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Two Multilevel Modeling Techniques for Analyzing Comparative Longitudinal Survey Datasets*

Published online by Cambridge University Press:  14 October 2013

Malcolm Fairbrother
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Abstract

Increasing numbers of comparative survey datasets span multiple waves. Moving beyond purely cross-sectional analyses, multilevel longitudinal analyses of such datasets should generate substantively important insights into the political, social and economic correlates of many individual-level outcomes of interest (attitudes, behaviors, etc.). This article describes two simple techniques for extracting such insights, which allow change over time in y to be a function of change over time in x and/or of a time-invariant x. The article presents results from simulation studies that assess the techniques in the presence of complications that are likely to arise with real-world data, and concludes with applications to the issues of generalized social trust and postmaterialist values, using data from World/European Values Surveys.

Type
Original Articles
Information
Political Science Research and Methods , Volume 2 , Issue 1 , April 2014 , pp. 119 - 140
Copyright
Copyright © The European Political Science Association 2013 

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Footnotes

*Malcolm Fairbrother is Lecturer in Global Policy and Politics, School of Geographical Sciences, University of Bristol, University Road Bristol BS8 1SS, United Kingdom (m.fairbrother@bristol.ac.uk). For useful comments and suggestions, he thanks Rima Wilkes, Andy Bell, Dmitriy Poznyak, David Manley and Kelvyn Jones; two anonymous PSRM reviewers; fellow members of the University of Bristol's School of Geographical Sciences Spatial Modelling Group; and audiences at ECPR 2011 in Reykjavik, ESRA 2011 in Lausanne, the 2011 ASA Spring Methodology Conference at Tilburg University and a 2012 symposium on The Quality of Measurement at the Technische Universität Dresden. Thanks also to Gethin Williams for help running simulations remotely and in parallel on multiple cores of a Linux server. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2013.24

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