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Using SPSS

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

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

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Handy buttons … and you will get variable info…

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

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

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

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

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

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

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

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

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

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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)

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

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Regression & Logistic Regression Revisited

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

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

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Specify variables Graphing Relationships

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Result in output window Graphing Relationships

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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) Graphing Relationships

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Now choose Chart | Options

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Graphing Relationships Then ask for a fit line

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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

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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 transformati on…

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Transforming variables

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The histogram of transformed variable is:

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Transforming variables Relationships are nicely linear !

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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

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

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Categorical Predictors Compute dummy variable for each category, except last

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Categorical Predictors And so on…

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Categorical Predictors Block 1

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Categorical Predictors Block 2

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Categorical Predictors Ask for R 2 change

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

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Categorical Predictors Zodiac is actually a categori cal variable

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Categorical Predictors Indicator coding scheme

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Categorical Predictors

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

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Categorical Predictors

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Outliers

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Saving residuals

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Influential Points

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Saving distances and influence measures as variables

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Multicollinearity Diagnostics

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Multicollinearity

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