Analysis of Data – Basic Concepts

1. 14 Analysis of Data – Basic Concepts 中央大學.資訊管理系 范錚強 mailto: ckfarn@mgt.ncu.edu.tw 2010.05

2. Descriptive Statistics描述性統計 • 描述樣本的特性 • 主要要呈現的是： • 你的研究樣本，和母體究竟有什麼差異？

3. Exploratory Data Analysis Exploratory Confirmatory

4. 一些探索性的資料呈現 (Ch.16) • Scatter-plot • Bar Chart, Pie chart • Frequency table • Histogram 長條圖 • Cross Tabulation

5. Statistical Procedures Inferential Statistics Descriptive Statistics

6. Confirmatory Studies • Hypothesis Testing 假說檢驗 • Research Hypothesis • Null Hypothesis H0 • Refutation 反証 • 基於想要驗證的研究假說，建立反面的一個「稻草人」 H0 （原來的研究假說就是統計裡的替代假說） • 用統計來推翻 H0 的真實性 • 因此替代假說獲得支持

7. Types of Hypotheses • Null • H0:  = 50 mpg • H0:  < 50 mpg • H0:  > 50 mpg • Alternate • HA:  = 50 mpg • HA:  > 50 mpg • HA:  < 50 mpg

8. Two-Tailed Test of Significance

9. One-Tailed Test of Significance

10. Take no corrective action if the analysis shows that one cannot reject the null hypothesis. Decision Rule

11. Statistical Decisions

12. Tests of Significance Parametric 參數統計 是「強」統計 Nonparametric 非參數、無母數 統計：弱統計

13. Assumptions for Using Parametric Tests Independent observations Normal distribution Equal variances Interval or ratio scales

14. Advantages of Nonparametric Tests Easy to understand and use Usable with nominal data Appropriate for ordinal data Appropriate for non-normal population distributions

15. How to Select a Test How many samples are involved? If two or more samples are involved, are the individual cases independent or related? Is the measurement scale nominal, ordinal, interval, or ratio?

16. Recommended Statistical Techniques

17. Measures of Association: Interval/Ratio

18. Pearson’s Product Moment Correlation r Is there a relationship between X and Y? What is the magnitude of the relationship? What is the direction of the relationship?

19. Scatterplots of Relationships

20. Scatterplots

21. Interpretation of Correlations X causes Y Y causes X X and Y are activated by one or more other variables X and Y influence each other reciprocally

22. Artifact Correlations

23. Interpretation of Coefficients A coefficient is not remarkable simply because it is statistically significant! It must be practically meaningful.

24. Coefficient of Determination: r2 Total proportion of variance in Y explained by X Desired r2: 80% or more

25. Classifying Multivariate Techniques Dependency Interdependency

26. Multivariate Techniques

27. Multivariate Techniques

28. Multivariate Techniques

29. Right Questions. Trusted Insight. When using sophisticated techniques you want to rely on the knowledge of the researcher. Harris Interactive promises you can trust their experienced research professionals to draw the right conclusions from the collected data.

30. Dependency Techniques Multiple Regression Discriminant Analysis MANOVA Structural Equation Modeling (SEM) Conjoint Analysis

31. Uses of Multiple Regression Develop self-weighting estimating equation to predict values for a DV Control for confounding Variables Test and explain causal theories

32. Generalized Regression Equation

33. Multiple Regression Example

34. Selection Methods Forward Backward Stepwise

35. Evaluating and Dealing with Multicollinearity Choose one of the variables and delete the other Create a new variable that is a composite of the others

36. Discriminant Analysis A. B.

37. MANOVA

38. MANOVA Output

39. Bartlett’s Test

40. MANOVA Homogeneity-of-Variance Tests

41. Multivariate Tests of Significance

42. Univariate Tests of Significance

43. Structural Equation Modeling (SEM) Model Specification Estimation Evaluation of Fit Respecification of the Model Interpretation and Communication

44. Structural Equation Modeling (SEM)

45. Interdependency Techniques Factor Analysis Cluster Analysis Multidimensional Scaling

46. Factor Analysis

47. Factor Matrices

48. Orthogonal Factor Rotations

49. Factor Matrix, Metro U MBA Study

50. Varimax Rotated Factor Matrix