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The ASA Spring Methodology Conference Organized in Europe by the Department of Methodology and Statistics at Tilburg University, the Netherlands. SESSION: Multilevel analysis SUMMARY Multilevel analysis is extensively used in cross-national survey research and in the current session the latest developments in this field are presented. The first talk is about distinguishing longitudinal from cross-sectional variation and explaining why some societies change more than others. The question “How many countries are needed for an accurate multilevel SEM?” is answered in the second presentation. Third, a Stata command is presented that became recently available to fit multilevel models in MLwiN from within Stata. Last, models for predicting (dichotomous) outcomes at the national-level from explanatory variables at the individual-level are presented. PRESENTERS: Malcolm Fairbrother m.fairbrother@bristol.ac.uk University of Bristol Bart Meuleman bart.meuleman@soc.kuleuven.be Katholieke Universiteit Leuven George Leckie g.leckie@bristol.ac.uk University of Bristol, Centre for Multilevel Modelling Margot Bennink(chair of the session) m.bennink@uvt.nl Dept. Methodology and Statistics, Tilburg School of Social and Behavioral Sciences
Author and presenter Fairbrother, Malcolm; University of Bristol Title On the Multiple Ways of Using Multilevel Models to Study Social Change Abstract Analyses of repeated cross-sectional survey data have relied increasingly on multilevel/random effects models, in two ways. First, multilevel models have been used to distinguish age, period, and cohort effects, where the goal is to understand the mechanism by which some social change is occurring. Second, models of survey respondents nested within social units (typically countries or states) have been used to examine the effects of society-level conditions on individual-level outcomes. Both approaches, however, provide limited insights into the drivers of change over time. The former approach does not exploit differences among societies experiencing more or less change, and the latter does not distinguish longitudinal from cross-sectional variation. This paper illustrates how to overcome these limitations, by group mean-centring time- varying covariates—allowing for longitudinal effects to be distinguished from cross-sectional effects—and by fitting growth curves at the group level. Growth curves, where units of analysis are presumed to have unique random slopes for time, allow for the rate of some social change to be a function of a time- invariant covariate. This is the relationship many social theories implicitly expect, no matter whether change is mostly due to period or cohort effects. The paper concludes with an application to the study of why religiosity has declined (or secularism expanded) in some countries and not others.
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