1. Non-Experimental designs: Developmental and Small-N Designs
Psych 231: Research Methods in Psychology

2. Reminders Don’t forget Quiz 9 is due on Friday at midnight
Post Exam 2 extra credit – posted on ReggieNet, due in-class on Wednesday
Journal Summary #2 Due in labs this week
Reminders

3. Non-Experimental designs
Sometimes you just can’t perform a fully controlled experiment
Because of the issue of interest
Limited resources (not enough subjects, observations are too costly, etc).
Surveys
Correlational
Quasi-Experiments
Developmental designs
Small-N designs
This does NOT imply that they are bad designs
Just remember the advantages and disadvantages of each
Non-Experimental designs

4. Developmental designs
Used to study changes in behavior that occur as a function of age changes
Age typically serves as a quasi-independent variable
Three major types
Cross-sectional
Longitudinal
Cohort-sequential
Developmental designs
Video lecture (~10 mins)

5. Developmental designs
Cross-sectional design
Groups are pre-defined on the basis of a pre-existing variable
Study groups of individuals of different ages at the same time
Use age to assign participants to group
Age is subject variable treated as a between-subjects variable
Age 4
Age 7
Age 11
Developmental designs
Examples:

6. Developmental designs
Cross-sectional design
Advantages:
Can gather data about different groups (i.e., ages) at the same time (a “snapshot” of a population at a particular time)
Participants are not required to commit for an extended period of time
Developmental designs

7. Developmental designs
Cross-sectional design
Developmental designs

8. Developmental designs
Longitudinal design
Follow the same individual or group over time
Age is treated as a within-subjects variable
Rather than comparing groups, the same individuals are compared to themselves at different times
Changes in dependent variable likely to reflect changes due to aging process
Changes in performance are compared on an individual basis and overall
Age 11
time
Age 20
Age 15
Developmental designs

9. Longitudinal Designs Examples Wisconsin Longitudinal Study (WLS)
Began in 1957 and is still on-going (~60 years)
10,317 men and women who graduated from Wisconsin high schools in 1957
Originally studied plans for college after graduation
Now it can be used as a test of aging and maturation
Data collected in:
1957, 1964, 1975, 1992, 2004, 2011
Physicians’ Health Study
Nurses’ Health Study
Longitudinal Designs

10. Developmental designs
Longitudinal design
Advantages:
Can see developmental changes clearly
Can measure differences within individuals
Avoid some cohort effects (participants are all from same generation, so changes are more likely to be due to aging)
Developmental designs

11. Developmental designs
Longitudinal design
Baby boomers
Generation X
Mellennials
Generation Z
Disadvantages
Can be very time-consuming
Can have cross-generational effects:
Conclusions based on members of one generation may not apply to other generations
Numerous threats to internal validity:
Attrition/mortality
History
Practice effects
Improved performance over multiple tests may be due to practice taking the test
Cannot determine causality
Developmental designs

12. Developmental designs
Cohort-sequential design
Measure groups of participants as they age
Example: measure a group of 5 year olds, then the same group 10 years later, as well as another group of 5 year olds
Age is both between and within subjects variable
Combines elements of cross-sectional and longitudinal designs
Addresses some of the concerns raised by other designs
For example, allows to evaluate the contribution of cohort effects
Developmental designs

13. Developmental designs
Cohort-sequential design
Time of measurement
Cross-sectional component
1975
1985
1995
Age 5
Age 15
Age 25
Cohort A
1970s
Age 5
Age 15
Cohort B
1980s
Age 5
Cohort C
1990s
Longitudinal component
Developmental designs

14. Developmental designs
Cohort-sequential design
Advantages:
Get more information
Can track developmental changes to individuals
Can compare different ages at a single time
Can measure generation effect
Disadvantages:
Still time-consuming
Need lots of groups of participants
Still cannot make causal claims about age variable
Developmental designs

15. Non-Experimental designs
Sometimes you just can’t perform a fully controlled experiment
Because of the issue of interest
Limited resources (not enough subjects, observations are too costly, etc).
Surveys
Correlational
Quasi-Experiments
Developmental designs
Small-N designs
This does NOT imply that they are bad designs
Just remember the advantages and disadvantages of each
Non-Experimental designs

16. Small N designs What are they?
In contrast to Large N-designs (comparing aggregated performance of large groups of participants)
One or a few participants
Data are typically not analyzed statistically; rather rely on visual interpretation of the data
Historically, these were the typical kind of design used until 1920’s when there was a shift to using larger sample sizes
Small N designs are common place in some areas of psych.
e.g., applied behavioral analysis, psychophysics, clinical settings, animal studies, expertise, etc.
Small N designs

17. Small N designs Small vs. Large N debate
Some researchers have argued that Small N designs are the best way to go.
The goal of psychology is to describe behavior of an individual
Looking at data collapsed over groups “looks” in the wrong place
Need to look at the data at the level of the individual
Smith and Little (2018). Small is beautiful: In defense of the small-N design, Psychonomic Bulletin & Review (online March)
Small N designs

18. Small N designs Baseline experiments – the basic idea is to show:
= observation
Treatment introduced
Steady state (baseline)
Baseline experiments – the basic idea is to show:
Observations begin in the absence of treatment (BASELINE)
Essentially our control/comparison level
Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded
Small N designs

19. Small N designs Baseline experiments – the basic idea is to show:
= observation
Reversibility
Treatment removed
Transition steady state
Steady state (baseline)
Treatment introduced
Baseline experiments – the basic idea is to show:
When the IV occurs, you get the effect
When the IV doesn’t occur, you don’t get the effect (reversibility)
This allows other comparisons, to the original baseline as well as to the transition steady state
Small N designs

20. Unstable
Stable
Before introducing treatment (IV), baseline needs to be stable
Measure level and trend
Level – how frequent (how intense) is behavior?
Are all the data points high or low?
Trend – does behavior seem to increase (or decrease)
Are data points “flat” or on a slope?
Small N designs

21. ABA design ABA design (baseline, treatment, baseline)
Steady state (baseline)
Transition steady state
Reversibility
ABA design (baseline, treatment, baseline)
The reversibility is necessary, otherwise
something else may have caused the effect
other than the IV (e.g., history, maturation, etc.)
There are other designs as well (e.g., ABAB see figure14.6 in your textbook)
ABA design

22. Small N designs Advantages
Focus on individual performance, not fooled by group averaging effects
Focus is on big effects (small effects typically can’t be seen without using large groups)
Avoid some ethical problems – e.g., with non-treatments
Allows to look at unusual (and rare) types of subjects (e.g., case studies of amnesics, experts vs. novices)
Often used to supplement large N studies, with more observations on fewer subjects
Small N designs

23. Small N designs Disadvantages
Difficult to determine how generalizable the effects are
Effects may be small relative to variability of situation so NEED more observation
Some effects are by definition between subjects
Treatment leads to a lasting change, so you don’t get reversals
Small N designs

24. Statistics Mistrust of statistics? It is all in how you use them
They are a critical tool in research
Statistics

25. Samples and Populations
Sampling methods
Sample
Samples and Populations

26. Samples and Populations
2 General kinds of Statistics
Descriptive statistics
Used to describe, simplify, & organize data sets
Describing distributions of scores
Inferential statistics
Used to test claims about the population, based on data gathered from samples
Takes sampling error into account. Are the results above and beyond what you’d expect by random chance?
Population
Inferential statistics used to generalize back
Sample
Samples and Populations

27. Samples and Populations
2 General kinds of Statistics
Descriptive statistics
Used to describe, simplify, & organize data sets
Describing distributions of scores
Inferential statistics
Used to test claims about the population, based on data gathered from samples
Takes sampling error into account. Are the results above and beyond what you’d expect by random chance?
Population
Inferential statistics used to generalize back
Sample
Samples and Populations

28. Recall that a variable is a characteristic that can take different values.
The distribution of a variable is a summary of all the different values of a variable
Both type (each value) and token (each instance)
How much do you like statistics?
Hate it Love it
5 values (1, 2, 3, 4, 5)
7 tokens (1,1,2,3,4,5,5) 1 5 5 4 1 3 2
Distribution

29. Distribution Many important distributions 5 1 2 5 3 Population Sample
All the scores of interest
Sample
All of the scores observed (your data)
Used to estimate population characteristics
Distribution of sample distributions
Used to estimate sampling error
Population 1 2 3 3 1 5 1 2 3 1
Sample
How do we describe these distributions?
Use descriptive statistics, focus on 3 properties
Distribution

30. Describing Distributions
Properties: Shape, Center, and Spread (variability)
Shape
Symmetric v. asymmetric (skew)
Unimodal v. multimodal
Center
Where most of the data in the distribution are
Mean, Median, Mode
Spread (variability)
How similar/dissimilar are the scores in the distribution?
Standard deviation (variance), Range
Describing Distributions

31. Describing Distributions
Properties: Shape, Center, and Spread (variability)
Visual descriptions - A picture of the distribution is usually helpful
Numerical descriptions of distributions f %
1 (hate)
200 20 2
100 10 3 4
5 (love)
300 30
Describing Distributions

32. Mean & Standard deviation
The mean (mathematical average) is the most popular and most important measure of center.
Divide by the total number in the population
Add up all of the X’s
The formula for the population mean is (a parameter):
The formula for the sample mean is (a statistic):
Divide by the total number in the sample
mean
Mean & Standard deviation

33. Mean & Standard deviation
The mean (mathematical average) is the most popular and most important measure of center.
The standard deviation is the most popular and important measure of variability.
The standard deviation measures how far off all of the individuals in the distribution are from a standard, where that standard is the mean of the distribution.
Essentially, the average of the deviations.
mean
recall
Mean & Standard deviation

34. An Example: Computing Standard Deviation (population)
Working your way through the formula:
Step 1: Compute deviation scores
Step 2: Compute the SS
Step 3: Determine the variance
Take the average of the squared deviations
Divide the SS by the N
Step 4: Determine the standard deviation
Take the square root of the variance
standard deviation = σ =
An Example: Computing Standard Deviation (population)

35. An Example: Computing Standard Deviation (sample)
Main difference:
Step 1: Compute deviation scores
Step 2: Compute the SS
Step 3: Determine the variance
Take the average of the squared deviations
Divide the SS by the n-1
Step 4: Determine the standard deviation
Take the square root of the variance
This is done because samples are biased to be less variable than the population. This “correction factor” will increase the sample’s SD (making it a better estimate of the population’s SD)
An Example: Computing Standard Deviation (sample)

36. Statistics 2 General kinds of Statistics Descriptive statistics
Used to describe, simplify, & organize data sets
Describing distributions of scores
Inferential statistics
Used to test claims about the population, based on data gathered from samples
Takes sampling error into account. Are the results above and beyond what you’d expect by random chance?
Population
Inferential statistics used to generalize back
Sample
Statistics

37. Inferential Statistics
Purpose: To make claims about populations based on data collected from samples
What’s the big deal?
Population
Sample A
Treatment
X = 80%
Sample B
No Treatment
X = 76%
Example Experiment:
Group A - gets treatment to improve memory
Group B - gets no treatment (control)
After treatment period test both groups for memory
Results:
Group A’s average memory score is 80%
Group B’s is 76%
Is the 4% difference a “real” difference (statistically significant) or is it just sampling error?
Inferential Statistics

38. Testing Hypotheses Step 1: State your hypotheses
Step 2: Set your decision criteria
Step 3: Collect your data from your sample(s)
Step 4: Compute your test statistics
Step 5: Make a decision about your null hypothesis
“Reject H0”
“Fail to reject H0”
Testing Hypotheses

39. Testing Hypotheses Step 1: State your hypotheses
This is the hypothesis that you are testing
Null hypothesis (H0)
Alternative hypothesis(ses)
“There are no differences (effects)”
Generally, “not all groups are equal”
You aren’t out to prove the alternative hypothesis (although it feels like this is what you want to do)
If you reject the null hypothesis, then you’re left with support for the alternative(s) (NOT proof!)
Testing Hypotheses

40. Testing Hypotheses Step 1: State your hypotheses
In our memory example experiment
Null H0: mean of Group A = mean of Group B
Alternative HA: mean of Group A ≠ mean of Group B
(Or more precisely: Group A > Group B)
It seems like our theory is that the treatment should improve memory.
That’s the alternative hypothesis. That’s NOT the one the we’ll test with inferential statistics.
Instead, we test the H0
Testing Hypotheses

41. Testing Hypotheses Step 1: State your hypotheses
Step 2: Set your decision criteria
Your alpha level will be your guide for when to:
“reject the null hypothesis”
“fail to reject the null hypothesis”
This could be correct conclusion or the incorrect conclusion
Two different ways to go wrong
Type I error: saying that there is a difference when there really isn’t one (probability of making this error is “alpha level”)
Type II error: saying that there is not a difference when there really is one
Testing Hypotheses

42. Error types Real world (‘truth’) H0 is correct H0 is wrong
Type I error
Reject H0
Experimenter’s conclusions
Fail to Reject H0
Type II error
Error types

43. Error types: Courtroom analogy
Real world (‘truth’)
Defendant is innocent
Defendant is guilty
Type I error
Find guilty
Jury’s decision
Type II error
Find not guilty
Error types: Courtroom analogy

44. Type I error: concluding that there is an effect (a difference between groups) when there really isn’t.
Sometimes called “significance level”
We try to minimize this (keep it low)
Pick a low level of alpha
Psychology: 0.05 and 0.01 most common
Type II error: concluding that there isn’t an effect, when there really is.
Related to the Statistical Power of a test
How likely are you able to detect a difference if it is there
Error types

45. Testing Hypotheses Step 1: State your hypotheses
Step 2: Set your decision criteria
Step 3: Collect your data from your sample(s)
Step 4: Compute your test statistics
Descriptive statistics (means, standard deviations, etc.)
Inferential statistics (t-tests, ANOVAs, etc.)
Step 5: Make a decision about your null hypothesis
Reject H0 “statistically significant differences”
Fail to reject H0 “not statistically significant differences”
Testing Hypotheses

46. Statistical significance
“Statistically significant differences”
When you “reject your null hypothesis”
Essentially this means that the observed difference is above what you’d expect by chance
“Chance” is determined by estimating how much sampling error there is
Factors affecting “chance”
Sample size
Population variability
Statistical significance

47. (Pop mean - sample mean)
Population mean
Population
Distribution x
Sampling error
(Pop mean - sample mean)
n = 1
Sampling error

48. (Pop mean - sample mean)
Population mean
Population
Distribution
Sample mean x x
Sampling error
(Pop mean - sample mean)
n = 2
Sampling error

49. (Pop mean - sample mean)
Generally, as the sample size increases, the sampling error decreases
Population mean
Population
Distribution
Sample mean x
Sampling error
(Pop mean - sample mean)
n = 10
Sampling error

50. Typically the narrower the population distribution, the narrower the range of possible samples, and the smaller the “chance”
Large population variability
Small population variability
Sampling error

51. Sampling error Population Distribution of sample means
These two factors combine to impact the distribution of sample means.
The distribution of sample means is a distribution of all possible sample means of a particular sample size that can be drawn from the population
Population
Distribution of sample means XC
Samples of size = n XA XD
Avg. Sampling error XB
“chance”
Sampling error

52. Significance “A statistically significant difference” means:
the researcher is concluding that there is a difference above and beyond chance
with the probability of making a type I error at 5% (assuming an alpha level = 0.05)
Note “statistical significance” is not the same thing as theoretical significance.
Only means that there is a statistical difference
Doesn’t mean that it is an important difference
Significance

53. Non-Significance Failing to reject the null hypothesis
Generally, not interested in “accepting the null hypothesis” (remember we can’t prove things only disprove them)
Usually check to see if you made a Type II error (failed to detect a difference that is really there)
Check the statistical power of your test
Sample size is too small
Effects that you’re looking for are really small
Check your controls, maybe too much variability
Non-Significance

54. From last time XA XB About populations Example Experiment:
Group A - gets treatment to improve memory
Group B - gets no treatment (control)
After treatment period test both groups for memory
Results:
Group A’s average memory score is 80%
Group B’s is 76%
H0: μA = μB
H0: there is no difference between Grp A and Grp B
Real world (‘truth’)
H0 is correct
H0 is wrong
Experimenter’s conclusions
Reject H0
Fail to Reject H0
Type I error
Type II error
Is the 4% difference a “real” difference (statistically
significant) or is it just sampling error?
Two sample
distributions XA XB
76%
80%
From last time

55. “Generic” statistical test
Tests the question:
Are there differences between groups due to a treatment?
H0 is true (no treatment effect)
Real world (‘truth’)
H0 is correct
H0 is wrong
Experimenter’s conclusions
Reject H0
Fail to Reject H0
Type I error
Type II error
Two possibilities in the “real world”
One population
Two sample
distributions XA XB
76%
80%
“Generic” statistical test

56. “Generic” statistical test
Tests the question:
Are there differences between groups due to a treatment?
Real world (‘truth’)
H0 is correct
H0 is wrong
Experimenter’s conclusions
Reject H0
Fail to Reject H0
Type I error
Type II error
Two possibilities in the “real world”
H0 is true (no treatment effect)
H0 is false (is a treatment effect)
Two populations XA XB XB XA
76%
80%
76%
80%
People who get the treatment change,
they form a new population
(the “treatment population)
“Generic” statistical test

57. “Generic” statistical testXB XA
ER: Random sampling error
ID: Individual differences (if between subjects factor)
TR: The effect of a treatment
Why might the samples be different?
(What is the source of the variability between groups)?
“Generic” statistical test

58. “Generic” statistical testXB XA
ER: Random sampling error
ID: Individual differences (if between subjects factor)
TR: The effect of a treatment
The generic test statistic - is a ratio of sources of variability
Observed difference
TR + ID + ER
ID + ER
Computed
test statistic = =
Difference from chance
“Generic” statistical test

59. Sampling error Population “chance” Distribution of sample means
The distribution of sample means is a distribution of all possible sample means of a particular sample size that can be drawn from the population
Population
Distribution of sample means XC
Samples of size = n XA XD
Avg. Sampling error XB
“chance”
Sampling error

60. “Generic” statistical test
The generic test statistic distribution
To reject the H0, you want a computed test statistics that is large
reflecting a large Treatment Effect (TR)
What’s large enough? The alpha level gives us the decision criterion
TR + ID + ER
ID + ER
Distribution of the test statistic
Test statistic
Distribution of sample means
α-level determines where these boundaries go
“Generic” statistical test

61. “Generic” statistical test
The generic test statistic distribution
To reject the H0, you want a computed test statistics that is large
reflecting a large Treatment Effect (TR)
What’s large enough? The alpha level gives us the decision criterion
Distribution of the test statistic
Reject H0
Fail to reject H0
“Generic” statistical test

62. “Generic” statistical test
The generic test statistic distribution
To reject the H0, you want a computed test statistics that is large
reflecting a large Treatment Effect (TR)
What’s large enough? The alpha level gives us the decision criterion
Distribution of the test statistic
Reject H0
“One tailed test”: sometimes you know to expect a particular difference (e.g., “improve memory performance”)
Fail to reject H0
“Generic” statistical test

63. “Generic” statistical test
Things that affect the computed test statistic
Size of the treatment effect
The bigger the effect, the bigger the computed test statistic
Difference expected by chance (sample error)
Sample size
Variability in the population
“Generic” statistical test

64. Significance “A statistically significant difference” means:
the researcher is concluding that there is a difference above and beyond chance
with the probability of making a type I error at 5% (assuming an alpha level = 0.05)
Note “statistical significance” is not the same thing as theoretical significance.
Only means that there is a statistical difference
Doesn’t mean that it is an important difference
Significance

65. Non-Significance Failing to reject the null hypothesis
Generally, not interested in “accepting the null hypothesis” (remember we can’t prove things only disprove them)
Usually check to see if you made a Type II error (failed to detect a difference that is really there)
Check the statistical power of your test
Sample size is too small
Effects that you’re looking for are really small
Check your controls, maybe too much variability
Non-Significance

66. Some inferential statistical tests
1 factor with two groups
T-tests
Between groups: 2-independent samples
Within groups: Repeated measures samples (matched, related)
1 factor with more than two groups
Analysis of Variance (ANOVA) (either between groups or repeated measures)
Multi-factorial
Factorial ANOVA
Some inferential statistical tests

67. T-test Design Formula: Observed difference X1 - X2 T =
2 separate experimental conditions
Degrees of freedom
Based on the size of the sample and the kind of t-test
Formula:
Observed difference
T =
X X2
Diff by chance
Based on sample error
Computation differs for
between and within t-tests
T-test

68. T-test Reporting your results
The observed difference between conditions
Kind of t-test
Computed T-statistic
Degrees of freedom for the test
The “p-value” of the test
“The mean of the treatment group was 12 points higher than the control group. An independent samples t-test yielded a significant difference, t(24) = 5.67, p < 0.05.”
“The mean score of the post-test was 12 points higher than the pre-test. A repeated measures t-test demonstrated that this difference was significant significant, t(12) = 5.67, p < 0.05.”
T-test

69. Analysis of Variance XB XA XC Designs Test statistic is an F-ratio
More than two groups
1 Factor ANOVA, Factorial ANOVA
Both Within and Between Groups Factors
Test statistic is an F-ratio
Degrees of freedom
Several to keep track of
The number of them depends on the design
Analysis of Variance

70. Analysis of Variance More than two groups F-ratio = XB XA XC
Now we can’t just compute a simple difference score since there are more than one difference
So we use variance instead of simply the difference
Variance is essentially an average difference
Observed variance
Variance from chance
F-ratio =
Analysis of Variance

71. 1 factor ANOVA 1 Factor, with more than two levels XB XA XC
Now we can’t just compute a simple difference score since there are more than one difference
A - B, B - C, & A - C
1 factor ANOVA

72. 1 factor ANOVA The ANOVA tests this one!! XA = XB = XC XA ≠ XB ≠ XC
Null hypothesis:
H0: all the groups are equal
The ANOVA tests this one!!
XA = XB = XC
Do further tests to pick between these
Alternative hypotheses
HA: not all the groups are equal
XA ≠ XB ≠ XC
XA ≠ XB = XC
XA = XB ≠ XC
XA = XC ≠ XB
1 factor ANOVA

73. 1 factor ANOVA Planned contrasts and post-hoc tests:
- Further tests used to rule out the different Alternative hypotheses
XA ≠ XB ≠ XC
Test 1: A ≠ B
XA = XB ≠ XC
Test 2: A ≠ C
XA ≠ XB = XC
Test 3: B = C
XA = XC ≠ XB
1 factor ANOVA

74. 1 factor ANOVA Reporting your results The observed differences
Kind of test
Computed F-ratio
Degrees of freedom for the test
The “p-value” of the test
Any post-hoc or planned comparison results
“The mean score of Group A was 12, Group B was 25, and Group C was 27. A 1-way ANOVA was conducted and the results yielded a significant difference, F(2,25) = 5.67, p < Post hoc tests revealed that the differences between groups A and B and A and C were statistically reliable (respectively t(1) = 5.67, p < 0.05 & t(1) = 6.02, p <0.05). Groups B and C did not differ significantly from one another”
1 factor ANOVA

75. We covered much of this in our experimental design lecture
More than one factor
Factors may be within or between
Overall design may be entirely within, entirely between, or mixed
Many F-ratios may be computed
An F-ratio is computed to test the main effect of each factor
An F-ratio is computed to test each of the potential interactions between the factors
Factorial ANOVAs

76. Factorial ANOVAs Reporting your results The observed differences
Because there may be a lot of these, may present them in a table instead of directly in the text
Kind of design
e.g. “2 x 2 completely between factorial design”
Computed F-ratios
May see separate paragraphs for each factor, and for interactions
Degrees of freedom for the test
Each F-ratio will have its own set of df’s
The “p-value” of the test
May want to just say “all tests were tested with an alpha level of 0.05”
Any post-hoc or planned comparison results
Typically only the theoretically interesting comparisons are presented
Factorial ANOVAs

77. Non-Experimental designs
Sometimes you just can’t perform a fully controlled experiment
Because of the issue of interest
Limited resources (not enough subjects, observations are too costly, etc).
Surveys
Correlational
Quasi-Experiments
Developmental designs
Small-N designs
This does NOT imply that they are bad designs
Just remember the advantages and disadvantages of each
Non-Experimental designs

78. Small N designs What are they?
Historically, these were the typical kind of design used until 1920’s when there was a shift to using larger sample sizes
Even today, in some sub-areas, using small N designs is common place
(e.g., psychophysics, clinical settings, animal studies, expertise, etc.)
Small N designs

79. In contrast to Large N-designs (comparing aggregated performance of large groups of participants)
One or a few participants
Data are typically not analyzed statistically; rather rely on visual interpretation of the data
Small N designs

80. = observation
Treatment introduced
Steady state (baseline)
Observations begin in the absence of treatment (BASELINE)
Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded
Small N designs

81. Small N designs Baseline experiments – the basic idea is to show:
= observation
Reversibility
Treatment removed
Transition steady state
Steady state (baseline)
Treatment introduced
Baseline experiments – the basic idea is to show:
when the IV occurs, you get the effect
when the IV doesn’t occur, you don’t get the effect (reversibility)
Small N designs

82. Unstable
Stable
Before introducing treatment (IV), baseline needs to be stable
Measure level and trend
Level – how frequent (how intense) is behavior?
Are all the data points high or low?
Trend – does behavior seem to increase (or decrease)
Are data points “flat” or on a slope?
Small N designs

83. ABA design ABA design (baseline, treatment, baseline)
Steady state (baseline)
Transition steady state
Reversibility
ABA design (baseline, treatment, baseline)
The reversibility is necessary, otherwise
something else may have caused the effect
other than the IV (e.g., history, maturation, etc.)
There are other designs as well (e.g., ABAB see figure13.6 in your textbook)
ABA design

84. Small N designs Advantages
Focus on individual performance, not fooled by group averaging effects
Focus is on big effects (small effects typically can’t be seen without using large groups)
Avoid some ethical problems – e.g., with non-treatments
Allows to look at unusual (and rare) types of subjects (e.g., case studies of amnesics, experts vs. novices)
Often used to supplement large N studies, with more observations on fewer subjects
Small N designs

85. Small N designs Disadvantages
Difficult to determine how generalizable the effects are
Effects may be small relative to variability of situation so NEED more observation
Some effects are by definition between subjects
Treatment leads to a lasting change, so you don’t get reversals
Small N designs

86. Some researchers have argued that Small N designs are the best way to go.
The goal of psychology is to describe behavior of an individual
Looking at data collapsed over groups “looks” in the wrong place
Need to look at the data at the level of the individual
Small N designs