Comparison of four analytic strategies for complex survey data: a case-study of Spanish data

Abstract

Purpose: The aim of this secondary data analysis was to investigate the effect of four different analytical strategies: Model Based Analysis (MBA), Design Based Analysis (DBA), Multilevel Model Based Analysis (MMBA), and Multilevel Design Based Analysis (MDBA), on the model estimates for complex survey data. Methods: Using data from the World Health Survey-Spain explanatory models for the outcome Metabolic Equivalent of Task (METs) were calculated using MBA, DBA, MMBA, and MDBA. Regression coefficients, standard errors (SE) and the Akaike Information Criterion (AIC) from all the models were compared. Results: DBA showed highest estimates for most of the variables, including consistently higher SE than all other model - 20% to 48% higher than estimates for MBA, 10% to 37% for MMBA and 23% to 35% for MDBA. The SE for MDBA were 2.5% to 13% higher than estimates derived from MMBA in level 1 predictors, but SE in MMBA was higher by 18% for level 2 predictors. Values of AIC suggested the model derived by MDBA was the best fit and DBA the poorest fit of the four models. Conclusion: With minimum AIC, MDBA appeared to be the most appropriate approach to analyze complex survey data. To confirm the finding of present study a future work on a simulation data would be required.