Multilevel Models and Comparative Longitudinal Survey Data: Advances, Prospects, and Challenges

Names session organizers: Dr Malcolm Fairbrother, Dr Alexander Schmidt-Catran
Name session chair: Dr Malcolm Fairbrother
Email address of session chair:

Description of session topic
Multilevel models have become predominant in analyses of comparative survey datasets, where respondents are clustered in higher-level units like countries or regions. Such models have also long been fitted to data clustered within units — i.e., repeated observations on individuals or countries. Increasingly, however, researchers are fitting multilevel models to data that are clustered both ways, such as multiple waves of surveys whose respondents are nested in countries or regions each observed multiple times. (Such datasets may be traditional panels, where each respondent is observed more than once, or they may draw new samples each time.) These comparative longitudinal survey datasets should be useful resources for studies of social change in the broadest sense, and for testing inferences previously based on only cross-sectional analyses. This session welcomes papers grappling with the challenges of analysing such datasets, whether using multilevel modelling or other related techniques with different capabilities/advantages. Papers might address recent methodological advances; present illuminating or innovative applications in some field of the social sciences; and/or discuss limitations and challenges that remain. They could focus on topics such as: problems of temporal autocorrelation; endogeneity; measurement error; omitted variable bias; missing data; age, period, and cohort confounding; spatial autocorrelation; misspecification; estimation; or the properties of different software packages.

Keywords: multilevel, comparative, longitudinal, surveys, change