Can Likert Scale Data ever be Continuous?

by Karen Grace-Martin


A very common question is whether it is legitimate to use Likert scale data in parametric statistical procedures that require interval data, such as Linear Regression, ANOVA, and Factor Analysis. A typical Likert scale item has 5 to 11 points that indicate the degree of agreement with a statement, such as 1=Strongly Agree to 5=Strongly Disagree. It can be a 1 to 5 scale, 0 to 10, etc.

The issue is that despite being made up of numbers, a Likert scale item is in fact a set of ordered categories.

One camp maintains that as ordered categories, the intervals between the scale values are not equal. Any mean, correlation, or other numerical operation applied to them is invalid. Only nonparametic statistics should be used on Likert scale data (i.e. Jamieson, 2004).

The other group maintains that while technically the Likert scale item is ordered, using it in parametric tests IS valid in some situations. For example, Lubke & Muthen (2004) found that it is possible to find true parameter values in factor analysis with Likert scale data, if assumptions about skewness, number of categories, etc., were met. Likewise, Glass et al. (1972) found that F tests in ANOVA could return accurate p-values on Likert items under certain conditions.

Meanwhile, the debate rages on.

What is a researcher with integrity supposed to do? In the absence of a definitive answer, these are my recommendations:

  1. Understand the difference between a Likert type item and a Likert Scale. A true Likert scale, as Likert defined it, is made up of many items, which all measure the same attitude. But many people use the term Likert Scale to refer to a single item. Confusion about what a Likert Scale is, no doubt, has contributed to the debate.
  2. Proceed with caution. Research the consequences of using your procedure on Likert scale data from your study design. The fact that everyone uses it is not sufficient justification. There are some circumstances and procedures for which it is more egregious than others.
  3. At the very least, insist that the item have at least 5 points (7 is better), that the underlying concept be continuous, and that there be some indication that the intervals between points are approximately equal. Make sure the other assumptions (normality & equal variance of residuals, etc.) be met.
  4. When you can, run the nonparametric equivalent to your test. If you get the same results, you can be confident about your conclusions.
  5. If you do choose to use Likert data in a parametric procedure, make sure you have strong results before making claims. Use a more stringent alpha level, like .01 or even .005, instead of .05. If you have p-values of .001 or .45, it’s pretty clear what the result is, even if parameter estimates are slightly biased. It’s when p-values are close to .05 that the effect of bending assumptions is unclear.
  6. Consider the consequences of reporting inaccurate results. Will anyone ever read your paper? Will your research be published? Will it be used to shape public policy or affect practices? The answers to these questions can inform the seriousness of potential problems.

Carifio, J. & Perla, R. (2007). Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes. Journal of Social Sciences, 2, 106-116.

Glass, Peckham, and Sanders (1972). Consequences of failure to meet assumptions underlying the analyses of variance and covariance, Review of Educational Research, 42, 237-288.

Jamieson, S. (2004). Likert scales: how to (ab)use them. Medical Education, 38, 1212-1218.

Lubke, Gitta H.; Muthen, Bengt O. (2004). Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons. Structural Equation Modeling, 11, 514-534.

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{ 27 comments… read them below or add one }

Bruce Weaver September 17, 2015 at 1:02 pm

Here is another article you could cite in support of using parametric tests with Likert scales, and even items.

Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in health sciences education, 15(5), 625-632.


Johnny July 23, 2015 at 11:06 am

Hi – great article, thank you! I’ve got a 20-item scale, each question in the form of a Likert (ranging from 0-3) – that means there are 80 possible scores. I’d like to use this variable as a dependent var in some sort of regression with several predictor variables. Technically this score is “ordinal” since it came from a sum of Likert scales, but an ordinal regression with 80 possible “categories” is a bit much. The score is also distributed very much like a binomial distribution and not at all normal (could I use negative binomial regression perhaps?). Any advice on how to regress this would be appreciated!


Max Thomsen July 13, 2015 at 8:39 am

Thanks a lot, Karen! :)


Max Thomsen July 10, 2015 at 6:27 am

Dear Karen,

thanks for your really helpful article. In point 3 you say that in order to (more or less) safely consider an ordinal predictor continuous it should have at least 5 to 7 points. Can you name a referance for that statement?

Thanks and best regards, Max


Karen July 10, 2015 at 2:06 pm

Hi Max,
The Lubke & Muthen article listed above discusses it. It’s been a while since I read it, but I believe they did it in the context of factor analysis, not predictor variables in regression.


Haitham Elsaid December 24, 2014 at 9:26 am

My dependent variable is measured on a 5 point likert scale and independent variable is measured on a 5 point likert scale. Is it appropriate to run linear regression analysis on such data? What if the same for multiregression?


Timi Joy December 13, 2014 at 1:02 pm

Can anyone suggest a academic study which supports the fact that different points in Likert scale(5, 7, 8, 11) can use in the same study.


jordyn December 11, 2014 at 2:20 pm


I ran PCA on likert scale variables (answers ranged 0-6). I found skewness and kurtosis ok on all variables. When I write up my paper, do I need to justify using likert scale items in PCA or is it just so common no one justifies it anymore? Would I use Lubke article above to cite to show how I could use likert data as cotinuous?



Madushani Gunathilake August 22, 2014 at 10:58 pm

Can i use regression analysis for my 5 point likert scale data. And can i link 5 point likert scale data with contonuous varuables??


aslam May 29, 2014 at 10:41 am

i have R value 1 how can reduce this value R VALUE plz help


Kerry Dungay October 18, 2013 at 6:55 am

Hi, loved your article, really helpful. I have a question. I have some confusion over what is exactly summed. If you have a questionnaire with say 7 likert items (questions), is the summed amount for the population the interval data? For example if it was a job satisfaction survey and I wanted to compare satisfaction between males and females, could I sum the overall score for females then males and compare means as interval data? I think I am confused at what point the data is summed to become interval data, is at individual level or population? The other thing, as likert is closed question survey, the research method is quantative, but at a statistical level it is qualitative, is this right? Thanks, this is for a research assignment due in soon, I have to explain my data analysis method, so desperately need advise!


Karen October 21, 2013 at 9:30 am

Hi Kerry,

Well, first of all, you don’t have population data, just samples. But you would sum scores for individuals. Now, there is more to it as to whether it’s truly interval. To be sure I would suggest reading more in the psychometric literature. As for the quantitative/qualitative, if you need that for an assignment, I think you need to figure that out on our own. :)


Faisal October 4, 2013 at 8:50 am

i have a question. please reply. i am doing statistical analysis. my independent variables are 5 point likert scale. and dependent variable is binary. should i use binary logistic regression? what options should i select?


Karen October 7, 2013 at 11:24 am

Hi Faisal,

I would need a lot more information to actually suggest an analysis. If your outcome is binary, then indeed logistic regression is one possibility. But it depends on a lot of other questions, including “what is it you want to test?”


JUSTICE MOSES K. AHETO September 16, 2013 at 3:13 pm

Some papers have it that one can combine likert type questions into likert scale by summing the responses under each construct to form scores which reduced the data from ordinal scale to interval scale in which parametric test can be conducted like ANOVA, Regression etc.
What about that?


jsny May 14, 2013 at 9:40 pm

how can i use data collected using likert scale for doing corelation


Karen May 17, 2013 at 11:17 am

Depends what you mean by likert scale. If you mean something like a 1-5 scale item, your best bet would be a spearman rank correlation. No assumptions of normality there.


rohail April 29, 2013 at 7:02 am

how can we convert the data into an independent variable so that i can use factor analysis, as i am new to this software can somebody help me in this?? have collected data on customer satisfaction on a 5-pint likert scaling..please help


Karen April 29, 2013 at 6:34 pm

Rohail, I’m not entirely sure what you’re asking, but generally people do use likert data for factor analysis.


Harleen October 8, 2011 at 5:34 am


I have done factor analysis on the data collected (likert items). Now i am lost as to how should i proceed further. SPSS has given me 9 factors out of 50 ordinal variables.
Can i apply regression or Anova on such data?
Also can i subdivide a factor into two or more factors by doing factor analysis again on those items which constitute a factor (originally computed) e.g. Brand image can be subdivided as quality, product attributes, so and so forth. Can i do so?


Karen October 10, 2011 at 12:47 pm

Hi Harleen,

There’s a lot to using the results from factor analysis in other analyses. More than I could ever answer here (it’s a book, really).

I would strongly suggest getting this book, even if you don’t use SAS. A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling by Larry Hatcher. He really explains everything step-by-step.

I recently suggested it to a client who needed to use Factor Analysis, and she said it cleared up all her confusion.



zik oseni September 15, 2010 at 1:42 pm

Can someone tell me why Firm’s age is used as a proxy for information asymmetry. You can post your response here or email to me @



Alireza January 3, 2010 at 12:59 pm

My question is: if our data were parametric, can we use Likert Scale data in Factor Analysis directly? Otherwise, to identifying important variables in my study with my Likert Scale data, what should I do?
Thank you


Karen January 5, 2010 at 3:35 pm


So are you asking if you can use Factor Analysis for Likert Scale data?

Theoretically, Likert items do not meet the assumptions for a Factor Analysis. That Lubke and Muthen paper referenced above, however, found that in some situations, the results are quite valid. I would suggest reading that paper and seeing if your data fit the situations where it works well.



ZIK OSENI October 1, 2009 at 6:51 am

I am a student. Can someone help me to locate a statistical software (free) to run data I gathered using Likert Scale. I am working on asymmetric information in the capital market. I can be reached via Thank you


Karen October 8, 2009 at 11:53 pm

Hi Zik,

Just to up, if you need free, you have two choices:

PSPP is an opensource version of SPSS base. Easy to use, but limited.

R requires more programming, but can do much, much more.


Andy October 17, 2008 at 5:46 am

That Carifio and Perla paper looks handy – ta for sharing!


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