Errors in Measurement
Psych 231: Research
Methods in Psychology
Turn in your class experiment results
 Pass the results over
 Pass the consent forms over
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Class Experiment
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Independent variables
Dependent variables
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Measurement
• Scales of measurement
• Errors in measurement
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Extraneous variables
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Control variables
Random variables
Confound variables
Variables
Example: Measuring intelligence?
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Reliability & Validity
How do we measure the
construct?
How good is our
measure?
How does it compare to
other measures of the
construct?
Is it a self-consistent
measure?
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Reliability
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If you measure the same thing twice (or have two
measures of the same thing) do you get the same
values?
Validity
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Does your measure really measure what it is
supposed to measure (the construct)?
• Is there bias in our measurement?
Errors in measurement
Reliability = consistency
Validity = measuring what is intended
Bull’s eye = the “true score”
unreliable
invalid
reliable
invalid
Dartboard analogy
reliable
valid
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True score + measurement error
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A reliable measure will have a small amount of
error
Multiple “kinds” of reliability
Reliability
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Test-restest reliability
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Test the same participants more than once
• Measurement from the same person at two
different times
• Should be consistent across different
administrations
Reliable
Reliability
Unreliable
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Internal consistency reliability
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Multiple items testing the same construct
Extent to which scores on the items of a measure
correlate with each other
• Cronbach’s alpha (α)
• Split-half reliability
• Correlation of score on one half of the measure with
the other half (randomly determined)
Reliability
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Inter-rater reliability
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At least 2 raters observe behavior
Extent to which raters agree in their observations
• Are the raters consistent?
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Requires some training in judgment
Reliability
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Does your measure really measure what it is
supposed to measure?
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There are many “kinds” of validity
Validity
VALIDITY
CONSTRUCT
INTERNAL
CRITERIONORIENTED
FACE
PREDICTIVE
CONVERGENT
CONCURRENT
DISCRIMINANT
Many kinds of Validity
EXTERNAL
VALIDITY
CONSTRUCT
INTERNAL
CRITERIONORIENTED
FACE
PREDICTIVE
CONVERGENT
CONCURRENT
DISCRIMINANT
Many kinds of Validity
EXTERNAL
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At the surface level, does it look as if the
measure is testing the construct?
“This guy seems smart to me,
and
he got a high score on my IQ measure.”
Face Validity
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Usually requires multiple studies, a large body
of evidence that supports the claim that the
measure really tests the construct
Construct Validity
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The precision of the results
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Did the change in the
DV result from the
changes in the IV or
does it come from
something else?
Internal Validity
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History – an event happens the experiment
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Maturation – participants get older (and other
changes)
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Selection – nonrandom selection may lead to biases
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Mortality – participants drop out or can’t continue
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Testing – being in the study actually influences how
the participants respond
Threats to internal validity
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Are experiments “real life” behavioral situations,
or does the process of control put too much
limitation on the “way things really work?”
External Validity
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Variable representativeness
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Subject representativeness
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Relevant variables for the behavior studied along
which the sample may vary
Characteristics of sample and target population
along these relevant variables
Setting representativeness
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Ecological validity - are the properties of the
research setting similar to those outside the lab
External Validity
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Control variables
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Holding things constant - Controls for excessive random
variability
Random variables – may freely vary, to spread variability
equally across all experimental conditions
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Randomization
• A procedure that assures that each level of an extraneous variable has an
equal chance of occurring in all conditions of observation.
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Confound variables
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Variables that haven’t been accounted for (manipulated,
measured, randomized, controlled) that can impact changes in
the dependent variable(s)
Co-varys with both the dependent AND an independent
variable
Extraneous Variables
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Pilot studies
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A trial run through
Don’t plan to publish these results, just try out the
methods
Manipulation checks
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An attempt to directly measure whether the IV
variable really affects the DV.
Look for correlations with other measures of the
desired effects.
“Debugging your study”
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Why do we do we use sampling methods?
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Typically don’t have the resources to test everybody,
so we test a subset
Sampling
Population
Sample
Sampling
Everybody that the
research is
targeted to be
about
The subset of the
population that
actually
participates in the
research
Population
Sampling to
make data
collection
manageable
Inferential
statistics used
to generalize
back
Sample
Sampling
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Why do we do we use sampling methods?
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Goals of “good” sampling:
– Maximize Representativeness:
– To what extent do the characteristics of
those in the sample reflect those in the
population
– Reduce Bias:
– A systematic difference between those in
the sample and those in the population
Sampling
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Probability sampling
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Simple random sampling
Systematic sampling
Stratified sampling
Have some element of
random selection
Non-probability sampling
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Convenience sampling
Quota sampling
Sampling Methods
Susceptible to biased
selection
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Every individual has a equal and independent
chance of being selected from the population
Simple random sampling
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Selecting every nth person
Systematic sampling
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Step 1: Identify groups (strata)
Step 2: randomly select from each group
Stratified sampling
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Use the participants who are easy to get
Convenience sampling
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Step 1: identify the specific subgroups
Step 2: take from each group until desired number of
individuals
Quota sampling