SPSS
Intro and Analysis
Hein Stigum
Presentation, data and programs at:
http://folk.uio.no/heins/
Analysis with SPSS
• SPSS introduction
– Files and menus
– syntax
• Analysis
– Continuous data
• Symmetrical
• Skewed
– Categorical data
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Files
• Data
.sav
Data Editor
• Syntax .sps
Syntax Editor
• Output .spo
Viewer+Chart Editor
Menus
Toolbars
file/editor
Statusbar
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Vary with
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Data Editor
• Variable view
– Each variable: name, type, label, value labels
• Data view
– Each case: values
• Save a master file, work on workfile
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Syntax Editor
• Syntax
– Comands ends with a ”.”
– Comments starts with ”*”
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Ways of working
• Use menus to run commands
• Use menus, paste commands, run
• Write commands, run
• Your main product: ”The Syntax File” !!
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Viewer
• Contains all output
– Show/hide or delete elements
– Double-click to edit element
– Double-click on chart to start Chart Editor
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Select and Filter
Do analysis on “old people”:
• Method 1, select
Select if (age>50).
• Method 2, filter
Compute ff=(age>50).
Filter by ff.
…
Filter off.
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Recode and label
• Cut age into 3 groups
recode age (missing=sysmis) (lowest thru 29=1)
(30 thru 39=2) (40 thru highest=3) into ageGr3.
• Add labels
variable label ageGr3 ’Age in 3 groups’.
value label ageGr3 1’29 years’ 2’30-39 years’
3’40 years’.
• Cut age into equal sized groups
Rank age /ntiles(3) into ageGr3.
Examine age by ageGr3 /plot=none.
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Compute and If
Compute ageSqr=age**2.
If (age<=50) old=0.
If (age>50) old=1.
Compute old=(age>50).
Comp oldMale=0.
If (age>50 and sex=1)
oldMale=1.
Compute oldMale=
(age>50 and sex=1).
Compute id=$casenum.
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Missing
• System missing
– Empty values are marked ”.” and called sysmis
• User missing
– Set to missing:
– Set to value:
missing age (999).
missing age ().
• Selection
– Remove all missing: select if (not missing(age))
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Options
• Show variable names
– Edit, options, general, show names
• Show label values
– Edit, options, output labels, Values and Labels
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Analysis
Datatypes
• Categorical data
– Nominal:
– Ordinal:
married/ single/ divorced
small/ medium/ large
• Numerical data
– Discrete:
number of children
– Continuous: weight
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Data type dictates type of analysis
Data
type
Numerical
Yes
Means
T-test
Linear regression
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Normal
data
Categorical
No
Medians
Non-par tests
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Freq table
Cross, Chisquare
Logistic regression
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Continuous
symmetrical
data
Check for normality
graph /histogram(normal) debut.
pplot debut /type=Q-Q /dist=normal.
Normal Q-Q Plot of Age of 1. intercourse
160
30
140
120
20
Expected Normal Value
100
80
60
40
20
0
13.1
22.1
31.1
Std. Dev = 3.52
Mean = 18.1
0
N = 406.00
0
44.6
35.6
26.6
17.6
8.6
10
40.1
10
20
30
40
60
50
49.1
Observed Value
Age of 1. intercourse
Deviations form normal
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Describe continuous data
What is the distribution and the mean of weight?
• Distribution
graph/histogram weight
• Describe
descriptive weight
Descriptive Statistics
N
Hva veide du sis t
du veide deg?
Valid N (listwise)
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6986
Minimum
Maximum
0
999
Mean
60,35
Std. Deviation
15,794
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Compare groups, equal variance?
• Equal
2
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• Not equal
2
4
2
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2
4
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Compare means
Do boys and girls have the same average weight?
• T-test
– Analyze, Compare means, Independent-Samples
T-test
Does weight vary with social group? (3 or more groups)
• Anova
– Analyze, Compare means, One-Way ANOVA
• Options, homogeniety of variance test
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Test situations
• 1 sample test
• Weight =10
• 2 independent samples
• Weight by sex
• K independent samples
• Weight by age groups
• 2 dependent samples (Paired)
• Weight last year = Weight today
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Continuous
skewed
data
Partners
Percentiles:
25%
50% (median)
75%
90%
Median
25., 50., 75. and 90. percentile
Mean
0 2 5
10
2 partners
5 partners
10 partners
20 partners
20
30
40
50
Number of lifetime partners
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Describe skewed data
• Medians and percentiles
– Analyze, Descriptive, Statistics=descriptives
and percentiles, Plots=Box
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Compare skewed distributions
Do boys and girls have the same height?
• 2 independent samples
– Analyze, Compare means, Means, height by sex,
Options=medians
– Analyze, Non-parametric, 2 independent Samples,
height by sex(1 2)
• K independent samples
– Analyze, Non-parametric, K independent samples
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Categorical
data
Describe and compare categorical data
Do boys and girls have the same educational plans?
Frequency tables
– Analyze, Descriptives, Frequencies
• Crosstables
– Analyze, Descriptives, Crosstabs, Row=plans,
Column=sex, Stat=chi, Cells=column
Syntax:
freq plans.
cross plans by sex /cells=col /stat=chi.
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Table of descriptives
Normal
Numerical data
Skewed
Proportions
Descriptives
Center
Dispersion
Mean
Standard deviation
Median
Fractiles
p
Confidence intervals for center estimates
Standard error
95% Confidence interval
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se(mean)
mean ± 2*se(mean)
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se(p)
p ± 2*se(p)
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Table of tests
Numerical data
Normal
Skewed
1 sample
One sample T-test
Wilcoxon signed rank test
2 independent samples Independent sample T-test Mann-Whitney U
K independent samples ANOVA
Kruskal-Wallis
2 dependent samples Paired sample T-test
Wilcoxon signed rank test
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Proportions
Binomial
Chi-square
Chi-square
Mc-Nemar (2x2)
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