By Dr. Eun Sul Lee, Dr. Ronald N. Forthofer
This e-book examines how you can learn complicated surveys, and specializes in the issues of weights and layout results. This re-creation accommodates contemporary perform of reading complicated survey info, introduces the recent analytic process for express information research (logistic regression), stories new software program and offers an advent to the model-based research that may be helpful examining well-designed, rather small-scale social surveys.
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Extra resources for Analyzing Complex Survey Data (Quantitative Applications in the Social Sciences)
M. F. Smith, 1976, 1983). A good analysis of survey data would require general understanding of both points of view as well as consideration of some practical issues, especially in social surveys. The model-based approach is consistent with the increasing use of model-based inference in other areas of statistical analysis, and it provides some theoretical advantages. Model-based estimates can be used with relatively small samples and even with nonprobability samples. In addition, model-based analysis can be done using standard statistical software such as SAS and SPSS without relying on survey packages such as SUDAAN and others that are reviewed in this book.
For an analytic study, we may want to investigate the percentage of adults approving of hitting, by education and gender. For this analysis we need to determine if there are a number of PSUs without observations in a particular education-by-gender category. If there are many PSUs with no observation for some education-by-gender category, this calls into question the estimation of the variance–covariance matrix, which is based on the variation in the PSU totals within the strata. The education (3 levels) by gender (2 levels) tabulation by PSU showed that 42 of the 84 PSUs had at least one gender-by-education cell with no observations.
For example, when the objective of the analysis is to examine associations between health measures and risk factors, conditioning on the design variables may interfere with the relational pathway. In complex large-scale surveys, it is often not possible to include in the model all the design information, especially when the sample weights are modified for nonresponse and poststratification adjustments (Alexander, 1987). Another practical problem in incorporating the design variables into the model is the lack of relevant information in the data set.