illustrates the value of using likelihood tests to contrast alternative models (rather than the typical reliance on tests of significance involving individual parameters), and it provides code in the open-source language R to allow readers to replicate the results. The article concludes with practical guidelines for estimating growth models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models
Singer, J. D. (1998) · Journal of Educational and Behavioral Statistics · doi:10.2307/1165280 · Cited by 1,092
Cited by
Longitudinal Research: The Theory, Design, and Analysis of Change
Ployhart, R. E., & Vandenberg, R. J. (2010) · Journal of Management · doi:10.1177/0149206309352110 · Cited by 1,496
Self-citationHülsheger, U. R., Alberts, H. J. E. M., Feinholdt, A., & Lang, J. W. B. (2013) · Journal of Applied Psychology · doi:10.1037/a0031313 · Cited by 1,433
Variable-Centered, Person-Centered, and Person-Specific Approaches: Where Theory Meets the Method
Howard, M. C., & Hoffman, M. E. (2018) · Organizational Research Methods · doi:10.1177/1094428117744021 · Cited by 1,121
Clarifying conditions and decision points for mediational type inferences in Organizational Behavior
Mathieu, J. E., & Taylor, S. R. (2006) · Journal of Organizational Behavior · doi:10.1002/job.406 · Cited by 961
Beal, D. J. (2015) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-032414-111335 · Cited by 436
Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations
Shek, D. T. L., & Ma, C. M. S. (2011) · The Scientific World JOURNAL · doi:10.1100/tsw.2011.2 · Cited by 425
Mathieu, J. E., Kukenberger, M. R., D'Innocenzo, L., & Reilly, G. (2015) · Journal of Applied Psychology · doi:10.1037/a0038898 · Cited by 316
Rai, A., Maruping, L. M., & Venkatesh, V. (2009) · MIS Quarterly · doi:10.2307/20650313 · Cited by 311
Banking sleep: realization of benefits during subsequent sleep restriction and recovery
Rupp, T. L., Wesensten, N. J., Bliese, P. D., & Balkin, T. J. (2009) · Sleep · doi:10.1093/sleep/32.3.311 · Cited by 270
Self-citationExplained variance measures for multilevel models
LaHuis, D. M., Hartman, M. J., Hakoyama, S., & Clark, P. C. (2014) · Organizational Research Methods · doi:10.1177/1094428114541701 · Cited by 239
Syrek, C. J., Weigelt, O., Peifer, C., & Antoni, C. H. (2017) · Journal of Occupational Health Psychology · doi:10.1037/ocp0000031 · Cited by 187
Twohig, M. P., Abramowitz, J. S., Smith, B. M., Fabricant, L. E., Jacoby, R. J., Morrison, K. L., Bluett, E. J., Reuman, L., Blakey, S. M., & Ledermann, T. (2018) · Behaviour Research and Therapy · doi:10.1016/j.brat.2018.06.005 · Cited by 156
Syrek, C. J., & Antoni, C. H. (2014) · Journal of Occupational Health Psychology · doi:10.1037/a0037127 · Cited by 143
Mensmann, M., & Frese, M. (2019) · Journal of Organizational Behavior · doi:10.1002/job.2333 · Cited by 75
Gooty, J., Serban, A., Thomas, J. S., Gavin, M. B., & Yammarino, F. J. (2012) · The Leadership Quarterly · doi:10.1016/j.leaqua.2012.10.002 · Cited by 71
Schaubroeck, J. M., Lam, S. S. K., & Peng, A. C. (2016) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2016.02.002 · Cited by 48
Weigelt, O., Siestrup, K., & Prem, R. (2021) · Journal of Organizational Behavior · doi:10.1002/job.2514 · Cited by 46
Jo, J., Chadwick, C., & Han, J. H. (2024) · Human Resource Management · doi:10.1002/hrm.22184 · Cited by 43
Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic
Batzdorfer, V., Steinmetz, H., Biella, M., & Alizadeh, M. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00298-6 · Cited by 38
“The differences in the level, slope, and nonlinear trends were tested following recommendations from the overall literature on multi-level models [66] and growth curve analyses [67].”
MethodWeigelt, O., & Syrek, C. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14121606 · Cited by 30
A Primer on Two-Level Dynamic Structural Equation Models for Intensive Longitudinal Data
McNeish, D. (2018) · doi:10.31234/osf.io/j56bm · Cited by 12
Metadata
- Authors
- Bliese, P. D., & Ployhart, R. E.
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2002-10
- Volume
- 5
- Issue
- 4
- Pages
- 362-387
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2031261250
- Cited by
- 786
- References
- 1
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
