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Shlomo S. Sawilowsky is professor of educational statistics and Distinguished Faculty Fellow at Wayne State University in Detroit, Michigan, where he has received teaching, mentoring, and research awards.[1][2][3]

Academic careerEdit

Sawilowsky obtained his Ph.D. in 1985 at the University of South Florida. He was inducted into the USF chapter of the Phi Kappa Phi honor society on May 17, 1981,[4] when he received his M.A.[5] In 2008 Sawilowsky served as president of the American Educational Research Association Special Interest Group/Educational Statisticians. He served as Assistant Dean in the College of Education at WSU for several years.[6]

Contributions to applied statistics and social/behavioral sciencesEdit

In 2000, the AMSTAT News, a publication of the American Statistical Association, described Professor Sawilowsky's award of Distinguished Faculty Fellow "in recognition of Sawilowsky's outstanding scholarly achievements in applied statistics, psychometrics, and experimental design in education and psychology."[7]

Applied statisticsEdit

He is the author of a statistics textbook that presents statistical methods via Monte Carlo simulation methods,[8] editor of a volume on real data analysis published by the American Educational Research Association SIG/Educational Statisticians,[9] and author of over a hundred articles in applied statistics and social sciences journals. Sawilowsky has also authored 24 entries in statistics encyclopedias.

His presentation titled "The Rank Transform," with co-author R. Clifford Blair, was awarded the 1985 Florida Educational Research Association & 1986 American Educational Research Association State/Regions Distinguished Paper Award.[5] Many of his publications are related to rank-based nonparametric statistics. For example, an examination of the robustness and comparative power properties of the rank transform statistic[10] was called a "major Monte Carlo study".[11][12] Hettmansperger and McKean stated that Sawilowsky provided "an excellent review of nonparametric approaches to testing for interaction" (p. 254-255).[13]

Sawilowsky's Monte Carlo work has been cited as an exemplar for designing simulation studies.[14] His work has been cited on a variety of statistical issues, such as

  • demonstrating sequential procedures of testing underlying assumptions of parametric tests, commonly recommended in textbooks and statistics software user manuals, "increases the rate of Type I error";[15]
  • rounding down degrees of freedom when using tabled critical values decreases statistical power;[16]
  • alternatives to the winsorized sample standard deviation can be invoked to increase the statistical power of Yuen's confidence interval;[17]
  • maximum likelihood methods (e.g., one-step Huber) are superior to trimming in constructing robust estimators;[18]
  • using effect sizes obtained when the null hypothesis has been retained inflates Type I errors in meta-analysis;[19] and
  • explicating "criteria for an appropriate Monte Carlo simulation."[20]

PsychometricsEdit

In psychological testing, Sawilowsky is a co-author of a self-determination assessment battery;[21] an instrument designed to assess locus of control, self-esteem, and self-concept among at-risk adolescents;[22] an instrument "which measures future orientation, knowledge of the realities of child rearing, personal intentions, and sexual self-efficacy;"[23][24] and a college well-being instrument.[25] Sawilowsky was the initial proponent in favor of psychometric theory (reliability refers to the test) over datametric theory[26] (reliability refers to the data), a controversy with implications for test theory, role of tests in expert testimony, test validity,[27][28] etc. The debate was discussed in Educational and Psychological Measurement[29] and elsewhere.[30] Although the issue has not been resolved, the current non-aligned opinion "lean[s] toward the Sawilowsky position."[31] In classical test theory, he developed the Sawilowsky I test, a statistical test used to help demonstrate evidence of construct validity in the multitrait-multimethod matrix.[32]

Experimental designEdit

Sawilowsky's Monte Carlo[33] work on comparing randomized vs quasi-experimental design has been described as "one of the strongest examples" demonstrating limitations of quasi-experimental design, and "provides possibly one of the strongest cases for the superiority of randomized designs."[34]

MentorshipEdit

In 1998, the AMSTAT News reported Sawilowsky's Awards for Excellence in Teaching, and Graduate Mentorship, and noted "Professor Sawilowsky's exceptional record as an academician is reflected in the excellence with which he mentors graduate students."[35] He has mentored 52 dissertations in applied statistics as major professor according to the Mathematics Genealogy Project.[36] His doctoral students include D. Lynn Kelley (1994),[37] Patrick D. Bridge (1996),[38] Todd C. Headrick (1997),[39] Michael J. Nanna (1997),[40][41][42] Gail C. Fahoome (1999),[43] Bruce Fay (2003),[44] David Fluharty (2007),[45] Boris Shulkin (2007),[46] and Tammy A. Grace (doctoral candidate).[47]

ProQuest indicates he has chaired dissertations in many other fields, such as kinesiology,[48] nursing education,[49] and teacher education;[50] and co-chaired a dissertation on process drama.[51] He also served as 2nd advisor on 19 doctoral dissertations, and numerous more as a committee member.[52]

EditorshipEdit

Sawilowsky is the founder and editor of the Journal of Modern Applied Statistical Methods. It was created to provide an outlet for research using Monte Carlo and other resampling methods, nonparemtric and other robust methods, permutation and other exact or approximately exact methods, and statistical algorithms.[53]

Template:Infobox religious biography


ReferencesEdit

  1. 1994 WSU President's Award For Excellence In Teaching; 1997 College of Education Excellence in Teaching Award
  2. 1998 WSU Outstanding Graduate Mentor Award
  3. 2000 - 2002 WSU Distinguished Faculty Fellow.
  4. Who's Who in American Education, 1990, II, 608. National Reference Institute.
  5. 5.0 5.1 http://coe.wayne.edu/faculty-staff/bio.php?id=60321
  6. With Detroit Roots, Organization Changes World Through Special Friendships - Stories - Chabad-Lubavitch News. URL accessed on 2010-04-18.
  7. Amstat News, May, 2000, p. 26.
  8. (2003) 'Statistics via Monte Carlo Simulation with Fortran', Rochester Hills, MI: JMASM.
  9. (2007). Real Data Analysis: A Volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching. American Educational Research Association SIG Educational Statisticians.
  10. Sawilowsky, S., Blair, R. C., and Higgins, J. J. (1989). An investigation of the type I error and power properties of the rank transform procedure in factorial ANOVA. Journal of Educational Statistics, 14, 255-267.
  11. (1998) Robust nonparametric statistical methods, First, xiv+467 pp., London: Edward Arnold. Template:MR (pages 269--270).
  12. Similarly, see F. Pesarin (2001), Multivariate permutation tests, Chichester: Wiley, p. 229.
  13. (1998) Robust nonparametric statistical methods, First, xiv+467 pp., London: Edward Arnold. Template:MR
  14. Maxwell, S. E., & Cole, D. A. (1995). "Tips for writing (and reading) methodological papers." Psychological Bulletin, 118(2), p. 196-197.
  15. Grissom, R. J., & Kim, J. J. (2005). Effect sizes for research: A broach practical approach. Mahwah, NJ: Erlbaum, p. 15.
  16. Grissom & Kim (2005), p. 33
  17. Grissom & Kim (2005), p. 38
  18. Grissom & Kim (2005), p. 42
  19. Grissom & Kim (2005), p. 60
  20. Grissom & Kim (2005), p. 131
  21. http://www.sdtac.uncc.edu/sd_assessments.asp
  22. Wood, P. C, Hillman, S. B., & Sawilowsky, S. S. (1996). Locus of control, self-concept, and self-esteem among at-risk African -American adolescents. Tests in Print, Volume 1‎, Oscar Krisen Buros, Buros Institute of Mental Measurements, Educational tests and measurements, 1999, p. 247.
  23. Herrman, J. W., & Waterhouse, J. K. (2010). What do adolescents think about teen parenting? Western Journal of Nursing Research, Sage, DOI:10.1177/0193945910381761
  24. Somers, C. L., Johnson, S. A., & Sawilowsky, S. S. 2002. A measure for evaluating the effectiveness of teen pregnancy programs. Psychology in the Schools, 39, 337-342.
  25. http://www.edgefoundation.org/information/research/edge-research-executive-summary/
  26. Sawilowsky coined the phrase "datametric theory" in Sawilowsky, S. (2000). Psychometrics vs datametrics. Educational and Psychological Measurement, 60, 157-173; see also Sawilowsky, S. (2000). Reliability. Educational and Psychological Measurement, 60, 196-200.
  27. S. Urbina (2004), Essentials of psychological testing. Hoboken: Wiley, p. 148.
  28. Note that in classical measurement theory (see, e.g., Sawilowsky (2000), Educational and Psychological Measurement), validity applies to the application of the test, but not to the test itself. Hence, the Wikipedia link name is problematic.
  29. 2000, v. 60
  30. B. Thompson (Ed.) (2003), Score reliability: Contemporary thinking on reliability issues, Thousand Oaks: Sage.
  31. J. C. Thomas and P. Traux (2008), Assessment and analysis of clinically significant change. In D. McKay (Ed.), Handbook of Research Methods in Abnormal and Clinical Psychology, p. 333.
  32. Sawilowsky, S. (2002). A quick distribution-free test for trend that contributes evidence of construct validity. Measurement and Evaluation in Counseling and Development, 35, 78-88.
  33. (2007). Real Data Analysis: A Volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching. American Educational Research Association SIG Educational Statisticians., Chapter 15
  34. Spence, P. R., & Lachlan, K. A. (2010). Disasters, crises, and unique populations: Suggestions for survey research. In L. A. Ritchie, & W. MacDonald (Eds.), Enhancing disaster and emergency preparedness, response, and recovery through evaluation: New directions for evaluation. American Evaluation Association, 126, p. 97.
  35. Amstat News, October, 1998.
  36. Cite error: Invalid <ref> tag; no text was provided for refs named genealogy
  37. Measurement Made Accessible: A Research Approach Using Qualitative, Quantitative and Quality Improvement Methods, Thousand Oaks, CA: Sage, 1999, ISBN 0-7619-1023-9; How to Use Control Charts for Healthcare, Milwaukee, WI: American Society for Quality, 1999, ISBN=0-87389-451-9
  38. http://books.google.com/books?id=75LczxSey3sC&pg=PA320&dq=%22increasing+physician's+awareness%22&hl=en&ei=yNj2TPbEDISglAfhst27BQ&sa=X&oi=book_result&ct=result&resnum=1&ved=0CCgQ6AEwAA#v=onepage&q=Bridge&f=false
  39. Statistical Simulation: Power Method Polynomials and Other Transformations, Boca Raton, FL: CRC Press, 2010, ISBN 978-1-4200-6490-2
  40. http://books.google.com/books?id=ZwqERlh1iEIC&pg=PA196&dq=nanna+likert&hl=en&ei=A9b2TJPRKcWBlAfEoJGHBg&sa=X&oi=book_result&ct=result&resnum=6&ved=0CD0Q6AEwBTgU#v=onepage&q=nanna&f=false
  41. http://books.google.com/books?id=-ufOfzVp6qYC&pg=PA5&dq=nanna+likert&hl=en&ei=ltX2TPH3DMb_lgfCsMnFBQ&sa=X&oi=book_result&ct=result&resnum=2&ved=0CCgQ6AEwAQ#v=onepage&q=nanna%20likert&f=false
  42. http://books.google.com/books?id=75LczxSey3sC&pg=PA340&dq=nanna+likert&hl=en&ei=A9b2TJPRKcWBlAfEoJGHBg&sa=X&oi=book_result&ct=result&resnum=2&ved=0CCUQ6AEwATgU#v=onepage&q=nanna&f=false
  43. (2003) 'Statistics via Monte Carlo Simulation with Fortran', Rochester Hills, MI: JMASM.
  44. Grissom & Kim (2005), p. 223
  45. Dave Fluharty was the first editor of the ASA's SPQR, and was the committee chair when the ASA granted Section status to Quality and Productivity. http://www.jstor.org/pss/2685334
  46. Shulkin, B., & Sawilowsky, S. Estimates of the population median. In (N. J. Salkind, Ed.) Encyclopedia of Measurement and Statistics, I, 316-318, Thousand Oaks: Sage.
  47. http://iospress.metapress.com/content/440t24600201019n/
  48. ProQuest 1594489921
  49. ProQuest 1269394702
  50. ProQuest 1269395621
  51. Proquest 733505421
  52. http://coe.wayne.edu/tbf/eer/bio.php?id=60321
  53. http://tbf.coe.wayne.edu/jmasm/

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