Applications of multilevel modeling in psychological science: Intensive repeated measures designs

John Nezlek , Błażej Mroziński


Multilevel modeling (MLM) is a statistical technique that can be used to analyze the data collected in various types of research. Although the use of and demand for MLM has increased dramatically over the past decade, instruction in MLM has not kept pace with these increases. The present paper provides an introduction to MLM that is intended to help researchers conduct MLM analyses and describe these results and to help them understand the results of MLM analyses that are presented in articles. Given the limits inherent in a single article, we do not cover all topics in depth. Nevertheless, we provide enough information so that readers should be able to conduct and understand MLM analyses. Examples of different types of analyses of diary style data (sometimes called intensive repeated measures), a design that is being used more and more often, are presented and sample data sets with worked examples are provided as on-line supplemental materials. Recommendations for best practice for conducting analyses and for reporting results are also provided.
Author John Nezlek (Wydział Psychologii i Prawa w Poznaniu)
John Nezlek,,
- Wydział Psychologii i Prawa w Poznaniu
, Błażej Mroziński (Wydział Psychologii w Warszawie)
Błażej Mroziński,,
- Wydział Psychologii w Warszawie
Other language title versionsApplications du modèle multiniveau dans les sciences psychologiques : les plans à mesures répétées intensives
Journal seriesAnnee Psychologique, ISSN 0003-5033, e-ISSN 1955-2580, (N/A 40 pkt)
Issue year2020
Publication size in sheets1.65
Keywords in Englishmultilevel modeling, intensive repeated measures, nested data, within-person analyses
ASJC Classification1201 Arts and Humanities (miscellaneous); 3200 General Psychology
Languageen angielski
2020-Année psychologique-MLM.pdf 1.16 MB
Additional file
2020-Annee-Affliation.pdf 804.52 KB
Oświadczenie_1_osiagniecia_naukowe.pdf 172.14 KB
Score (nominal)40
Score sourcejournalList
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.641; WoS Impact Factor: 2017 = 0.209 (2) - 2017=0.566 (5)
Citation count*5 (2021-04-12)
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