Proximate Measures and Hidden Assumptions: Teaching Statistics to Social Science Students

By Walter Zwirner.

Published by The Learner Collection

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Article: Print $US10.00
Article: Electronic $US5.00

Teaching statistics to social science students demands an in-depth look at the assumptions needed to design a valid study using statistical inferential methods. The hidden assumptions relate to problems in measurements in the social science research that are not addressed in the usual study in the physical sciences. These include measures of non-preciseness and fuzzy data, measurements of low reliability and validity, use of Bayesian statistics. Although even modern textbooks usually do not mention these problems, discussions on these topics can be found on the Internet. Course in research design thus are heavily depended on actual development of the mentioned difficulties on the Internet. There is also a need to discuss assumptions which are used for statistical inference, social science research is probably not as robust as physical science claims to be. The usual non-randomness of the sample seems to be the most difficult problem to deal with; that is where Bayesian statistics might be enabling a solution.

Keywords: Proximate Measurements, Non-Precise Data, Fuzzy Logic, Bayesian Statistics

The International Journal of Learning, Volume 14, Issue 7, pp.201-204. Article: Print (Spiral Bound). Article: Electronic (PDF File; 477.472KB).

Dr. Walter Zwirner

Associate Professor, University of Calgary, University of Calgary, Calgary, Alberta, Canada

After receiving a PhD from Stanford Univerity I joined the University of Calgary as a teacher and researcher. My interests include statistical founations as applied to research in social sciences.

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