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

PSC In The News

RSS Feed icon

Lam looks at population and development in next 15 years in UN commission keynote address

Mitchell et al. find harsh family environments may magnify disadvantage via impact on 'genetic architecture'

Frey says Arizona's political paradoxes explained in part by demography

Highlights

Raghunathan appointed director of Survey Research Center

PSC newsletter spring 2014 issue now available

Kusunoki wins faculty seed grant award from Institute for Research on Women and Gender

2014 PAA Annual Meeting, May 1-3, Boston

Next Brown Bag

Monday, April 21
Grant Miller: Managerial Incentives in Public Service Delivery

Comparing Measures of Survey Data Quality

a PSC Research Project

Investigators:   James Robert 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