Srinivasulu Rajendran
Centre for the Study of Regional Development (CSRD)
Jawaharlal Nehru University (JNU)
New Delhi
India
r.srinivasulu@gmail.com
Objective of the session
To understand
consumption pattern
through software
packages
1. How to Analyze consumption
pattern?
2. What are procedure available
for estimating consumption
pattern and how to do with
Econometric software
Two-way ANOVA using SPSS
 The two-way ANOVA compares the mean differences
between groups that have been split on two
independent variables (called factors). You need two
independent,
categorical
variables and
one
continuous, dependent variable .
Objective
 We are interested in whether an monthly per capita
food expenditure was influenced by their level of
education and their gender head. Monthly per capita
food expenditure with higher value meaning a better
off. The researcher then divided the participants by
gender head of HHs i.e Male head & Female head HHs
and then again by level of education.
 In SPSS we separated the HHs into their appropriate
groups by using two columns representing the two
independent variables and labelled them “Head_Sex"
and “Head_Edu". For “head_sex", we coded males as
"1" and females as “0", and for “Head_Edu", we coded
illiterate as "1", can sign only as "2" and can read only as
"3“ and can read & write as “4”. Monthly per capita food
expenditure was entered under the variable name,
“pcmfx".
How to correctly enter your data into SPSS in order to
run a two-way ANOVA
Testing of Assumptions
 In SPSS, homogeneity of variances is tested using
Levene's Test for Equality of Variances. This is
included in the main procedure for running the twoway ANOVA, so we get to evaluate whether there is
homogeneity of variances at the same time as we get
the results from the two-way ANOVA.
Perform
the two-anova test
procedure which is explained in the
previous session.
Tests of Between-Subjects Effects Table
 The table shows the actual results of the two-way ANOVA as
shown
 We are interested in the head of hhs gender, education and
head_sex*head_edu rows of the table as highlighted above.
These rows inform us of whether we have significant mean
differences between our groups for our two independent
variables, head_sex and head_edu, and for their interaction,
head_sex*head_edu.
We
must
first
look
at
the
head_sex*head_edu interaction as this is the most important
result we are after. We can see from the Sig. column that we have
a statistically NOT significant interaction at the P = .686 level.
You may wish to report the results ofhead_sex and head_edu as
well. We can see from the above table that there was no
significant difference in monthly per capita food exp between
head_sex (P = .675) but there were significant differences
between educational levels (P < .000).
Tests of Between-Subjects Effects
Dependent Variable:Per capita monthly food expenditure (taka)
Source
Corrected Model
Type III Sum of
Squares
10669432
df
6
Mean Square
1778239
F
6.773
Sig.
.000
Intercept
279013110
1
279013110
1062.753
.000
head_sex
46145
1
46145
.176
.675
head_edu
5527869
3
1842623
7.019
.000
head_sex *
head_edu
197900
2
98950
.377
.686
Error
322396593
1228
262538
Total
1708644528
1235
Corrected Total
333066026
1234
Multiple Comparisons Table
Multiple Comparisons
Per capita monthly food expenditure (taka)
Tukey HSD
95% Confidence
Interval
We can see from the table that
there is some repetition of the
results but, regardless of
which row we choose to read
from, we are interested in the
differences
between
(1)
illiterate, (2) can sign, (3) can
read, (4) can read & write.
From the results we can see
that there is a significant
difference between selected
different combinations of
educational level (P < .0005).
(J) (sum)
Mean
(I) (sum) head_ed Difference (Ihead_edu
u
J)
Std. Error
1
2
-50.5163
42.12953
2
3
4
Sig.
.628
Lower
Upper
Bound
Bound
-158.8968 57.8641
118.47081
.890
-219.7329 389.8118
3
85.0395
4
-200.2444
36.46704
.000
-294.0578 -106.4310
1
50.5163
42.12953
.628
-57.8641 158.8968
3
135.5558
118.29353
.661
-168.7605 439.8721
4
-149.7281
35.88692
.000
-242.0491 -57.4071
1
-85.0395
118.47081
.890
-389.8118 219.7329
2
-135.5558
118.29353
.661
-439.8721 168.7605
4
-285.2839
116.39719
.068
-584.7218 14.1540
1
200.2444
36.46704
.000
106.4310 294.0578
2
149.7281
35.88692
.000
57.4071 242.0491
3
285.2839
116.39719
.068
-14.1540 584.7218
*
*
*
*
Homogeneous Subsets
Per capita monthly food expenditure (taka)
Tukey HSDa,,b,,c
Subset
(sum)
head_edu
N
3
20
858.3107
1
289
943.3501
943.3501
2
303
993.8665
993.8665
4
623
Sig.
1
2
1143.5946
.409
.101
Overall, both subset shows insignificant, there was no homogeneous among subsets
Plot of the Results
 The following plot is not of sufficient quality to
present in your reports but provides a good graphical
illustration of your results. In addition, we can get an
idea of whether there is an interaction effect by
inspecting whether the lines are parallel or not.
From this plot we
can see how our
results from the
previous
table
might
make
sense. Remember
that if the lines
are not parallel
then there is the
possibility of an
interaction taking
place.
Procedure for Simple Main Effects
in SPSS
 You can follow up the results of a significant interaction
effect by running tests for simple main effects - that is,
the mean difference in monthly per capita food
expenditure between head of gender HHs at each
education level. SPSS does not allow you to do this
using the graphical interface you will be familiar with,
but requires you to use syntax.
Step 1
Click File > New > Syntax from the main menu as shown below
You will be presented with the Syntax Editor as shown below:
 Type text into the syntax editor so that you end up with the
following (the colours are automatically added):
 [Depending on the version of SPSS you are using you might
have suggestion boxes appear when you type in SPSSrecognised commands, such as, UNIANOVA. If you are
familiar with using this type of auto-prediction then please
feel free to do so, but otherwise simply ignore the pop-up
suggestions and keep typing normally
 UNIANOVA pcmfx BY head_sex head_edu
 /EMMEANS TABLES(head_sex*head_edu) COMPARE(head_sex)
 Basically, all text you see above that is in CAPITALS, is
required by SPSS and does not change when you enter
your own data. Non-capitalised text represents your
variables and will change when you use your own data.
Breaking it all down, we have:
UNIANOVA
Tells SPSS to use the Univariate Anova command
pcmfx BY head_sex, head_edu
Your dependent variable BY your two independent
variables (with a space between them)
/EMMEANS
Tells SPSS to calculate estimated marginal means
Generate statistics for the interaction term. Put your
TABLES(head_sex*head_edu) two independent variables here, separated by a * to
denote an interaction
COMPARE(head_sex)
Tells SPSS to compare the interaction term between
genders
Making sure that the cursor is at
the end of row 2 in the syntax
editor click the
button, which
will run the syntax you have typed.
Your results should appear in the
Output Viewer below the results
you have already generated.
SPSS Output of Simple Main
Effects
Univariate Tests
Dependent Variable:Per capita monthly food expenditure (taka)
This table shows us whether
there are statistical differences in
mean monthly per capita food
expenditure between head of
gender for each educational
level. We can see that there are
no statistically significant mean
differences between male and
females' headed HHs in pcmfx
when head of HHs are educated
to illetrate (P = .785) or can sign
(P = .718) so on.
Sum of
(sum) head_edu
Squares
1
Contrast
19272
Error
2
Contrast
Error
3
Contrast
Error
4
Contrast
Error
32239659
3
34207
32239659
3
0
Mean
df
Square
1 19272
F
Sig.
.073 .786
1228 262538
1
34207
.130
.718
.
.
.828
.363
1228 262538
0
.
32239659
3
1228 262538
217485
1 217485
32239659
3
1228 262538
Reporting the results of a two-way
ANOVA
 You should emphasize the results from the interaction first,
before you mention the main effects. In addition, you should
report whether your dependent variable was normally
distributed for each group and how you measured it (we will
provide an example below).
 A two-way ANOVA was conducted that examined the effect of
head of gender and education level on per capita monthly
food expenditure. There was no homogeneity of variance
between groups as assessed by Levene's test for equality of
error variances. There was a no significant interaction
between the effects of head of gender and education level on
per capita monthly food expenditure, F =0.377, P = .686.
Simple main effects analysis showed that male headed HH
were NOT significantly different in monthly per capita food
expenditure than female headed HH when educated to read
& write, but there were differences in monthly per capita food
expenditure when the head of HHs educated to read & write
(P = .000), However, there was no significant different
between male head and female head HHs in pcmfx.
Hands-on Exercises
1. Find out whether an monthly per capita total
expenditure was influenced by their gender head and
districts.
2. Find out whether an monthly per capita total
expenditure was influenced by the village those who
adopted technology and districts.