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

2. Journal Summary 1 due in labs this week
Announcements

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

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

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

6. 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

7. 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

8. 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

9. 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

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

11. Selecting every nth person
Systematic sampling

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

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

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

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

16. 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

17. 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

18. Blue
Green
Red
Purple
Yellow
List 1

19. 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?

20. Blue
Green
Red
Purple
Yellow
List 2

21. 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

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

23. 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

24. 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
Majors, class level, seating in classroom,…
These are not confounds, because they don’t co-vary with the IV
Blue
Green
Red
Purple
Yellow
Blue
Green
Red
Purple
Yellow

25. Experimental Control Our goal:
To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV.
Control is used to:
Minimize excessive variability
To reduce the potential of confounds (systematic variability not part of the research design)
Experimental Control

26. Experimental Control Our goal:
To test the possibility of a systematic relationship between the variability in our IV and how that affects the variability of our DV.
T = NRexp + NRother + R
Nonrandom (NR) Variability
NRexp: Manipulated independent variables (IV)
Our hypothesis: the IV will result in changes in the DV
NRother: extraneous variables (EV) which covary with IV
Condfounds
Random (R) Variability
Imprecision in measurement (DV)
Randomly varying extraneous variables (EV)
Experimental Control

27. Experimental Control: Weight analogy
Variability in a simple experiment:
T = NRexp + NRother + R
Absence of the treatment
(NRexp = 0)
Treatment group
Control group
“perfect experiment” - no confounds (NRother = 0) R NR
exp
other R NR
other
Experimental Control: Weight analogy

28. Experimental Control: Weight analogy
Variability in a simple experiment:
T = NRexp + NRother + R
Control group
Treatment group NR
exp R R
Difference Detector
Our experiment is a “difference detector”
Experimental Control: Weight analogy

29. Experimental Control: Weight analogy
If there is an effect of the treatment then NRexp will ≠ 0
Control group
Treatment group R NR
exp R
Difference Detector
Our experiment can detect the
effect of the treatment
Experimental Control: Weight analogy

30. Things making detection difficult
Potential Problems
Confounding
Excessive random variability
Difference Detector
Things making detection difficult

31. Potential Problems Confound
If an EV co-varies with IV, then NRother component of data will be present, and may lead to misattribution of effect to IV IV DV
Co-vary together EV
Potential Problems

32. Confounding Confound R NR R
Hard to detect the effect of NRexp because the effect looks like it could be from NRexp but could be due to the NRother R NR R
other NR
exp
Difference
Detector
Experiment can detect an effect,
but can’t tell where it is from
Confounding

33. Confound
Hard to detect the effect of NRexp because the effect looks like it could be from NRexp but could be due to the NRother
These two situations look
the same R NR
exp
other
Difference
Detector R R NR
other
Difference
Detector
There is an effect of the IV
There is not an effect of the IV
Confounding

34. Potential Problems Excessive random variability
If experimental control procedures are not applied
Then R component of data will be excessively large, and may make NRexp undetectable
Potential Problems

35. Excessive random variability
If R is large relative to NRexp then detecting a difference may be difficult R R NR
exp
Difference
Detector
Experiment can’t detect the
effect of the treatment
Excessive random variability

36. Reduced random variability
But if we reduce the size of NRother and R relative to NRexp then detecting gets easier
So try to minimize this by using good measures of DV, good manipulations of IV, etc. R NR
exp R
Difference
Detector
Our experiment can detect the
effect of the treatment
Reduced random variability

37. Controlling Variability
How do we introduce control?
Methods of Experimental Control
Constancy/Randomization
Comparison
Production
Controlling Variability

38. Methods of Controlling Variability
Constancy/Randomization
If there is a variable that may be related to the DV that you can’t (or don’t want to) manipulate
Control variable: hold it constant
Random variable: let it vary randomly across all of the experimental conditions
Methods of Controlling Variability

39. Methods of Controlling Variability
Comparison
An experiment always makes a comparison, so it must have at least two groups
Sometimes there are control groups
This is often the absence of the treatment
Training
group
No training
(Control) group
Without control groups if is harder to see what is really happening in the experiment
It is easier to be swayed by plausibility or inappropriate comparisons
Useful for eliminating potential confounds
Methods of Controlling Variability

40. Methods of Controlling Variability
Comparison
An experiment always makes a comparison, so it must have at least two groups
Sometimes there are control groups
This is often the absence of the treatment
Sometimes there are a range of values of the IV
1 week of
Training group
2 weeks of
Training group
3 weeks of
Training group
Methods of Controlling Variability

41. Methods of Controlling Variability
Production
The experimenter selects the specific values of the Independent Variables
1 week of
Training group
2 weeks of
Training group
3 weeks of
Training group
Need to do this carefully
Suppose that you don’t find a difference in the DV across your different groups
Is this because the IV and DV aren’t related?
Or is it because your levels of IV weren’t different enough
Methods of Controlling Variability

42. So far we’ve covered a lot of the about details experiments generally
Now let’s consider some specific experimental designs.
Some bad (but common) designs
Some good designs
1 Factor, two levels
1 Factor, multi-levels
Between & within factors
Factorial (more than 1 factor)
Experimental designs

43. Poorly designed experiments
Bad design example 1: Does standing close to somebody cause them to move?
“hmm… that’s an empirical question. Let’s see what happens if …”
So you stand closely to people and see how long before they move
Problem: no control group to establish the comparison group (this design is sometimes called “one-shot case study design”)
Poorly designed experiments

44. Poorly designed experiments
Bad design example 2:
Testing the effectiveness of a stop smoking relaxation program
The participants choose which group (relaxation or no program) to be in
Poorly designed experiments

45. Poorly designed experiments
Bad design example 2:
Non-equivalent control groups
Self
Assignment
Independent Variable
Dependent Variable
Training
group
Measure
participants
No training
(Control) group
Random
Assignment
Measure
Problem: selection bias for the two
groups, need to do
random assignment to groups
Poorly designed experiments

46. Poorly designed experiments
Bad design example 3: Does a relaxation program decrease the urge to smoke?
Pretest desire level – give relaxation program – posttest desire to smoke
Poorly designed experiments

47. Poorly designed experiments
Bad design example 3:
One group pretest-posttest
design
Dependent Variable
Independent Variable
Dependent Variable
participants
Pre-test
Training
group
Post-test
Measure
Pre-test
No Training
group
Post-test
Measure
Add another factor
Problems include: history, maturation, testing, and more
Poorly designed experiments

48. 1 factor - 2 levels Good design example
How does anxiety level affect test performance?
Two groups take the same test
Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success
Grp2 (low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough
1 Factor (Independent variable), two levels
Basically you want to compare two treatments (conditions)
The statistics are pretty easy, a t-test
1 factor - 2 levels

49. 1 factor - 2 levels Good design example
How does anxiety level affect test performance?
participants
Low
Moderate
Test
Random
Assignment
Anxiety
Dependent Variable
1 factor - 2 levels

50. 1 factor - 2 levels Good design example
How does anxiety level affect test performance?
One factor
Use a t-test to see if these points are statistically different
low
moderate
test performance
anxiety
anxiety
Two levels
low
moderate 60 80
Observed difference between conditions
T-test =
Difference expected by chance
1 factor - 2 levels

51. 1 factor - 2 levels Advantages:
Simple, relatively easy to interpret the results
Is the independent variable worth studying?
If no effect, then usually don’t bother with a more complex design
Sometimes two levels is all you need
One theory predicts one pattern and another predicts a different pattern
1 factor - 2 levels

52. 1 factor - 2 levels Interpolation Disadvantages:
“True” shape of the function is hard to see
Interpolation and Extrapolation are not a good idea
low
moderate
test performance
anxiety
What happens within of the ranges that you test?
Interpolation
1 factor - 2 levels

53. 1 factor - 2 levels Extrapolation Disadvantages:
“True” shape of the function is hard to see
Interpolation and Extrapolation are not a good idea
Extrapolation
low
moderate
test performance
anxiety
What happens outside of the ranges that you test?
high
1 factor - 2 levels

54. 1 Factor - multilevel experiments
For more complex theories you will typically need more complex designs (more than two levels of one IV)
1 factor - more than two levels
Basically you want to compare more than two conditions
The statistics are a little more difficult, an ANOVA (Analysis of Variance)
1 Factor - multilevel experiments

55. 1 Factor - multilevel experiments
Good design example (similar to earlier ex.)
How does anxiety level affect test performance?
Two groups take the same test
Grp1 (moderate anxiety group): 5 min lecture on the importance of good grades for success
Grp2 (low anxiety group): 5 min lecture on how good grades don’t matter, just trying is good enough
Grp3 (high anxiety group): 5 min lecture on how the students must pass this test to pass the course
1 Factor - multilevel experiments

56. 1 factor - 3 levels participants Low Moderate Test Random Assignment
Anxiety
Dependent Variable
High
1 factor - 3 levels

57. 1 Factor - multilevel experiments
low
mod
test performance
anxiety
anxiety
low
mod
high
high 80 60 60
1 Factor - multilevel experiments

58. 1 Factor - multilevel experiments
Advantages
Gives a better picture of the relationship (function)
Generally, the more levels you have, the less you have to worry about your range of the independent variable
1 Factor - multilevel experiments

59. Relationship between Anxiety and Performance
low
moderate
test performance
anxiety
2 levels
high
low
mod
test performance
anxiety
3 levels
Relationship between Anxiety and Performance

60. 1 Factor - multilevel experiments
Disadvantages
Needs more resources (participants and/or stimuli)
Requires more complex statistical analysis (analysis of variance and pair-wise comparisons)
1 Factor - multilevel experiments

61. Pair-wise comparisons
The ANOVA just tells you that not all of the groups are equal.
If this is your conclusion (you get a “significant ANOVA”) then you should do further tests to see where the differences are
High vs. Low
High vs. Moderate
Low vs. Moderate
Pair-wise comparisons