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Pairwise maximum likelihood (PML) estimation is a promising method for multilevel models with discrete responses. Multilevel models take into account that units within a cluster tend to be more alike than units from different clusters. The pairwise likelihood is then obtained as the product of bivariate likelihoods for all within-cluster pairs of units and items. In this study, we investigate the PML estimation method with computationally intensive multilevel random intercept and random slope structural equation models (SEM) in discrete data. In pursuing this, we first reconsidered the general ‘wide format’ (WF) approach for SEM models and then extend the WF approach with random slopes. In a small simulation study we the determine accuracy and efficiency of the PML estimation method by varying the sample size (250, 500, 1000, 2000), response scales (two-point, four-point), and data-generating model (mediation model with three random slopes, factor model with one and two random slopes). Overall, results show that the PML estimation method is capable of estimating computationally intensive random intercept and random slopes multilevel models in the SEM framework with discrete data and many (six or more) latent variables with satisfactory accuracy and efficiency. However, the condition with 250 clusters combined with a two-point response scale shows more bias.

Neighbours have been found to influence each other’s behaviour (contagion effect). However, little is known about the influence on sport club membership. This while increasing interest has risen for the social role of sport clubs. Sport clubs could bring people from different backgrounds together. A mixed composition is a key element in this social role. Individual characteristics are strong predictors of sport club membership. Western high educated men are more likely to be members. In contrast to people with a non Western migration background. The neighbourhood is a more fixed meeting place, which provides unique opportunities for peo ple from different backgrounds to interact. This study aims to gain more insight into the influence of neighbours on sport club membership. This research looks especially at the composition of neighbour’s migration background, since they tend to be more or less likely to be members and therefore could encourage of inhibit each other. A population database including the only regis try data of all Dutch inhabitants was merged with data of 11 sport unions. The results show a cross-level effect of neighbours on sport club membership. We find a contagion effect of neighbours’ migra tion background; having a larger proportion of neighbours with a migration background from a non-Western country reduces the odds, as expected. However, this contagion effect was not found for people with a Moroccan or Turkish background.