PATH
ANALYSIS CONCEPT AND EXAMPLE USING SPSS 16
Good night friends..
Howdy? Hope you’re fine today.. Hahaha.. Okay, this time I will give you a nice
post about Path Analysis. Not only explaining you about the concept, but I also
will give you an easy understandable example that we will execute by using SPSS
16.
You must have been
remembered that in the former I have ever explained you about multiple
regression analysis.. Had you understood its concept, it would have been
helpful for you to understand this path analysis.
Note: In path analysis, we are introduced to what
called “direct effect”, “indirect effect” and “total effect”. For make it easy,
I will illustrate it like this:
A case to know the
direct and indirect effect of variables A, B, and C to variable E by using D as
the mediator variable.
For example, we have
independent variables A, B and C that we want to know each of their effects to
E as a dependent variable. Okay, in this case as I have said, we used a
mediator variable, say D. Why do we use this variable? Something we can be
confronted for a case that some
independent variables can instantly effect the dependent one (direct effect)
but for any condition, the independent variables cannot instantly effect the
dependent one
Please look at the
illustration above:
Here, we shall set two
sub structural equation, right?
First, see that
variable A, B and C can instantly effect variable D (direct effect). In SPSS,
we regress independent variables A, B and C to variable D. So, this is the sub structural
equation:
D = ρ DA + ρ DB + ρ DC + Є1
Second, Variable A, C and D can instanly
effect E though variable B can’t. Just look to the arrow at the illustration
given. Then, we make the sub structural equation:
E = ρ EA + ρ EC + ρ ED + Є2
For not wasting time, I
will give an example with fictive data.. Nevermind, the example can be found
but you should remember that you havo to understand the concept and
philosophy.. Okay, if you want to practice, you can download the data here
Oops, you should also
know that in path analysis, we also use the test of assumption in regression
OLS. This classical test won’t be explained here because I have ever put it in
a last post. When you have downloaded the data you can see that you’ll have
data view alike:
First step: regress
variable A, B and C to variable D. Just click Analyze, Regression, Linier.
Then, put variable A, B and C in Independent Part and variable D in Dependent
one.
You will have output:
Simultaneously,
variable A, B and contribute 73,1 percent in explaining the changes happen in
variable D and the rest for about 16,9 percent is explained by the other
variables out of the model.
In Anova F test, see that simultaneously, the
independent variables statistically has a significant effect to variable D
that’s shown by Sig. value 0,000 less than Alpha 0,05 (rejecting null
hypothesis and accepting the alternative one, so the F test is significant.
At coefficient, the
partial t test, variable B and C statistically have a significant effect to
variable D that’s shown by each Sig. value that less than Alpha, viz 0,048 and
0,000. Variable A statistically don’t have a significant effect to variable D
(Sig. value is 0,615; more than Alpha 5 percent. For that case, we eliminate
variable A.
The sub structural
equation (using Standardized Beta) becomes:
D= 0,320B +
0,640C + Є1
Heemm, see that the
sign of coefficient is positive, It means that variable B and C are positively
effect variable D.
Interpretation of
variable B: The increase for one unit of variable B will increase variable D
for 0,320 unit with assumption that another independent variable is constant.
Interpretation of
variable C: The increase for one unit of variable C will increase variable D
for 0,640 unit with assumption that another independent variable is constant.
Next, we go to the
second suu structural equation. Regress variable A, C and D to variable E. Just
the same way and you will get this output:
The result shows that
variable A, C and D can only explain the variance of variable E for 34,4
percent and the rest is explained by the other variables out of the model.
Next, we see that simultaneous test ( F test) is still significant, shown by
Sig. value 0,011 < (less than) 0,05. From partial test, only variable D that
statistically significant to effect variable E (Sig. value 0,005 < Alpha
0,05). Here is the structural model:
E= 0,868D +
Є2
Statistically, variable
A doesn’t have direct effect to E because in our test, variable A is not
significant to effect E. For its indirect effect doesn’t need to be counted
because we have eliminated this variable when we regress it with the variable
dependent D.
The indirect effect
from B to E through the moderator variable D is 0,320 x 0,868 = 0,27776 or 0,28. For
the direct one is suitable with the beginning ilustration. In a research, we
must reckon this moderator variable by strengthening the theories related. See
also that B doesn’t have a direct arrow to E; it means variable D just have the
indirect effect (only by using moderator variable).
Variable C; we can count its
indirect effect 0,640 x 0,868 = 0,5552 or 0,56. Okay, for the direct effect,
remember that at second sub strutural we place variable E as dependent. From
A,C and D, only D that statistically significant to effect variable E.
And now, there is one question for
you my friends about this path analysis. Imagine that variable D (moderator
variable) is not significant.. Is there any indirect effect?? Hehehe.. Can you
give your argument? Right, hope that anyone can give argument for this
question..l.o.l.
So, I suggest that we should be
careful to choose the moderator variable for this path analysis. Read and
deepen the theories related friends. You can also download some free ebooks
about this analysis and moderator intervening concepts.
The last but not the least, we can
find the total effect. Just totalize the direct and indirect effect. In our
case (may be, because we just need fictive data), the direct effect is not
significant, so for the total we only use the indirect one.. Total effect from
B to E is 0,28 while total effect from C to E is 0,56. Heeemmm,, for the
interpretation, I have explained it so long hahaha..
Okay, that’s all that I can tell you
about this path analysis.. more or less, I apologize.. Keep spirit and success
for us.. :-)
Thank you for your spss path analysis example . It can be very useful for those who will get such a task. This statistical technique is used to investigate an compare the strength of the variable relations. And it can be hard to perform if you are a newbie in this field. SO you may need some help instead of learning useful examples. There are special services that will provide you with the non-academic assistance with any data analysis using such software as SPSS, STATA and EXCEL. Everything will depend on your requirements and expectations. But their expetts ill do it for you.
BalasHapus