Hello Thesisfun, Satchi;

Based on my research and from what I have understood t-tests can be used and are widely used with ordinal scales in surveys. Here is a journal paper on the matter: http://pareonline.net/pdf/v15n11.pdf

I think although the data is not continuous a value such as 3.5 would still have some meaning in this context.

The scale was presented to respondents as follows:

(not important) 1 2 3 4 5 (essential)

So 3 could be interpreted as important or neutral.

Any benchmark value could have been used from 3 - 5 (I would say that it should be slightly more than 3). The reason for using 4 is based on research that suggests 80% is a good benchmark so I am using 80% of 5 i.e. 4.

I think even if I change the benchmark value you could still end up in the situation described earlier where one group regards a component to be important whilst another does not.


At the moment the conclusion seems to be that there is no significant reason to drop component A from my framework.

Hello Satchi;

Thanks for the response.

The one-sample t-test was needed because in the first case I was looking at the teachers and students separately. The logic is basically as follows:

1) I have a framework with a number of components
2) Experts evaluate framework and rank components using scale 1 (not important), 2 (slightly important), 3 (important), 4 (very important) or 5 (essential)
3) I keep component in framework if it has a mean of 4 or more
4) A one-sample t-test is performed against benchmark of 4
5) Results reveal experts believe component A is not very important (i.e. mean is below 4)
6) I run another survey but this time with students who use the same ranking scale
7) I carry out one-sample t-test against a benchmark of 4 as before
8) Results reveal that students believe component A is very important (i.e. mean is 4 or more)
9) I check if there is a significant difference between the two results by carrying out an f-test followed by a t-test
10) Two-sample t-test reveals no significant difference

So the question I am left with is whether or not component A is very important i.e. should it be left in the framework?

I was wondering whether the confidence interval could be used for this purpose?

I hope the above makes sense.

Hello Everybody;

I would like some advice regarding my results. I carried out two surveys - one involving teachers and the other involving students - and in both surveys I used the same questionnaire. The participants were asked to rank components on a 5 point scale. Here are the results for one of the components

- one sample t-test against benchmark of 4 with results of teacher's survey.

Component A had the following results:

mean: 3.3
p-value: 0.0048
95% C.I: -Infinity and 3.6913
reject null hypothesis (mu >= 4) and accept alternative hypothesis (mu < 4)


- one sample t-test against benchmark of 4 with results of student's survey:

Component A had the following results:

mean: 3.9565
p-value: 0.4262
95% C.I: -Infinity and 4.3529
accept null hypothesis (mu >= 4)

- two sample t-test with equal variance comparing means of both samples:

p-value: 0.0933
95% C.I: -1.4298 to 0.1167
Accept null hypothesis (mu1 == mu2)


My question is this:

The teacher's sample suggests that the mu < 4 for component A. The student's sample suggests that the mu >= 4 for component A. The two-sample t-test suggests there is not significant difference between the samples. What conclusion can be made here or what other test can I do to decide if overall mu < 4 or mu >= 4?

Any advice would be greatly appreciated.

Thank you Reenie for trying to encourage people to contribute. I still have not come up with a suitable solution to this problem. One possible approach I have investigated is the use of Noun-Verb Analysis but I do not believe it to be suitable for my needs. I will continue to try and identify a suitable technique but support from friends here at postgraduateforum.com would be greatly appreciated.

Hello Everybody;

I have a question regarding the construction of my framework which I hope makes sense. Basically, I have reviewed research related to two different domains, let us call this Domain X and Domain Y. I identified themes in the literature related to Domain X and to Domain Y and ended up with (for example) something like this:

Domain X:

Theme: Environment Components: Economics, Law, Technology
etc

Domain Y:

Theme: Setting Components: Location
etc


I then tried to identify common cross-domain themes. At the moment I have made these cross-domain links by looking at the semantic meanings of each of the components and linking similar components. For the example above I grouped the components Economics, Law, Technology and Location together since they address the same theme (which I called 'Context').

This technique allows me to construct a conceptual framework but it relies on semantics and my interpretation. I would like to reduce this reliance by linking the components using a technique which is independent of the person making the links. That is the technique reduces bias and produces the same groupings no matter who performs the groupings.

I was wondering if anyone can point to any established techniques that can be applied to achieve this linking?

Any help would be greatly appreciated.

Prins