Using SPSS

Handy buttons

Switch between values & value labels

Info about variables (& ‘Go To’)

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…

Handy buttons

… and you will get variable info…

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

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

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

SPSS output viewer

Now, let’s look at the Pivoting Trays

SPSS output viewer

The pivots of the table…

This pivot represents the statistics

This pivot represents the variables

This pivot represents the variables

SPSS output viewer

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

SPSS Syntax

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

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

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

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)

SPSS OptionsMake your SPSS life easy with Edit | Options

For instance by using the session journal file as a syntax file…

Regression & Logistic Regression Revisited

Graphing relationships

Transforming variables

Missing Values

Outliers & Influential Points

Categorical predictors

Regression revisited; topics:

Graphing Relationships

Matrixplot to make a plot of a lot of variables

Specify variables


Result in output window


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)



Now choose Chart | Options


Then ask for a fit line


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


Gross domestic product / capita

22000.0

20000.0

18000.0

16000.0

14000.0

12000.0

10000.0

8000.0

6000.0

4000.0

2000.0

0.0

30

20

10

0

Std. Dev = 6479.84

Mean = 5860.0

N = 109.00

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…


The histogram of transformed variable is:

LNGDP

9.54

8.75

7.96

7.18

6.39

5.61

4.82

16

14

12

10

8

6

4

2

0

Std. Dev = 1.43

Mean = 7.88

N = 109.00


LNGDP

Gross domestic produ

People living in cit

Daily calorie intake

Average female life

Average male life ex

Aids cases

Relationships are nicely linear !


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

Categorical Predictors

Is income dependent on years of age and religion ?

Categorical PredictorsCompute dummy variable for each category, except last


And so on…


Block 1

Categorical PredictorsBlock 2


Ask for R2 change


Model Summary

.101a .010 .010 5.424 .010 14.688 1 1421 .000

.172b .030 .026 5.379 .019 7.064 4 1417 .000

Model1

2

R

RSqua

re

Adjusted RSquar

e

Std. Errorof the

EstimateR SquareChange F Change df1 df2

Sig. FChange

Change Statistics

Predictors: (Constant), Age of Respondenta.

Predictors: (Constant), Age of Respondent, Jewish, Cath, None, Protb.

Look at R Square change for

importance of categorical

variable


Zodiac is actually a categorical variable

Categorical PredictorsIndicator coding scheme


Categorical Variables Codings

120 1 0 0 0 0 0 0 0 0 0 0

92 0 1 0 0 0 0 0 0 0 0 0

128 0 0 1 0 0 0 0 0 0 0 0

130 0 0 0 1 0 0 0 0 0 0 0

135 0 0 0 0 1 0 0 0 0 0 0

100 0 0 0 0 0 1 0 0 0 0 0

99 0 0 0 0 0 0 1 0 0 0 0

107 0 0 0 0 0 0 0 1 0 0 0

115 0 0 0 0 0 0 0 0 1 0 0

104 0 0 0 0 0 0 0 0 0 1 0

107 0 0 0 0 0 0 0 0 0 0 1

136 0 0 0 0 0 0 0 0 0 0 0

Aries

Taurus

Gemini

Cancer

Leo

Virgo

Libra

Scorpio

Sagittarius

Capricorn

Aquarius

Pisces

RespondentsAstrologicalSign

Frequency (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Parameter coding

Annotated output of regression analysis (it uses the file

data/elemapi.sav )http://www.ats.ucla.edu/stat/spss/webbooks/reg/chapter1/annotated1.htm

For more on regression, see:

http://www.ats.ucla.edu/stat/spss/webbooks/reg/chapter1/spssreg1.htm




Variables in the Equation

-.003 .004 .584 1 .445 .997

10.515 11 .485

.153 .299 .262 1 .609 1.166

.375 .312 1.441 1 .230 1.455

-.167 .309 .294 1 .587 .846

-.348 .317 1.200 1 .273 .706

.211 .288 .535 1 .464 1.235

.260 .310 .705 1 .401 1.297

-.014 .323 .002 1 .966 .986

.219 .306 .515 1 .473 1.245

.176 .302 .339 1 .560 1.192

.206 .308 .448 1 .503 1.229

-.298 .333 .800 1 .371 .743

-1.170 .274 18.221 1 .000 .310

AGE

ZODIAC

ZODIAC(1)

ZODIAC(2)

ZODIAC(3)

ZODIAC(4)

ZODIAC(5)

ZODIAC(6)

ZODIAC(7)

ZODIAC(8)

ZODIAC(9)

ZODIAC(10)

ZODIAC(11)

Constant

Step1

a

B S.E. Wald df Sig. Exp(B)

Variable(s) entered on step 1: AGE, ZODIAC.a.

Outliers

Outliers

Saving residuals

Influential Points

Influential Points

Saving distances and influence measures as variables

Multicollinearity

Diagnostics

Multicollinearity

Using SPSS

Documents

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