Geographic Information System mapping as a tool to assess nonresponse bias in survey research.
Geographic Information System mapping as a tool to assess nonresponse bias in survey research.
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DOI：
10.1016/j.sapharm.2006.10.001
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发表时间：
2007-09-01
期刊：
影响因子：
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通讯作者：
Roth, Mary T
中科院分区：
文献类型：
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作者：
Hansen, Richard A;Henley, Amanda Clarke;Roth, Mary T
背景技术背景：调查是评估专业实践模式的有用工具，尽管答复率下降引起了对外部有效性的关注。这与网上调查特别相关，因为网上调查的答复率传统上低于纸质邮件调查。在2005年的北卡罗来纳州社区药房经理使用基于Web的数据收集工具的调查中，我们实现了23%.OBJECTIVE的总体响应率：探索无响应偏倚使用公认的方法，并测试地理信息系统映射是否是一个有用的工具来评估响应偏倚.通过比较早期应答者与晚期应答者（即波形分析）以及通过比较应答者与非应答者的已知药房、药剂师和人群特征，对无应答偏倚进行定量评估。结果：药房类型被确定为反应的预测因子，独立药房比连锁药房反应更少（优势比0.75，95%可信区间0.59-0.95）。这一结论在波动分析和已知种群特征分析中是一致的。其他县级变量，如人均医生数量、收入和符合医疗补助条件的居民百分比显示出趋势，但没有统计学意义（P
BACKGROUND: Surveys are a useful tool for assessing professional practice patterns, although declining response rates have caused concern over external validity. This is particularly relevant to Web-based surveys, where response rates traditionally have been lower than with paper mail surveys. In a 2005 survey of North Carolina community pharmacy managers using a Web-based data collection instrument, we achieved an overall response rate of 23%.OBJECTIVE: To explore nonresponse bias using accepted methods and to test whether Geographic Information System mapping is a useful tool for assessing response bias.METHODS: Cross-sectional survey of 1593 community pharmacy managers in North Carolina using a Web-based tool. Nonresponse bias was assessed quantitatively by comparing early responders with late responders (ie, wave analysis) and by comparing respondents with nonrespondents with regard to known pharmacy, pharmacist, and population characteristics. Significant variables from these analyses were then mapped using ArcGIS 9.1.RESULTS: Pharmacy type was identified as a predictor of response, with independent pharmacies less likely to respond than chain pharmacies (odds ratio 0.75; 95% confidence interval 0.59-0.95). This conclusion was consistent in the wave analysis and the analysis of known population characteristics. Other county-level variables such as the number of physicians per capita, income, and the percentage of residents eligible for Medicaid showed trends but were not statistically significant (P