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Title: Likert Scales: How To (Ab)Use Them?
Descriptive title : Commentary on the appropriate use of data derived from
Likert-type rating scales
Word count 749 words (excluding references & pull-out quotes)
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Dipping my toe in the water of educational research, I have recently used Likert-type
rating scales to measure student views on various educational interventions. Likert
scales are commonly used to measure attitude, providing “a range of responses to a
given question or statement”1. Typically, there are five categories of response, from
(for example) 1=strongly disagree to 5 =strongly agree, although there are arguments
in favour of scales with seven, or with an even number of response categories1.
Likert scales fall within the ordinal level of measurement2,3,4. That is, the response
categories have a rank order, but the intervals between values cannot be presumed
equal, although as Blaikie3 points out, “researchers frequently assume that they are”.
However, Cohen et al1 contend that it is “illegitimate” to infer that the intensity of
feeling between ‘strongly disagree’ and ‘disagree’ is equivalent to the intensity of
feeling between other consecutive categories on the Likert scale. The legitimacy of
assuming an interval scale for Likert-type categories is an important issue, because the
appropriate descriptive and inferential statistics differ for ordinal and interval
variables1,5. And if the wrong statistical technique is used, the researcher increases the
chance of coming to the wrong conclusion about the significance (or otherwise) of his
research.
Methodological and statistical texts are clear that for ordinal data one should employ
the median or mode as the ‘measure of central tendency’5 since the arithmetic
manipulations required to calculate the mean (and standard deviation) are
inappropriate for ordinal data3,5, where the numbers generally represent verbal
statements. In addition, ordinal data may be described using frequencies/percentages
of response in each category3. Standard texts also advise that the appropriate
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inferential statistics for ordinal data are those employing non-parametric tests, such as
Chi-square, Spearman’s Rho, or the Mann-Whitney U-test1, since parametric tests
require data of interval or ratio level2,5.
However, these ‘rules’ are commonly ignored by authors, including some who have
published in Medical Education. For example, in two recent papers the authors have
used Likert scales, but have described their data using means and standard deviations
and have performed parametric analyses such as ANOVA6,7. This is consistent with
Blaikie’s observation that it has become common practice to assume that Likert-type
categories constitute interval-level measurement3. Generally, it is not made clear by
authors whether they are aware that some would regard this as illegitimate; no
statement is made about an assumption of interval status for Likert data, and no
argument made in support.
All of which is very confusing for the novice in pedagogical research. What approach
should one take when specialist texts say one thing, yet actual practice differs?
Delving further, treating ordinal scales as interval scales has long been controversial
(discussed by Knapp8) and, it would seem, remains so. Thus while Kuzon Jr et al9
contend that using parametric analysis for ordinal data is the first of “The Seven
Deadly Sins of Statistical Analysis”, Knapp8 sees some merit in the argument that
sample size and distribution are more important than level of measurement in
determining whether it is appropriate to use parametric statistics. Yet even if one
accepts that it is valid to assume interval status for Likert-derived data, data sets
generated with Likert-type scales often have a skewed or polarised distribution (e.g.,
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where most students ‘agree’ or ‘strongly agree’ that a particular intervention was
useful; or where students have polarised views about a ‘wet lab’ in biochemistry,
depending on their interest in basic science).
It seems to me that if we want to “raise the quality of research” in medical
education10, such issues as levels of measurement and appropriateness of mean,
standard deviation and parametric statistics should be considered in the design stage
and must be addressed by authors when they discuss their chosen methodology.
Knapp8 gives advice that essentially boils down to this: the researcher should decide
what level of measurement is in use (to paraphrase, if it’s interval level, for a score of
3, one should be able to answer “3 what?”); non-parametric tests should be employed
if the data is clearly ordinal; and if the researcher is confident that the data can
justifiably be classed as interval, attention should nevertheless be paid to the sample
size and to whether the distribution is normal.
Finally, is it valid to assume that Likert scales are interval-level? I remain convinced
by the argument of Kuzon Jr. et al9: to paraphrase, the average of ‘fair’ and ‘good’ is
not ‘fair-and-a-half’, and this is true even when one assigns integers to represent ‘fair’
and ‘good’!
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REFERENCES
1. Cohen L, Manion L, Morrison K Research Methods in Education. 5th edn.
London: RoutledgeFalmer, 2000.
2. Pett MA. Nonparametric Statistics for Health Care Research. London: SAGE
Publications, 1997.
3. Blaikie N. Analyzing Quantitative Data London: SAGE Publications Ltd.,
London, 2003
4. Hansen JP. “CAN’T MISS – Conquer Any Number Task by Making Important
Statistics Simple. Part 1. Types of variables, mean, median, variance, and standard
deviation”. J. Healthcare Qual 2003; 25 (4): 19-24.
5. Clegg F. “Simple Statistics”, Cambridge University Press, 1998.
6. Santina M, Perez J. “Health professionals’ sex and attitudes of health science
students to health claims.” Med Educ. 2003; 37: 509-513.
7. Hren D, Lukic IK, Marusic A, Vodopivec I, Vujaklija A, Hrabak M, Marusic M.
“Teaching research methodology in medical schools: students’ attitudes towards
and knowledge about science.” Med. Educ. 2004; 38: 81-86.
8. Knapp, TR “Treating ordinal scales as interval scales: an attempt to resolve the
controversy” Nursing Res. 1990; 39: 121-123.
9. Kuzon, Jr. WM, Urbanchek MG, McCabe S. “The seven deadly sins of statistical
analysis” Annals Plastic Surg. 1996; 37: 265-272.
10. Bligh J “Ring the changes: some resolutions for the new year and beyond” Med.
Educ. 2004; 38: 2-4.
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POTENTIAL ‘PULL-OUT’ QUOTATIONS
“Likert scales fall within the ordinal level of measurement … the response categories
have a rank order, but the intervals between values cannot be presumed equal”
“… the mean (and standard deviation) are inappropriate for ordinal data”
“Treating ordinal scales as interval scales has long been controversial”
“…it has become common practice to assume that Likert-type categories constitute
interval-level measurement”
“…such issues as levels of measurement and appropriateness of mean, standard
deviation and parametric statistics should be considered in the design stage and must
be addressed by authors…”
... Both the 3D printed negative mold and the final silicone product were included. The survey variables were considered ordinal data (7)(8) and were presented using descriptive statistics, both as distribution frequencies for each question and as average and standard deviation (SD). The data were loaded and analyzed in Microsoft Excel® 16 ( Microsoft Corp, Redmond, United States ). ...
... This model helped improve my confidence in performing the procedure. 8 This model helped improve my ability to perform the procedure. Fidelity (scale from 1 to 5) 9 ...
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