7. Characteristics of True Designs
Manipulation (treatment)
Randomization
Control group
Characteristics of simple true designs
One IV with 2 levels (T, C)
One DV
11. Advantages & Disadvantages
Advantages of pretest design
Equivalency of groups
Can measure extent of change
Determine inclusion
Assess reasons for and effects of mortality
Disadvantages of pretest design
Time-consuming
Sensitization to pre-test
16. Latin Squares
1 2 3 4
A B D C
Row 1
(60) (0) (120) (180)
B C A D
Row 2
(0) (180) (60) (120)
C D B A
Row 3
(180) (120) (0) (60)
D A C B
Row 4
(120) (60) (180) (0)
17. Dealing with Order Effects
Counterbalancing
n!
Latin
squares
Randomized blocks
Time interval between treatments
18. Variations
Independent groups (between) vs.
repeated measures (within) designs
Consider external validity when
deciding which design to use.
20. Characteristics of True Designs
Manipulation (treatment)
Randomization
Control group
Characteristics of simple true designs
One IV with 2 levels (T, C)
One DV
21. Complex True Experimental
Randomized matched control group
design
Increased levels of IV
Factorial design
Multiple DVs
22. Complex True Experimental
Randomized matched control group
design
Increased levels of IV
Factorial design
Multiple DVs
23. Randomized matched control group
design
M R T Post
M R C Post
• Used in small samples
∀ ↑ cost in time & money
24. Complex True Experimental
Randomized matched control group
design
Increased levels of IV
Factorial design
Multiple DVs
25. Increased Levels of IV
Provides more complete information about
the relationship between the IV & DV
Detects curvilinear relationships
Examines effects of multiple treatments
26. Reward Amount
$0 $1 $2 $3
DV
Performance level (% complete)
Amount of reward promised ($) IV
27. Increased Levels of IV
DV
Performance level (% complete)
Amount of reward promised ($) IV
28. Complex True Experimental
Randomized matched control group
design
Increased levels of IV
Factorial design
Multiple DVs
29. Factorial Design
>1 IV (factor)
Simultaneously determine effects of 2 or
more factors on the DV (real world)
Between Factor vs. Within Factor
ID’d by # of factors and levels of factors
2X2
30. Do differing exercise regimens (hi,
med, lo intensity) have the same effect
on men as they do on women?
3 X 2 (Exercise Regimen X Gender)
2 factors
Exercise Regimen – 3 levels
Gender – 2 levels
Between factors
DV?
Experimental IVs or Participant IVs?
31. Gender
Male Female
High
Exercise
Intensity
Medium
Low
32. Do strength gains occur at the same rate in men
as they do in women over a 6 mo. training period?
Measurements are taken at 0, 2, 4, 6 mo.
2 X 4 (Gender X Time)
? factors
Time – 4 levels
Gender – 2 levels
Between or within factors?
DV?
Experimental IVs or Participant IVs?
33. Time
0 mo. 2 mo. 4 mo. 6 mo.
Gender
Male
Female
34. Cell means, Margin means
Main Effects, Interactions
Time
0 mo. 2 mo. 4 mo. 6 mo.
Gender
Male 50 70 90 130 85
Female 30 60 75 90 64
40 65 83 110 74
Cell means
Margin means Grand mean
36. Parallel lines indicate no Is there
a main
interaction. effect?
Interaction of Exercise Intensity and Gender
70
65
VO2 Max (ml/kg/min)
60
55 Male
50 Female
45
40
35
High Medium Low
Exercise Intensity
37. Is there
a main
Interaction of Exercise Intensity and Gender effect?
70
65
VO2 Max (ml/kg/min)
60
55 High
Medium
50
Low
45
40
35
Male Female
Gender
38. Non-parallel lines indicate an there
Is
interaction. a main
Interaction of Gender and Time
effect?
140
120
Weight Lifted (lbs.)
100
80 Male
60 Female
40
20
0
0 mo. 2 mo. 4 mo. 6 mo.
Time
39. Is there
a main
Interaction Between Gender and Time effect?
140
120
Weight Lifted (lbs.)
100
0 mo.
80 2 mo.
60 4 mo.
6 mo.
40
20
0
Male Female
Gender
41. Advantages of factorial designs:
Greater protection against Type I error
More efficient
Can examine the interaction
Disadvantages:
↑ subject # for between factor designs
Consider external validity when
deciding which design to use.
42. IV A: Exposure to Violence – violent vs.
nonviolent video
IV B: Gender – male vs. female
B1
DV: # ads recalled (0-8)
B2
9
B
1 2
1 1 5 3
A 5
2 9 5 7
5 5 1
1 2
A
43. IV A: Exposure to Violence – violent vs.
nonviolent video
IV B: Gender – male vs. female
B1
DV: # ads recalled (0-8)
B2
9
A: Yes
B: No 5
AxB: Yes 1
1 2
A
44. Complex True Experimental
Randomized matched control group
design
Increased levels of IV
Factorial design
Multiple DVs
45. Do strength gains occur at the same rate in men
as they do in women over a 6 mo. training period?
Measurements are taken at 0, 2, 4, 6 mo.
Time
0 mo. 2 mo. 4 mo. 6 mo.
Gender
Male 50 70 90 130 85
Female 30 60 75 90 64
40 65 83 110 74
50. Quasi-experimental Designs
One group posttest-only design
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
51. Quasi-experimental Designs
One group posttest-only design
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
54. Quasi-experimental Designs
One shot study
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
56. One group pretest-posttest design
Pre T Post
•History
•Maturation
•Testing Use control group
•Instrument decay
•Regression
57. Quasi-experimental Designs
One shot study
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
60. Quasi-experimental Designs
One shot study
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
63. Quasi-experimental Designs
One shot study
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
65. Quasi-experimental Designs
One shot study
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
66. Quasi-experimental Designs
One shot study
One group pretest-posttest design
Non-equivalent control group design
Non-equivalent control group pretest-posttest
design
Time series
Single subject designs (Case study)
Developmental designs
67. Developmental Research Designs
Longitudinal Cross Sectional
Powerful (within Less time consuming
subject) Cohorts problem
Time consuming
Attrition
Testing effect
68. Choosing a Research Design
Best addresses the problem
Ethics
Cost in time and money
Validity (internal & external)
Notas do Editor
Difficult to establish cause-and-effect. Correlational research often done first to establish relationships that may be examined for cause-and-effect. Cause-and-effect are not established by statistics but rather by logical thinking and sound research design. You must establish that no other plausible explanation exists for the changes in the DV except the manipulation done to the IV.
Random groups, controls for past history, maturation, testing, and sources of invalidity based on nonequivalent groups (statistical regression, selection biases, selection-maturation interaction Investigator must control present history, instrumentation, experimental mortality
Makes it possible to ascertain that groups were equivalent at the beginning of the study. Not necessary if Randomization was used Large sample size
Advantages: Fewer subjects needed (less costly) Sensitive to finding statistical differences because of control over participant differences Disadvantages: Order effect (practice, fatigue, carry-over)
Counterbalancing - all possible orders of presentation are included in experiment Latin squares – a limited set of orders constructed to ensure that Each condition appears at each ordinal position Each condition precedes and follows each condition one time
Time interval – may counteract the effects of treatment, but also increases time demands on subjects
Obtain measure of matching variable from each subject Rank from highest to lowest based on score Form matched pairs Randomly assign members of pairs to conditions
Use Figures 10.1 and 10.2 to demonstrate graphically at end of slide.
Significant interaction means that the effect that one factor has on the dependent variable depends on which level of the other factor is being administered
Advantages of factorial designs: Greater protection against Type I error More efficient (1 analysis vs. multiple one-ways) Can examine the interaction (not possible with one-way ANOVAs) Disadvantages: Only for fixed models (levels of IV chosen by researcher) and when subjects are assigned randomly
Makes it possible to ascertain that groups were equivalent at the beginning of the study. Not necessary if Randomization was used Large sample size
Makes it possible to ascertain that groups were equivalent at the beginning of the study. Not necessary if Randomization was used Large sample size
Makes it possible to ascertain that groups were equivalent at the beginning of the study. Not necessary if Randomization was used Large sample size
Developmental research – studies the ways that individuals change as a function of age; age is the independent variable Longitudinal (similar to repeated measures) Powerful (within subject) but several problems Time consuming Attrition due to move, death, school rezoning may change sample characteristics (e.g., more obese subjects die, leaving non-obese subjects in sample – knowledge about obesity is not changing but rather sample is changing) Subjects become familiar with test items (learning effect or items may cause change in behavior) Cross-sectional (similar to independent groups) Less time consuming, but problems Cohorts – a group of people born at about the same time, exposed to same events in society, and influenced by same demographic trends such as divorce rates and family size. Are all age-groups really from same population? Are environmental circumstances that affect jumping performance the same today for 6 yr olds as they were when the 10 yr olds were 6?