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Doing Psychology
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Doing Psychology (Lectures 1-3; 16-19)Psychological Measurement
18/Sept (Wednesday) and 20/Sept (Friday)
Measurement is a subtle concept, but basically it comes down to finding some way of
assigning numbers, or labels, or some other kind of well-defined descriptions to “stuff”.
• My age is 33 years.
• I do not like anchovies.
• My chromosomal gender is male.
• My self-identified gender is male.
The bolded part is “the thing to be measured”, and the italicised part is “the
measurement itself. The way in which you specify the allowable measurement values is
important (age could be measured in years, months, days, since birth or conception…)
Methodology: What specific “measurement method” are you going to use to find out
someone’s age? As before, there are lots of different possibilities: self-report, asking
authorities, looking up certificates…
Operationalisation: is the process by which we take a meaningful but somewhat vague
concept, and turn it into a precise measurement. The process of operationalisation can
involve several different things:
• Being precise about what you are trying to measure (age: since birth or conception?), •
Determining what method you will use to measure it (self-report, certificates, how to
phrase the question…)
• Defining the set of the allowable values that the measurement can take. Note that
these values don’t always have to be numerical, though they often are. (years/months?,
male/female or more options?)
The way in which you choose to operationalise the informal concept of “age” or “gender”
into a formal measurement depends on what you need to use the measurement for.
Operationalisation needs to be thought through on a case by case basis. Nevertheless,
while there are a lot of issues that are specific to each individual research project, there
are some aspects to it that are pretty general.
Some terminology:
•A theoretical construct. This is the thing that you’re trying to take a measurement of,
like “age”, “gender” or an “opinion”. A theoretical construct can’t be directly observed,
and often they’re actually a bit vague.
• A measure. The measure refers to the method or the tool that you use to make your
observations. A question in a survey, a behavioural observation or a brain scan could all
count as a measure.
• An operationalisation. The term “operationalisation” refers to the logical connection
between the measure and the theoretical construct, or to the process by which we try to
derive a measure from a theoretical construct.
, Doing Psychology (Lectures 1-3; 16-19)
• A variable: is what we end up with when we apply our measure to something in the
world. That is, variables are the actual “data” that we end up with in our data sets.
Scales/levels of measurement:
- A nominal scale variable (also referred to as a categorical variable) is one in
which there is no particular relationship between the different possibilities: for
these kinds of variables it doesn’t make any sense to say that one of them is
“bigger’ or “better” than any other one, and it absolutely doesn’t make any
sense to average them. The classic example for this is “eye colour”. nominal
scale variables are those for which the only thing you can say about the
different possibilities is that they are different. (transportation by car, train,
bus… there’s no average one, but more/less popular)
- An ordinal scale variable is one in which there is a natural, meaningful way to
order the different possibilities, but you can’t do anything else. The usual
example given of an ordinal variable is “finishing position in a race”. You can
say that the person who finished first was faster than the person who finished
second, but you don’t know how much faster. We can use the natural ordering
of these items to construct sensible groupings (groups 1, 2, 3 partially endorse
the science, groups 2, 3, 4 registered at least some disagreement with the
dominant scientific view), what we can’t do is average them.
- Interval scale variables: the variable doesn’t have a “natural” zero value. e.g.
measuring temperature in degrees celsius. If it was 15˝ yesterday and 18˝
today, then the 3˝ difference between the two is genuinely meaningful.
Moreover, that 3˝ difference is exactly the same as the 3˝ difference between 7˝
and 10˝ . In short, addition and subtraction are meaningful for interval scale
variables. However, notice that the 0˝ does not mean “no temperature at all”: it
actually means “the temperature at which water freezes”, which is pretty
arbitrary. As a consequence, it becomes pointless to try to multiply and divide
temperatures. It is wrong to say that 20˝ is twice as hot as 10˝ , just as it is
weird and meaningless to try to claim that 20˝ is negative two times as hot as
´10˝ .
- Ratio scale variable, in which zero really means zero, and it’s okay to multiply
and divide. A good psychological example of a ratio scale variable is response
time (RT). Suppose that Alan takes 2.3 seconds to respond to a question,
whereas Ben takes 3.1 seconds. As with an interval scale variable, addition and
subtraction are both meaningful here. Ben really did take 3.1 ´ 2.3 “ 0.8 seconds
longer than Alan did. However, notice that multiplication and division also make
sense here too: Ben took 3.1{2.3 “ 1.35 times as long as Alan did to answer the
question. And the reason why you can do this is that, for a ratio scale variable
such as RT, “zero seconds” really does mean “no time at all”.
2 types of variables:
• A continuous variable is one in which, for any two values that you can think of, it’s
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