量的データを扱うときに 知っておきたいこと —データの分布、有意性、効果量、信頼区間—

Bac20b7719109838d6be162a560272a0?s=47 Ken Urano
August 07, 2016

量的データを扱うときに 知っておきたいこと —データの分布、有意性、効果量、信頼区間—

外国語教育メディア学会第56回全国研究大会
@早稲田大学
2016. 8. 7.

Bac20b7719109838d6be162a560272a0?s=128

Ken Urano

August 07, 2016
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  1. 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/
  2. 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
  3. 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
  4. 8.

    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?
  5. 12.

    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
  6. 13.

    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
  7. 14.

    ൺ΂ͯΈΑ͏ 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
  8. 17.

    • ݸʑͷ਺஋ͱฏۉ஋ͱͷࠩΛ̎৐͠ɺ
 ͦͷ߹ܭΛσʔλͷ਺Ͱׂͬͨ΋ͷͷฏํࠜ ඪ४ภࠩ 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
  9. 18.

    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
  10. 19.

    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 ඪ४ภࠩ = 10 ඪ४ภࠩ = 20
  11. 24.

    ΋͏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
  12. 25.

    ΋͏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
  13. 26.

    ΋͏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
  14. 31.

    Figure 1. (N = 10) Figure 2. (N = 119)

    r = .627 r = .184 p = .052 p = .045 > ༗ҙͰͳ͍ʛ༗ҙ 0 2 4 6 8 10 0 2 4 6 8 10 0 3 6 9 12 15 0 3 6 9 12 15
  15. 36.

    ฼ूஂ
 μ = 15.3 ඪຊA
 M = 14.7 ඪຊB
 M

    = 15.9 ඪຊC
 M = 15.2 ඪຊD
 M = 15.4 ඪຊE
 M = 15.1
  16. 39.

    N 3 4 5 10 20 30 ྟք஋ (α =

    .05) 0.997 0.950 0.878 0.632 0.444 0.361 N 40 50 100 200 500 1000 ྟք஋ (α = .05) 0.312 0.279 0.197 0.139 0.088 0.062 *͜ΕΑΓେ͖͍਺஋ͩͬͨΒۮવͰͳ͍ͱΈͳ͢ ૬ؔ܎਺ʢrʣͱྟք஋*
  17. 41.

    Figure 1. (N = 10) Figure 2. (N = 119)

    r = .627 r = .184 p = .052 p = .045 0 2 4 6 8 10 0 2 4 6 8 10 0 3 6 9 12 15 0 3 6 9 12 15 ΋͏Ұ౓ൺ΂ͯΈΑ͏
  18. 42.

    ༗ҙͳࠩʢ·ͨ͸ɺ༗ҙͳ૬ؔʣ͕ಘΒΕ΍͍͢ݚڀΛ͢ΔͨΊ ͷ͖ΘΊͯ༗ޮͳํ๏͕͋Γ·͢ɻͦΕ͸ɺͱʹ͔͘ଟ͘ͷσʔ λΛूΊΔ͜ͱͰ͢ɻͳͥͳΒ͹ɽɽɽ౷ܭతݕఆͷ݁Ռ͸σʔ λ਺͕ଟ͍΄Ͳ༗ҙʹͳΓ΍͍͔͢ΒͰ͢ɻͦͷͨΊɺσʔλ਺ Λ૿΍͑͢͠͞Ε͹ɺ͖ΘΊͯখ͞ͳࠩͰ΋ “ʢ౷ܭతʹ͸ʣ༗ ҙͰ͋Δ” ͱ͍͏݁ՌʹͳΔՄೳੑ͕ߴ·Γ·͢ɻྫ͑͹ɺN = 1000

    ͷ৔߹ʹ͸ɺr = 0.062 ͱ͍͏͖ΘΊͯখ͞ͳ૬ؔ܎਺ʢ͢ ͳΘͪɺඇৗʹऑ͍ؔ܎ʣͰ΋༗ҙʹͳΓ·͢ɽɽɽɻ͜ͷΑ͏ ʹɺ౷ܭతਪఆʹ͸ɺ“σʔλ਺ͱ͍͏ɺݚڀऀ͕೚ҙʹܾΊΒ ΕΔཁҼʹΑͬͯ݁Ռ͕ࠨӈ͞Εͯ͠·͏” ͱ͍͏ࠜຊతͳ໰୊ ͕͋Γ·͢ɻʢ٢ా, 1998, p. 232; Լઢ͸Ӝ໺ʹΑΔʣ ༗ҙੑݕఆͱඪຊαΠζ
  19. 48.

    pooled SD X X d 2 1 − = ←ɹฏۉͷࠩ

    ←ɹඪ४ภࠩ Cohen’s d ʮ̎ͭͷάϧʔϓͷࠩ͸ඪ४ภࠩԿݸ෼ʯ
  20. 49.

    pooled SD X X d 2 1 − = |

    52.1 - 57.1| = (15.1 + 16.4) / 2* *ඪຊαΠζ͕ҟͳΔͱ͖ɺSDpooled ͷܭࢉ͸΋͏গ͠ෳࡶʹͳΓ·͢ Cohen’s d Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4
  21. 50.

    pooled SD X X d 2 1 − = 5.0

    = 15.75 *ඪຊαΠζ͕ҟͳΔͱ͖ɺSDpooled ͷܭࢉ͸΋͏গ͠ෳࡶʹͳΓ·͢ Cohen’s d Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ 15.1 16.4 = 0.32
  22. 51.

    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 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
  23. 52.

    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
  24. 53.
  25. 56.

    pooled SD X X d 2 1 − = ←ɹখ͍͞ํ͕ྑ͍

    ←ɹେ͖͍ํ͕ྑ͍ d ஋͕େ͖͘ͳΔʹ͸ ʮฏۉͷ͕ࠩେ͖͘ɺඪ४ภ͕ࠩখ͘͞ͳΔͱɺ ޮՌྔ͸େ͖͘ͳΔɻʯ
  26. 58.

    • 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
  27. 59.

    • Plonsky & Oswald (2014) • “L2 field-specific benchmarks” ޮՌྔͷղऍ

    ܈ؒൺֱ ܈಺ൺֱ small d = 0.40 d = 0.60 medium d = 0.70 d = 1.00 large d = 1.00 d = 1.40
  28. 60.
  29. 65.
  30. 66.

    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
  31. 68.

    pooled SD X X d 2 1 − = ←ɹฏۉͷࠩ

    ←ɹඪ४ภࠩ Cohen’s d ʮ͜Εʹ n Λ଍͢ͱ t ͬΆ͍ʂʯ
  32. 69.

    pooled SD X X d 2 1 − = 1

    2 2 2 1 2 1 − + − = n SD SD X X t ʮt ͸ɺޮՌྔʹඪຊαΠζΛՃຯͨ͠΋ͷʯ
  33. 70.
  34. 71.

    n 1 2 3 4 5 ྟք஋ ྆ଆݕఆ5% 12.71 4.3

    3.18 2.78 2.57 n 10 20 50 100 200 ྟք஋ ྆ଆݕఆ5% 2.23 2.09 2.01 1.98 1.97 *͜ΕΑΓେ͖͍਺஋ͩͬͨΒۮવͰͳ͍ͱΈͳ͢ t ͷྟք஋*
  35. 72.

    1 2 2 2 1 2 1 − + −

    = n SD SD X X t *2܈Ͱ n ͕ҟͳΔͱ͖ͷܭࢉ͸΋ 
 ͏গ͠ෳࡶʹͳΓ·͢ * | 52.1-57.1| = √(15.32 + 16.72) / (30 - 1) ܭࢉͯ͠ΈΑ͏ Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ** 15.3 16.7 **ਪଌ౷ܭʢ༗ҙੑݕఆʣͰ࢖͏ͷ͸
 ෆภ෼ࢄʹجͮ͘ඪ४ภࠩɻn ͷ
 ୅ΘΓʹ n–1 Λܭࢉʹ࢖͍·͢ɻ
  36. 73.

    1 2 2 2 1 2 1 − + −

    = n SD SD X X t * 5 = 4.21 ܭࢉͯ͠ΈΑ͏ = 1.18 Group A Group B ฏۉ஋ 52.1 57.1 ඪ४ภࠩ** 15.3 16.7 *2܈Ͱ n ͕ҟͳΔͱ͖ͷܭࢉ͸΋ 
 ͏গ͠ෳࡶʹͳΓ·͢ **ਪଌ౷ܭʢ༗ҙੑݕఆʣͰ࢖͏ͷ͸
 ෆภ෼ࢄʹجͮ͘ඪ४ภࠩɻn ͷ
 ୅ΘΓʹ n–1 Λܭࢉʹ࢖͍·͢ɻ
  37. 74.

    n 1 2 3 4 5 ྟք஋ ྆ଆݕఆ5% 12.71 4.3

    3.18 2.78 2.57 n 10 20 50 100 200 ྟք஋ ྆ଆݕఆ5% 2.23 2.09 2.01 1.98 1.97 t ͷྟք஋ t = 1.18 ͸༗ҙͰͳ͍
  38. 76.

    • ޮՌྔ Cohen’s d • 2ͭͷάϧʔϓؒͷࠩΛඪ४Խͨ͠΋ͷ • t ݕఆ •

    ޮՌྔʹඪຊޡࠩͷӨڹΛՃຯͯ͠ɺͦͷ͕ࠩ ۮવ؍࡯͞ΕΔ֬཰Λࣔͨ͠΋ͷ • ݕఆ౷ܭྔ = ޮՌͷେ͖͞ x ඪຊͷେ͖͞ ʢೆ෩ݪ, 2002, p. 163ʣ
  39. 82.

    ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 847.609 2

    423.805 0.955 .389 .022 Error 37259.655 84 443.567 Total 38107.264 MacR ʹ෇ଐͷσʔλΛ࢖ͬͯ MacR Ͱܭࢉ
  40. 83.

    ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 847.609 2

    423.805 0.955 .389 .022 Error 37259.655 84 443.567 Total 38107.264 / = / = MS = SS / df
  41. 84.

    ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 847.609 2

    423.805 0.955 .389 .022 Error 37259.655 84 443.567 Total 38107.264 / = ↑ɹඪຊαΠζ͕େ͖͍ͱ F ஋͕େ͖͘ͳΔ F = MSA / MSE = 423.805 / 443.567 = 0.955
  42. 85.

    ҰཁҼ෼ࢄ෼ੳ SS df MS F p η2 A 847.609 2

    423.805 0.955 .389 .022 Error 37259.655 84 443.567 Total 38107.264 + = η2 = SSA / SST = 847.609 / 38107.264 = .022
  43. 87.

    • small: η2 = .01 • medium: η2 = .06

    • large: η2 = .14 ਫຊɾ஛಺ (2008) • ͜ͷΑ͏ͳࢦඪ͸͋͘·Ͱ໨҆ • ࣮ࡍͷղऍ͸ݚڀऀࣗ਎ͷ੹೚Ͱ
  44. 89.

    • 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
  45. 90.

    • η2 ͷ஥ؒ: • partial η2 • ෼฼ʹ SSA +

    SSE Λ࢖͏ • ω2 • ฼෼ࢄਪఆͷͨΊͷόΠΞεΛऔΓআ ͍ͨ΋ͷ
  46. 104.

    • σʔλ෼ੳ͕ͳΜͰ΋
 Ͱ͖ͪΌ͏΋ͷ͍͢͝
 ΢ΣϒΞϓϦ • ޮՌྔ d, g ͱͦͷ৴པ۠ ؒΛܭࢉͯ͘͠ΕΔ

    • ਫຊಞ͞Μʢؔ੢େֶʣ ͕։ൃ͠ɺແྉͰެ։ • ͓ྱ͸Ϗʔϧ·ͨ͸
 νϣίϨʔτͰ
  47. 105.
  48. 106.
  49. 107.
  50. 108.
  51. 109.
  52. 115.

    1. ෼෍ͱඪ४ภࠩ 2. ༗ҙੑʢݕఆʣͱޮՌྔ 3. ޮՌྔͷछྨ • ࠩͷେ͖͞Λද͢ d ଒

    • ؔ܎ͷڧ͞Λද͢ r ଒ 4. ޮՌྔͷ৴པ۠ؒ 5. ޮՌྔͷܭࢉ Ken Urano urano@hgu.jp http://bit.ly/let2016ws ྔతσʔλΛѻ͏ͱ͖ʹ ஌͓͖͍ͬͯͨ͜ͱ —σʔλͷ෼෍ɺ༗ҙੑɺޮՌྔɺ৴པ۠ؒ—
  53. 116.

    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. Retrieved from http://www.mizumot.com/files/ EffectSize_KELES31.pdf • େ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๪.