Analysis of non-response bias in a mailed health survey
ABSTRACT The objective of this study was to identify characteristics of non-respondents and late respondents to a mailed health survey. Persons who returned and those who did not return the questionnaire were compared using health insurance data, which indicated their age, sex, and health care expenditures in the previous year. Insurance and questionnaire data were used to compare early and late survey respondents and to compare categories of non-respondents. Questions covered use of health services, health status, and sociodemographic characteristics. Participants were members of health insurance plans in Geneva, Switzerland, 19–45 years old (n = 1822). Respondents (n = 1424) and non-respondents (n = 398) were of similar age and sex. The proportion of persons who had health care expenditures greater than zero Swiss francs (SFr) was higher among respondents (75%) than among non-respondents (69%, p = 0.03). Among non-respondents, expenditures of persons who explicitly refused to participate (2378 SFr) were higher than expenditures of persons who moved out of Geneva (1085 SFr) or who failed to return the questionnaire (1592 SFr, p = 02). Among respondents, being born in a Switzerland, having completed elementary school, having generated health care expenditures, and reporting good physical health were independent predictors of early response. In conclusion, low response rates to mailed health surveys may result in overestimating the utilization of health services. However, non-respondents did not constitute a homogenous group, and the strength and even direction of non-response bias depended on the mechanisms of non-response.
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ABSTRACT: To some extent, results always depend on the methods used, and the complete picture of the phenomenon of interest can be drawn only by combining results of different data processing techniques. This emphasizes the use of a wide arsenal of methods for processing and analyzing patient satisfaction surveys.The purpose of this study was to introduce the self-organizing map (SOM) to nursing science and to illustrate the use of the SOM with patient satisfaction data. The SOM is a widely used artificial neural network suitable for clustering and exploring all kind of data sets. The study was partly a secondary analysis of data collected for the Attractive and Safe Hospital Study from four Finnish hospitals in 2008 and 2010 using the Revised Humane Caring Scale. The sample consisted of 5,283 adult patients. The SOM was used to cluster the data set according to (a) respondents and (b) questionnaire items. The SOM was also used as a preprocessor for multinomial logistic regression. An analysis of missing data was carried out to improve the data interpretation. Combining results of the two SOMs and the logistic regression revealed associations between the level of satisfaction, different components of satisfaction, and item nonresponse. The common conception that the relationship between patient satisfaction and age is positive may partly be due to positive association between the tendency of item nonresponse and age. The SOM proved to be a useful method for clustering a questionnaire data set even when the data set was low dimensional per se. Inclusion of empty responses in analyses may help to detect possible misleading noncausative relationships.Nursing Research 09/2014; 63(5):333-345. DOI:10.1097/NNR.0000000000000054 · 1.50 Impact Factor
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ABSTRACT: Accurate information concerning alcohol consumption level and patterns is vital to formulating public health policy. The objective of this paper is to critically assess the extent to which survey design, response rate and alcohol consumption coverage obtained in random digit dialling, telephone-based surveys impact on conclusions about alcohol consumption and its patterns in the general population. Our analysis will be based on the Canadian Alcohol and Drug Use Monitoring Survey (CADUMS) 2008, a national survey intended to be representative of the general population. The conclusions of this paper are as follows: (1) ignoring people who are homeless, institutionalized and/or do not have a home phone may lead to an underestimation of the prevalence of alcohol consumption and related problems; (2) weighting of observations to population demographics may lead to a increase in the design effect, does not necessarily address the underlying selection bias, and may lead to overly influential observations; and (3) the accurate characterization of alcohol consumption patterns obtained by triangulating the data with the adult per capita consumption estimate is essential for comparative analyses and intervention planning especially when the alcohol coverage rate is low like in the CADUMS with 34%.03/2012; 21(1):17-28. DOI:10.1002/mpr.1345
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ABSTRACT: while low response rates need not introduce bias into research, having a lower percentage of responders does increase the potential for this to occur. This is of particular concern given the decline that has been occurring in response rates since the 1950s. However, there are various methods that can be incorporated into the study design, which can assist in increasing levels of participation. To outline the methods used by the King's Centre for Military Health Research (KCMHR) when conducting a recent telephone survey of serving and ex-Service military personnel. Using participants who had already taken part in a questionnaire-based study on the health effects of serving in the UK Armed Forces (n=10,272), a subsample was selected for an in-depth telephone interview-based follow-up study. The subsample consisted of 1,105 participants, selected on the basis of their mental health status. An adjusted response rate of 76% was achieved (n=821). Various methods of contact were used in this study to ensure an adequate response rate was achieved. Simple research strategies increase response rates and are likely to reduce bias. Use of multiple simultaneous tracing methods and customisation of the approach to the target population increases rapport between participants, ensuring that those who take part feel valued as members of the study. In the current climate of decreasing participation in studies, research teams need to engage with their study population and devise innovative strategies to keep participants involved in the research being undertaken.European Journal of Psychotraumatology 12/2010; 1. DOI:10.3402/ejpt.v1i0.5516 · 2.40 Impact Factor