Using multilevel analyses with sibling data to increase analytic power: an illustration and simulation study.
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.
Krull
Jennifer L
JL
Department of Psychological Sciences, University of Missouri, Columbia, MO 65211, USA. krulljl@missouri.edu
D000293:Adolescent; D000328:Adult; D000428:Alcohol Drinking / Q000453:epidemiology* / Q000523:psychology; D000437:Alcoholism / Q000453:epidemiology / Q000523:psychology; D000704:Analysis of Variance; D002648:Child; D016241:Child of Impaired Parents / Q000523:psychology / Q000706:statistics & numerical data; D003220:Conflict, Psychological; D003430:Cross-Sectional Studies; D005191:Family Characteristics; D005195:Family Relations; D005260:Female; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D015999:Multivariate Analysis; D012307:Risk Factors; D035781:Siblings; D013223:Statistics as Topic; D014481:United States