A two-way ANOVA was conducted to examine the effects of gender head and education level on monthly per capita food expenditure. There was no significant interaction between gender head and education level. While there were no differences between gender heads within each education level, there were significant differences in expenditure between education levels overall. Simple main effects tests showed no significant differences in expenditure between gender heads at each education level.

1. Srinivasulu Rajendran
Centre for the Study of Regional Development (CSRD)
Jawaharlal Nehru University (JNU)
New Delhi
India
r.srinivasulu@gmail.com

2. Objective of the session
To understand
consumption pattern
through software
packages

3. 1. How to Analyze consumption
pattern?
2. What are procedure available
for estimating consumption
pattern and how to do with
Econometric software

4. 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 .

5. 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.

6.  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".

7. How to correctly enter your data into SPSS in order to
run a two-way ANOVA

8. 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 two-
way 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.

9. Perform the two-anova test
procedure which is explained in the
previous session.

10. 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).

11. Tests of Between-Subjects Effects
Dependent Variable:Per capita monthly food expenditure (taka)
Type III Sum of
Source Squares df Mean Square F Sig.
Corrected Model 10669432 6 1778239 6.773 .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 * 197900 2 98950 .377 .686
head_edu
Error 322396593 1228 262538
Total 1708644528 1235
Corrected Total 333066026 1234

12. Multiple Comparisons Table

13. Multiple Comparisons
Per capita monthly food expenditure (taka)
Tukey HSD
95% Confidence
Interval
(J) (sum) Mean
We can see from the table that (I) (sum) head_ed Difference (I- Lower Upper
there is some repetition of the head_edu
1
u
2
J)
-50.5163
Std. Error
42.12953
Sig.
.628
Bound Bound
-158.8968 57.8641
results but, regardless of
3 85.0395 118.47081 .890 -219.7329 389.8118
which row we choose to read
*
from, we are interested in the 4 -200.2444 36.46704 .000 -294.0578 -106.4310
differences between (1) 2 1 50.5163 42.12953 .628 -57.8641 158.8968
illiterate, (2) can sign, (3) can 3 135.5558 118.29353 .661 -168.7605 439.8721
read, (4) can read & write. 4 -149.7281
*
35.88692 .000 -242.0491 -57.4071
From the results we can see 3 1 -85.0395 118.47081 .890 -389.8118 219.7329
that there is a significant
2 -135.5558 118.29353 .661 -439.8721 168.7605
difference between selected
different combinations of 4 -285.2839 116.39719 .068 -584.7218 14.1540
educational level (P < .0005). 4 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

14. Homogeneous Subsets
Per capita monthly food expenditure (taka)
Tukey HSDa,,b,,c
(sum) Subset
N
head_edu 1 2
3 20 858.3107
1 289 943.3501 943.3501
2 303 993.8665 993.8665
4 623 1143.5946
Sig. .409 .101
Overall, both subset shows insignificant, there was no homogeneous among subsets

15. Plot of the Results

16.  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.

17. 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.

18. 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.

19. Step 1

20. Click File > New > Syntax from the main menu as shown below

21. 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 SPSS-
recognised 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

22.  UNIANOVA pcmfx BY head_sex head_edu
 /EMMEANS TABLES(head_sex*head_edu) COMPARE(head_sex)

23.  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
Your dependent variable BY your two independent
pcmfx BY head_sex, head_edu
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
Tells SPSS to compare the interaction term between
COMPARE(head_sex)
genders

24. 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.

25. SPSS Output of Simple Main
Effects

26. Univariate Tests
Dependent Variable:Per capita monthly food expenditure (taka)
This table shows us whether
there are statistical differences in Sum of Mean
(sum) head_edu Squares df Square F Sig.
mean monthly per capita food 1 Contrast 19272 1 19272 .073 .786
expenditure between head of Error 32239659 1228 262538
gender for each educational 3
2 Contrast 34207 1 34207 .130 .718
level. We can see that there are
no statistically significant mean Error 32239659 1228 262538
3
differences between male and 3 Contrast 0 0 . . .
females' headed HHs in pcmfx
Error 32239659 1228 262538
when head of HHs are educated 3
to illetrate (P = .785) or can sign 4 Contrast 217485 1 217485 .828 .363
(P = .718) so on. Error 32239659 1228 262538
3

27. Reporting the results of a two-way
ANOVA

28.  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.

29. 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.