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