Comparing Methods for Assessing Reliability

The usual method for assessing the reliability of survey data has been to conduct reinterviews a short interval (such as one to two weeks) after an initial interview and to use these data to estimate relatively simple statistics, such as gross difference rates (GDRs). More sophisticated approaches have also been used to estimate reliability. These include estimates from multi-trait, multi-method experiments, models applied to longitudinal data, and latent class analyses. To our knowledge, no prior study has systematically compared these different methods for assessing reliability. The Population Assessment of Tobacco and Health Reliability and Validity (PATH-RV) Study, done on a national probability sample, assessed the reliability of answers to the Wave 4 questionnaire from the PATH Study. Respondents in the PATH-RV were interviewed twice about two weeks apart. We examined whether the classic survey approach yielded different conclusions from the more sophisticated methods. We also examined two ex ante methods for assessing problems with survey questions and item nonresponse rates and response times to see how strongly these related to the different reliability estimates. We found that kappa was highly correlated with both GDRs and over-time correlations, but the latter two statistics were less highly correlated, particularly for adult respondents; estimates from longitudinal analyses of the same items in the main PATH study were also highly correlated with the traditional reliability estimates. The latent class analysis results, based on fewer items, also showed a high level of agreement with the traditional measures. The other methods and indicators had at best weak relationships with the reliability estimates derived from the reinterview data. Although the Question Understanding Aid seems to tap a different factor from the other measures, for adult respondents, it did predict item nonresponse and response latencies and thus may be a useful adjunct to the traditional measures.