1. Manipulation and Measurement of Variables
Psych 231: Research Methods in Psychology

2. Announcements
For labs this week you’ll need to download (and bring to lab):
Class experiment packet

3. Choosing your independent variable
Choosing 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
And in the middle of the range

4. Identifying potential problems
These are things that you want to try to avoid by careful selection of the levels of your IV (may be issues for your DV as well).
Demand characteristics
Experimenter bias
Reactivity
Floor and ceiling effects

5. 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?

6. 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

7. Reactivity 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
Cooperative
“You seem like a nice person: I’ll help you get the right results”
Defensive
“I don’t want to look stupid/evil. I’ll do what a smart/good person is expected to do (rather than what I normally would do).”
Noncooperative
“This experiment is annoying. Let me screw up the results.”

8. 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.

9. 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%
So while there may be an effect of the IV, that effect can’t be seen because everybody has “maxed out”
To avoid floor and ceiling effects you want to pick levels of your IV that result in middle level performance in your DV

10. Measuring your dependent variables
Scales of measurement
Errors in measurement

11. Measuring your dependent variables
Scales of measurement
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

12. Scales of measurement Categorical variables Quantitative variables
Nominal scale

13. 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

14. Scales of measurement Categorical variables Quantitative variables
Nominal scale
Ordinal scale
Quantitative variables

15. 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:
XXL
XL,
Lrg,
Med,
Small,

16. Scales of measurement Categorical variables Quantitative variables
Nominal scale
Ordinal scale
Quantitative variables
Interval scale

17. 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
40º
20º
“Not Twice as hot”

18. Scales of measurement Categorical variables Quantitative variables
Nominal scale
Ordinal scale
Quantitative variables
Interval scale
Ratio scale

19. Scales of measurement
Ratio scale: An interval scale with the additional feature of an absolute zero point.
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

20. Scales of measurement
Ratio scale
8 cards high

21. Scales of measurement
Interval scale
5 cards high

22. Scales of measurement Ratio scale Interval scale 8 cards high
0 cards high means ‘no height’
0 cards high means ‘as tall as the table’

23. Scales of measurement Categorical variables Quantitative variables
Nominal scale
Ordinal scale
Quantitative variables
Interval scale
Ratio scale
“Best” Scale?
Given a choice, usually prefer highest level of measurement possible

24. Measuring your dependent variables
Scales of measurement
Errors in measurement

25. Reliability & Validity
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?

26. Errors in measurement Reliability Validity
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?

27. Reliability & Validity
Reliability = consistency
Validity = measuring what is intended
reliable valid
unreliable
invalid
reliable
invalid

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

29. 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

30. 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)

31. 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

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

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

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

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

36. 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.”

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

38. 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

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

40. 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

41. “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.

42. Next time Read chapters 8.
Remember: For labs this week you’ll need to download:
Class experiment packet