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Using SPSS. Handy buttons. Switch between values & value labels. Info about variables (& ‘Go To’). Handy buttons. In dialog boxes you always have help nearbye…. Click with the right mouse button on a variable you want to know more about…. Handy buttons. … and you will get variable info….

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Using SPSS

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    1. Using SPSS

    2. Handy buttons Switch between values & value labels Info about variables (& ‘Go To’)

    3. Handy buttons In dialog boxes you always have help nearbye… Click with the right mouse button on a variable you want to know more about…

    4. Handy buttons … and you will get variable info…

    5. SPSS output viewer Just let’s make a table with some corre-lations

    6. SPSS output viewer Now, click with the right mouse button on table en choose Open…

    7. SPSS output viewer … and you will get a new window wherein you can edit the table

    8. SPSS output viewer Now, let’s look at the Pivoting Trays

    9. SPSS output viewer The pivots of the table… This pivot represents the variables This pivot represents the variables This pivot represents the statistics

    10. SPSS output viewer Now put the pivot of the statistics in the layer (‘capa’) and the form of the table will change!

    11. SPSS Syntax In each dialogbox you will see a button Paste (‘Pegar’) to create syntax.

    12. SPSS Syntax After having done Paste (‘Pegar’) you will see a ‘command’ in the syntax window.

    13. SPSS Syntax You can as well open a specific syntax file, i.e. Ridge Regression (in the SPSS program folder)

    14. SPSS Syntax Why would you use syntax??? • To do analyses repeatedly • To use all the functions of SPSS (in dialogboxes +/- 95% is incorporated) • To be independent of dialogboxes, that keep changing…(and syntax never changes)

    15. SPSS Options Make your SPSS life easy with Edit | Options For instance by using the session journal file as a syntax file…

    16. Regression & Logistic Regression Revisited

    17. Regression revisited; topics: • Graphing relationships • Transforming variables • Missing Values • Outliers & Influential Points • Categorical predictors

    18. Graphing Relationships Matrixplot to make a plot of a lot of variables

    19. Graphing Relationships Specify variables

    20. Graphing Relationships Result in output window

    21. Graphing Relationships You can edit the Graph like you edited a table by opening the graph (click with right mouse button on the graph and choose Open)

    22. Graphing Relationships Now choose Chart | Options

    23. Graphing Relationships Then ask for a fit line

    24. Graphing Relationships • Some remarks: • GDP is related in a non linear way with other variables • variable Aids Cases we have a very influential point (not an outlier, but influential!) • correlation between female life expectation and male life expectation is almost 1

    25. Graphing Relationships Let’s try to transform gdp_cap in order to get linear relationships with other variables. First let’s look at the distribution of gdp_cap with a histogram: We need to bring values on the right closer to values on the left. We might try a LN transformation…

    26. Transforming variables

    27. Transforming variables The histogram of transformed variable is:

    28. Transforming variables Relationships are nicely linear !

    29. Transforming variables Note: you probably want to make a variable lifeexp out of life expectancy males and life expectancy females: Tip: use function Mean in stead of using the ‘+’ and dividing by 2

    30. Categorical Predictors Is income dependent on years of age and religion ?

    31. Categorical Predictors Compute dummy variable for each category, except last

    32. Categorical Predictors And so on…

    33. Categorical Predictors Block 1

    34. Categorical Predictors Block 2

    35. Categorical Predictors Ask for R2 change

    36. Categorical Predictors Look at R Square change for importance of categorical variable

    37. Categorical Predictors Zodiac is actually a categorical variable

    38. Categorical Predictors Indicator coding scheme

    39. Categorical Predictors

    40. Annotated output of regression analysis (it uses the file data/elemapi.sav ) For more on regression, see:

    41. Categorical Predictors

    42. Outliers

    43. Outliers

    44. Outliers Saving residuals

    45. Influential Points

    46. Influential Points

    47. Influential Points Saving distances and influence measures as variables

    48. Multicollinearity Diagnostics

    49. Multicollinearity

    50. Multicollinearity