外国語教育（研究）における量的データの視覚化と解釈

FLEAT VII (LET2019) ワークショップ
2019/08/06
@早稲田大学

Transcript

1. ֎ࠃޠڭҭʢݚڀʣʹ͓͚Δ ྔతσʔλͷࢹ֮Խͱղऍ Ӝ໺ ݚʢ๺ւֶԂେֶʣ email: urano@hgu.jp FLEAT VII / LET2019 @ Waseda University ɹɹ2019. 8. 6. https://www.urano-ken.com/research/let2019
2. ຊ೔ͷࢿྉ
3. ֎ࠃޠڭҭʹܞΘΔࢲͨͪ͸ɺݚڀʹ͓͍͚ͯͩͰͳ ͘ɺςετ΍੒੷ॲཧͱ͍ͬͨ৔໘Ͱ೔͝Ζ͔Β਺ྔԽ ͞ΕͨσʔλΛѻ͍ͬͯ·͢ɻຊϫʔΫγϣοϓͰ͸ɺ ڭҭ΍ݚڀͰྔతσʔλΛѻ͏ࡍʹ·ͣߦ͏΂͖σʔλ ͷࢹ֮Խͱɺσʔλͷಛ௃Λཧղ͢ΔͨΊͷجຊతͳ֓ ೦ͱͯ͠ͷ୅ද஋ɾ෼෍ɾޮՌྔͷҙຯʹֶ͍ͭͯͼɺ ϑϦʔͰΦʔϓϯιʔεͷ౷ܭιϑτ jamovi Λ࢖ͬͯɺ ࣮ࡍʹσʔλͷ؆୯ͳ෼ੳ͕Ͱ͖ΔΑ͏ʹͳΔ͜ͱΛ໨ ࢦ͠·͢ɻ ཁࢫ
4. ՍۭͷσʔλΛ ༻ҙ͠·ͨ͠
5. Name* Test A খ૔ ರ 70 Տ੢ େޒ࿠ 38 খਿ Ꮺ 58 ௶Ҫ ج༞ 48 ӬҪ ج༞ 28 ڮޱ ๏࢚ 54 ݪ ཽ໵ 58 ޿੉ ༎ 38 ౻ా ࢰಐ 42 ຊؒ խ඙ 47 ٶ࡚ ৎ༤ 78 ଜҪ ࿣࿠ 68 ࢁ࡚ ޹ଠ࿠ 40 ԣҪ ޛࢤ 50 ґా ༸հ 68 एࢁ ప 57 ༗അ Ղ೫ 64 ࿨ઘ ګࢠ 76 ؠҪ ඒՂ 43 ߐ઒ ༝Ӊ 90 ਆ୩ ࣿق 58 ๺઒ ઍՂࢠ 38 ࡔా Ѫࡊ 38 ਿా ඒՂ 43 ⁋ຊ ᜫ 58 ౔୩ ே߳ 60 Ӭ੉ ͘ΔΈ 48 দ໦ ಹಸ 45 ଜҪ ݁ࢠ 24 ए௬ ·Έ 36 *ʮͳΜͪΌͬͯݸਓ৘ใʯͰੜ੒ http://kazina.com/dummy/
6. Group A Test A খ૔ ರ 70 Տ੢ େޒ࿠ 38 খਿ Ꮺ 58 ௶Ҫ ج༞ 48 ӬҪ ج༞ 28 ڮޱ ๏࢚ 54 ݪ ཽ໵ 58 ޿੉ ༎ 38 ౻ా ࢰಐ 42 ຊؒ խ඙ 47 ٶ࡚ ৎ༤ 78 ଜҪ ࿣࿠ 68 ࢁ࡚ ޹ଠ࿠ 40 ԣҪ ޛࢤ 50 ґా ༸հ 68 एࢁ ప 57 ༗അ Ղ೫ 64 ࿨ઘ ګࢠ 76 ؠҪ ඒՂ 43 ߐ઒ ༝Ӊ 90 ਆ୩ ࣿق 58 ๺઒ ઍՂࢠ 38 ࡔా Ѫࡊ 38 ਿా ඒՂ 43 ⁋ຊ ᜫ 58 ౔୩ ே߳ 60 Ӭ੉ ͘ΔΈ 48 দ໦ ಹಸ 45 ଜҪ ݁ࢠ 24 ए௬ ·Έ 36 Group B Test A ؠӬ ࿡࿠ 52 ২໦ ҭೋ 59 ย੉ ཽ໵ 61 ࡔݩ ᠳଠ 76 ౡଜ ༏ 45 ৓ా ௕ར 68 ௕୔ ஌࢙ 63 দҪ Ұಙ 69 ࡾݪ ༟࣍࿠ 43 क԰ ཽ࣍ 51 ੨໺ Έ͋ 36 ஑୩ ༏ 51 ؠ୩ ౧ࢠ 39 ্ݪ ܠࢠ 71 ߐޱ Ί͙Έ 26 ٴ઒ ͳͭΈ 79 େ௩ ·͞Έ 55 Ԭ໺ ࿏ࢠ 61 ֯ా ౧ࢠ 89 ઒୺ ݁ҥ 51 ਆށ ࡊʑඒ 71 ֎ࢁ Έ͋ 63 রҪ Έ͖ 41 ࠜ؛ ༏ 41 ࠜ؛ ྱࢠ 83 Ӌా ѥر 93 ෱࢜ ΈΏ͖ 47 ෍ࢪ ༑߳ 37 ଜా จੈ 52 ٢Ӭ ܙས߳ 41
7. Group A Test A খ૔ ರ 70 Տ੢ େޒ࿠ 38 খਿ Ꮺ 58 ௶Ҫ ج༞ 48 ӬҪ ج༞ 28 ڮޱ ๏࢚ 54 ݪ ཽ໵ 58 ޿੉ ༎ 38 ౻ా ࢰಐ 42 ຊؒ խ඙ 47 ٶ࡚ ৎ༤ 78 ଜҪ ࿣࿠ 68 ࢁ࡚ ޹ଠ࿠ 40 ԣҪ ޛࢤ 50 ґా ༸հ 68 एࢁ ప 57 ༗അ Ղ೫ 64 ࿨ઘ ګࢠ 76 ؠҪ ඒՂ 43 ߐ઒ ༝Ӊ 90 ਆ୩ ࣿق 58 ๺઒ ઍՂࢠ 38 ࡔా Ѫࡊ 38 ਿా ඒՂ 43 ⁋ຊ ᜫ 58 ౔୩ ே߳ 60 Ӭ੉ ͘ΔΈ 48 দ໦ ಹಸ 45 ଜҪ ݁ࢠ 24 ए௬ ·Έ 36 Group B Test A ؠӬ ࿡࿠ 52 ২໦ ҭೋ 59 ย੉ ཽ໵ 61 ࡔݩ ᠳଠ 76 ౡଜ ༏ 45 ৓ా ௕ར 68 ௕୔ ஌࢙ 63 দҪ Ұಙ 69 ࡾݪ ༟࣍࿠ 43 क԰ ཽ࣍ 51 ੨໺ Έ͋ 36 ஑୩ ༏ 51 ؠ୩ ౧ࢠ 39 ্ݪ ܠࢠ 71 ߐޱ Ί͙Έ 26 ٴ઒ ͳͭΈ 79 େ௩ ·͞Έ 55 Ԭ໺ ࿏ࢠ 61 ֯ా ౧ࢠ 89 ઒୺ ݁ҥ 51 ਆށ ࡊʑඒ 71 ֎ࢁ Έ͋ 63 রҪ Έ͖ 41 ࠜ؛ ༏ 41 ࠜ؛ ྱࢠ 83 Ӌా ѥر 93 ෱࢜ ΈΏ͖ 47 ෍ࢪ ༑߳ 37 ଜా จੈ 52 ٢Ӭ ܙས߳ 41 ൺ΂ͯΈΑ͏ How?
8. ᶃ ਤʹͯ͠ΈΑ͏
9. ώετάϥϜ (Histogram) B A 20 40 60 80 100 Score Group
10. ๘܈ਤ (Beeswarm) 20 40 60 80 A B Group Score
11. ശͻ͛ਤ (Box Plot) 20 40 60 80 A B Group Score
12. ϰΝΠΦϦϯਤ (Violin Plot) 20 40 60 80 A B Group Score
13. ֬཰ີ౓ (Density) B A 30 60 90 Score Group
14. ֬཰ີ౓ (Density) B A 30 60 90 Score Group
15. ਤʹͯ͠ΈΑ͏ • ऩूͨ͠σʔλʹͲͷΑ͏ͳಛ௃͕͋Δ͔ɺ ͬ͘͟Γ೺Ѳ͢Δ͜ͱ͕Ͱ͖Δɻ • ໨ͰݟΔ͚ͩͳͷͰɺݫີͳൺֱ΍෼ੳʹ͸ ద͞ͳ͍ɻ
16. ᶄ ཁ໿ͯ͠ΈΑ͏
17. σʔλͷத৺ͱ ͹Β͖ͭ σʔλͷத৺
18. ฏۉ஋ ͢΂ͯͷσʔλͷ߹ܭΛσʔλͷݸ਺Ͱ ׂͬͨ΋ͷ தԝ஋ ͢΂ͯͷσʔλΛখ͍͞ॱʢ·ͨ͸େ͖͍ ॱʣʹฒ΂ͨͱ͖ɺਅΜதʹདྷΔ஋ ࠷ස஋ ͢΂ͯͷσʔλͷதͰग़ݱճ਺͕࠷΋ଟ͍ ஋ σʔλͷத৺
19. Group A Group B ฏۉ஋ 52.1 57.1 தԝ஋ 49.0 53.5 ࠷ස஋ 38, 58 41, 51 σʔλͷத৺
20. ඪ४ภࠩ σʔλͷ͹Β͖ͭ
21. • ݸʑͷ਺஋ͱฏۉ஋ͱͷࠩΛ̎৐͠ɺ
 ͦͷ߹ܭΛσʔλͷ਺Ͱׂͬͨ΋ͷͷฏํࠜ Group A Test A খ૔ ರ 70 Տ੢ େޒ࿠ 38 খਿ Ꮺ 58 ௶Ҫ ج༞ 48 ӬҪ ج༞ 28 ڮޱ ๏࢚ 54 ݪ ཽ໵ 58 ޿੉ ༎ 38 ౻ా ࢰಐ 42 (70–52.1)2 = 320.4 (38–52.1)2 = 198.8 (58–52.1)2 = 034.8 . . . ߹ܭ 6828.7 / 30 = 227.6 √ 227.6 = 15.1 Group A ฏۉ஋ 52.1 ←ʢ෼ࢄʣ ඪ४ภࠩ
22. • ݸʑͷ਺஋ͱฏۉ஋ͱͷࠩΛ̎৐͠ɺ
 ͦͷ߹ܭΛσʔλͷ਺Ͱׂͬͨ΋ͷͷฏํࠜ Group A Test A খ૔ ರ 70 Տ੢ େޒ࿠ 38 খਿ Ꮺ 58 ௶Ҫ ج༞ 48 ӬҪ ج༞ 28 ڮޱ ๏࢚ 54 ݪ ཽ໵ 58 ޿੉ ༎ 38 ౻ా ࢰಐ 42 (70–52.1)2 = 320.4 (38–52.1)2 = 198.8 (58–52.1)2 = 034.8 . . . ߹ܭ 6828.7 / 30 = 227.6 √ 227.6 = 15.1 Group A ฏۉ஋ 52.1 ඪ४ภࠩ 15.1 ←ʢ෼ࢄʣ ඪ४ภࠩ
23. 0 20 40 60 80 100 0.00 0.01 0.02 0.03 0.04 0 20 40 60 80 100 0.00 0.01 0.02 0.03 0.04 ฏۉ஋ = 50 ͷ৔߹ ඪ४ภࠩ = 10 ඪ४ภࠩ = 20 34.1% 13.6% 34.1% 34.1% 13.6% 34.1% 13.6% 13.6% ඪ४ภࠩ
24. 0 20 40 60 80 100 0.00 0.01 0.02 0.03 0.04 0 20 40 60 80 100 0.00 0.01 0.02 0.03 0.04 ฏۉ஋ = 50 ͷ৔߹ ඪ४ภࠩ = 10 ඪ४ภࠩ = 20 ඪ४ภࠩ
25. ʢ٢ా, 1998, p. 173ʣ ඪ४ภࠩ
26. ʢ٢ా, 1998, p. 173ʣ ࠩ͸ಉ͡ ඪ४ภࠩ
27. ॏͳΓͷྔ͕ҧ͏ ඪ४ภࠩ
28. Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4
29. Group A Group B 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030
30. Group A Group B 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030
31. ฏۉ஋ͷࠩ Group A Group B Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030 ෼෍ͷҧ͍
32. ݴ͑ͦ͏ͳ͜ͱ • ฏۉ஋ͷൺֱ͚ͩͰ͸ෆे෼ • σʔλͷ෼෍ʢ͹Β͖ͭʣ΋߹Θͤͯݕ౼ • ෼෍ͷॏͳΓ͕গͳ͍ํ͕͕ࠩେ͖͍
33. ΋͏Ұ౓ൺ΂ͯΈΑ͏ Group A Group B 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0 20 40 60 80 100 0.000 0.005 0.010 0.015 0.020 0.025 0.030
34. ΋͏Ұ౓ൺ΂ͯΈΑ͏ 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 0,10" 11,20" 21,30" 31,40" 41,50" 51,60" 61,70" 71,80" 81,90" 91,100" Group"A" Group"B" ࣮ࡍͷ෼෍Λϓϩοτͨ͠΋ͷ
35. ΋͏Ұ౓ൺ΂ͯΈΑ͏ 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 0,10" 11,20" 21,30" 31,40" 41,50" 51,60" 61,70" 71,80" 81,90" 91,100" Group"A" Group"B" ͜ͷॏͳΓ͸େ͖͍ͷʁখ͍͞ͷʁ
36. ࢦඪ͕΄͍͠
37. ޮՌྔʢEffect Sizeʣ • ޮՌͷେ͖͞Λ͋ΒΘ͢౷ܭతͳࢦඪ
 ʢେٱอɾԬా, 2012, p. 44ʣ
38. ޮՌྔͷछྨ
39. • ࠩͷେ͖͞Λද͢ࢦඪʢd ଒ʣ • ؔ܎ͷڧ͞Λද͢ࢦඪʢr ଒ʣ େ͖͘෼͚ͯ̎ͭ
40. ࠩͷେ͖͞Λද͢ࢦඪ Cohen’s d
41. pooled SD X X d 2 1 − = ←ɹฏۉͷࠩ ←ɹඪ४ภࠩ Cohen’s d ʮ̎ͭͷάϧʔϓͷࠩ͸ඪ४ภࠩԿݸ෼ʯ
42. pooled SD X X d 2 1 − = | 52.1 - 57.1| = (15.1 + 16.4) / 2* *ඪຊαΠζ͕ҟͳΔͱ͖ɺSDpooled ͷܭࢉ͸΋͏গ͠ෳࡶʹͳΓ·͢ Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4 Cohen’s d
43. pooled SD X X d 2 1 − = 5.0 = 15.75 *ඪຊαΠζ͕ҟͳΔͱ͖ɺSDpooled ͷܭࢉ͸΋͏গ͠ෳࡶʹͳΓ·͢ Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4 = 0.32 Cohen’s d
44. d 0 0.1 0.2 0.3 0.4 0.5 0.6 ॏͳΓ ʢˋʣ 100 92.3 85.7 78.7 72.6 67 61.8 d 0.7 0.8 0.9 1.0 1.1 1.2 1.3 ॏͳΓ ʢˋʣ 57 52.6 48.4 44.6 41.1 37.8 34.7 ޮՌྔ d ͱ෼෍ͷॏͳΓ
45. 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 0,10" 11,20" 21,30" 31,40" 41,50" 51,60" 61,70" 71,80" 81,90" 91,100" Group"A" Group"B" d = 0.32 ͳͷͰॏͳΓ͸ 3/4 ͙Β͍ ࠶ͼ͜ͷάϥϑ
46. ͭ·Γ
47. Group A ͱ Group B ͸ɺ ฏۉ఺ʹ 5 ఺͕ࠩ͋Δ͕ɺ શମͷ 3/4 ͸ॏͳ͍ͬͯΔɻ
48. ޮՌྔ d ͱॏͳΓͷؔ܎
49. pooled SD X X d 2 1 − = ←ɹখ͍͞ํ͕ྑ͍ ←ɹେ͖͍ํ͕ྑ͍ d ஋͕େ͖͘ͳΔʹ͸ ʮฏۉͷ͕ࠩେ͖͘ɺඪ४ภ͕ࠩখ͘͞ͳΔ ͱɺޮՌྔ͸େ͖͘ͳΔɻʯ
50. • Cohen (1988) • small: d = 0.2, overlap: 85.7% • e.g., 15ࡀͱ16ࡀͷঁࢠͷ਎௕ࠩ • medium: d = 0.5, overlap: 67.0% • e.g., 14ࡀͱ18ࡀͷঁࢠͷ਎௕ࠩ • large: d = 0.8, overlap: 52.6% • e.g., େֶ৽ೖੜͱPhDऔಘऀͷIQࠩ ޮՌྔͷղऍ
51. • Plonsky & Oswald (2014) • “L2 field-specific benchmarks” ܈ؒൺֱ ܈಺ൺֱ small d = 0.40 d = 0.60 medium d = 0.70 d = 1.00 large d = 1.00 d = 1.40 ޮՌྔͷղऍ
52. ͨͩ͠
53. • ͜ͷΑ͏ͳࢦඪ͸͋͘·Ͱ໨҆ • ࣮ࡍͷղऍ͸ݚڀऀࣗ਎ͷ੹೚Ͱ
54. ༗ҙੑݕఆ ॏͳΓͷେ͖͞͸Θ͔͚ͬͨͲɺ
 ͜ͷࠩ͸ۮવʁ
55. ؍࡯͞Ε͕ͨࠩۮવੜͨ͡΋ͷͰ͋Δ Մೳੑʢ֬཰ʣ ༗ҙੑݕఆ
56. ʮʢ౷ܭతʣ༗ҙੑʯͱ͸ • ໨ͷલͷσʔλʢඪຊʣ͔ΒΑΓେ͖ͳจ຺ ʢ฼ूஂʣΛਪఆ͢Δ • ඪຊͰ؍࡯͞ΕΔࠩɾؔ܎͕ɺ฼ूஂ͔Βͷ ඪຊநग़࣌ͷޡࠩͰੜ͡Δ֬཰ʢp ஋ʣΛ
 ܭࢉ͢Δ • p ஋͕ج४஋ʢྟք஋ʣҎԼͰ͋Ε͹ʮ༗ҙʯ Ͱ͋ΔʢޡࠩͰ͸ͳ͍ʣͱ൑அ͢Δ
57. ฼ूஂ ඪɹຊ ਪఆ σʔλղੳ Σ, F, t, p... ฼ूஂͱඪຊ
58. • ͋ΔඪຊͰಘΒΕͨ୅ද஋ʢe.g., ฏۉʣ ͱ฼ूஂͷ୅ද஋ͱͷࠩ ඪຊޡࠩ
59. ฼ूஂ
 μ = 15.3 ඪຊA
 M = 14.7 ඪຊB
 M = 15.9 ඪຊC
 M = 15.2 ඪຊD
 M = 15.4 ඪຊE
 M = 15.1
60. ฼ूஂ
 μ = 14.7 ඪຊA
 M = 14.7 ࣮ࡍ͸ M = μ ͱͯ͠ਪఆ
61. • ඪຊͷαΠζ͕େ͖͚Ε͹େ͖͍΄Ͳɺ ඪຊޡࠩ͸খ͘͞ͳΔ • ͭ·Γਪఆͷਫ਼౓͕ߴ͘ͳΔ ඪຊޡࠩ
62. t ݕఆ ← ฏۉͷࠩ ← ඪ४ภࠩ2ͷ࿨
 1 2 2 2 1 2 1 − + − = n SD SD X X t ↑ ʢ֤܈ͷඪຊαΠζʣ ʢඪຊαΠζ͕౳͍͠৔߹ʣ ʢ٢ా, 1998, p. 186ʣ
63. ͜Ε͖ͬ͞ݟͨʁ
64. pooled SD X X d 2 1 − = ←ɹฏۉͷࠩ ←ɹඪ४ภࠩ Cohen’s d ʮ͜Εʹ n Λ଍͢ͱ t ͬΆ͍ʂʯ
65. pooled SD X X d 2 1 − = 1 2 2 2 1 2 1 − + − = n SD SD X X t ʮt ͸ɺޮՌྔʹඪຊαΠζΛՃຯͨ͠΋ͷʯ
66. ←ɹখ͍͞ํ͕ྑ͍ ←ɹେ͖͍ํ͕ྑ͍ t ஋͕େ͖͘ͳΔʹ͸ 1 2 2 2 1 2 1 − + − = n SD SD X X t ↑ɹେ͖͍ํ͕ྑ͍
67. ࣗ༝౓** 3 4 5 10 20 30 ྟք஋ ྆ଆݕఆ5% 3.182 2.776 2.571 2.228 2.086 2.042 ࣗ༝౓ 40 50 100 200 500 1,000 ྟք஋ ྆ଆݕఆ5% 2.021 2.009 1.984 1.972 1.965 1.962 *͜ΕΑΓେ͖͍਺஋ͩͬͨΒۮવͰͳ͍ͱΈͳ͢ **n1 +n2 -2 t ͷྟք஋*
68. 1 2 2 2 1 2 1 − + − = n SD SD X X t *2܈Ͱ n ͕ҟͳΔͱ͖ͷܭࢉ͸΋ 
 ͏গ͠ෳࡶʹͳΓ·͢ * | 52.1-57.1| = √(15.12 + 16.42) / (30 - 1) ܭࢉͯ͠ΈΑ͏ Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4
69. 1 2 2 2 1 2 1 − + − = n SD SD X X t * 5 = 4.14 ܭࢉͯ͠ΈΑ͏ = 1.21 Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4 *2܈Ͱ n ͕ҟͳΔͱ͖ͷܭࢉ͸΋ 
 ͏গ͠ෳࡶʹͳΓ·͢
70. ࣗ༝౓** 3 4 5 10 20 30 ྟք஋ ྆ଆݕఆ5% 3.182 2.776 2.571 2.228 2.086 2.042 ࣗ༝౓ 40 50 100 200 500 1,000 ྟք஋ ྆ଆݕఆ5% 2.021 2.009 1.984 1.972 1.965 1.962 t ͷྟք஋ t (58) = 1.21 ͸༗ҙͰͳ͍
71. ͜͜·Ͱͷ·ͱΊ
72. • ޮՌྔ Cohen’s d • 2ͭͷάϧʔϓؒͷࠩΛඪ४Խͨ͠΋ͷ • t ݕఆ • ޮՌྔʹඪຊޡࠩͷӨڹΛՃຯͯ͠ɺͦͷ͕ࠩ ۮવ؍࡯͞ΕΔ֬཰Λࣔͨ͠΋ͷ • ݕఆ౷ܭྔ = ޮՌͷେ͖͞ x ඪຊͷେ͖͞ ʢೆ෩ݪ, 2002, p. 163ʣ
73. • Cohen’s d ͷ஥ؒ: • Hedges’ g • ෼฼ʹ฼ूஂͷඪ४ภࠩʢෆภ෼ࢄʹ جͮ͘ඪ४ภࠩʣΛ࢖͏ • Glass’ ⊿ • ෼฼ʹ౷੍܈ͷඪ४ภࠩΛ࢖͏
74. ؔ܎ͷڧ͞Λද͢ࢦඪ Pearson’s r / r2
75. • ม਺ؒͷؔ܎ͷେ͖͞Λද͢ • ࠷େ: 1.0ʢઈର஋ʣ • ࠷খ: 0 • ϐΞιϯͷੵ཰૬ؔ܎਺ r • r2 ʢ෼ࢄઆ໌཰ʣ Pearson’s r / r2
76. ෼ࢄ෼ੳͷ৔߹ ௐ΂͍ͨཁҼͷ෼ࢄ η2 = ૯෼ࢄ SSA = SSTotal
77. ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 
 (Class) 848 2 424 0.955 .389 .022 Error (Residuals) 37260 84 444 ஛಺ɾਫຊ (2014) ୈ6ষͷσʔλΛ࢖ͬͯ jamovi Ͱܭࢉ
78. ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 
 (Class) 848 2 424 0.955 .389 .022 Error (Residuals) 37260 84 444 / = / = MS = SS / df
79. ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 
 (Class) 848 2 424 0.955 .389 .022 Error (Residuals) 37260 84 444 / = ↑ɹඪຊαΠζ͕େ͖͍ͱ F ஋͕େ͖͘ͳΔ F = MSA / MSError = 424 / 444 = 0.955
80. ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 
 (Class) 848 2 424 0.955 .389 .022 Error (Residuals) 37260 84 444 + η2 = SSA / SSTotal = 848 / 38108 = .022 SSTotal = SSA + SSError = 848 + 37260 = 38108
81. ޮՌྔͷղऍ
82. • small: η2 = .01 • medium: η2 = .06 • large: η2 = .14 ਫຊɾ஛಺ (2008) • ͜ͷΑ͏ͳࢦඪ͸͋͘·Ͱ໨҆ • ࣮ࡍͷղऍ͸ݚڀऀࣗ਎ͷ੹೚Ͱ
83. ͜͜·Ͱͷ·ͱΊ
84. • r ଒ͷޮՌྔ • ม਺ؒͷؔ܎ͷڧ͞Λ਺஋Ͱࣔͨ͠΋ͷ • ࠷େͰ 1.0ɺ࠷খͰ 0 • ෼ࢄ෼ੳͰ࢖͏ η2 ͸ r2 ͱࣅͨײ͡ • F ͱ η2 ͷҧ͍͸ඪຊαΠζΛߟྀ͢Δ͔Ͳ͏͔ • ݕఆ౷ܭྔ = ޮՌͷେ͖͞ x ඪຊͷେ͖͞ ʢೆ෩ݪ, 2002, p. 163ʣ
85. • η2 ͷ஥ؒ: • partial η2 • ෼฼ʹ SSA + SSError Λ࢖͏ • ω2 • ฼෼ࢄਪఆͷͨΊͷόΠΞεΛऔΓআ ͍ͨ΋ͷ
86. ࣮ࡍʹ ܭࢉͯ͠Έ·͠ΐ͏
87. • Φʔϓϯιʔεͷ౷ܭϓϩάϥϛϯάݴޠ
 ɹɹΛ࢖͍΍͍͢ܗʹͨ͠ιϑτ΢ΣΞɻ • GUIͷͨΊ௚ײతʹ࢖͑Δɻ • ΦʔϓϯιʔεͰແྉͰ࢖͑Δɻ
88. https://www.jamovi.org
89. ϋϯζΦϯ
90. • Must-read: • Navarro, D. J., & Foxcroft, D. R. (2019). Learning statistics with jamovi: A tutorial for psychology students and other beginners. (Version 0.70).
 DOI: 10.24384/hgc3-7p15 • ೔ຊޠ༁΋͋Γ·͢: • ࣳా੐࢘༁. jamoviͰֶͿ৺ཧ౷ܭ.
 https://bookdown.org/sbtseiji/lswjamoviJ/
91. ஫ҙ఺
93. • ϑΝΠϧಡΈࠐΈ࣌ʹࣗಈత ʹ൑அ͞ΕΔม਺ͷछྨ͕ؒ ҧ͍ͬͯΔ͜ͱ͕͋Δɻ • Continuous ࿈ଓม਺ • Ordinal ॱংม਺ • Nominal ໊ٛม਺
94. http://www.langtest.jp Effect Size Calculator @
95. • σʔλ෼ੳ͕ͳΜͰ΋
 Ͱ͖ͪΌ͏΋ͷ͍͢͝
 ΢ΣϒΞϓϦ • ޮՌྔ d, g ͱͦͷ৴པ۠ ؒΛܭࢉͯ͘͠ΕΔ • ਫຊಞ͞Μʢؔ੢େֶʣ ͕։ൃ͠ɺແྉͰެ։ • ͓ྱ͸Ϗʔϧ·ͨ͸
 നϫΠϯͰ
98. ஫. ਪଌ౷ܭʢ༗ҙੑݕఆʣͰ࢖͏ ඪ४ภࠩʢSDʣ͸ෆภ෼ࢄʹجͮ͘ ΋ͷɻn ͷ୅ΘΓʹ n–1 Λܭࢉʹ࢖ ͍·͢ɻ
100. ࢀߟจݙ • ӳޠڭࢣͷͨΊͷڭҭσʔλ ෼ੳೖ໳ • ༗ҙੑݕఆͷ͘͠Έ΍ͦͷݶ քʹ͍ͭͯ΋ղઆ
101. ࢀߟจݙ • ຊ౰ʹΘ͔Γ΍͍͘͢͢͝େ ੾ͳ͜ͱ͕ॻ͍ͯΔ͘͝ॳา ͷ౷ܭͷຊ • େ੾ͳ͜ͱΛ਺ࣜΛަ͑ͯஸ ೡʹղઆ
102. ࢀߟจݙ • ֎ࠃޠڭҭݚڀϋϯυϒοΫ • هड़౷ܭɺਪଌ౷ܭɺޮՌྔ ΋ؚΊͯ໢ཏతͳҰ࡭
103. ࢀߟจݙ • ఻͑ΔͨΊͷ৺ཧ౷ܭ • ޮՌྔʹ͍ͭͯษڧ͢ΔͳΒ ඞಡ
104. ࢀߟจݙ • ͸͡Ίͯͷӳޠڭҭݚڀ • ݚڀͷೖޱΛղઆ͢ΔҰ࡭ɻ ࠓ೔ͷ಺༰͸ୈ6ষΛิ଍͢ Δ΋ͷ
105. 1. σʔλͷࢹ֮Խʢਤࣔʣ 2. σʔλͷཁ໿ʢத৺ͱ͹Β͖ͭʣ 3. ޮՌྔ • ࠩͷେ͖͞Λද͢ d ଒ • ؔ܎ͷڧ͞Λද͢ r ଒ 4. ༗ҙੑݕఆʢਪଌ౷ܭʣ 5. jamovi ͱ langtest.jp Ken Urano urano@hgu.jp https://www.urano-ken.com/research/let2019 ֎ࠃޠڭҭʢݚڀʣʹ͓͚Δ ྔతσʔλͷࢹ֮Խͱղऍ
106. ࢀߟจݙ • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates. • ೆ෩ݪே࿨. (2002). ʰ৺ཧ౷ܭֶͷجૅ: ౷߹తཧղͷͨΊʹʱ౦ژ: ༗൹ֳ. • લాܒ࿕ɾࢁ৿ޫཅ (ฤ). (2004). ʰӳޠڭࢣͷͨΊͷڭҭσʔλ෼ੳೖ໳: तۀ͕มΘ ΔςετɾධՁɾݚڀʱ౦ژ: େमؗॻళ. • ਫຊಞɾ஛಺ཧ. (2008). ʮݚڀ࿦จʹ͓͚ΔޮՌྔͷใࠂͷͨΊʹ: جૅత֓೦ͱ஫ҙ ఺ʯʰӳޠڭҭݚڀʱୈ31߸, 57–66. http://www.mizumot.com/files/ EffectSize_KELES31.pdf • Navarro, D. J., & Foxcroft, D. R. (2019). Learning statistics with jamovi: A tutorial for psychology students and other beginners. (Version 0.70). doi: 10.24384/hgc3-7p15
 ʢࣳా੐࢘༁. jamoviͰֶͿ৺ཧ౷ܭ. https://bookdown.org/sbtseiji/lswjamoviJ/ʣ • େٱอ֗ѥɾԬాݠհ. (2012). ʰ఻͑ΔͨΊͷ৺ཧ౷ܭ: ޮՌྔɾ৴པ۠ؒɾݕఆྗʱ ౦ژ: Ⴛ૲ॻ๪. • Plonsly, L., & Oswald, F. (2014). How big is “big”? Interpreting effect sizes in L2 research. Language Learning, 64, 878–912. doi: 10.1111/lang.12079 • ஛಺ཧɾਫຊಞ (ฤ). (2014). ʰ֎ࠃޠڭҭݚڀϋϯυϒοΫ: ݚڀख๏ͷΑΓྑ͍ཧղ ͷͨΊʹ (վగ൛)ʱ౦ژ: দദࣾ. • Ӝ໺ݚɾ࿱ཧཅҰɾాத෢෉ɾ౻ా୎࿠ɾ∁໦ѥرࢠɾञҪӳथ. (2016). ʰ͸͡Ίͯͷ ӳޠڭҭݚڀ: ԡ͓͖͍͑ͯͨ͞ίπͱϙΠϯτʱ౦ژ: ݚڀࣾ. • ٢ాण෉. (1998). ʰຊ౰ʹΘ͔Γ΍͍͘͢͢͝େ੾ͳ͜ͱ͕ॻ͍ͯ͋Δ͘͝ॳาͷ౷ܭ ͷຊʱژ౎: ๺େ࿏ॻ๪.