Measurement Concepts - faculty.buffalostate.edu

PSY 450W 4/12/2011
Dr. Schuetze 1

 No one is ready
 4 Levels

 Nominal
 Ordinal
 Ratio
 Interval

 Properties: Identity
 Classification data
 No ordering (makes no sense to state that M>F)
 Number assigned to each category is arbitrary (m/f

= 0/1)

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 Properties: Identity and Magnitude
 Ordered but differences between values are

not important.

 e.g., restaurant ratings

 Properties: Identity, magnitude,
equal distance

 Ordered, constant scale
 No natural zero
 Difference makes sense but

ratios do not (e.g., 30°-20°=20°-
10°, but 20°/10° is not twice as
hot!

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 Properties: Properties: Identity, magnitude,
equal distance, absolute/true zero

 E.g., height, weight, age, length

 Salary earned last year
 Quality of food: Good, Average, Poor
 Number of children
 Political Affiliation: Democrat, Republican,

Independent
 Temperature in degrees Fahrenheit
 Marital status: Married, Single
 Reaction time
 Order people finished race
 Number correct on exam
 Score on intelligence test

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 Most statistical analyses have scale 4/12/2011
requirements 4
 Can no do means on ordinal or nominal data.
 Most analyses require at least interval scale.

 Will need to tell SPSS what scale of
measurement each variable has

 Some statistical packages call both ratio and
interval scales – continuous

 Only certain operations can be performed on
certain scales of measurement

 Can only examine if data are equal to some
particular value or count the number of
occurrences of each value
 E.g., gender – can examine if gender of a person
is m or f; can count the number of males in a
sample.

 Can do everything we discussed with nominal
data, plus…

 Can exam if data point is less than or greater
than another value
 Can rank ordinal data but cannot quantify
differences between 2 ordinal values
 E.g., ratings of restaurants where 10=good,
1=poor. The difference between a 10 ranking
and an 8 ranking can’t be quantified.

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 Can quantify difference between 2 interval
scales.

 E.g., temperature. 75 degrees versus 70
degrees. 5 degree difference has some
meaning

 Does not make sense to say that 80 degrees
is twice as hot as 40 degrees.

 Can take a ratio between 2 values.
 It is now meaningful to say that 24 pounds is
twice as heavy as 12 pounds.

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 Degree to which a measurement is
consistent and reproducible.

 Test-retest Reliability: compare scores of
people who have been measured twice
with same instrument.

 Reliability established when the two
scores are very similar

 Reliability coefficient – a correlation
coefficient that ranges from 0.00 to 1.00

 Highly similar scores are close to 1.00

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 Inter-item Reliability: extent to which 4/12/2011
different parts of a questionnaire or test 7
assess the same variable.

 Sometimes you do have multiple measures,
as in a 20-item personality measure

 Do the items correlate highly with one
another?

 Interrater Reliability: level of agreement
between measurements of different
raters.

 A test of “truth” or “accuracy”
 Extent to which a procedure measures what

it’s intended to measure.

 Agreement between a theoretical concept 4/12/2011
and a specific measuring device or 8
procedure.

 Face validity
 Convergent validity
 Discriminant validity
 Criterion validity

 The degree to which a measurement
device appears to accurately measure a
variable

 Do scores on the the measure relate to 4/12/2011
other measures in expected ways? 9

 Convergent validity: actual general
agreement among ratings, gathered
independently of one another, where
measures should be theoretically related.

 Example: do people with high self-
efficacy predict that they will perform
better on a task? If so, this would be
evidence for the construct validity of the
measure.

 The measure of the variable is NOT
related to other variables that it
theoretically should not be related to.

 E.g., scores on the self-efficacy measure
are not related to reaction time

 The degree to which a measurement
device accurately predicts behavior on a
criterion measure

 A paper-and-pencil measure of leadership
ability predicts actual leadership behavior
in a group

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 Content Validity: degree to which our
measurements reflect the variable of
interest.

 Face Validity: degree to which a
manipulation or measurement technique is
self-evident.

 Predictive Validity: degree to which a
measuring instrument yields information
allowing us to predict later behavior or
performance.

 Concurrent Validity: degree to which scores
on a measurement instrument correlate with
another known standard for measuring the
variable being studied.

Reliability Versus Validity

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