Hi, I'm new to the forum, and worried that I have screwed up my thesis project. I thought I designed a standard one group pretest/postest design. But I was working with kids, and the advice I was given was they do better with Likert scale (1-False 2-Somewhat False 3-Neither 4-True 5-Very True) as opposed to a 10 point scale. (Rate how true XYZ is to you- 1 being Not at all true and 10 being very true) Now I have this data, and I'm not sure it is kosher to do a paired t-test on it, because a Lickert scale is technical ordinal data, not interval data. I have taken statistics but it was 10 years ago, and I'm very rusty.

Here is my study in a nutshell:

I wanted to study the effect of a curriculum I designed on the students anxiety, confidence, and motivation for learning English. I administered the pretest, taught the curriculum, and administered the posttest. The pretest/posttest is a lickert scale survey with 5 questions each in 3 categories which are: Anxiety, Confidence, and Motivation toward learning English. Pretest and Posttest were exactly the same survey.

Can I add up the reponses in each section and get a mean and compare the means of the pretest and posttest using a paired t-test? Or should I treat each question as a seperate variable (each question covers a different aspect of the main category) and do a paired t-test for each question? Or is there a completely different test I should be doing?

This is not my only data. This is a mixed methods study with both qualitative and quantitative data. I've triangulated the data with my observations and interviews. I don't know if that helps any with not doing the perfect test.

2. Hi SoLost,

You should be fine. While it's technically true that Likert Scale data is ordinal categories, it is often assumed to be interval as long as:

1. The intervals between the values are generally equal: is the difference between a 1 and a 2 the same as between a 2 and a 3?

2. The underlying concept you are measuring is itself continuous.

The sum of ordinal variables is still ordinal, so running them separately instead of summing them doesn't help anything.

You have a few options. Run the paired t-test as planned, and check your assumptions well. If they look good, stick with the t-test.

If the assumptions don't turn out well, there is a non-parametric equivalent to the paired t-test called the Wilcoxan sign-rank test. It is available in SPSS under nonparametrics.

Good luck,
Karen

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