Two Multilevel Modeling Techniques for Analyzing Comparative Longitudinal Survey Datasets

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 paper 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 paper presents results from simulation studies assessing the techniques in the presence of complications 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.