agreement to most, which is pretty important information. The other options are to treat it
as a ranked variable or a measurement variable.
Treating a Likert item as a measurement variable lets you summarize the data using a
mean and standard deviation, and analyze the data using the familiar parametric tests
such as anova and regression. One argument against treating a Likert item as a
measurement variable is that the data have a small number of values that are unlikely to
be normally distributed, but the statistical tests used on measurement variables are not
very sensitive to deviations from normality, and simulations have shown that tests for
measurement variables work well even with small numbers of values (Fagerland et al.
2011).
A bigger issue is that the answers on a Likert item are just crude subdivisions of some
underlying measure of feeling, and the difference between “Strongly Disagree” and
“Disagree” may not be the same size as the difference between “Disagree” and “Neither
Agree nor Disagree”; in other words, the responses are not a true “interval” variable. As
an analogy, imagine you asked a bunch of college students how much TV they watch in a
typical week, and you give them the choices of 0=None, 1=A Little, 2=A Moderate
Amount, 3=A Lot, and 4=Too Much. If the people who said “A Little” watch one or two
hours a week, the people who said “A Moderate Amount” watch three to nine hours a
week, and the people who said “A Lot” watch 10 to 20 hours a week, then the difference
between “None” and “A Little” is a lot smaller than the difference between “A Moderate
Amount” and “A Lot.” That would make your 0-4 point scale not be an interval variable.
If your data actually were in hours, then the difference between 0 hours and 1 hour is the
same size as the difference between 19 hours and 20 hours; “hours” would be an interval
variable.
Personally, I don’t see how treating values of a Likert item as a measurement variable
will cause any statistical problems. It is, in essence, a data transformation: applying a
mathematical function to one variable to come up with a new variable. In chemistry, pH is
the base-10 log of the reciprocal of the hydrogen activity, so the difference in hydrogen
activity between a ph 5 and ph 6 solution is much bigger than the difference between ph 8
and ph 9. But I don’t think anyone would object to treating pH as a measurement variable.
Converting 25-44 on some underlying “agreeicity index” to “2” and converting 45-54 to
“3” doesn’t seem much different from converting hydrogen activity to pH, or
micropascals of sound to decibels, or squaring a person’s height to calculate body mass
index.
The impression I get, from briefly glancing at the literature, is that many of the people
who use Likert items in their research treat them as measurement variables, while most
statisticians think this is outrageously incorrect. I think treating them as measurement
variables has several advantages, but you should carefully consider the practice in your
particular field; it’s always better if you’re speaking the same statistical language as your
peers. Because there is disagreement, you should include the number of people giving
each response in your publications; this will provide all the information that other
researchers need to analyze your data using the technique they prefer.
All of the above applies to statistics done on a single Likert item. The usual practice is
to add together a bunch of Likert items into a Likert scale; a political scientist might add
the scores on Likert questions about abortion, gun control, taxes, the environment, etc. and
come up with a 100-point liberal vs. conservative scale. Once a number of Likert items are
added together to make a Likert scale, there seems to be less objection to treating the sum
as a measurement variable; even some statisticians are okay with that.
Independent and dependent variables
Another way to classify variables is as independent or dependent variables. An
independent variable (also known as a predictor, explanatory, or exposure variable) is a