Take the 2-minute tour ×
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It's 100% free, no registration required.

I have carried out my pilot study on language learning motivation using a 6 point Likert scale but from 0 (strongly disagree) to 5 (very much agree). I noticed a colleague in his survey used 1 to 6. Will my computed variables (sum and mean) be the same as if I had used 1 to 6? Is it normally recommended to not use a 0 for some reason? I am new to SPSS but have managed to do most of what I need to do but I'm now worrying my values are 'distorted'. I don't understand how SPSS adds a 0 into an equation.

Thanks

share|improve this question
2  
I suspect--but without being able to offer evidence--that there may be a psychological difference between the 1..6 and 0..5 scales, primary due to differences in how 0 is experienced and understood by people compared to their perceptions of positive integers, and perhaps because people may be thinking in terms relative to the maximum, so that in one case the change from 5 to 6 is 1/6 of the maximum whereas in the other case it would be (erroneously) considered equal to 1/5 of the maximum. Perhaps psychologists in our community could offer some thoughts about this? –  whuber Jul 16 '12 at 15:29
1  
@whuber Indeed, this is a long standing topic in questionnaire design - especially with regards to uni- vs. bipolar scales. For an overview, see Schwarz. 1996. Cognition and communication: Judgmental biases, research methods, and the logic of conversation. Mahwah, NJ: Lawrence Erlbaum. Some more references. –  caracal Jul 16 '12 at 16:45
    
I think this previous post on Likert scale categories might help. –  David Jul 16 '12 at 19:14
5  
5-point Likert-type item are often used, where response categories are "Strongly disag.", "Disag.", "Neither ag. nor disag.", "Ag.", and "Strongly ag.", without explicit numerical score. What's your 6th category? Response coding is immaterial, for regression, data checking, or summary statistics. Any scaling, linking or equating method can take that into account. What's really matter is the underlying measurement model you are ready to assume, because it will determine whether scores are to be treated as sample-dependent discrete scores or a 'reflection' of a latent trait on a discrete scale. –  chl Jul 16 '12 at 21:31
add comment

3 Answers

Let me make a couple of points. First, if you just have 1 question, you don't technically have a likert scale, but just an ordinal rating. At any rate, I can't see as how there will be any meaningful difference. This is just a linear shift. This will neither make a difference whether you use an ordinal analysis like ordinal logistic regression or a Mann-Whitney U test, or a more standard option like OLS regression or a t-test.

share|improve this answer
add comment

I must partially disagree with @MichaelChernick. While answers to a single Likert question (whether 0 to 5 or 1 to 6 or whatever) are clearly ordinal, usually there is a sum of several Likert scale items. At some point, the number of possible values becomes so high that it is essentially continuous.

As you know (but the poster of the question may not) OLS regression does not assume that the dependent variable is normally distributed, only that the errors (as estimated by the residuals) are.

If we sum a bunch of Likert items, do we know that the intervals are really equal? No, not really. But do we know that for, say IQ? Or even income? Is the difference between an IQ of 130 and 140 the same as 100 and 110? Does that question even make sense? What about a \$10,000 raise for someone who makes \$10,000 vs. $100,000 per year?

I wrote a whole blog post on this.

In addition, it's not clear to me whether this Likert scale is going to be a dependent or independent variable.

share|improve this answer
    
I guess it depends on whether the OP wants to compare individual questions or groups of questions (commonly called domainsin the survey literature). But how do the sums of integers bscome continuous. They are larger but they are still integers and not continuous. Also the sum does not magically change from ordinal to nominal so I don't get what you are trying to say. OLS assumes the covariates are fixed and that the residuals are normal. In effect that means the dependent variable conditioned on the covariates is normal. –  Michael Chernick Jul 16 '12 at 22:17
2  
@Michael You may want to look at some earlier discussions on whether to treat Likert scales as continuous or discrete. Variable scoring sometimes reflect the way we envision the data. I can also recommend De Boeck, P., Wilson, M., and Acton, G.S. (2005). A Conceptual and Psychometric Framework for Distinguishing Categories and Dimensions. Psychological Review, 112(1): 129-158. –  chl Jul 16 '12 at 22:41
    
@Chi Thanks for your suggestions. –  Michael Chernick Jul 16 '12 at 22:45
add comment

To do analysis with ordinal scales like the Likert you would use nonparametric methods based on ranks. What matters with ordinal scales is the order if 5 is best, 0 is worst, 1 is better than 0, 2 is better than 1 etc. Both ratios and intervals are meaningless for ordinal data. So a scale of 1-6 versus 0-5 doesn't matter and won't affect the analysis. Starting with 1 is due to tradition rather than necessity.

share|improve this answer
1  
"Both ratios and intervals are meaningless for ordinal data" depends on the underlying measurement model you are willing to assume. –  chl Jul 16 '12 at 22:00
    
@Chl What is your point? –  Michael Chernick Jul 16 '12 at 22:10
2  
My point is that certain psychometric models explicitly consider discrete response as a proxy reflecting one's location (or liability) on a latent trait, as detailed in an answer of mine. I'm ok with the rest of your answer, which is basically founded on the idea of treating sum (not in this case, but you'll get the idea) scores as mean to rank individuals on a given construct (under the CTT framework). –  chl Jul 16 '12 at 22:23
2  
But variables are usually not strictly ordinal or interval. The blind application of Stevens' taxonomy is just as bad as the blind application of any other rule. For strictly ordinal, you could recode it a 0, 2, 2.1, 2.2, 2.3, 19288191. But would you? And, other than physical measurements, what is really interval? –  Peter Flom Jul 17 '12 at 21:47
1  
@Peter This reminds me of this older thread: Does it ever make sense to treat categorical data as continuous? –  chl Jul 18 '12 at 16:44
show 3 more comments

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

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