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外国語教育(研究)における量的データの視覚化と解釈

Ken Urano
August 06, 2019

 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

Ken Urano

August 06, 2019
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  1. ֎ࠃޠڭҭʢݚڀʣʹ͓͚Δ
    ྔతσʔλͷࢹ֮Խͱղऍ
    Ӝ໺ ݚʢ๺ւֶԂେֶʣ
    email: [email protected]
    FLEAT VII / LET2019 @ Waseda University
    ɹɹ2019. 8. 6.
    https://www.urano-ken.com/research/let2019

    View Slide

  2. ຊ೔ͷࢿྉ

    View Slide

  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

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  4. ՍۭͷσʔλΛ
    ༻ҙ͠·ͨ͠

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  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/

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  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

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  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?

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  8. ਤʹͯ͠ΈΑ͏

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  9. ώετάϥϜ (Histogram)
    B
    A
    20 40 60 80 100
    Score
    Group

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  10. ๘܈ਤ (Beeswarm)
    20
    40
    60
    80
    A B
    Group
    Score

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  11. ശͻ͛ਤ (Box Plot)
    20
    40
    60
    80
    A B
    Group
    Score

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  12. ϰΝΠΦϦϯਤ (Violin Plot)
    20
    40
    60
    80
    A B
    Group
    Score

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  13. ֬཰ີ౓ (Density)
    B
    A
    30 60 90
    Score
    Group

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  14. ֬཰ີ౓ (Density)
    B
    A
    30 60 90
    Score
    Group

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  15. ਤʹͯ͠ΈΑ͏
    • ऩूͨ͠σʔλʹͲͷΑ͏ͳಛ௃͕͋Δ͔ɺ
    ͬ͘͟Γ೺Ѳ͢Δ͜ͱ͕Ͱ͖Δɻ
    • ໨ͰݟΔ͚ͩͳͷͰɺݫີͳൺֱ΍෼ੳʹ͸
    ద͞ͳ͍ɻ

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  16. ཁ໿ͯ͠ΈΑ͏

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  17. σʔλͷத৺ͱ
    ͹Β͖ͭ
    σʔλͷத৺

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  18. ฏۉ஋ ͢΂ͯͷσʔλͷ߹ܭΛσʔλͷݸ਺Ͱ
    ׂͬͨ΋ͷ
    தԝ஋ ͢΂ͯͷσʔλΛখ͍͞ॱʢ·ͨ͸େ͖͍
    ॱʣʹฒ΂ͨͱ͖ɺਅΜதʹདྷΔ஋
    ࠷ස஋ ͢΂ͯͷσʔλͷதͰग़ݱճ਺͕࠷΋ଟ͍

    σʔλͷத৺

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  19. Group A Group B
    ฏۉ஋ 52.1 57.1
    தԝ஋ 49.0 53.5
    ࠷ස஋ 38, 58 41, 51
    σʔλͷத৺

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  20. ඪ४ภࠩ
    σʔλͷ͹Β͖ͭ

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  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

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  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

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  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

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  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

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  25. ʢ٢ా, 1998, p. 173ʣ
    ඪ४ภࠩ

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  26. ʢ٢ా, 1998, p. 173ʣ
    ࠩ͸ಉ͡
    ඪ४ภࠩ

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  27. ॏͳΓͷྔ͕ҧ͏
    ඪ४ภࠩ

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  28. Group A Group B
    ฏۉ஋ 52.1 57.1
    ඪ४ภࠩ 15.1 16.4

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  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

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  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

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  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

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  32. ݴ͑ͦ͏ͳ͜ͱ
    • ฏۉ஋ͷൺֱ͚ͩͰ͸ෆे෼
    • σʔλͷ෼෍ʢ͹Β͖ͭʣ΋߹Θͤͯݕ౼
    • ෼෍ͷॏͳΓ͕গͳ͍ํ͕͕ࠩେ͖͍

    View Slide

  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

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  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

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  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

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  36. ࢦඪ͕΄͍͠

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  37. ޮՌྔʢEffect Sizeʣ
    • ޮՌͷେ͖͞Λ͋ΒΘ͢౷ܭతͳࢦඪ

    ʢେٱอɾԬా, 2012, p. 44ʣ

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  38. ޮՌྔͷछྨ

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  39. • ࠩͷେ͖͞Λද͢ࢦඪʢd ଒ʣ
    • ؔ܎ͷڧ͞Λද͢ࢦඪʢr ଒ʣ
    େ͖͘෼͚ͯ̎ͭ

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  40. ࠩͷେ͖͞Λද͢ࢦඪ
    Cohen’s d

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  41. pooled
    SD
    X
    X
    d
    2
    1 −
    =
    ←ɹฏۉͷࠩ
    ←ɹඪ४ภࠩ
    Cohen’s d
    ʮ̎ͭͷάϧʔϓͷࠩ͸ඪ४ภࠩԿݸ෼ʯ

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  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

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  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

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  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

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  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

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  46. ͭ·Γ

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  47. Group A ͱ Group B ͸ɺ
    ฏۉ఺ʹ 5 ఺͕ࠩ͋Δ͕ɺ
    શମͷ 3/4 ͸ॏͳ͍ͬͯΔɻ

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  48. ޮՌྔ d ͱॏͳΓͷؔ܎

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  49. pooled
    SD
    X
    X
    d
    2
    1 −
    =
    ←ɹখ͍͞ํ͕ྑ͍
    ←ɹେ͖͍ํ͕ྑ͍
    d ஋͕େ͖͘ͳΔʹ͸
    ʮฏۉͷ͕ࠩେ͖͘ɺඪ४ภ͕ࠩখ͘͞ͳΔ
    ͱɺޮՌྔ͸େ͖͘ͳΔɻʯ

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  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

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  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

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  52. ͨͩ͠

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  53. • ͜ͷΑ͏ͳࢦඪ͸͋͘·Ͱ໨҆
    • ࣮ࡍͷղऍ͸ݚڀऀࣗ਎ͷ੹೚Ͱ

    View Slide

  54. ༗ҙੑݕఆ
    ॏͳΓͷେ͖͞͸Θ͔͚ͬͨͲɺ

    ͜ͷࠩ͸ۮવʁ

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  55. ؍࡯͞Ε͕ͨࠩۮવੜͨ͡΋ͷͰ͋Δ
    Մೳੑʢ֬཰ʣ
    ༗ҙੑݕఆ

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  56. ʮʢ౷ܭతʣ༗ҙੑʯͱ͸
    • ໨ͷલͷσʔλʢඪຊʣ͔ΒΑΓେ͖ͳจ຺
    ʢ฼ूஂʣΛਪఆ͢Δ
    • ඪຊͰ؍࡯͞ΕΔࠩɾؔ܎͕ɺ฼ूஂ͔Βͷ
    ඪຊநग़࣌ͷޡࠩͰੜ͡Δ֬཰ʢp ஋ʣΛ

    ܭࢉ͢Δ
    • p ஋͕ج४஋ʢྟք஋ʣҎԼͰ͋Ε͹ʮ༗ҙʯ
    Ͱ͋ΔʢޡࠩͰ͸ͳ͍ʣͱ൑அ͢Δ

    View Slide

  57. ฼ूஂ ඪɹຊ
    ਪఆ
    σʔλղੳ
    Σ, F, t, p...
    ฼ूஂͱඪຊ

    View Slide

  58. • ͋ΔඪຊͰಘΒΕͨ୅ද஋ʢe.g., ฏۉʣ
    ͱ฼ूஂͷ୅ද஋ͱͷࠩ
    ඪຊޡࠩ

    View Slide

  59. ฼ूஂ

    μ = 15.3
    ඪຊA

    M = 14.7
    ඪຊB

    M = 15.9
    ඪຊC

    M = 15.2
    ඪຊD

    M = 15.4
    ඪຊE

    M = 15.1

    View Slide

  60. ฼ूஂ

    μ = 14.7
    ඪຊA

    M = 14.7
    ࣮ࡍ͸ M = μ ͱͯ͠ਪఆ

    View Slide

  61. • ඪຊͷαΠζ͕େ͖͚Ε͹େ͖͍΄Ͳɺ
    ඪຊޡࠩ͸খ͘͞ͳΔ
    • ͭ·Γਪఆͷਫ਼౓͕ߴ͘ͳΔ
    ඪຊޡࠩ

    View Slide

  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

    View Slide

  63. ͜Ε͖ͬ͞ݟͨʁ

    View Slide

  64. pooled
    SD
    X
    X
    d
    2
    1 −
    =
    ←ɹฏۉͷࠩ
    ←ɹඪ४ภࠩ
    Cohen’s d
    ʮ͜Εʹ n Λ଍͢ͱ t ͬΆ͍ʂʯ

    View Slide

  65. pooled
    SD
    X
    X
    d
    2
    1 −
    =
    1
    2
    2
    2
    1
    2
    1

    +

    =
    n
    SD
    SD
    X
    X
    t
    ʮt ͸ɺޮՌྔʹඪຊαΠζΛՃຯͨ͠΋ͷʯ

    View Slide

  66. ←ɹখ͍͞ํ͕ྑ͍
    ←ɹେ͖͍ํ͕ྑ͍
    t ஋͕େ͖͘ͳΔʹ͸
    1
    2
    2
    2
    1
    2
    1

    +

    =
    n
    SD
    SD
    X
    X
    t
    ↑ɹେ͖͍ํ͕ྑ͍

    View Slide

  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஋*

    View Slide

  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

    View Slide

  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·͢

    View Slide

  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

    View Slide

  71. ͜͜·Ͱͷ·ͱΊ

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  72. • ޮՌྔ Cohen’s d
    • 2ͭͷάϧʔϓؒͷࠩΛඪ४Խͨ͠΋ͷ
    • t ݕఆ
    • ޮՌྔʹඪຊޡࠩͷӨڹΛՃຯͯ͠ɺͦͷ͕ࠩ
    ۮવ؍࡯͞ΕΔ֬཰Λࣔͨ͠΋ͷ
    • ݕఆ౷ܭྔ = ޮՌͷେ͖͞ x ඪຊͷେ͖͞
    ʢೆ෩ݪ, 2002, p. 163ʣ

    View Slide

  73. • Cohen’s d ͷ஥ؒ:
    • Hedges’ g
    • ෼฼ʹ฼ूஂͷඪ४ภࠩʢෆภ෼ࢄʹ
    جͮ͘ඪ४ภࠩʣΛ࢖͏
    • Glass’ ⊿
    • ෼฼ʹ౷੍܈ͷඪ४ภࠩΛ࢖͏

    View Slide

  74. ؔ܎ͷڧ͞Λද͢ࢦඪ
    Pearson’s r / r2

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  75. • ม਺ؒͷؔ܎ͷେ͖͞Λද͢
    • ࠷େ: 1.0ʢઈର஋ʣ
    • ࠷খ: 0
    • ϐΞιϯͷੵ཰૬ؔ܎਺ r
    • r2 ʢ෼ࢄઆ໌཰ʣ
    Pearson’s r / r2

    View Slide

  76. ෼ࢄ෼ੳͷ৔߹
    ௐ΂͍ͨཁҼͷ෼ࢄ
    η2 =
    ૯෼ࢄ
    SSA
    =
    SSTotal

    View Slide

  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

    View Slide

  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

    View Slide

  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

    View Slide

  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

    View Slide

  81. ޮՌྔͷղऍ

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  82. • small: η2 = .01
    • medium: η2 = .06
    • large: η2 = .14
    ਫຊɾ஛಺ (2008)
    • ͜ͷΑ͏ͳࢦඪ͸͋͘·Ͱ໨҆
    • ࣮ࡍͷղऍ͸ݚڀऀࣗ਎ͷ੹೚Ͱ

    View Slide

  83. ͜͜·Ͱͷ·ͱΊ

    View Slide

  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

    View Slide

  85. • η2 ͷ஥ؒ:
    • partial η2
    • ෼฼ʹ SSA
    + SSError Λ࢖͏
    • ω2
    • ฼෼ࢄਪఆͷͨΊͷόΠΞεΛऔΓআ
    ͍ͨ΋ͷ

    View Slide

  86. ࣮ࡍʹ
    ܭࢉͯ͠Έ·͠ΐ͏

    View Slide

  87. • Φʔϓϯιʔεͷ౷ܭϓϩάϥϛϯάݴޠ

    ɹɹΛ࢖͍΍͍͢ܗʹͨ͠ιϑτ΢ΣΞɻ
    • GUIͷͨΊ௚ײతʹ࢖͑Δɻ
    • ΦʔϓϯιʔεͰແྉͰ࢖͑Δɻ

    View Slide

  88. https://www.jamovi.org

    View Slide

  89. ϋϯζΦϯ

    View Slide

  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/

    View Slide

  91. ஫ҙ఺

    View Slide

  92. View Slide

  93. • ϑΝΠϧಡΈࠐΈ࣌ʹࣗಈత
    ʹ൑அ͞ΕΔม਺ͷछྨ͕ؒ
    ҧ͍ͬͯΔ͜ͱ͕͋Δɻ
    • Continuous ࿈ଓม਺
    • Ordinal ॱংม਺
    • Nominal ໊ٛม਺

    View Slide

  94. http://www.langtest.jp
    Effect Size Calculator @

    View Slide

  95. • σʔλ෼ੳ͕ͳΜͰ΋

    Ͱ͖ͪΌ͏΋ͷ͍͢͝

    ΢ΣϒΞϓϦ
    • ޮՌྔ d, g ͱͦͷ৴པ۠
    ؒΛܭࢉͯ͘͠ΕΔ
    • ਫຊಞ͞Μʢؔ੢େֶʣ
    ͕։ൃ͠ɺແྉͰެ։
    • ͓ྱ͸Ϗʔϧ·ͨ͸

    നϫΠϯͰ

    View Slide

  96. View Slide

  97. View Slide

  98. ஫. ਪଌ౷ܭʢ༗ҙੑݕఆʣͰ࢖͏
    ඪ४ภࠩʢSDʣ͸ෆภ෼ࢄʹجͮ͘
    ΋ͷɻn ͷ୅ΘΓʹ n–1 Λܭࢉʹ࢖
    ͍·͢ɻ

    View Slide

  99. View Slide

  100. ࢀߟจݙ
    • ӳޠڭࢣͷͨΊͷڭҭσʔλ
    ෼ੳೖ໳
    • ༗ҙੑݕఆͷ͘͠Έ΍ͦͷݶ
    քʹ͍ͭͯ΋ղઆ

    View Slide

  101. ࢀߟจݙ
    • ຊ౰ʹΘ͔Γ΍͍͘͢͢͝େ
    ੾ͳ͜ͱ͕ॻ͍ͯΔ͘͝ॳา
    ͷ౷ܭͷຊ
    • େ੾ͳ͜ͱΛ਺ࣜΛަ͑ͯஸ
    ೡʹղઆ

    View Slide

  102. ࢀߟจݙ
    • ֎ࠃޠڭҭݚڀϋϯυϒοΫ
    • هड़౷ܭɺਪଌ౷ܭɺޮՌྔ
    ΋ؚΊͯ໢ཏతͳҰ࡭

    View Slide

  103. ࢀߟจݙ
    • ఻͑ΔͨΊͷ৺ཧ౷ܭ
    • ޮՌྔʹ͍ͭͯษڧ͢ΔͳΒ
    ඞಡ

    View Slide

  104. ࢀߟจݙ
    • ͸͡Ίͯͷӳޠڭҭݚڀ
    • ݚڀͷೖޱΛղઆ͢ΔҰ࡭ɻ
    ࠓ೔ͷ಺༰͸ୈ6ষΛิ଍͢
    Δ΋ͷ

    View Slide

  105. 1. σʔλͷࢹ֮Խʢਤࣔʣ
    2. σʔλͷཁ໿ʢத৺ͱ͹Β͖ͭʣ
    3. ޮՌྔ
    • ࠩͷେ͖͞Λද͢ d ଒
    • ؔ܎ͷڧ͞Λද͢ r ଒
    4. ༗ҙੑݕఆʢਪଌ౷ܭʣ
    5. jamovi ͱ langtest.jp Ken Urano
    [email protected]
    https://www.urano-ken.com/research/let2019
    ֎ࠃޠڭҭʢݚڀʣʹ͓͚Δ
    ྔతσʔλͷࢹ֮Խͱղऍ

    View Slide

  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๪.

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