manipulation and measurement of variables psych 231: research methods in psychology
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Manipulation and Measurement of Variables
Psych 231: Research Methods in Psychology
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Announcements For labs this week you’ll need to download
(and bring to lab):– Class experiment packet
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Choosing your independent variable
Choosing the right range (the right levels) of your independent variable– Review the literature– do a pilot experiment– consider the costs, your resources, your limitations – be realistic– pick levels found in the “real world”– pick a large enough range to show the effect
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Potential problems
These are things that you want to try to avoid by careful selection of the levels of your IV (issues for your DV as well).
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Demand characteristics
Characteristics of the study that may give away the purpose of the experiment
May influence how the participants behave in the study– Examples:
• Experiment title: The effects of horror movies on mood• Obvious manipulation: Ten psychology students looking
straight up• Biased or leading questions: Don’t you think it’s bad to
murder unborn children?
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Experimenter Bias
Experimenter bias (expectancy effects)– the experimenter may influence the results
(intentionally and unintentionally)• E.g., Clever Hans
– One solution is to keep the experimenter “blind” as to what conditions are being tested
• Single blind - experimenter doesn’t know the condition
• Double blind - neither the participant nor the experimenter knows the condition
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Knowing that you are being measured– just being in an experimental setting, people don’t
always respond the way that they “normally” would.
• Cooperative• Defensive• Non-cooperative
Reactivity
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Floor effects
A value below which a response cannot be made– Imagine a task that is so difficult, that none of your
participants can do it. – As a result the effects of your IV (if there are
indeed any) can’t be seen.
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Ceiling effects
When the dependent variable reaches a level that cannot be exceeded– Imagine a task that is so easy, that everybody
scores a 100% (imagine accuracy is your measure).
– So while there may be an effect of the IV, that effect can’t be seen because everybody has “maxed out”.
So you want to pick levels of your IV that result in middle level performance in your DV
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Measuring your dependent variables:
Scales of measurement - the correspondence between the numbers representing the properties that we’re measuring– The scale that you use will (partially) determine
what kinds of statistical analyses you can perform
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Scales of measurement
Categorical variables– Nominal scale
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Scales of measurement
Nominal Scale: Consists of a set of
categories that have different names. – Measurements on a nominal scale label and
categorize observations, but do not make any quantitative distinctions between observations.
– Example:• Eye color: blue, green, brown, hazel
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Scales of measurement
Categorical variables– Nominal scale– Ordinal scale
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Scales of measurement
Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. – Measurements on an ordinal scale rank
observations in terms of size or magnitude.– Example:
• T-shirt size:
Small, Med, Lrg, XL, XXL
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Scales of measurement
Categorical variables– Nominal scale– Ordinal scale
Quantitative variables– Interval scale
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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. – With an interval scale, equal differences between numbers
on the scale reflect equal differences in magnitude. – Ratios of magnitudes are not meaningful.– Example:
• Fahrenheit temperature scale
20º40º“Not Twice as hot”
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Scales of measurement
Categorical variables– Nominal scale– Ordinal scale
Quantitative variables– Interval scale– Ratio scale
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Scales of measurement
Ratio scale: An interval scale with the additional feature of an absolute zero point.
With a ratio scale, ratios of numbers DO reflect ratios of magnitude.– It is easy to get ratio and interval scales confused– Consider the following example: Measuring your height with
playing cards
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Scales of measurementRatio scale
8 cards high
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Scales of measurementInterval scale
5 cards high
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Scales of measurementInterval scaleRatio scale
8 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
“Best Scale?”: • Given a choice, usually prefer highest level of measurement possible
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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? • Does our measure really measure the construct?• Is there bias in our measurement?
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Reliability & Validity
Reliability =consistencyValidity = measuring what is intended
unreliable reliable reliableinvalid invalid valid
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Example: How can we measure intelligence?
Reliability & Validity
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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
• Sensitive to type of measure
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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– Extent to which raters agree in their observations
• Are the raters consistent?
– At least 2 raters observe behavior• Need a second opinion
– 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 INTERNAL EXTERNAL
FACE CRITERION-ORIENTED
PREDICTIVE
CONCURRENT
CONVERGENT
DISCRIMINANT
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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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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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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
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Internal Validity
The precision of the results Did the change result from the changes in the
DV or does it come from something else?
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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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“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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Next time Read chapters 8. Remember: For labs this week you’ll need to
download:– Class experiment packet