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This study proposes a simple and practical Composite Marginal Likelihood (CML) inference approach to estimate ordered-response discrete choice models with flexible copula-based spatial dependence structures across observational units. The approach is applicable to data sets of any size, provides standard error estimates for all parameters, and does not require any simulation machinery. The combined copula–CML approach proposed here should be appealing for general multivariate modeling contexts because it is simple and flexible, and is easy to implement The ability of the CML approach to recover the parameters of a spatially ordered process is evaluated using a simulation study, which clearly points to the effectiveness of the approach. In addition, the combined copula–CML approach is applied to study the daily episode frequency of teenagers’ physically active and physically inactive recreational …