ArticleStatistics in medicine2011
The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects.
Article in Statistics in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it, 135 citations in OpenAlex.
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- An adapted scale to measure perceived TB and HIV stigma during household contact investigation.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2024Article
- Secondary Endpoint Utilization and Publication Rate among Phase III Oncology Trials.Cancer research communications · 2024Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Response fatigue can cause measurement error and misclassification problems in survey research. Questions asked later in a long survey are often prone to more measurement error or misclassification. The response given is a function of both the true response and participant response fatigue. We investigate the identifiability of survey order effects and their impact on estimators of treatment effects. The focus is on fatigue that affects a given answer to a question rather than fatigue that causes non-response and missing data. We consider linear, Gamma, and logistic models of response that incorporate both the true underlying response and the effect of question order. For continuous data, survey order effects have no impact on study power under a Gamma model. However, under a linear model that allows for convergence of responses to a common mean, the impact of fatigue on power will depend on how fatigue affects both the rate of mean convergence and the variance of responses. For binary data and for less than a 50% chance of a positive response, order effects cause study power to increase under a linear probability (risk difference) model but decrease under a logistic model. The results suggest that measures designed to reduce survey order effects might have unintended consequences. We present a data example that demonstrates the problem of survey order effects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.