2. FUNCTION OF MANCOVA TEST
● Manova = Mancova : use to analyse data more than one dependent variable AND
one or more independent variable
● Have additional function : controlcontrol/fixed variable (covariate)
● Fixed variable : Factor that not be study in research, but it might influences
dependent variable
3. Basic Requirement for MANCOVA Test
1 More than 1 dependent variable. (measure in interval or ratio scale)
2 Independent Variables: Consist of at least 2 groups (categories) independent data
3 Fixed Variable: Consist of at least 2 groups (categories) independent data
4
Homogeneity of variances must be met for each dependent variable.
The variances in the difference groups must be the same.
4. 5
Sample size for each group is not less than 15. When subject size is 30,
research data are assumed normally distributed.
6
Research data is normally distributed. Normality test can be done by using
Skewness and Kurtosis, Kolmogorov-Smirnov, Shapiro-Wilk and Normal
Probability Plot.
7 Linear correlation. There must be a significant linear relationship between
the dependent variable and the covariate. Identify by scatterplot graph.
5. MANCOVA
• The one-way multivariate analysis of covariance (MANCOVA)
can be thought of as an extension of the one-way MANOVA to
incorporate a covariate or an extension of the one-way
ANCOVA to incorporate multiple dependent variables.
• This covariate is linearly related to the dependent variables and
its inclusion into the analysis can increase the ability to detect
differences between groups of an independent variable.
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6. • A one-way MANCOVA is used to determine whether there are
any statistically significant differences between the adjusted
means of three or more independent (unrelated) groups, having
controlled for a continuous covariate.
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7. BASIC RULE USING MANCOVA
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• More than 1
• Measure by ratio scale
Dependent
(bersandar)
• At least 2 groups
Independent
(bebas)
• At least 2 group of
independent data
Fixed
(kawalan)
8. For example, you could use a one-way MANCOVA to determine
whether a number of different exam performances differed based
on test anxiety levels amongst students, whilst controlling for
revision time (i.e., your dependent variables would be “Biology
exam performance", “Additional Maths exam performance" and
“Physics exam performance", all measured from 0-100, your
independent variable would be "test anxiety level", which has
three groups – "low-stressed students", "moderately-stressed
students" and "highly-stressed students" – and your covariate
would be "revision time", measured in hours).
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10. ➢ The one-way MANCOVA is very useful, but it is important to realize that the one-
way MANCOVA is an omnibus test statistic. It will tell you whether the groups of
the independent variable statistically significantly differed based on the
combined dependent variables, after adjusting for the covariate, but it will not
explain the result further. In other words, the one-way MANCOVA will not tell
you about the differences between specific groups.
11. Analysis Procedure for MANCOVA Test
1 State the research hypothesis:
● Null hypothesis:
Gender is not a factor in the knowledge and ability of school teachers
in the Batu Pahat district to apply PPBI by controlling school factors.
● Alternative hypothesis:
Gender is a factor in the knowledge and ability of school teachers
in the Batu Pahat district to apply PPBI by controlling school factors.
16. 16
1
Result
For Knowledge : (mean: men = 3.03, women = 4.43)
For Application : (mean: men = 22.18, women = 8.97)
17. 17
2
Multivariate Pillai’s Trace show significance result for gender : [F(2,70) = 15.50, p < 0.05]
Multivariate Pillai’s Trace show no significance result for location : [F(2,70) = 2.70, p > 0.05]
18. 18
3
Multivariate Pillai’s Trace show significance result of gender towards 2 dependent variable
Knowledge: [F(1,70) = 9.66, p < 0.05]
Application: [F(1,70) = 20.28, p < 0.05]
19. Null hypothesis is rejected
Multivariate Pillai’s Trace show significance result for gender : [F(2,70) = 15.50, p < 0.05]
Multivariate Pillai’s Trace show no significance result for location : [F(2,70) = 2.70, p > 0.05]
Knowledge: [F(1,70) = 9.66, p < 0.05]
Application: [F(1,70) = 20.28, p < 0.05]
