Hi, I have a data set in SPSS with several Likert scale questions. I wanted to see if there are any differences with how males and females answer several satisfaction questions (e.g., satisfied with personal life, job, etc.). I have taken a couple of these ordinal statements and transformed them into nominal measurements. Not sure if that was the right first step. After initial research, it sounds like I can use Spearman.
Can I use Spearman and do I have to transform all of my ordinal statements to nominal measured variables in order for the analysis to make sense? Any help would be greatly appreciated.
A heads up, even though in graduate school I took and passed several statistics classes, I haven't used these skills for a few years, so I am a little rusty. :)
Likert-type scales are frequently used in medical education and medical education research. Common uses include end-of-rotation trainee feedback, faculty evaluations of trainees, and assessment of performance after an educational intervention. A sizable percentage of the educational research manuscripts submitted to the Journal of Graduate Medical Education employ a Likert scale for part or all of the outcome assessments. Thus, understanding the interpretation and analysis of data derived from Likert scales is imperative for those working in medical education and education research. The goal of this article is to provide readers who do not have extensive statistics background with the basics needed to understand these concepts.
Developed in 1932 by Rensis Likert1 to measure attitudes, the typical Likert scale is a 5- or 7-point ordinal scale used by respondents to rate the degree to which they agree or disagree with a statement.
In an ordinal scale, responses can be rated or ranked, but the distance between responses is not measurable. Thus, the differences between “always,” “often,” and “sometimes” on a frequency response Likert scale are not necessarily equal. In other words, one cannot assume that the difference between responses is equidistant even though the numbers assigned to those responses are. This is in contrast to interval data, in which the difference between responses can be calculated and the numbers do refer to a measureable “something.” An example of interval data would be numbers of procedures done per resident: a score of 3 means the resident has conducted 3 procedures. Interestingly, with computer technology, survey designers can create continuous measure scales that do provide interval responses as an alternative to a Likert scale.
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Well, dear friend, I think both of your work is wrong. 1. If your data is Likert and Ordinal, then there is no reason to convert them to Nominal. SPSS or any other statistical software can be well analyzed with the data from the Ordinal. So do not change the data Measure. 2. You are talking about differences between women and men. So your task is to compare and not to measure relationships. So instead of using the Pearson correlation test, you should use parametric comparisons like T-test or non-parametric tests such as Mann-Whitney. In the end, you have a very simple task.
Thank you Abolfazl, that is true. I would need to use the T-test or Mann-Whitney which came up in my search. But can Pearsons or Spearman be used to make comparisons in a data set using Likert scale questions? Can I look for strong or weak relationships between lets say higher job satisfaction and age?
Yes, if you want to measure the relationship (stronger or weaker), you can use Spearman's correlation coefficient here.
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