Multilevel approaches to survey data analysis |
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| Coordinator 1 | Dr Dalila Failli (University of Florence) |
Data with hierarchically structured units, also known as multilevel data, are often encountered in survey research. In this context, a multilevel approach offers a very useful methodological tool to properly account for the hierarchical structure of the data. Indeed, this type of approach allows not only to deal with unobserved sources of heterogeneity at multiple levels, but also to take into account the possible correlation between the responses provided by units belonging to the same hierarchical level. This session is specifically focused on multilevel approaches to survey data analysis. In this regard, the first contribution of the session extends a model-based clustering method in a multilevel context for the analysis of network data, with the aim of analyzing the gray digital divide among different European countries. A different use of the multilevel approach is that of the second contribution, in which a multilevel regression with post-stratification is performed to model individual survey responses based on demographic and geographic predictors in order to estimate voting choice in the 2023 Estado de México governor election. Finally, the third contribution of the session exploits the hierarchical nature of organizational survey data to explore the antecedents of organizational-level engagement in a setting in which employees are nested within service sector organizations in India.