Estimating the Effect of Nonresponse Bias in a Survey of Hospital Organizations

Lewis, Emily F.; Hardy, Maryann; Snaith, Beverly

doi:10.1177/0163278713496565

Cited by 66 publications

(53 citation statements)

References 38 publications

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“…Overall, respondents differed somewhat from non‐respondents with respect to user type (60% of users were respondents versus 37% of registered non‐users and 35% of non‐registrants) and clinical specialty (49% of PCPs versus 43% in emergency and 37% in pain medicine). There was no evidence of a statistically significant association between length of time until survey response and our main outcome variables, suggesting the absence of response wave bias, any difference between early and late responders .…”

Section: Resultsmentioning

confidence: 79%

Physician attitudes and experiences with Maryland's prescription drug monitoring program (PDMP)

et al. 2016

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Section: Resultsmentioning

confidence: 79%

Physician attitudes and experiences with Maryland's prescription drug monitoring program (PDMP)

et al. 2016

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“…Early respondents were assumed to be the most motivated, those with longer response times less motivated, and non-respondents the least motivated. 27 As the outcome measures of non-respondents are unknown, inferences can be made based on the relationship between how frequently clinicians were exposed to abuse and the time it took to respond. We adapted this methodology for continuous outcomes by examining the correlations between the time it took for an individual to respond to the survey and each of their five exposure scores.…”

Section: Methodsmentioning

confidence: 99%

Prevalence and predictors of exposure to disruptive behaviour in the operating room

Villafranca

Hiebert

Hamlin

et al. 2019

Can J Anesth/J Can Anesth

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Prevalence and predictors of exposure to disruptive behaviour in the operating room Prévalence et prédicteurs d'exposition à des comportements perturbateurs en salle d'opération Abstract Purpose Disruptive intraoperative behaviour ranges from incivility to abuse. This behaviour can have deleterious effects on clinicians, students, institutions, and patients. Previous investigations of this behaviour used underdeveloped tools or small sampling frames. We therefore examined the prevalence and predictors of perceived exposure to disruptive behaviour in a multinational sample of operating room clinicians. Methods A total of 134 perioperative associations in seven countries were asked to distribute a survey examining five types of exposure to disruptive behaviour: personal, directed toward patients, directed toward colleagues, directed toward others, or undirected. To compare the average amount of exposure with each type, we used a Friedman's test with select post hoc Wilcoxon tests. A negative binomial regression model identified socio-demographic predictors of personal exposure. Results Of the 134 organizations approached, 23 (17%) complied. The total response rate was estimated to be 7.6% (7465/101,624). Almost all (97.0%; 95% confidence interval [CI], 96.6 to 97.4) of the respondents reported exposure to disruptive behaviour in the past year, with the average respondent experiencing 61 incidents per year (95% CI, 57 to 65). Groups reporting higher personal exposure included clinicians who were young, inexperienced, female, non-heterosexual, working as nurses, or working in clinics with private funding (all P \ 0.05). Conclusion Perceived exposure to disruptive behaviour was prevalent and frequent, with the most common behaviours involving speaking ill of clinicians and patients. These perceptions, whether accurate or not, can result in detrimental consequences. Greater efforts are required to eliminate disruptive intraoperative behaviour, with recognition that specific groups are more likely to report experiencing such behaviours. RésuméObjectif Les comportements perturbateurs en salle d'opération vont de l'incivilité à l'abus. Ce type de comportement peut avoir des effets délétères sur les cliniciens, les étudiants, les institutions et les patients. Les études précédentes de ce type de comportement se sont servies d'outils sous-développés ou de cadres d'échantillonnage restreints. Nous avons donc examiné la prévalence et les prédicteurs d'une exposition perçue à un

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“…However, off-site angler surveys are prone to a low response rate and to reporting bias (Roach et al 1999;Zarauz et al 2015). Surveys with a low response rate are at risk of producing biased population estimates (Brick 2013) and should be viewed with skepticism if a nonresponse bias assessment is not undertaken (Fisher 1996;Lewis et al 2013). In essence, the potential benefits of off-site angler surveys, such as cheaper cost per contact (McCormick et al 2015), can easily be negated by nonresponse bias.…”

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confidence: 99%

Concurrently Assessing Survey Mode and Sample Size in Off‐Site Angler Surveys

Barrett

Poorten

Cooper

et al. 2017

N American J Fish Manag

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Off‐site angler surveys are commonly administered via two or more survey modes in the form of a mixed‐mode survey. Mixed‐mode surveys allow survey administrators to attain the benefits inherent to different survey modes, reduce total survey error, and control survey cost. However, these benefits can only be simultaneously attained after undertaking sample size planning. Sample size planning is a trade‐off analysis wherein a researcher concurrently assesses survey administration cost, the accuracy and precision of estimates, the magnitude and direction of biases, and variance of the test statistic to determine an optimal sample size. We used data from an off‐site angler survey administered to anglers targeting White Sturgeon Acipenser transmontanus to illustrate a systematic approach to sample size planning. Our survey design included a mixed‐mode design with three survey modes (e‐mail, mail, and telephone) and a two‐phase sampling design that had a first contact and a follow‐up contact with a subsample of nonrespondents. Sample size planning was undertaken in the form of a sensitivity analysis wherein four survey design alternatives were simulated and assessed based on four criteria (i.e., bias, precision, accuracy, and cost). We also incorporated tests for nonresponse bias and survey mode effect. We found that (1) response rates were lower for e‐mail surveys (22%) than for mail surveys and telephone surveys (39–44%); (2) nonresponse bias did not have a substantial effect on survey estimates from the mixed‐mode design; and (3) estimates (total effort and total catch) from the mail and e‐mail survey modes were significantly different, indicating a survey mode effect. The high variability of anglers’ annual catch made survey estimates highly imprecise at lower sample sizes. The level of acceptable error varies for each study. Therefore, a systematic approach to sample size planning is necessary to determine the point where acceptable error is reached while considering multiple survey design alternatives. Received September 25, 2016; accepted April 24, 2017 Published online June 19, 2017

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Estimating the Effect of Nonresponse Bias in a Survey of Hospital Organizations

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Cited by 66 publications

References 38 publications

Physician attitudes and experiences with Maryland's prescription drug monitoring program (PDMP)

Physician attitudes and experiences with Maryland's prescription drug monitoring program (PDMP)

Prevalence and predictors of exposure to disruptive behaviour in the operating room

Concurrently Assessing Survey Mode and Sample Size in Off‐Site Angler Surveys

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