Updated: 4 Scales Every Researcher Should Remember!
One of our most-viewed articles is an article about four scales every researcher should remember.
Since that post was written, some changes have taken place that have
affected the four scales, so we figured it was time for an update! Here,
we’ll dive into a bit more about considerations behind which scale or
question type you should be using, as well as an update on the most
powerful questions that can drive insight into what your respondents are
thinking.
The First Rule of Research
The first rule for any research project – whether it’s a one-time
project or a customer feedback survey distributed regularly – is this:
focus your project to the one primary purpose or question you want to
have answered. This will automatically help you narrow down what type of
scale or question to use for your survey. For example, let’s say your
focus is a feedback survey. The primary question might be: are our
customers having a good experience in our stores? From there, choose the
primary 3-5 items that you want to measure as related to the question.
In this example, they might be: products easy to find; helpful staff;
clean store. Now, you’re ready to check out the scales and questions
that could provide you the best insight into these questions.
Four Types of Data to Measure
There are four types of data. Any data falls under one of these
categories. They are all used for different purposes and are analyzed
differently.
Nominal: names, labels. Examples include days of the
week, colors, and geographic area names. These are difficult to analyze
using most statistical methods. Basically, you are limited to count, or
frequency of distribution.
Ordinal: order of items, but no measurable
difference in numbers between the items in the list. Examples include
satisfaction ratings and importance ratings. Analysis includes median
(midpoint of distribution, rather than the average distribution) and
count.
Interval: numeric values for which a difference in
value is measurable, but no true zero exists (a point at which the
quality being measure does not exist). Examples include time and
temperature. Analysis includes most statistical measures, but not any
that would include multiplying or dividing values.
Ratio: numeric values for which a difference in value is measurable AND a true zero exists. Examples include age and height.
Now that we understand the difference between the types of scales, let’s look at some of the most used scales!
Types of Measurement
There are two types of measurement: comparative and non-comparative. They are pretty straight-forward.
Comparative measurement: As the name implies,
comparative measurements typically involves two brands, or two things,
being compared against each other. One example would be comparing one
brand of smartphone against another brand of smartphone to determine
what people like better about one or the other.
Non-comparative measurement: Again, the name says it
all. The scales we’re going to review today all fall in the
non-comparative realm of measurement, where only one item or one brand
is being measured.
The Four Question Types/Scales You Should Know
1. Likert Items
A Likert scale is technically the sum of a list of Likert items. What
makes a question a Likert item? There are equal numbers of positive and
negative options in the scale; Likert items always have a
central neutral option, which means there will always be an odd set of
answers available to the respondent – typically, a Likert item has a
total of seven answer options. Likert items measure ordinal data.
2. Unipolar scales
Where Likert items are bipolar, meaning the respondent has to decide
between opposing items, unipolar scales are more streamlined, allowing
users to instead focus on the absence or presence of a single item. The
scale is still measuring ordinal data, but research has shown that
unipolar scales generate more accurate answers. An example of a unipolar
satisfaction scale is: not at all satisfied, slightly satisfied,
moderately satisfied, very satisfied, and completely satisfied.
3. Slider Scales
Slider scales (also known as continuous rating scales) allow the
survey designer to measure ordinal data (using a categorical slider), or
interval or ratio data (using a numeric slider). For example, a slider
scale could be used for a unipolar satisfaction question, allowing a
respondent to perhaps more precisely define their feeling about an item.
A slider scale could also be used to let respondents answer questions
about how much time they expected to wait for an order versus how much
time they actually waited for the order. Based on what type of data you
are gathering using the slider scale is the type of analysis you can
conduct with the data. If you’re gathering interval data, you can
calculate averages, standard deviations, etc. If you’re gathering
ordinal data, however, be sure to stick to frequencies and medians to
analyze your data.
4. Side-by-Side Matrix
The side-by-side matrix remains another highly-used item for
measuring ordinal data. The side-by-side matrix allows a respondent to
respond to two qualities about an item. The most common use is to
determine a respondent’s opinion about the importance of an attribute
and the satisfaction of their experience with that attribute. This helps
a company determine if perhaps there are items they are focusing on
that respondents don’t find terribly important, versus items that the
respondent finds important, but unsatisfactory.
The most common analysis tool used with the side-by-side matrix is a
Gap analysis, which is actually pretty self-explanatory – it shows where
there are gaps between the two attributes measured, using the first
attribute listed (in this case, importance) as the primary attribute to
measure against. In this example, you can see that the red arrow
indicates the greatest gap exists for the topic On-time Arrival. For the
best impact, this would be where this organization should focus, as
this item is important to respondents but has the lowest satisfaction
rating.
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