M.A.
(IVth Semester)
Time Series Analysis & Computer Application
(Eco-592)
SPSS
Dr. Rekha Gupta
Department of Economics
University of Allahabad
SPSS (Statistical Package for Social Science)
Introduction: What is SPSS?
❖ Originally it is an acronym of Statistical Package for the Social Science but
now it stands for Statistical Product and Service Solutions
❖ One of the most popular statistical packages which can perform highly
complex data manipulation and analysis with simple instructions
Scales of Measurement
•Nominal Scale - groups or classes
✓Gender
•Ordinal Scale - order matters
✓Ranks (top ten videos)
•Interval Scale - difference or distance matters – has arbitrary zero
value.
✓Temperatures (0F, 0C), Like Scale
•Ratio Scale - Ratio matters – has a natural zero value.
✓Salaries
Contents
• SPSS interface:
– Data view and Variable view
• How to enter data in SPSS
• How to import external data into SPSS
• How to clean and edit data
• How to get descriptive statistics
5
SPSS interface
• Data view
– The place to enter data
– Columns: variables
– Rows: records
• Variable view
– The place to enter variables
– List of all variables
– Characteristics of all variables
6
Opening SPSS
❖The default window will have the data editor
❖There are two sheets in the window:
1. Data view 2. Variable view
Enter data in SPSS directly
8
Example: Hospital-stay data
9
Columns:
variables
Rows:
cases
Under Data
View
10
Enter Variables
1. Click Variable View
2. Type variable name under
2. Type variable 4. Description Name column (AGE).
name of variable
NOTE: Variable name can be
3. Type:
64 bytes long, and the first
numeric or
string character must be a letter
or one of the characters @,
#, or $.
3. Type: Numeric, string, etc.
1. Click this 4. Label: description of
Window
variables.
11
Enter variables
Based on your
code book!
12
Enter cases
13
Import data from Excel
• Select File Open Data
• Choose Excel as file type
• Select the file you want to import
• Then click Open
14
Open Excel files in SPSS
15
Continue
Save this
file as
SPSS data
16
Clean data after import data files
• Run cases summaries for all variables
• Run frequency for qualitative variables and Descriptives for
quantitative variables
• Check outputs to see if you have variables with wrong
values.
• Check missing values and physical surveys if you use paper
surveys, and make sure they are real missing.
• Sometimes, you need to recode string variables into numeric
variables
17
cases summaries
18
19
20
The basic analysis of SPSS that will be
introduced in this class
❖Frequencies
✓This analysis produces frequency tables showing frequency counts
and percentages of the values of individual variables.
❖Descriptives
✓This analysis shows the maximum, minimum, mean, and standard
deviation of the variables
❖Linear regression analysis
✓Linear Regression estimates the coefficients of the linear equation
Basic Statistical Analysis
• Descriptive statistics:
1. Find wrong entries
2. Have basic knowledge about the sample
and targeted variables in a study
3. Summarize data.
22
Qualitative Variables
Analyze Descriptive statistics Frequency
23
Frequencies
❖Click ‘Analyze,’ ‘Descriptive statistics,’
then click ‘Frequencies’
25
26
Quantitative Variables
Analyze Descriptive statistics Descriptives
28
Descriptives
❖The options allows you to analyze other descriptive
statistics besides the mean and Std.
❖Click ‘variance’ and ‘kurtosis’
❖Finally click ‘Continue’
Click
Click
Descriptives
❖Finally Click OK in the Descriptives box. You will be able
to see the result of the analysis.
Graphs
Click ‘Graphs,’ ‘Legacy Dialogs,’ ‘Interactive,’ and
‘Scatter plot’ from the main menu.
Bar Chart
4.4
60
50 4.3
40
4.2
30
4.1
20
Mean EQ1
4.0
10
Percent
3.9
0
Missing Female Male
Missing Female Male
GENDER
GENDER
Pie Chart
Male Missing
EQ5
EQ1
Female EQ4
EQ2
EQ3
Line Chart
60 4.4
50
4.3
40
4.2
30
4.1
20
Mean EQ1
4.0
10
Percent
0 3.9
Missing Female Male Missing Female Male
GENDER GENDER
Histogram
❖Histogram
Really just a bar chart that displays “Num of Cases”
only
Click “Display Normal Curve” to inspect if your
distribution deviates from normal
300
200
100
Std. Dev = .86
Mean = 4.3
0 N = 614.00
1.0 2.0 3.0 4.0 5.0
EQ1
Regression Analysis
❖Click ‘Analyze,’
‘Regression,’ then click
‘Linear’ from the main menu.
Regression Analysis
One-Sample t Test
❖Tests for difference between sample mean and pre-determined population
mean
Click “Analyze” → “Compare Means” → “One- Sample T Test…”
“Test Value” = Predetermined population mean
Options:
Exclude Cases Listwise = If multiple variables used, only use cases
that have values on ALL variables
Exclude Cases Analysis by Analysis
One-Sample T Test
One-Sample Statistics
Std. Error
N Mean Std. Deviation Mean
EQ2 613 2.83 1.026 .041
One-Sample Test
Test Value = 0
95% Confidence
Interval of the
Mean Difference
t df Sig. (2-tailed) Difference Lower Upper
EQ2 68.368 612 .000 2.83 2.75 2.91
Independent-Samples t Test
❖Tests if two unrelated samples differ significantly from one
another
Click “Analyze” → “Compare Means” → “Independent-
Samples T Test…”
“Test Variable(s)” = DV
“Grouping Variable” = IV
Click “Define Groups…”
MergeFile1.sav – Male = 1; Female = 0
If IV dimensional, can use cut point to create groups –
i.e. x > 7 = Grp 1, x ≤ 7 = Grp 2
Levene’s Test for Equality of Variances
If significant, equal variances cannot be assumed
Independent-Samples t Test
Group Statistics
Std. Error
GENDER N Mean Std. Deviation Mean
EQ1 Female 326 4.30 .769 .043
Male 286 4.21 .962 .057
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
95% Confidence
Interval of the
Mean Std. Error Difference
F Sig. t df Sig. (2-tailed) Difference Difference Lower Upper
EQ1 Equal variances
5.118 .024 1.203 610 .230 .08 .070 -.053 .222
assumed
Equal variances
1.185 543.961 .236 .08 .071 -.055 .224
not assumed
Paired-Samples t Test
❖Tests if two related samples differ significantly from one another
Click “Analyze” → “Compare Means” → “Paired-Samples T
Test…”
Paired Samples Statistics
Std. Error
Mean N Std. Deviation Mean
Pair EQ1 4.26 613 .860 .035
1 EQ2 2.83 613 1.026 .041
Paired Samples Correlations
N Correlation Sig.
Pair 1 EQ1 & EQ2 613 .016 .684
Paired Samples Test
Paired Differences
95% Confidence
Interval of the
Std. Error Difference
Mean Std. Deviation Mean Lower Upper t df Sig. (2-tailed)
Pair 1 EQ1 - EQ2 1.43 1.327 .054 1.32 1.53 26.657 612 .000
