Survey design, sampling, and analysis with applications

Survey theory developed as a means to overcome problems with design and analysis is inherent in early research. Survey sampling methodology improves the quality of information collected, ensures the accuracy of data analysis, and reduces the cost of research. Technology drives the evolution of data collection and analysis that is required in survey sampling. In turn, this influences survey sampling techniques. I investigate the history of survey sampling, current survey sampling theory, and current theory applied to two examples: 1) a stratified market research survey, and 2) a psychological survey for health science research. The market research survey was an original design using a specific methodology: conduct pre-interviews on a small sample, develop survey questions based on the qualitative research, stratify the target sample during data collection, and perform data analysis on the resulting cross-sectional data. The second survey utilizes well-developed and tested health measurement instruments that have already been developed and tested. The resulting longitudinal data are then scored and analyzed