I think I know what you did last summer : improving data quality in panel surveys

Five specific studies investigate how the methodology of panel surveys can be improved. 1) Propensity Score Matching to separate different sources of measurement error. Errors of non-observation and measurement errors are easily confounded in mixed-mode surveys. I propose propensity score matching as a method to deal with this problem 2 and 3) Dependent Interviewing (DI) to improve data quality of measures of change. In Dependent Interviewing, data from the previous interviews are used in the current interview to check whether the old situation of the respondent has changed. It is unclear whether DI leads to higher data quality, or rather, that correlated errors decrease data quality. Two papers study this question. 4) What is change? When a questionnaire is administered twice, we cannot simply compare the scores on the tests from both measurements. Errors of measurements in the questions result change estimates to be biased. A study that combined open interviews (qualitatively), with an analysis of measurement equivalence (quantitatively) helps us to understand how change estimates can be biased, making it impossible to study change scores. 5) methods to separate different types of dropout in a panel surveys and possibe ways to deal with dropout. Attrition or dropout models cannot at the same time study variation among individuals and the process of attrition over time. I show how Growth Mixture Models (GMM) can be used to study how the attrition process over individuals. There are several distinct groups of attriters, that have clearly different background characteristics