Descriptive Statistical Analyses Reliability Analyses

1. Review of Last Class • Descriptive Statistical Analyses • Reliability Analyses

2. Computing Scale Scorese.g., Global Life Satisfaction • Recode Negatively worded items • How can you check you did it correctly? • Compute a global life satisfaction score by taking the mean of all items • Can only do after reverse scoring • Why not take the sum of all items? Advantages vs. disadvantages • What types of things can/should you take sums of?

3. How to check if you recoded correctly? • Compute frequencies of variables to be recoded before and after recoding • The freq of people who are responding to specific categories of scale should shift appropriately based on the recoding • Items that are negatively worded and positively worded should be positively correlated after recoding but negatively correlated before recoding • Change the output view setting to show all commands you have run to see that you have only run the recode command once

4. Students check sample output • Correlations of un recoded items vs. recoded items • What’s next…. • Change the output view setting to show all commands you have run to see that you have only run the recode command once

5. To change output view, Go to “edit”, click “options”, pick “viewer” tab, click on “Display commands in the log”

6. Other issues When Computing Scale Scores • Always compute reliabilities before computing scale scores. • Why? • See output for specific satisfaction & stress • Compute scale scores for each • Ensure you recode appropriate items • Drop items that have no variance and report in results • Decide on sum/mean based on meaning of scale

7. Correct Syntax for previous slide • Example syntax file has the commands for • Social relationship satisfaction • Social relationship stress • Notes about decisions made to drop specific items • Students review output file generated & answer orally • What is the correlation between • Social relationship satisfaction & social stress • Social relationship satisfaction & life satisfaction stress • life satisfaction & social stress

8. Review of Types of Variables • Continuous • Interval • Ratio • Discontinuous (Categorical) • Nominal • Ordinal • Students provide examples from questionnaires completed in this course (e.g., 1st day of class, student satisfaction survey etc.)

9. Types of Inferential Statistics

10. When both variables are continuous • Correlation • Regression

11. Review of Correlation • Assesses whether 2 variables are ‘linearly’ related to each other • Varies from –1 to +1 to reflect the direction and the strength of the relation • Associated with a significance level to determine its likelihood of occurring due to chance • .05 likelihood of correlation occurring due to chance is regarded as significant; • Anything more than .05 means it is not significant • Significance Determined via t-test

12. r = .76; r2 = 58% Vince Carter Tom Cruise Julia Roberts Calista Flockhart

13. Review of Variance Explained • Better measure of the strength of a relation is the amount of explained variance (r2) • Ranges from 0 to 100 • Difference between r=.3 & r=.4 is not the same as difference between r=.7 & r=.8 • When comparing correlation charts for height & weight for women vs. men one can directly compare the amount of variance whereas one cannot directly compare size of correlations unless one does a transformation to the ‘r’s

14. For Male Celebrities:r = .27; r2 = 7%

15. For Female Celebrities:r = .78; r2 =61 %

16. What is a regression analyses? • Also known as multiple correlational analyses • Describes the relationship (R) between 3 or more variables (see example on next slide) • Note: correlation (r) that only examines 2 variables • Uses the concepts of variance explained & significance levels as in r • Significance determined differently • Uses (new) concept of regression coefficients • ß & B

17. Conducting a Regression Analyses • What is the combined relationship between the three variables housing satisfaction, leisure satisfaction and global life satisfaction

18. How example regression was done • Bec there was insufficient class participation, for this illustration, prof used part of the correlation matrix from Student Satisfaction & Performance article by Rode et al(handout article from which student satisfaction survey was created) directly into SPSS data window & then used syntax window • See raw data vs. correlational matrix • See syntax

19. Raw data file for regression looks like this…

20. A correlation matrix for regression looks like this…

21. Syntax for simple regression with a matrix regression / matrix in (*) / var housesat lifesat leisure / dep lifesat / method enter housesat leisure. • Here the three variables are listed next to ‘var’ • The primary dependent variable is listed next to ‘dep’ • More on “method enter” later

22. Syntax for simple regression with raw data regression / var housesat lifesat leisure / dep lifesat / method enter housesat leisure. • VS (note differences to below) regression / matrix in (*) / var housesat lifesat leisure / dep lifesat / method enter housesat leisure.

23. How to run a simple regression in menus?

24. Under analyze, Choose regression & Linear

25. Click on appropriate var to be your dependent Click on predictor var to be independent

26. What we did so far…what’s next • What is correlation? • What is regression? • An example analysis • Syntax/menu to use for regression analyses • Data file/correlation to use • Reviewing the output to learn about regression concepts • Similarity to and differences from correlation

27. Examine results of simple regression analysis to learn about common concepts in correlation & regression

28. r2 vs R2 r2=.432 r2=.222 Life sat Life sat Leisure sat Housing sat R2=.432 Life sat Housing sat Leisure sat

29. Significance test for R vs. r & Variance explained • R is significant at F=77.89 p<.0001 or p=.000 • Note significance of correlations is determined by t-test • Variance explained (R2 )=.19 • Same as variance explained in correlations

30. Examine the output of simple regression example to learn new concepts in regression • Regression Coefficients • Standardized • Unstandardized

31. Similarities & differences between r and ß • Similar to r • Vary from -1 to 1 and indicate strength & direction of relations • Their significance determined by t-test • Different from r • Estimate the relationship between 2 variable (e.g., life sat & leisure) after taking the relationship between 1st and 3rd variable into account (e.g., life sat & ) housing)

32. Another additional concept in regression: Unstandardized regression coefficient (B) • Similarities & differences between ß & B • Vary on the scale of the variable rather than between -1 to +1 (i.e., as in ß) • Used predominantly in economics • Can be used (along with its standard error) to calculate how much change in predictor (e.g., housing satisfaction) is needed to obtain a specific amount of change in dependent (e.g., life satisfaction)

33. What we learned so far • How is correlation similar and different from regression • R vs. r • Variance explained is the common concept • Coefficients • Standardized= ß vs. r • Unstandardized= B vs. r

34. Conducting a More Sophisticated Regressional Analyses • Which type of satisfaction best predicts life satisfaction? • Stepwise (hierarchical) regression analyses

35. Syntax for stepwise/hierarchical regression • What happens if house satisfaction is entered into the equation first? • regression / matrix in (*) / var housesat lifesat leisure / dep lifesat / method enter housesat /method enter leisure. • What happens if leisure satisfaction is entered into the equation first? • regression / matrix in (*) / var housesat lifesat leisure / dep lifesat / method enter leisure /method enter housesat .

36. How to run a stepwise/hierarchical regression in menus?

37. Under analyze, Choose regression & Linear

38. Click on appropriate var to be your dependent Click on first predictor to be independent

39. When you click on “next” button, you should come here...

40. Choose your next dependent to be entered in the ‘next’ step

41. Modifications to hierarchical analyses • You can enter multiple dependent variables in same block or in separate blocks using the previous and next buttons

42. Interpreting the output from stepwise regression

43. Using regression as a preliminary test of an explanation • Test the explanation for a finding via a mediator analysis • Why might a particular type of satisfaction (e.g., housing) affect your performance? • Implies a corr b/w housing sat & perf • Because that makes you less satisfied with your life which, in turn, affects your performance • Implies that corr b/w housing sat & perf is due to the corr between housing sat and life sat and between life sat & perf

44. Conditions to be met before running a mediator analyses Life sat r2=.102 r2=.142 Performance Performance Housing sat Life sat r2=.222 Housing sat

45. Results of Mediator Regressional Analyses

46. Types of Inferential Statistics

47. Using t-test to test the hypothesis whether the women in the sample are older than men?

48. 1st Step= “Analyze”, 2nd Step=“Compare means” 3rd Step=“Independent samples t-test”

49. Move “age” to test-variable window & move “gender” to “grouping variable” window

50. Click on Define Groups,