5
$\begingroup$

I have data on 26 participants (13 from computing and remaining 13 from non computing) who have participated in my research. Each participant is treated with a lab module (Hands on Robotics Session). Now each participant will be evaluated using a rubric on scale of 1 to 4. This experiment has both pre and post test.

For my research i want to evaluate the following questions:

Research question 1

  • Null Hypothesis: students do not learn about computational thinking (programming basics and algorithmic thinking) with the help of robotics.
  • Alternate Hypothesis: Students learn about computational thinking (programming basics and algorithmic thinking) with the help of robotics.

To evaluate the above question, the categories i will be considering are Plan, Implementation and Knowledge gained on a scale of Excellent, Good, Fair and Poor.

I think i should use DEPENDENT T-TEST FOR PAIRED SAMPLES. Am i correct?

Research question 2:

  • Null Hypothesis: Participants background (computing and non computing) has no effect in learning about algorithmic thinking with help of robotics
  • Alternate Hypothesis:Participants background (computing and non computing) has an effect in learning about algorithmic thinking with help of robotics

I will also evaluate question 2 on a scale of Excellent, Good, Fair and Poor, but with respect to background.

My Statistical Question

Which T test should I use for each of my research questions?

$\endgroup$
1

3 Answers 3

5
$\begingroup$

Since you are interested in measuring an increase pre- and post-test, it seems to me that you should use a paired test.

An issue here is that your variables are likely non Gaussian since they are among 4 categories. If your sample size is big (very roughly larger than 50), then it is no big deal. Otherwise, I would use the Wilcoxon signed rank test which is a non parametric analog of the paired t-test.

$\endgroup$
6
  • 2
    $\begingroup$ very roughly is right. What if $P( {\rm category \ 1} ) = 1 - 10^{-200}?$ ;) $\endgroup$
    – Macro
    Jun 18, 2012 at 23:40
  • 1
    $\begingroup$ Aha (+1), then I am f... But how likely is that? Close to $10^{-200}$ I'd say :-) $\endgroup$
    – gui11aume
    Jun 18, 2012 at 23:45
  • 1
    $\begingroup$ It's also worth noting that this would also require an assumption that the categories comprise an interval scale. It also seems (unless I misread) that, in question 1, the role of the null and alternative are reversed from the way they normally are in a paired $t$-test; the null appears to be that the module does help. Finally, question 2 appears to require a two independent samples test, where the paired differences are the outcome variables. $\endgroup$
    – Macro
    Jun 18, 2012 at 23:51
  • 1
    $\begingroup$ I do not subscribe to using the Wilcoxon signed rank test alternative. If you do a simulation study, you can still show the power of the t-test is good --comprable to the signed rank test-- in moderately skewed distributions. If the researchers are interested in testing for a geometric difference in responses, then a log transformation would be justified. $\endgroup$
    – AdamO
    Jun 18, 2012 at 23:54
  • $\begingroup$ @marco: Thanks for pointing out that my formation for null and alternate was wrong and thanks for your suggestion on the second question. $\endgroup$
    – Dumb_Shock
    Jun 19, 2012 at 0:59
1
$\begingroup$

gui11aume beat me to it. He said exactly what I had in mind when I read the question. Since scaled measurements are not going to fit a normal error distribution very well a nonparametric paired test is the best way to go in my view and for that I would have recommended the Wilcoxon signed rank test. On the other hand if you were comparing scores by domains where many responses are summed, normality is not a bad assumption and the t test is fairly robust anyway. So in that situation a paired t test might be okay.

$\endgroup$
0
$\begingroup$

I think you are having problems with the nature and elaboration of your alternative hipotesis. Your investigation/reserach hypotesis (and all the theoretical model associated with it) is your alternative hipothesis. Since the variable is clearly ordinal, i´d prefer a Wilcoxon rank test. But anyway i suggest to work out a little bit more your hipothesis.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.