variables cont. psych 231: research methods in psychology
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Variables cont.
Psych 231: Research Methods in Psychology
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Announcements
Download the class experiment results from the web page and bring to labs this week
Class experiment due dates: First draft: in labs Oct 23 & 24 Final draft: in class Nov. 19th (no labs that
week)
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Scales of measurement
Categorical variables Nominal scale
• Consists of a set of categories that have different names.
Ordinal scale• Consists of a set of categories that are organized in an
ordered sequence.
Quantitative variables Small, Med, Lrg,
blue, green,brown,
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Scales of measurement
Categorical variables Nominal scale
• Consists of a set of categories that have different names.
Ordinal scale • Consists of a set of categories that are organized in an
ordered sequence.
Quantitative variables Interval scale
Ratio scale
Small, Med, Lrg,
blue, green,brown,
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Scales of measurement
Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. Example: Fahrenheit temperature scale
20º40º “Not Twice as hot”
With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude.
However, Ratios of magnitudes are not meaningful.
20º 40º The amount of temperature increase is the same60º 80º
20º increase
20º increase
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Scales of measurement
Categorical variables Nominal scale Ordinal scale
Quantitative variables Interval scale Ratio scale
Categories
Categories with order
Ordered Categories of same size
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Scales of measurement
Ratios of numbers DO reflect ratios of magnitude.
It is easy to get ratio and interval scales confused
• Example: Measuring your height with playing cards
Ratio scale: An interval scale with the additional feature of an absolute zero point.
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Scales of measurement
Ratio scale
8 cards high
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Scales of measurement
Interval scale5 cards high
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Scales of measurement
Interval scaleRatio scale8 cards high 5 cards high
0 cards high means ‘no height’
0 cards high means ‘as tall as the table’
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Scales of measurement
Categorical variables Nominal scale Ordinal scale
Quantitative variables Interval scale Ratio scale
Categories
Categories with order
Ordered Categories of same size
Ordered Categories of same size with zero point
• Given a choice, usually prefer highest level of measurement possible
“Best” Scale?
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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?
Measuring the true score
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
In search of the “true score”
Reliability • Do you get the same value with multiple measurements?
Validity • Does your measure really measure the construct?
• Is there bias in our measurement? (systematic error)
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Dartboard analogy
Bull’s eye = the “true score”
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Dartboard analogy
Bull’s eye = the “true score” Validity = measuring what is intended
Reliability = consistency of measurement
reliablevalid
reliable invalid
unreliable
invalid
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Reliability
True score + measurement error A reliable measure will have a small amount of
error Many “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 judgment5:00
4:56
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VALIDITY
CONSTRUCT
CRITERION-ORIENTED
DISCRIMINANT
CONVERGENTPREDICTIVE
CONCURRENT
FACE
INTERNAL EXTERNAL
Validity
Does your measure really measure what it is supposed to measure?
: many varieties
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VALIDITY
CONSTRUCT
CRITERION-ORIENTED
DISCRIMINANT
CONVERGENTPREDICTIVE
CONCURRENT
FACE
INTERNAL EXTERNAL
Validity: many varieties
Does your measure really measure what it is supposed to measure?
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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.