Survey Methods Musings

One issue that we have been discussing is indicators for the risk of nonresponse bias. There are some indicators that use observed information (i.e. largely sampling frame data) to determine whether respondents and nonrespondents are similar. The R-Indicator is an example of this type of indicator. It's not the only one. There are several sample balance indicators. There is an implicit model that the observed characteristics are related to the survey data and controlling for them will, therefore, also control the potential for nonresponse bias. Another indicator uses the observed data, including the observed survey data, and a model to fill in the missing survey data. The goal here is to predict whether nonresponse bias is likely to occur. Here, the model is explicit. An issue that impacts either of these approaches is that if you are able to predict the survey variables with the sampling frame data, then why bother addressing imbalances on them during data collection? One answ

I have been talking to undergraduates about survey methodology. The students I talk to have learned either some social research methods or statistics. I think that many are interested in data science and/or big data. From these conversations, I found it was useful to describe survey methodologists as "data quality specialists." Survey methodology is not a field that most undergraduates are even aware of. But when I started talking about how we evaluate the quality of data, I could see ears perking up. It reinforced for me the idea that the Total Survey Error perspective is valuable for Big Data .We can talk about nonresponse and measurement error in a coherent way. Raising questions about the quality of the data, the need to understand the processes that generated those data, and methods for evaluation of the data were all ideas that seemed to resonate with undergraduates... well, at least some. It was energizing and exciting to speak with them. Hopefully they bring that