1. Experiment Basics: Variables
Psych 231: Research Methods in Psychology

2. Journal Summary 1 due in labs this week
Announcements

3. Results
Mean: 78.3%
Median: 78%
Range: 59-92
If you want to go over your exam set up a time to see me
Exam 1

4. Many kinds of Variables
Independent variables (explanatory)
Dependent variables (response)
Extraneous variables
Control variables
Random variables
Confound variables
Many kinds of Variables

5. Many kinds of Variables
Independent variables (explanatory)
Dependent variables (response)
Extraneous variables
Control variables
Random variables
Confound variables
Many kinds of Variables

6. Measuring your dependent variables
Scales of measurement
Errors in measurement
Reliability & Validity
Sampling error
Measuring your dependent variables

7. Measuring the true score
Example: Measuring intelligence?
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?
Measuring the true score
Mythbusters

8. 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)
Errors in measurement

9. Bull’s eye = the “true score”
Dartboard analogy

10. Dartboard analogy reliable valid unreliable invalid reliable invalid
Bull’s eye = the “true score”
Reliability = consistency
Validity = measuring what is intended
Measurement error
Estimate of true score
biased
reliable valid
unreliable
invalid
reliable
invalid
Dartboard analogy

11. Reliability True score + measurement error
A reliable measure will have a small amount of error
Multiple “kinds” of reliability
Reliability

12. 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

13. 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

14. 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
5:00
4:56
Reliability

15. Does your measure really measure what it is supposed to measure?
There are many “kinds” of validity
Validity

16. Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE
CRITERION-
ORIENTED
PREDICTIVE
CONVERGENT
CONCURRENT
DISCRIMINANT
Many kinds of Validity

17. Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE
CRITERION-
ORIENTED
PREDICTIVE
CONVERGENT
CONCURRENT
DISCRIMINANT
Many kinds of Validity

18. 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.”
Face Validity

19. Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct
Construct Validity

20. Internal Validity The precision of the results
Did the change in the DV result from the changes in the IV or does it come from something else?
Internal Validity

21. 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
Threats to internal validity

22. Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”
External Validity

23. 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
External Validity

24. Measuring your dependent variables
Scales of measurement
Errors in measurement
Reliability & Validity
Sampling error
Measuring your dependent variables

25. Sampling Errors in measurement Sampling error
Population
Everybody that the research is targeted to be about
The subset of the population that actually participates in the research
Sample
Sampling

26. Sampling Population Sampling to make data collection manageable
Inferential statistics used to generalize back
Sampling to make data collection manageable
Sample
Allows us to quantify the Sampling error
Sampling

27. Sampling Goals of “good” sampling: Key tool: Random selection
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

28. Sampling Methods Probability sampling Non-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
Sampling Methods

29. Simple random sampling
Every individual has a equal and independent chance of being selected from the population
Simple random sampling

30. Selecting every nth person
Systematic sampling

31. Cluster sampling Step 1: Identify groups (clusters)
Step 2: randomly select from each group
Cluster sampling

32. Use the participants who are easy to get
Convenience sampling

33. Quota sampling Step 1: identify the specific subgroups
Step 2: take from each group until desired number of individuals
Quota sampling

34. Variables Independent variables Dependent variables
Measurement
Scales of measurement
Errors in measurement
Extraneous variables
Control variables
Random variables
Confound variables
Variables

35. 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
Extraneous Variables

36. 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?
Colors and words

37. Blue
Green
Red
Purple
Yellow
List 1

38. 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?

39. Blue
Green
Red
Purple
Yellow
List 2

40. 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.
Our results

41. List 2 Men List 1 Women Blue Green Red Purple Yellow Blue Green Red
Matched
Mis-Matched

42. 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
Blue
Green
Red
Purple
Yellow
Blue
Green
Red
Purple
Yellow IV DV
Confound
Co-vary together

43. 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
Blue
Green
Red
Purple
Yellow
Blue
Green
Red
Purple
Yellow

44. “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.
“Debugging your study”