Using multilevel analyses with sibling data to increase analytic power

An illustration and simulation study

Bibliographic Data

ID9788511
AuthorsJennifer L Krull (University of Missouri, corresponding author)
Year2007
Volume43
Issue3
Pages602-619
Publication date2007-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueDevelopmental Psychology (JOURNAL)
Journal identifiersISSN: 0012-1649 • E-ISSN: 1939-0599
PublisherAmerican Psychological Association (APA) (PUBLISHER)
DOI10.1037/0012-1649.43.3.602
PMID17484574
OpenAlexW2091486278
LanguageEN
Citations received3
References cited16

This study investigates the extent to which analytic power can be increased through the inclusion of siblings in a data set and the concomitant use of random coefficient multilevel models. Analyses of real-world data regarding the predictors of young adult alcohol use illustrate how parallel single-level analyses of a 1-child-per-family data set and multilevel analyses of a data set including all siblings in each family would be conducted. A simulation study, closely based on the illustrative analyses, compares the empirical power to detect main, moderation, and mediation effects under three conditions: (a) single-level analyses of 1-child-per-family data, (b) multilevel analyses of all-siblings data, and (c) single-level analyses of independent data with sample size equivalent to the all-siblings condition. Supplementary analyses are conducted to determine the conditions under which greater analytic power could be achieved with the addition of siblings to a data set than with the addition of a lesser number of independent individuals at equivalent cost

Data set · Developmental psychology · Meta-analysis · Moderation · Multilevel model · Random effects model · Set (abstract data type · Sibling · Statistics · Cognitive Abilities and Testing · Computer Science · Family Dynamics and Relationships · Mathematics · Psychology · Social Psychology · Urban, Neighborhood, and Segregation Studies

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