Drawing Inferences From Self-selected Samples

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Drawing Inferences from Self-Selected Samples

Close Preview. Toggle navigation Additional Book Information. Description Table of Contents. Summary This volume contains a collection of essays and discussions which serve as an introduction and guide to current research in the area of drawing inferences from self-selected samples. This topic is of direct interest to a professional audience of survey researchers, pollsters, market researchers, policymakers, statisticians, demographers, economists, and sociologists. The essays themselves and their associated critical discussions are clear and careful; the contributors are among the foremost experts in the field.

Drawing Inferences From Self-selected Samples by Howard Wainer, Paperback | Barnes & Noble®

Moreover, the in-depth analysis of a small-N purposive sample or a case study enables the "discovery" and identification of patterns and causal mechanisms that do not draw time and context-free assumptions. Non-probability sampling is often not appropriate in statistical quantitative research, though, as these assertions raise some questions —how can one understand a complex social phenomenon by drawing only the most convenient expressions of that phenomenon into consideration? What assumption about homogeneity in the world must one make to justify such assertions?

Alas, the consideration that research can only be based in statistical inference focuses on the problems of bias linked to nonprobability sampling and acknowledges only one situation in which a non-probability sample can be appropriate —if one is interested only in the specific cases studied for example, if one is interested in the Battle of Gettysburg , one does not need to draw a probability sample from similar cases Lucas a.

Making Inferences from a Random Sample

Non-probability sampling is however widely used in qualitative research. Examples of nonprobability sampling include:. Nonprobability sampling should not intend to meet the same type of results neither to be assessed with the quality criteria of probabilistic sampling Steinke, Studies intended to use probability sampling sometimes end up using nonprobability samples because of characteristics of the sampling method.

For example, using a sample of people in the paid labor force to analyze the effect of education on earnings is to use a non-probability sample of persons who could be in the paid labor force.

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Because the education people obtain could determine their likelihood of being in the paid labor force, technically the sample in the paid labor force is a nonprobability sample for the question at issue. In such cases results are biased. The statistical model one uses can also render the data a non-probability sample.


For example, Lucas b notes that several published studies that use multilevel modeling have been based on samples that are probability samples in general, but nonprobability samples for one or more of the levels of analysis in the study. Evidence indicates that in such cases the bias is poorly behaved, such that inferences from such analyses are unjustified. These problems occur in the academic literature, but they may be more common in non-academic research.

For example, in public opinion polling by private companies or other organizations unable to require response , the sample can be self-selected rather than random. Document Type: Book review. Length: words. Howard Wainer ed. New York: Springer-Verlag, The four papers and authors are: 1.

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Wainer 2. Singer 3.

Robb 4.