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Gender Differences in Academic Success Werner W. Wittmann University of Mannheim, Germany

Gender Differences in Academic Success Werner W. Wittmann University of Mannheim, Germany. Symposium: Individual Differences and Academic Performance Organized by Phillip L. Ackerman (Georgia Institute of Technology, Atlanta, GA)

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Gender Differences in Academic Success Werner W. Wittmann University of Mannheim, Germany

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  1. Gender Differences in Academic SuccessWerner W. WittmannUniversity of Mannheim, Germany Symposium:Individual Differences and Academic Performance Organized by Phillip L. Ackerman(Georgia Institute of Technology, Atlanta, GA) 12th Biennial Meeting of the International Society for the Study of Individual Differences (ISSID) Adelaide, 18th – 22nd, July, 2005

  2. Outline • For those who know me, this talk is about Brunswik-Symmetry and PINC research • For those who don‘t know me, this talk is about PINC research and Brunswik-Symmetry.

  3. Data from the United States Gender enrollment proportions in higher education

  4. R 2 = .283 R 2ADJ = .263 R 2 = .367 R 2ADJ = .339 R 2 = .084 A snapshot from a German Gymnasium. The grades you get there are the entrance ticket to Higher Education; GPA determines what you can study! MATH Grades at Gymnasium – Boys (N = 74) Stand.-Coeff. Beta BIS-B (mental speed) -.280 BIS-K (reasoning) -.342 BIS-B (mental speed) -.245 BIS-K (reasoning) -.379 O_B Openness for Aesthetics (NEO-PI-R) .292 Opening for aesthetics is negatively related to math grades and adds 8.4 % of variance accounted for. Do you see the typical European male intellectual who is proud about his poor math grades? Maybe you have seen some of them on TV?

  5. R 2 = .278 R 2ADJ = .259 R 2 = .408 R 2ADJ = .377 R 2 = .130 MATH Grades at Gymnasium – Girls (N = 81) Stand.-Coeff. Beta BIS-F (figural intelligence) -.352 BIS-N (numerical intelligence) -.254 BIS-F (figural intelligence) -.372 BIS-N (numerical intelligence) -.247 O_C Openness for Feelings (NEO-PI-R) .319 O_F Openness for Values (NEO-PI-R) -.201 The two openness facets add 13 % of variance accounted for. Girls with higher openness to values get better MATH grades, high openness to feelings is probably closely related to anxiety (anxiety eats up your soul, but probably also your intelligence and working memory resources)

  6. R 2 = .235 R 2ADJ = .227 R 2 = .366 R 2ADJ = .346 R 2 = .131 Grades in German – Girls (N = 97) Stand.-Coeff. Beta BIS-V (verbal intelligence) -.485 BIS-V (verbal intelligence) -.415 O_B Openness for Aesthetics (NEO-PI-R) -.160 O_F Openness for Values (NEO-PI-R) -.314 Openness for values and aesthetics add 13.1 % to verbal intelligence. For boys (N = 89) openness facets didn‘t contribute over verbal intelligence. Only BIS_V accounts for variance in German language grades. BIS-V: R 2 = .162, R 2ADJ = .152)

  7. Criterion ISchool/College/ University Criterion IIReal Business Life Predictor-Box ?CR NPR NCR gCR GPACR gPR VPR VCR FPR FCR The danger of Brunswik-asymmetry in validation strategies

  8. Criterion ISchool/College/ University Criterion IIReal Business Life Predictor-Box ?CR NPR NCR gCR GPACR gPR VPR VCR FPR FCR The danger of asymmetry in validation strategies

  9. Demands on the workforce • CP SNOW‘s distinction of the two cultures • Buz Hunt‘s question: Will we be smart enough? • Camilla Benbow and David Lubinski‘s focus on tilted profiles in aptitude and achievement

  10. 800 800 Upper third QUANT VERBAL VERBAL QUANT QUANT VERBAL Middle third 500 500 Lower third 200 200 Tilted Profiles

  11. 800 800 500 500 200 200 Tilted Profiles VERBAL QUANT University of .... (e.g. Harvard School of Education)

  12. 800 800 500 500 200 200 Tilted Profiles QUANT VERBAL University of .... (e.g. MIT, Caltech, Georgiatech)

  13. 800 800 500 500 200 200 Tilted Profiles QUANT VERBAL University of .... (e.g. your University ?)

  14. 800 800 500 500 200 200 Tilted Profiles as demands of the workplace VERBAL QUANT

  15. 800 800 500 500 200 200 Tilted Profiles as demands of a different workplace QUANT VERBAL

  16. 800 800 500 500 200 200 Even Profiles of still another one QUANT VERBAL

  17. 800 800 Upper third QUANT VERBAL Middle third 500 500 Lower third 200 200 Tilted Profiles

  18. 800 800 Upper third VERBAL QUANT Middle third 500 500 Lower third 200 200 Tilted Profiles

  19. Pisa2003-profiles: Level and shape group percentages The following results are for: 32 OECD Countries Percents of total count all PISA_OECD countries Tiltedness/shape (rows) by level (columns) of PISA-profiles lower middle upper Total N third third third Verbal 11.0 12.4 9.9 33.3 74713 Even 10.6 11.1 11.6 33.3 74690 Quant 11.7 9.8 11.8 33.3 74691 Total 33.3 33.3 33.3 100.0 N 74691 74690 74713 224094

  20. Pisa 2003-profiles: Level and shape group percentages The following results are for: United States Percents of total count Tiltedness (rows) by level of profile (columns) m = male; f = female low middle high Total N Verbalf 18.0 24.3 21.7 64.0 1738 m 6.6 10.1 7.5 24.2 662 Evenf 10.5 10.1 9.4 29.9 811 m 17.8 15.0 12.8 45.7 1252 Quant f3.1 1.7 1.3 6.1 166 m 14.2 8.17.9 30.1 826 Total f 31.6 36.0 32.4 100.0 m 38.6 33.1 28.3 100.0 N f 858 978 879 2715 m 1057 908 775 2740

  21. Pisa 2003-profiles: Level and shape group percentages The following results are for: Australia Percents of total count Tiltedness (rows) by level of profile (columns) m = male; f = female low middle high Total N Verbalf 9.4 20.0 26.9 56.3 3500 m 4.8 7.0 7.3 19.2 1216 Evenf 6.0 9.1 16.5 31.6 1963 m 8.1 11.7 16.8 36.6 2319 Quant f3.0 3.2 6.0 12.1 753 m 13.0 12.518.6 44.2 2800 Total f 18.4 32.3 49.4 100.0 m 26.0 31.3 42.7 100.0 N f 1142 2005 3069 6216 m 1647 1981 2707 6335

  22. Pisa 2003-profiles: Level and shape group percentages The following results are for: New Zealand Percents of total count Tiltedness (rows) by level of profile (columns) m = male; f = female low middle high Total N Verbalf 7.5 17.0 27.9 52.4 1166 m 3.2 5.6 9.3 18.2 416 Evenf 8.0 10.6 15.3 33.9 755 m 7.8 10.5 17.2 35.5 811 Quant f4.8 3.5 5.4 13.6 303 m 14.3 13.019.0 46.3 1059 Total f 20.4 31.0 48.6 100.0 m 25.3 29.1 45.5 100.0 N f 453 690 1081 2224 m 579 666 1041 2286

  23. Pisa 2003-profiles: Level and shape group percentages The following results are for: United Kingdom Percents of total count Tiltedness (rows) by level of profile (columns) m = male; f = female low middle high Total N Verbalf 9.3 17.7 19.3 46.3 2257 m 3.0 5.5 5.9 14.4 671 Evenf 10.3 13.5 17.8 41.5 2023 m 10.4 15.5 17.4 43.3 2020 Quant f3.0 4.1 5.0 12.2 592 m 13.4 13.115.8 42.3 1972 Total f 22.7 35.2 42.1 100.0 m 26.8 34.1 39.1 100.0 N f 1104 1716 2052 4872 m 1248 1590 1825 4663

  24. Pisa 2003-profiles: Level and shape group percentages The following results are for: Canada Percents of total count Tiltedness (rows) by level of profile (columns) m = male; f = female low middle high Total N Verbalf 7.3 18.8 21.9 48.1 6609 m 2.4 5.3 5.3 13.0 1754 Evenf 6.8 13.4 17.7 37.9 5210 m 8.7 13.0 15.2 36.8 4958 Quant f3.2 4.5 6.4 14.0 1929 m 14.6 15.620.0 50.2 6757 Total f 17.3 36.7 46.0 100.0 m 25.6 33.9 40.5 100.0 N f 2379 5043 6326 13748 m 3454 4562 5453 13469

  25. Gender differences in the level of the PISA_2003 scores of the composite of all verbal and math scales

  26. Gender differences in the verbal/math contrast factor

  27. Profile tiltedness PISA 2003 – OECD_CountriesHigh Level Group only 11 % OECD-Benchmark 11 % OECD-Benchmark

  28. OECD-Benchmark

  29. 800 800 QUANT/SPATIAL 500 500 VERBAL 200 200 A Prominent Tilted Profile !Would a guy like him still have a chance today ?

  30. Conclusions • Sorry about asking more questions than answering them. • But gender differences in academic knowledge are a worldwide phenomenon and fact. IRT and g-type scaling contribute in camouflaging them. • The dominance of grades with their heavy emphasis on reading and writing as a selection device give girls a tremendous edge over boys. Are some of them left behind? • Why do we find so little research with respect to fairness of grades compared to fairness of selection devices like SAT, GRE etc.? • Disregarding the impact of tilted profiles may be a major drawback of selection as practiced. • Are the demands on the workforce really so much verbally tilted today? What is their proportional distribution? • What types of profiles contribute most to gross national product? • Will we be smart enough to react, adapt and fit the levels and the shape of those educated by us to the demands of the future workplaces? • As always in this process there will be winners and losers • Hopefully it is your and my team which are among the winners!

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