主讲题目：Online Survey Data Quality and Impact on Social Welfare Estimates
Dr. ZhifengGao is an associate professor in the Food and Resource Economics Department, University of Florida. His research program aims to enhance understanding of the factors that affect individual behavior related to food choice and agriculture technology. Dr. Gao has used choice experiment, sensory analysis, and experiment auctions to examine (1) the effects of various factors on the estimations of consumer preferences; (2) the impacts of food labels, packages and information on consumer food choices and demand; and (3) growers adoption of more sustainable farming practices. Dr. Gao has also worked on the tools and methods that can improve the efficiency of estimating individual preference and online survey data quality. In addition, Dr. Gao actively involves in interdisciplinary research to determine the economic feasibility of and growers' attitude towards new technology related to sustainable agriculture. He has published about 50 articles in applied economics as well as interdisciplinary journals such as Food Policy, American Journal of Agricultural Economics, Agricultural Economics, Journal of Agricultural Economics, China Economic Review, Journal of Australian Agricultural and Resource Economics, Economics Letters, Food Quality and Preference, Appetite, Public Opinion Quarterly, Agribusiness: International Journal, British Food Journals, HortScience, etc. He is on the editorial board of Journal of Integrative Agriculture and Journal of Agriculture and Applied Economics.
Using surveys to obtain accurate estimates of preference, attitudes, and opinions of products, services, and public policies is critical because it provides essential information for the development of business strategy, public program, and policy design. With advances in internet technology, the use of online surveys is increasing dramatically in both industry and academia. Despite several advantages, online surveys are perceived to suffer from lower data quality than traditional survey modes.
This talk is based on a series of work focusing on 1) the use of different instruments to detect low-quality data; 2) the application of validation questions to identifying respondents with satisficing behavior; 3) cross-country comparison of online data quality; and 4) the impact of monetary incentives on online data quality. Results show that validation/trap question or low-probability (LP) screening questions are the effective instruments to identify respondents with satisficing behavior; respondents with satisficing behavior can result in different social welfare estimates; online data quality differs by countries, and monetary incentive has a nonlinear effect on online data quality. Results of these studies can be extended to other survey modes such as mail or mall intercept survey. They provide essential implication to people who rely on survey methods for data collection and research