A Comparison of Design-Based and Model-Based Analysis of Sample Surveys in Geography

Wheeler, David C.; VanHorn, Jason E.; Paskett, Electra D.

doi:10.1080/00330120802212180

Cited by 2 publications

(1 citation statement)

References 30 publications

(21 reference statements)

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“…The stratified multistage cluster sampling design creates non-independence among units, along with disproportionate sampling where some groups may be oversampled or a probability proportional to size has been applied [5,6]. This creates two primary challenges that must be addressed when analyzing complex survey data: 1) homogeneity that results from the non-simple random sample (i.e., non-independence) and 2) disproportionate sampling that results in unequal selection probabilities (e.g., oversampling or adjustment for non-response) [7,8]. Failure to address these issues results in incorrect estimation of standard errors, which often results in underestimation and an increased probability of a type I error [6,9].…”

Section: Introductionmentioning

confidence: 99%

Inappropriate Survey Design Analysis of the Korean National Health and Nutrition Examination Survey May Produce Biased Results

Kim

Park

Kim

et al. 2013

J Prev Med Public Health

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ObjectivesThe inherent nature of the Korean National Health and Nutrition Examination Survey (KNHANES) design requires special analysis by incorporating sample weights, stratification, and clustering not used in ordinary statistical procedures.MethodsThis study investigated the proportion of research papers that have used an appropriate statistical methodology out of the research papers analyzing the KNHANES cited in the PubMed online system from 2007 to 2012. We also compared differences in mean and regression estimates between the ordinary statistical data analyses without sampling weight and design-based data analyses using the KNHANES 2008 to 2010.ResultsOf the 247 research articles cited in PubMed, only 19.8% of all articles used survey design analysis, compared with 80.2% of articles that used ordinary statistical analysis, treating KNHANES data as if it were collected using a simple random sampling method. Means and standard errors differed between the ordinary statistical data analyses and design-based analyses, and the standard errors in the design-based analyses tended to be larger than those in the ordinary statistical data analyses.ConclusionsIgnoring complex survey design can result in biased estimates and overstated significance levels. Sample weights, stratification, and clustering of the design must be incorporated into analyses to ensure the development of appropriate estimates and standard errors of these estimates.

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Section: Introductionmentioning

confidence: 99%

Inappropriate Survey Design Analysis of the Korean National Health and Nutrition Examination Survey May Produce Biased Results

Kim

Park

Kim

et al. 2013

J Prev Med Public Health

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Multi-country health surveys: are the analyses misleading?

Masood

Reidpath

2014

Current Medical Research and Opinion

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While there is general agreement among survey statisticians that complex surveys are most appropriately analyzed using design-based analyses, most researchers continued to use the more common model-based approaches. Recent developments in design-based multi-level analysis may be one approach to include all the survey design characteristics. This is a relatively new area, however, and there remains statistical, as well as applied analytic research required. An important limitation of this study relates to the selection of the surveys used and the choice of year for the analysis, i.e., year 2010 only. There is, however, no strong reason to believe that analytic strategies have changed radically in the past few years, and 2010 provides a credible snapshot of current practice.

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A Comparison of Design-Based and Model-Based Analysis of Sample Surveys in Geography

Cited by 2 publications

References 30 publications

Inappropriate Survey Design Analysis of the Korean National Health and Nutrition Examination Survey May Produce Biased Results

Inappropriate Survey Design Analysis of the Korean National Health and Nutrition Examination Survey May Produce Biased Results

Multi-country health surveys: are the analyses misleading?

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