Home > Research . Search . Country . Browse . Small Grants

PSC In The News

RSS Feed icon

Almirall says comparing SMART designs will increase treatment quality for children with autism

Thompson says America must "unchoose" policies that have led to mass incarceration

Alter says lack of access to administrative data is "big drag on research"


Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

Next Brown Bag

Monday, Oct 12 at noon, 6050 ISR
Joe Grengs: Policy & planning for transportation equity

James Wagner photo

Comparing Measures of Survey Data Quality

a PSC Research Project

Investigators:   James Wagner, James Wagner, Michael R. Elliott

The risk of nonresponse bias is a major threat to the validity of health surveys that can impact the results of important health measures. Little guidance is available on how to evaluate the risk of nonresponse bias. Many surveys rely on the response rate as a key statistic. However, a recent meta-analysis indicates that the response rate is a poor indicator for nonresponse bias. The lack of a good indicator for the risk of bias is harmful to health surveys in two ways. First, surveys with high response rates and relatively high nonresponse bias may be accepted as valid and published. Second, surveys with low response rates and relatively low nonresponse bias may be questioned and rejected for publication. Both of these situations may lead to incorrect conclusions about health policies and practices.

This project attempts to fill this void by evaluating a set of indicators for the risk of nonresponse bias. Each of these measures (including the response rate) makes assumptions that are untestable in most practical situations. The goal of this research is to understand the strengths and weaknesses of each of several alternative measures and the implications of incorrect assumptions. These indicators are compared through derivation of their key properties. These properties can include a description of how each measure can be used to place bounds on the potential nonresponse bias and the assumptions required to do so. In addition, a simulation study will be conducted to demonstrate how each measure performs under a varied set of conditions. Finally, all the measures will be applied to existing survey data collections. The goal of the proposed project is to aid in the development of a common understanding of a set of measures that can be used to evaluate the risk of nonresponse bias. This should greatly facilitate efforts to evaluate the quality of health-related survey data.

Funding Period: 08/16/2011 to 07/31/2013

Search . Browse