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Sources of Variance in the Accuracy of Interviewer Observations.

ABSTRACT: In face-to-face surveys, interviewer observations are a cost-effective source of paradata for nonresponse adjustment of survey estimates and responsive survey designs. Unfortunately, recent studies have suggested that the accuracy of these observations can vary substantially among interviewers, even after controlling for household-, area-, and interviewer-level characteristics, limiting their utility. No study has identified sources of this unexplained variance in observation accuracy. Motivated by theoretical expectations from the observer bias literature, this study analyzed more than 45,000 open-ended justifications provided by interviewers in the U.S. National Survey of Family Growth (NSFG) for their observations on two key features of all sampled NSFG households: presence of children and expected probability of household response. The study finds that variability among interviewers in the cues used to record these observations (evident from the open-ended justifications) explains much of the previously unexplained variance in observation accuracy.

SUBMITTER: West BT

PROVIDER: S-EPMC6905517 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Sources of Variance in the Accuracy of Interviewer Observations.

West Brady T BT Li Dan D

Sociological methods & research 20170925 3

In face-to-face surveys, interviewer observations are a cost-effective source of paradata for nonresponse adjustment of survey estimates and responsive survey designs. Unfortunately, recent studies have suggested that the accuracy of these observations can vary substantially among interviewers, even after controlling for household-, area-, and interviewer-level characteristics, limiting their utility. No study has identified sources of this unexplained variance in observation accuracy. Motivated ...[more]

PMID: 31827308

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Project description:BackgroundFor many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure; however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The transformed problem can then be approached by either the one-sample Student's t-test or Wilcoxon signed rank test.ResultsIn simulation studies, the proposed methods outperform commonly used alternative methods under both normal and double exponential distributions. In particular, the PB-transformed t-test produces notably better results than the weighted LMER test, especially in the high correlation case, using only a small fraction of computational cost (3 versus 933 s). We apply these two methods to a set of RNA-seq gene expression data collected in a breast cancer study. Pathway analyses show that the PB-transformed t-test reveals more biologically relevant findings in relation to breast cancer than the weighted LMER test.ConclusionsAs fast and numerically stable replacements for the weighted LMER test, the PB-transformed tests are especially suitable for "messy" high-throughput data that include both independent and matched/repeated samples. By using our method, the practitioners no longer have to choose between using partial data (applying paired tests to only the matched samples) or ignoring the correlation in the data (applying two sample tests to data with some correlated samples). Our method is implemented as an R package 'PBtest' and is available at https://github.com/yunzhang813/PBtest-R-Package .

Dataset Information

Sources of Variance in the Accuracy of Interviewer Observations.

Publications

Sources of Variance in the Accuracy of Interviewer Observations.

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