True-Value Regression with Non-Response
Volume 9, Issue 4 (2011), pp. 501–512
Pub. online: 10 July 2021 Type: Research Article Open Access
10 July 2021
10 July 2021
Abstract: True-value theory (Bechtel, 2010), as an extension of randomization theory, allows arbitrary measurement errors to pervade a survey score as well as its predictor scores. This implies that true scores need not be expectations of observed scores and that expected errors need not be zero within a respondent. Rather, weaker assumptions about measurement errors over respondents enable the regression of true scores on true predictor scores. The present paper incorporates Sarndal-Lundstrom (2005) weight calibration into true-value regression. This correction for non-response is illustrated with data from the fourth round of the European Social Survey (ESS). The results show that a true-value regression coefficient can be corrected even with a severely unrepresentative sample. They also demonstrate that this regression slope is attenuated more by measurement error than by non-response. Substantively, this ESS analysis establishes economic anxiety as an important predictor of life quality in the financially stressful year of 2008.