Project

Parallelism in Research Questions and Hypotheses

(Creswell p.308 has a good example of a more complex hypothesis with control IVs predicted to have interaction effects with the treatment IV)

Research Question (what do you want to find out?):

Do students who seek help from professors learn more from their courses?

(IV) (DV)

Conceptual Hypothesis (what do you predict you will find?):

Students who seek help from professors learn more from their courses than students who do not seek help.

(IV) (DV)

**These variables are considered ‘conceptual’ because they are very general…there are many ways to define or measure these concepts**

Operational Hypothesis:

(IV) (DV)

Students who attend office hours with a course’s professor get higher grades in the course than students who do not attend office hours.

**These variables are considered ‘operational’ because they are specified/defined in a measurable way**

Null Hypothesis:

Students who attend office hours with a course’s professor WILL NOT get higher grades in the course than students who do not attend office hours.

From the internet I found some good explanations of the issue of null hypotheses: http://www.ruf.rice.edu/~lane/hyperstat/A29337.html

http://www.ruf.rice.edu/~lane/hyperstat/B33945.html

The null hypothesis either will or will not be rejected as a viable possibility. The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it. [You can never actually ACCEPT a null or an alternative hypothesis, you can only REJECT a null hypothesis…that is you can only draw the conclusion from your data that it reveals a difference (or relationship) greater than zero with some level of confidence (p<.05) that the difference is not merely a product of chance.]

Data not sufficient to show convincingly that a difference between means is not zero do not prove that the difference is zero. Such data may even suggest that the null hypothesis is false but not be strong enough to make a convincing case that the null hypothesis is false. For example, if the probability value were p=.15, then one would not be ready to present one's case that the null hypothesis is false to the (properly) skeptical scientific community. More convincing data would be needed to do that. However, there would be no basis to conclude that the null hypothesis is true. It may or may not be true, there just is not strong enough evidence to reject it. Not even in cases where there is no evidence that the null hypothesis is false is it valid to conclude the null hypothesis is true. No experiment can distinguish between the case of no difference between means and an extremely small difference between means.