The DOI matches its Crossref record.
Multilevel Modeling of Categorical Outcomes Using IBM SPSS
Abstract
This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“GEEs also allow the inclusion of patients for whom measures are missing for certain time periods (e.g. patients attached in August 2014 who did not have a measure for T0 + 2 years).”
In the citing paper
“Robust (Huber-White sandwich) estimators were used given departure from normality and large sample size [69, 70].”
Citations
Cited by
Sorted by citations
Ordinal Regression Models in Psychology: A Tutorial
Bürkner, P.-C., & Vuorre, M. (2019) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918823199 · Cited by 689
“Useful references include Heck, Thomas, and Tabata (2013) for IBM SPSS; Bender and Benner (2000) for SAS and S-Plus; and Long, Long, and Freese (2006) for Stata.”
Thakkar, A., & Lohiya, R. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10037-9 · Cited by 394
Otto, C., Reiss, F., Voss, C., Wüstner, A., Meyrose, A.-K., Hölling, H., & Ravens-Sieberer, U. (2021) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-020-01630-4 · Cited by 235
“Findings from sensitivity analyses using general estimation equation models (GEEs) for categorical outcomes [64] were similar.”
MethodAbeele, M. V., Campbell, S. W., Eggermont, S., & Roe, K. (2014) · Media Psychology · doi:10.1080/15213269.2013.801725 · Cited by 153
“Following the procedure sketched by Heck, Thomas, and Tabata (2012), we first estimated the unconditional (null) model for both sexting and mobile porn use, in which only intercepts (in our case: one fixed effect for the intercept and three random effects expressing its variability across the…”
MethodEXPOSURE FREQUENCY IN L2 READING
Mohamed, A. (2017) · Studies in Second Language Acquisition · doi:10.1017/s0272263117000092 · Cited by 140
Metadata
- Authors
- Heck, R. H., Thomas, S., & Tabata, L.
- ORCID
- Heck, Ronald H
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2013-05-07
- OpenAlex
- W1586960195
- Cited by
- 316
- References
- 0
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 21 · 2023: 27 · 2022: 24 · 2021: 29 · 2020: 34 · 2019: 36 · 2018: 34 · 2017: 19 · 2016: 29 · 2015: 22 · 2014: 16 · 2013: 2
- FWCI
- 5.22
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- multilevel modeling, categorical outcomes, generalized linear models, longitudinal data, multinomial data, ordinal data, count data, population-averaged models, SPSS, data management, model specification, model interpretation
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