Question

ANY STATISTICIAN OR ANYONE WHO HAS TAKEN STATISTICS?

I just need some clarification on confounding variable

Answer 1

What is exactly troubling you, perhaps I won't be able to help? But probably no, since you are much more knowledgeable and you are asking the question .

Answer 2

I think I have the basic concept of CONFOUNDED VARIABLE, but I think it is a little bit confusing or I just haven't zeroed in to the concept

Answer 3

https://answers.yahoo.com/question/index?qid=20080928171124AAze3Iv maybe

Answer 4

correction: CONFOUNDING VARIABLE

I am self-studying so I am relying solely on my textbook's definition and examples
it is defined as:
confounding variable is one that is related to both group membership and
the response variable of interest in a research study.

This is a bit vague for me and I don't think it is clear at all.

Answer 5

textbooks are never clear, are they ?

Answer 6

yeah the examples in that link are not good.

Answer 7

-:(

Answer 8

mmm statistics have few rules just memorize them :-|

Answer 9

Hmm what about this example I found...

"These are variables other than your independent variable, which might have an effect on your dependent variable. Confounding variables increase the variance of the measurements of the dependent variable.

Imagine an example where you want to test whether men or women are taller. The independent variable is gender and the dependent variable is height. Anything else that effects height is a confounding variable in this study. An obvious example would be age. The more different age groups you measure, the more variation you will see in heights. Nationality would be another. If you are lucky, confounding variables will only increase variance but if you are unlucky, they could introduce bias. In our current example, bias might be introduced if the men you measured were all jockeys (and so quite short) and the women were all basketball players. In this case, the confounding variable (sport) would eclipse the dependent variable (gender)."

Answer 10

From what I have gathered, confounding variable arise from an observational study that renders one unable to conclude a cause-and-effect.

Answer 11

Thank weldon !!

Answer 12

very good example

Answer 13

well this is almost a common sense. We cannot make a cause and effect conclusion because we are not able to rule anything out in an observational study. Meaning, that there are other possibilities that we can use to explain the observed phenomena.

Answer 14

yeah, bias is a separate sampling issue and may be, may be, affect our conclusion.

Answer 15

thanks for the input, guys
@johnweldon1993 @SolomonZelman

Answer 16

johnweldon is the man :)