experiment basics: variables
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Experiment Basics: Variables. Psych 231: Research Methods in Psychology. Journal summary 1 due in labs this week See link on syllabus. Announcements. Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables - PowerPoint PPT PresentationTRANSCRIPT
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Experiment Basics: Variables
Psych 231: Research Methods in Psychology
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Announcements
Journal summary 1 due in labs this week See link on syllabus
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Variables
Independent variables (explanatory) Dependent variables (response) Extraneous variables
Control variables Random variables
Confound variables
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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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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
Experimenter bias & reactivity History – an event happens the experiment Maturation – participants get older (and other changes) Selection – nonrandom selection may lead to biases Mortality (attrition) – participants drop out or can’t
continue Regression to the mean – extreme performance is
often followed by performance closer to the mean The SI cover jinx
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External Validity
Do the research results generalize to other individuals, methods, or settings?
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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
• Is your sample size large enough?• Is there bias in your sampling procedure?
Setting representativeness Ecological validity - are the properties of the research setting
similar to those outside the lab• Do the materials, methods, & setting approximate the ‘real life’
situation?• Often confused with external validity (they are related concepts,
and sound similar)
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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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Sampling
Population
Everybody that the research is targeted to be about
The subset of the population that actually participates in the research
Sample
Errors in measurement Sampling error
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Sampling
Sample
Inferential statistics used to generalize back
Sampling to make data collection manageable
Population
Allows us to quantify the Sampling error
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Sampling
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
Key tool: Random selection
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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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Cluster sampling
Step 1: Identify groups (clusters) 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
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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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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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Colors and words
Divide into two groups: men women
Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand.
Women first. Men please close your eyes. Okay ready?
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BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 1
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Okay, now it is the men’s turn. Remember the instructions: Read aloud the
COLOR that the words are presented in. When done raise your hand.
Okay ready?
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BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 2
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Our results
So why the difference between the results for men versus women?
Is this support for a theory that proposes: “Women are good color identifiers, men are not” Why or why not? Let’s look at the two lists.
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BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 2Men
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 1Women
Matched Mis-Matched
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What resulted in the performance difference? Our manipulated independent variable
(men vs. women) The other variable match/mis-match?
Because the two variables are perfectly correlated we can’t tell
This is the problem with confounds
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
BlueGreenRed
PurpleYellowGreenPurpleBlueRed
YellowBlueRed
Green
IVDV
Confound
Co-vary together
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What DIDN’T result in the performance difference?
Extraneous variables Control
• # of words on the list
• The actual words that were printed Random
• Age of the men and women in the groups
These are not confounds, because they don’t co-vary with the IV
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
BlueGreenRed
PurpleYellowGreenPurpleBlueRed
YellowBlueRed
Green
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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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Reminders
This week: Journal summary 1 due in labs
Next week: In lab turning in Methods, Appendix (stimuli), and
IRB form for group projects