1. 研究法簡介 何明洲 中山醫學大學心理系

2. Single Factor – Two Levels

3. Independent groups design: use random assignment –IV, manipulated –Between-subject Matched groups design: use matching procedure –IV, manipulated –Between-subject

4. Single Factor – Two Levels Nonequivalent groups design –IV, subject –Still need to match participants –Between-subject Repeated-measures design –IV, manipulated –Within-subject

5. Analyzing single factor and two- level design t test for independent groups and dependent groups

6. EXAMPLE: THE t AND F TESTS t value is a ratio of two aspects of the data, the difference between the group means and the variability within groups t = group difference within group variability

7. Multilevel designs 如果只有 2 levels (e.g., no reward and $4) ， 資訊量太少， 可能誤判為 linear Add more levels for replication and extension

8. LINEAR VERSUS POSITIVE MONOTONIC FUNCTIONS

9. Analyzing Single-Factor, >2 levels F Test (analysis of variance, ANOVA 變異 數分析 ) ≥ 2 conditions (or groups) When 2 conditions, F = t 2 When more than 2 conditions, why not use t test?

10. EXAMPLE: F TEST 用 t test 作多重比較, 至少出現一個 type I error 機率 1 – (1 – alpha) c (C: # of paired comparisons) 噪音程度 ( 無, 低, 中, 高 ) 對記憶的影響 –C = 4!/(2!2!) = 6 –1-(1-.05) 6 =.26 (=26%!!!) F test 同時比較多組, alpha 控制在.05 H 0 : μ 1 = μ 2 = μ 3 = μ 4 …

11. EXAMPLE: F TEST Total variance = systematic variance + error variance Systematic variance: deviation of group means from the grand mean  between-group variance Grand mean: 全部皆平均, 假設無任何因 IV 所引發的 差異 Error variance: deviation of individual scores in each group from their respective group means  within-group variance

12. EXAMPLE: F TEST Total variance = systematic variance + error variance 變異的程度反應整個實驗情境和總人數所帶有 的變異程度 實驗情境越多, 總人數越多, 則變異程度大 不能只考慮變異程度, 須考慮平均每個實驗情 境和人數, 所帶有的變異程度 F = (systematic variance/df systematic ) / (error variance/df error )

13. Factorial Designs > 1 IVs

14. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS > 1 IVs Factorial Designs 多因子設計 : Designs with more than one independent variable (or factor) 噪音（高 vs. 低）影響雙字詞記憶 噪音（高 vs. 低） x 詞頻（高 vs. 低）

15. Presentation rate 2-sec4-sec Type of training Imagery Rote Factorial matrix

16. Factor B B1B2 Factor AA1A1B1A1B2 A2A2B1A2B2 Factorial matrix

18. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS Simplest Factorial Design 2 x 3x 4 (two-by-two) factorial design Has two independent variables, each IV has 2 levels 4 conditions Number of levels of first IV x Number of levels of second IV x Number of levels of third IV…

19. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS Interpretation of Factorial Designs (A x B) Main effects of an independent variable ： effect of A factor ONLY (regardless of B factor, average out B factor) Interaction between the independent variables (how does effect of A factor vary with B factor?) ，條件機率 A (A1, A2) x B (B1, B2) 使用圖表讓讀者瞭解

20. 詞頻 高低 噪音程 度 高 A1B1A1B2A1 低 A2B1A2B2A2 B1B2

21. 詞頻 高低 噪音程度 高 AB 低 CD A - B = C – D 詞頻的效果是否隨者噪音程度改變 A – C = B – D 噪音效果是否隨者詞頻程度改變

22. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS 詞頻 高低 噪音程度 高 105 低 5 7.5 105 Interaction  NS Main effect of 噪音  NS Main effect of 難度  *

23. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS 詞頻 高低 噪音程度 高 510 低 5 7.5 Interaction  * Two main effects  NS

24. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS Type of question misleadingunbiased Knowledge naive1813 knowledgeable4113 15.5 27 29.5 13 Interaction  * Two main effects  *

25. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS

27. 2 x 3 x 4 factorial design –How many IVs? –How many levels of each IV –How many total conditions –How many DVs?

28. Varieties of Factorial Design Mixed factorial design: 有 between and within subject variables, 沒有 subject variable P x E factorial designs: 有 subject variable 和 manipulated variable （均為 between) Mixed P x E factorial : 有 subject variable 和 manipulated variable （有 between 和 within)

29. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS Interactions and Simple Main Effects( 單純主要效 果 ) 當有 interaction 時，必作的統計分析 Simple main effect: examine mean differences at each level of the independent variable 依研究目的決定要作哪些 simple main effect

30. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS Interactions and Simple Main Effects Simple main effect of B1: A1|B1 vs. A2|B1 Simple main effect of A1: B1|A1 vs. B2|A1

31. Anxiety level LowModerateHigh Task Difficulty Easy4710 Hard741 (Easy vs. Hard)|Low (L vs.M vs.H)|Easy

32. INCREASING THE NUMBER OF VARIABLES: FACTORIAL DESIGNS

33. INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS Increasing the Number of Independent Variables in a Factorial Design 2 x 2 x 2 噪音高低 x 作業難易 x 性別

34. 男 女 作業難易 噪音高低噪音高低 噪音高低噪音高低 噪音高低 x 作業難易 x 性別 3-way interaction  2-way interaction ( 男生中， 噪音高 低 x 作業難易 )  simple simple main effect

35. Presenting data Text Table Figure

37. Discrete and continuous variable

38. Continuous variable

39. 圖表 運用之法，存乎一心，沒有絕對對錯， 重要的是「好理解」且「點出重點」

40. 點出文章重點

41. 圖表尺度的影響 1. 注意尺度 2. 加上信賴區間