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SPSS 分析簡介

SPSS 分析簡介. 何明洲 中山醫學大學心理系. 資料在 SPSS 上之排列. Between-subject design, one factor with three levels. Within-subject design, one factor with three levels. 分析方法的選擇. 以 within-subject design 為主. Within-subject design. Single Factor – Three Levels Two Factors – 2 x 2 Two Factors – 2 x 3.

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SPSS 分析簡介

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  1. SPSS分析簡介 何明洲 中山醫學大學心理系

  2. 資料在SPSS上之排列

  3. Between-subject design, one factor with three levels

  4. Within-subject design, one factor with three levels

  5. 分析方法的選擇 以 within-subject design 為主

  6. Within-subject design • Single Factor – Three Levels • Two Factors – 2 x 2 • Two Factors – 2 x 3

  7. Single Factor – Three Levels

  8. Single Factor – Three Levels • 情緒雙字詞對於詞彙判斷作業的影響 • 情緒雙字詞種類：中性、負向、正向 • AnalyzeGeneral Linear ModelRepeated Measures。接著填上獨變項 (emotion)及其Numbers of Levels (3)，最後按下Define。

  9. Compare with alpha = .05 / #of comparison 兩兩比較，alpha = .05 / 3 = .016

  10. Two Factors – 2 x 2

  11. Two Factors – 2 x 2 • 情緒雙字詞（正向或負向）x 詞頻高低（高或低）對於詞彙判斷作業的影響 • AnalyzeGeneral Linear ModelRepeated Measures。接著填上獨變項 (emotion和freq)及其Numbers of Levels (各為2)，最後按下Define。

  12. 填上獨變項 (emotion和freq)及其Numbers of Levels (各為2)，最後按下Define

  13. Main effect Main effect Interaction effect

  14. 因為只有2個levels, main effect看我即可 因為只有2個levels, main effect看我即可

  15. Interaction effect。需再作simple (main) effect

  16. Compare with alpha = .05 / #of comparison e.g., in this case, alpha = .025 Factorial matrix

  17. Factorial matrix

  18. Two Factors – 2 x 3

  19. Two Factors – 2 x 3 • 情緒雙字詞（正向或負向）x 詞頻高低（高、中、低）對於詞彙判斷作業的影響 • AnalyzeGeneral Linear ModelRepeated Measures。接著填上獨變項 (emotion和freq)及其Numbers of Levels (各為2和3)，最後按下Define。

  20. Significant interaction simple (main) effect

  21. Emotion = 1: Freq 1 vs. 2 vs. 3 Emotion = 2: Freq 1 vs. 2 vs. 3 Freq = 1: Emo 1 vs. 2 Freq = 2: Emo 1 vs. 2 Freq = 3: Emo 1 vs. 2

  22. Compare with alpha = .05 / #of comparison e.g., in this case, alpha = .025 Factorial matrix

  23. 如果剛剛的比較有顯著（如當emotion=正向時，freq=高中低至少會有一個不同於其他），就需要更進一步，兩兩比較（高低、高中、低中），alpha = .025/3 = .008

  24. Compare with alpha = .05 / #of comparison e.g., in this case, alpha = .016 Factorial matrix

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