SPSS: Beyond the Basics
A “next steps” class on
SPSS 16 for PCs
Consultant: Betty Zou
What we will cover:
 PART 1
 Open Excel files in SPSS
 Merge SPSS files
 Multiple Response Questions
 PART 2
 The Wonders of Statistics Coach
 Bivariate Correlation
 T-Tests
 One-Sample
 Independent
 Paired
PART 1
Open an Excel file in SPSS
 In your Excel document
 Set-up should be like in SPSS
 Variable names in the first row
 Each row is a case
 In SPSS
 File > open data
 Specify “Files of type”
Open:
“Beyond_ExampleData_1.xls”
 (notice all the file types)
 In pop-up window--specify worksheet
Merge SPSS files
 Some sources will give you data sets in separate files
 For example:
 New Immigrant Survey: http://nis.princeton.edu/
 Same respondents, different questions
 Income, Assets, Employment all in separate files
 Merge
 Sort data in all files in ascending order
 Keep one file as working file and merge into that file
 Data > Merge Files > Add Variables
 Match cases on key variables: “ID”
Merge: “Sample_Data_2” to “Sample_Data_Main”
Multiple response questions
 Respondents might give more than
one answer to a question.
What is your favorite beer?
a. PBR
b. Corona
e. Budweiser
f. Other
c. Fat Tire
d. Mannies
g. Don’t drink beer
What is your favorite genre of music?
a. Hip-hop
b. Rock
c. Punk
d. Folk
e. Country
f. Other (please specify) _________
Multiple Response Questions
(cont’d)
 2 Options when entering data
 1. Multiple category method
(Beer 1, Beer 2, Beer 3)
 2. Multiple dichotomy method
Create a variable for each answer choice
(Hip-hop, Rock, Punk…)
Enter “1” if the answer choice was chosen, “0” for not
 Other (fill-in answer)
 Enter data & automatic recode OR
 Code the data yourself
Define Multiple Response Sets
 Which beer is more popular? PBR or
Bud?
 Analyze > Multiple Response > Define
Variable Sets
 Set Variables Beer 1 – Beer 3
 Set Variables are coded as “Categories”
 Range: 1 through 7 (the value labels)
 Analyze > Multiple Response >
Frequencies/ Crosstabs
More on Multiple Response
 Are men or women more likely to
listen to hip-hop?
 Same process as before
 Set Variables are coded as “Dichotomies”
 Counted Value: 1
 Go to crosstabs
 It may ask you to define range for “gender”
 Enter 1 for Min, 2 for Max
PART 2
Statistics Coach
 Help > Statistics Coach
 What do you want to do?
 Ex: Compare groups for significant
differences
 Data in categories
 Show Crosstabs Case Studies
Bivariate Correlation
 Is there a correlation between age
and income?
Using dataset: “Sample_Data_Main”
2 scale variables: “age” and “income”
Analyze > Correlate > Bivariate
Check Pearson, Flag significant correlations
Interpret: Pearson (0.665), Sig (0.00)
 There is a positive relationship between age and
income. The higher the age, the higher the wage.
This correlation is highly significant.
 Scatter Plot
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T-Test: One Sample
 You know that the average number of hours people
work per week is 40.
 You want to know if the average number of hours
worked in your sample is different from the known
value.
 Analyze > Compare Means > One-Sample T Test
 Enter “WorkHrs” ; Test value: 40
 Interpret: The sample mean is 44.6750
 Sig. 0.014 significant at the 5% significance level
 t = 2.564
 The sample mean could be 44.675 +2.564 or -2.564
 These people are generally over worked
 The difference is significant
T-Test: Independent
 Is the average annual income of females
different from males?
 You want to compare the mean of two
unrelated groups.
 Analyze > Independent-Samples T Test
 Test variables: Income
 Grouping variables: Gender
 Define Groups: 1, 2
 Interpret: Sig: 0.301 The difference is not
significant.
T-Test: Paired
 A soda with ginko biloba in it claims to
improve test scores.
 Your sample was given a general
knowledge test before and after drinking
the soda. (Test score out of 100)
 You want to test if drinking the soda
significantly improves test scores.
 Use Paired because you are comparing two
measurements for the same individuals.
T-Test: Paired (cont’d)
 Analyze> Paired-Samples T Test
 Variable 1: PreTest
 Variable 2: PostTest
 Notice that you can compare more pairs
 Interpret:
 Drinking the soda actually reduces test
scores by an average of about 40 points!
 Sig: 0.000, it is highly significant