June 14, 2015
June 14, 2015
June 17, 2015
Educational Research and Methods
26.1570.1 - 26.1570.14
The Role of Outlier Analysis in Reducing Purposeful Sampling Bias: A Sequential Mixed-Method ApproachSampling is an important step in researching. Depending on the research question and qualitativeor quantitative nature of the study the eligibility and size of the population may vary widely. Dueto small size of samples for qualitative analysis, bias may have a larger effect in this type ofresearch where convenient samples are commonly used. The aim of purposefully selectedsamples is to find information-rich cases allowing in-depth analysis instead of generalizablefindings. Using statistical analysis for identifying information-rich cases may reduce bias whileallowing qualitative analysis for in-depth research questions.The purpose of this paper is to describe an outlier analysis followed by a cluster analysis toinform purposeful sampling as part of sequential mixed-methods studies. Three hypotheses aretested: 1) Purposeful sampling can be performed using statistical methods that weight criteriaequally for all prospective participants. 2) Outliers represent critical cases of groups within adesired population for maximum variation or contrast sampling techniques 3) Due to outliernature, sample size affects the quality of critical cases identification.The sample included adults in academia and industry who competed a lifelong learning scale andbackground survey. Using cluster analysis, outliers in four groups were identified based on theinteraction between participants’ lifelong learning and STEM background. Two cloudrepresentations were used for increasing confidence in outlier identification, one using raw scalesfrom surveys and other using ranked data from highest to lowest scores. The first method tookbetween-scales variation into account by calculating linkage to the cluster using distance to anelliptical cloud, while the second took that variation into account by ranking values within eachscale. The purposeful sample comprised all data points identified as outliers using bothstrategies. Central tendencies were analyzed to assure that outliers were representing significantdifferences between groups. This analysis resulted in the identification of outliers withconfidence and show statistically that the outliers were part of a sub-cluster, representing aspecific group in the population.The study provides a valid and rigorous approach to purposeful sampling, enabling to select aconvenient yet unbiased sample. The statistically rigorous selection of participants based incluster analysis led to a wide variety of cases. This range and representation of sub-groups withina larger population may provide a useful selection of participants for qualitative analyses.
Tafur-Arciniegas, M., & Purzer, S. (2015, June), The Role of Outlier Analysis in Reducing Purposeful Sampling Bias: A Sequential Mixed-Method Approach Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24908
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