Conference Programme 2015
Tuesday 14th July Wednesday 15th July Thursday 16th July Friday 17th July
Friday 17th July, 09:00 - 10:30 Room: L-101
Multilevel Models for the Analysis of Comparative (Longitudinal) Survey Data 1
|Convenor||Dr Alexander Schmidt-catran (Institute of Sociology and Social Psychology, University of Cologne )|
|Coordinator 1||Professor Bart Meuleman (Centre for Sociological Research, University of Leuven)|
Session DetailsMultilevel 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 longitudinal data, where repeated observations are clustered within units. Additionally, 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. These comparative longitudinal survey datasets should be useful resources for studies of social change in the broadest sense, and for drawing inferences previously based on only cross-sectional analyses. This session welcomes papers using multilevel models for the analysis of cross-sectional data, longitudinal data and, in particular, data that is clustered both ways. 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.
Paper Details1. Modelling the Dynamics of Social Trust in 15 European countries (2002-2012): Keeping it maximal and the small-N problem in cross-national research
Dr Jan Mewes (Örebro University)
Social trust, the belief that most other people can be trusted, is remarkably stable over time. Using data from the first 6 rounds of the ESS, this paper examines if the crisis of 2008 has undermined social trust in 15 European countries. Results from Bayesian MCMC multilevel analysis suggest that long-term unemployment rates determine change in trust, whereas changes in GDP or unemployment rates have no impact. The presentation concludes with discussing the robustness of the findings. Special emphasis is paid to the small-N problem in cross-national research.
2. Multilevel strategies for the complex nested structures of international repeated cross-sectional surveys
Dr Raül Tormos (University of Barcelona & Centre d'Estudis d'Opinió)
This paper presents two multilevel techniques to deal with international repeated cross-sectional surveys. The first one accounts for the comparative and dynamic nature of this data. It implies defining multilevel models with distinct levels for individuals and country-years combined (with contextual time-invariant and time-varying covariates). The need and choice of a third level of clustering is discussed. The second strategy implies the nesting of cross-classified random effects models within countries. This approach adds a comparative dimension to the study of APC effects, and also allows for the inclusion of time-varying/-invariant contextual covariates.
3. The Random Effects in Multilevel Models: Getting Them Wrong and Getting Them Right
Dr Malcolm Fairbrother (University of Bristol)
Dr Alexander Schmidt-catran (University of Cologne)
This paper shows that the multilevel models used in many published analyses of comparative longitudinal survey data are specified erroneously. They typically omit one or more relevant random effects, leading to downward biases in the standard errors. These biases occur even if the fixed effects are specified correctly; if the fixed effects are incorrect, erroneous specification of the random effects worsens biases in the coefficients. We illustrate these problems using Monte Carlo simulations and two empirical examples, and conclude with straightforward recommendations that will help applied researchers avoid such problems in the future.
4. Historical analysis of survey results and survey data: The incredible possibilities afforded by longitudinal multilevel analysis using time as the higher level
Professor Claire Durand (Université de Montréal)
Mrs Isabelle Valois (Université de Montréal)
Mr François Yale (ASSSMM)
As information generated by surveys and polls, sometimes decades old, becomes available, we are now able to perform “historical analyses” of how various socio-political attitudes have changed over time. The presentation shows how multilevel analysis can estimate change over time, taking into account the different goals and wordings of poll questions and the various features of the methods used. Three case studies are presented: a) Change in support for Quebec sovereignty over 40 years, b) Change in voting intentions for Obama in 2012, and c) Change in trust in institutions in Canada over 40 years.