1. Analis Data dan Penyajian
Pertemuan 12

2. Analisis Data Kuantitatif2

3. Getting the Data Ready for Analysis
Data coding: assigning a number to the participants’ responses so they can be entered into a database.
Data Entry: after responses have been coded, they can be entered into a database. Raw data can be entered through any software program (e.g., SPSS)

4. Editing Data
An example of an illogical response is an outlier response. An outlier is an observation that is substantially different from the other observations.
Inconsistent responses are responses that are not in harmony with other information.
Illegal codes are values that are not specified in the coding instructions.

5. Transforming Data

6. Getting a Feel for the Data

7. Frequencies

8. Descriptive Statistics: Central Tendencies and Dispersions

9. Reliability Analysis

10. Quantitative Data Analysis: Hypothesis Testing10

11. Type I Errors, Type II Errors and Statistical Power
Type I error (): the probability of rejecting the null hypothesis when it is actually true.
Type II error (): the probability of failing to reject the null hypothesis given that the alternative hypothesis is actually true.
Statistical power (1 - ): the probability of correctly rejecting the null hypothesis.

12. Choosing the Appropriate Statistical Technique

13. Testing Hypotheses on a Single Mean
One sample t-test: statistical technique that is used to test the hypothesis that the mean of the population from which a sample is drawn is equal to a comparison standard.

14. Testing Hypotheses about Two Related Means
Paired samples t-test: examines differences in same group before and after a treatment.
The Wilcoxon signed-rank test: a non-parametric test for examining significant differences between two related samples or repeated measurements on a single sample. Used as an alternative for a paired samples t-test when the population cannot be assumed to be normally distributed.

15. Testing Hypotheses about Two Related Means - 2
McNemar's test: non-parametric method used on nominal data.
It assesses the significance of the difference between two dependent samples when the variable of interest is dichotomous.
It is used primarily in before-after studies to test for an experimental effect.

16. Testing Hypotheses about Two Unrelated Means
Independent samples t-test: is done to see if there are any significant differences in the means for two groups in the variable of interest.

17. Testing Hypotheses about Several Means
ANalysis Of VAriance (ANOVA) helps to examine the significant mean differences among more than two groups on an interval or ratio-scaled dependent variable.

18. Regression Analysis
Simple regression analysis is used in a situation where one metric independent variable is hypothesized to affect one metric dependent variable.

19. Scatter plot

20. Simple Linear RegressionY 1 ? `0 X

21. Ordinary Least Squares EstimationXi Yi ˆ ei Yi

22. SPSS
Analyze  Regression  Linear

23. SPSS cont’d

24. Model validation Face validity: signs and magnitudes make sense
Statistical validity:
Model fit: R2
Model significance: F-test
Parameter significance: t-test
Strength of effects: beta-coefficients
Discussion of multicollinearity: correlation matrix
Predictive validity: how well the model predicts
Out-of-sample forecast errors

25. SPSS

26. Measure of Overall Fit: R2
R2 measures the proportion of the variation in y that is explained by the variation in x.
R2 = total variation – unexplained variation
total variation
R2 takes on any value between zero and one:
R2 = 1: Perfect match between the line and the data points.
R2 = 0: There is no linear relationship between x and y.

27. = r(Likelihood to Date, Physical Attractiveness)
SPSS
= r(Likelihood to Date, Physical Attractiveness)

28. Model Significance H1: Not H0
H0: 0 = 1 = ... = m = 0 (all parameters are zero)
H1: Not H0

29. Model Significance
H0: 0 = 1 = ... = m = 0 (all parameters are zero)
H1: Not H0
Test statistic (k = # of variables excl. intercept)
F = (SSReg/k) ~ Fk, n-1-k
(SSe/(n – 1 – k)
SSReg = explained variation by regression
SSe = unexplained variation by regression

30. SPSS

31. Parameter significance
Testing that a specific parameter is significant (i.e., j  0)
H0: j = 0
H1: j  0
Test-statistic: t = bj/SEj ~ tn-k-1
with bj = the estimated coefficient for j
SEj = the standard error of bj

32. SPSS cont’d

33. Physical Attractiveness
Conceptual Model +
Likelihood
to Date
Physical Attractiveness

34. Multiple Regression Analysis
We use more than one (metric or non-metric) independent variable to explain variance in a (metric) dependent variable.

35. Conceptual Model + + Perceived Intelligence Likelihood
to Date
Physical Attractiveness

37. Conceptual Model + + + Gender Perceived Intelligence Likelihood
to Date
Physical Attractiveness

38. Moderators
Moderator is qualitative (e.g., gender, race, class) or quantitative (e.g., level of reward) that affects the direction and/or strength of the relation between dependent and independent variable
Analytical representation
Y = ß0 + ß1X1 + ß2X2 + ß3X1X with Y = DV X1 = IV X2 = Moderator

40. interaction significant effect on dep. var.

41. Conceptual Model + + + + + Gender Perceived Intelligence Likelihood
to Date
Physical Attractiveness + +
Communality of Interests
Perceived Fit

42. Mediating/intervening variable
Accounts for the relation between the independent and dependent variable
Analytical representation
Y = ß0 + ß1X => ß1 is significant
M = ß2 + ß3X => ß3 is significant
Y = ß4 + ß5X + ß6M => ß5 is not significant => ß6 is significant
With Y = DV
X = IV
M = mediator

43. Step 1

44. significant effect on dep. var.
Step 1 cont’d
significant effect on dep. var.

45. Step 2

46. significant effect on mediator
Step 2 cont’d
significant effect on mediator

47. Qualitative Data Analysis47

48. Qualitative Data Qualitative data: data in the form of words.
Examples: interview notes, transcripts of focus groups, answers to open-ended questions, transcription of video recordings, accounts of experiences with a product on the internet, news articles, and the like.

49. Analysis of Qualitative Data
The analysis of qualitative data is aimed at making valid inferences from the often overwhelming amount of collected data.
Steps:
data reduction
data display
drawing and verifying conclusions

50. Data Reduction
Coding: the analytic process through which the qualitative data that you have gathered are reduced, rearranged, and integrated to form theory.
Categorization: is the process of organizing, arranging, and classifying coding units.

51. Data Display
Data display: taking your reduced data and displaying them in an organized, condensed manner.
Examples: charts, matrices, diagrams, graphs, frequently mentioned phrases, and/or drawings.

52. Drawing Conclusions
At this point where you answer your research questions by determining what identified themes stand for, by thinking about explanations for observed patterns and relationships, or by making contrasts and comparisons.

53. Reliability in Qualitative Research
Category reliability “depends on the analyst’s ability to formulate categories and present to competent judges definitions of the categories so they will agree on which items of a certain population belong in a category and which do not.” (Kassarjian, 1977, p. 14).
Interjudge reliability can be defined degree of consistency between coders processing the same data (Kassarjian 1977).

54. Validity in Qualitative Research
Validity refers to the extent to which the qualitative research results:
accurately represent the collected data (internal validity)
can be generalized or transferred to other contexts or settings (external validity).

55. martani@ui.ac.id atau dwimartani@yahoo.com
TERIMA KASIH
Dwi Martani
atau