Experiment Basics: Variables Psych 231: Research Methods in Psychology
Mar 13, 2016
Experiment Basics: Variables
Psych 231: Research Methods in Psychology
Announcements
CITI training forms due this week
Many kinds of Variables
Independent variables (explanatory) Dependent variables (response) Extraneous variables
Control variables Random variables
Confound variables
Many kinds of Variables
Independent variables (explanatory) Dependent variables (response) Extraneous variables
Control variables Random variables
Confound variables
Measuring your dependent variables
Scales of measurement Errors in measurement
Reliability & Validity Sampling error
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?
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)
Dartboard analogy
Bull’s eye = the “true score”
Dartboard analogy
Bull’s eye = the “true score” Reliability = consistency
Validity = measuring what is intended
reliablevalid
reliable invalid
unreliable
invalid
Measurement error
Estimate of true score biased
Reliability
True score + measurement error A reliable measure will have a small amount of
error Multiple “kinds” of reliability
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
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)
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
5:004:56
Validity
Does your measure really measure what it is supposed to measure? There are many “kinds” of validity
VALIDITY
CONSTRUCT
CRITERION-ORIENTED
DISCRIMINANT
CONVERGENTPREDICTIVE
CONCURRENT
FACE
INTERNAL EXTERNAL
Many kinds of Validity
VALIDITY
CONSTRUCT
CRITERION-ORIENTED
DISCRIMINANT
CONVERGENTPREDICTIVE
CONCURRENT
FACE
INTERNAL EXTERNAL
Many kinds of Validity
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.”
Construct Validity
Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct
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
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
External Validity
Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”
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
Measuring your dependent variables
Scales of measurement Errors in measurement
Reliability & Validity Sampling error
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
Sampling
Sample
Inferential statistics used to generalize back
Sampling to make data collection manageable
Population
Allows us to quantify the Sampling error
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
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
Simple random sampling
Every individual has a equal and independent chance of being selected from the population
Systematic sampling
Selecting every nth person
Cluster sampling
Step 1: Identify groups (clusters) Step 2: randomly select from each group
Convenience sampling
Use the participants who are easy to get
Quota sampling
Step 1: identify the specific subgroups Step 2: take from each group until desired number of
individuals
Variables
Independent variables Dependent variables
Measurement• Scales of measurement• Errors in measurement
Extraneous variables Control variables Random variables
Confound variables
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
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?
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 1
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?
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 2
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.
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 2Men
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 1Women
Matched Mis-Matched
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
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
“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.