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Table of Contents• Data management
– Computing new variables– To sort data– Data selection and split files– Merging files
• Statistical procedures– Linear regressions– Regression for aggregated data– Chi-square test for grouped data – Nonparametric tests– Testing Normality
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Computing New Variables
• Open data sample1.sav• To compute a new variable we can
– Use a standard formula– Use a statistical function to compute
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Using a Formula
To compute the average income for the past three years for each person:
• Click Compute in the Transform menu, • Enter the new variable with the name of
“mean” for the target variable Mean = (ptoi92+ptoi93+ptotinc)/3• Click OK to compute the mean
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Using a Statistical Function
• Click the Compute in the Transform menu• Click the Reset button to clear the old formula• Enter average as the target variable• Locate Mean on function list and move it to the
Numeric Expression area (using Up arrow )• Enter ptoi92, ptoi93 and ptotinc inside the
parentheses• Click OK to compute the average
Log transformation
• Click the Compute in the Transform menu• Click the Reset button to clear the old formula• Enter lnincome as the target variable• Click on Arithmetic in Function group: text box• Locate Ln on functions and Special Variables: list
and move it to the Numeric Expression area (using Up arrow )
• Enter ptotinc inside the parentheses• Click OK to compute log of ptotinc.
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Sorting Data
Sorting data involves reordering of data using values of one or more variables.
• Sorting data on one variable• Sorting data on more than one variables
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Sorting Data on One Variable
• Click Data/Sort Cases in the Data Editor Window
• Click age and move it to the “Sort by:” text box
• Click Ascending radio button• Click OK
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Data Sorted by Age
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Sorting Data on Two Variables
• Click Data/Sort Cases• Click age and move it to the “Sort by:” text
box• Click educ and move it to the “Sort by:” text
box• Click Ascending radio button• Click OK
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Data Sorted by Two Variables
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Three Ways of Data Selection
• If condition is satisfied : to select data that meet if conditions
• Random sample of cases:randomly chose a specified percentage of cases
• Based on time or case range: to select data from a specified range
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If Condition Is Satisfied
To choose data that meet If conditions:• Click the Select Cases in the Data menu• Click the If condition is satisfied radio button• Click If push button to open the Select Cases:
If dialog box
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The If condition
If we are interested in the personal total income for females, we need to select the only observations whose sex is female.
• Type in sex = 1 in the Select Cases: If dialog box, (1 = “female”)
• Click Continue to confirm the rule
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Two Choices for Unselected Cases
• If one clicks the Filtered radio button, the unselected cases remain in the Data Editor, but are not used in analyses.
• If one clicks the Deleted radio button the unselected cases are deleted from the Data Editor Window.
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Complex If conditions
Suppose we want to select cases meeting two conditions: region = 1 and age >= 30
• Type in “region = 1 & age>=30” in the Select Cases: If window
• Click Continue to confirm the rule
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The Case Deletion Choice
• Switch to the Data Editor Window• Click the Select Cases in the Data menu• Click the Deleted radio button in the
Unselected Cases Are: area • Click the OK to delete unselected cases from
Data Editor Window
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The Data Editor Window Containing Only Selected Observations
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Split File
• The data file is split into separate groups for analysis based on the values of a grouping variable
• The same analysis is applied to separate subgroups simultaneously
• The results for all the subgroups will be presented together
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To Split a Data file
• Open sample2.por• Click the Split File in the Data menu• Click the Organize output by groups radio
button• Move sex to the the Groups Based on list box• Click the OK push button to Split File
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Descriptive Statistics Based on Split File
• Click Statistics/Summarize/descriptive• Click age in variable list box• Click OK
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Presenting results by selecting Compare groups
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Turn Off the Split File Processing
• Select Split File in the Data menu• Click Analyze all cases in the Split File dialog
box• click OK to set analyses to all cases (turn off
split file)
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Merging Files
Data can be combined in two ways• Merging different cases according to the same
variables (adding observations)• Merging different variables according to
the same cases (adding variables)
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Merging Cases In the Data Editor Window
• Open a data file row1.sav• Click Data/Merge Files/Add Cases, the dialog box of
Add cases: Read File is open as shown in the note page
• Select file row2.sav and Click open, then the dialog box of Add Cases from... is open
• Click OK, the observation from row2.sav are placed in Data Editor Window after row1.sav
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The Add cases from … dialog box
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Merging Variables
• Open file col1.sav• Click Data/Merge Files/Add Variables. The dialog
box Add Variable: Read File shown in the note page will be displayed.
• Select file col2.save and Click open. Then the dialog box of Add Variable from... Will appear
• Click OK.
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The Add Variable from … dialog box
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The Merged File
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Introduction to Regression
• Simple Regression• Multiple Regression• Regression Plots• Regression for aggregated data
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Simple Regression
• Click Analyze/Regression/Linear then the Linear Regression dialog box is open
• Use ptotinc (personal total income) as the dependent variable
• Use educ as the independent variable• Click OK
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The Dialog Box of Linear Regression
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The Output for the Regression
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The Estimated Regression Equation
xY 25815.13285
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The Fitted Line
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Examing the Residual
• Click Dialog Recall Tool • Click Linear Regression• Click plots… in the Linear Regression dialog box • In the Linear Regression: Plots dialog box, chose
ZRESID as the Y and ZPRED as the X variables.• Click Histogram• Click Continue • Click Ok
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The Scatterplot of Residuals
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Multiple Regression
xy 0
332110 xxxy x
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Running a Multiple Regression
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The Output of Multiple Regression
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The Fitted Model
Y = -13301+ 2672 X1-13106 X2 + 145 X3
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Residual Plots
• Click Plots in Linear Regression Dialog Box• Put ZRESID as the Y variable and ZPRED as the
X variable in a scatterplot• Chose Histogram and Normal probability plot
in the Standardized Residual Plots
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Histogram
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Normal Probability plot
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To aggregate data
• Using Current Population Survey 2006 (CPS2006) data
• Click on Data/Aggregate Data– Break Variable(s):– Summaries of Variable(s):
• Mean, Median, and Sum• First, Last, Minimum, and Maximum values
– To save aggregated variables
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A random sample from Current Population Survey (CPS2006)
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The mean and median wages by years of education
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A regression line of average income
Linear Regression
8 12 16 20
edu
20000.00
40000.00
60000.00
80000.00
pw
ages
_mea
n
pwages_mean = -41548.30 + 5888.49 * eduR-Square = 0.95
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A regression line of median income
Linear Regression
8 12 16 20
edu
0.00
25000.00
50000.00
75000.00
100000.00
pw
ages
_med
ian
pwages_median = -54975.43 + 6592.25 * eduR-Square = 0.88
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An Example taken from 1982 General Social Survey
Death PenaltyGunRegistration Favor OpposeFavor 784 236Oppose 311 66
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Death PenaltyGunRegistration Favor OpposeFavor 784 / 76.9% 236 / 23.1%Oppose 311 / 78.4% 66 / 17.5%
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Death Penalty Data in SPSS
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Crosstabulation with Row Percentage
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Comparison of Row Percentages
Cases weighted by COUNT
Death Penalty
FavorOppose
Pe
rce
nt
100
80
60
40
20
0
Gun Registration
Oppose
Favor
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Repeated Measures Analysis
Repeated measures analysis of variance involves testing for significant differences in mean when the observation appears in multiple levels of a factor.
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Opening the data
• Click File/Open in the Data Editor Window• Click SPSS (*.sav) choice on the File of type
pull-down list• Look in Floppy (A) and click blood in the file
list• Click Open
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Opening the Repeated Measures Dialog Box
• Click Analyze/General Linear Model/Repeated Measures
• Replace factor1 with time in the Within-Subject Factor Name text box
• Press Tab key to move to the number of levels text box and type 3
• Click Add push-button and then click Define push-button
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• Click and drag from time1 to time3• Click right arrow to move time1 to time3 into
the Within-Subject Variables box• Click gender and move it to the Between-
Subjects Factor box• Click OK push button
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Examining the results
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Compare the Main Effects
• Click Dialog Recall Tool • Click Define Push-button• Click Options push-button• Move time into the Display Means for list box• Click Compare Main Effects checkbox• Click Continue to process the request• Click Ok
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Pairwise Comparisons
Testing the Normality
• To test if Age variable is normal distributed.• Using file: sam1000.sav• Using both graphs and tests• Click on Analyze/Descriptive Statistics/Explore…
– To chose Age variable in the dependent list– Click on Plots push button.– Check Normality plots with tests.
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