Download - Chapter 8 class version 2
EXPERIMENTAL RESEARCH DESIGN
Chapter 8 -- Continued
10/16/2012
Roadmap
Discuss:Exam 2Reflection Assignment #1
Quick Review of Weak and Strong Between-Participants designs
New: Within-Participants and factorial designs
Experimental Research Design• Weak vs. Strong experimental design
One-group Posttest-only Design
Treatment Posttest Measure
X O
One-group Pretest-Posttest Design
A treatment condition is interjected between pre- and posttest of the dependent variable.
Pretest measure Treatment Posttest Measure
O X O
Compare
Nonequivalent Posttest-Only Design
Performance of an experimental group is compared with that of a nonequivalent control group at posttest
TreatmentPosttest Measure
Experimental Group X O
Control Group OCompare
Strong Experimental Research Designs Designs that effectively control extraneous
variables and provide strong evidence of cause and effect
Strong Experimental Research Designs Basic designs – one IV and one DV
Between-participantsWithin-Participants (repeated measures)
Factorial Designs – multiple IVs
Posttest-Only Control Group Design
This design looks familiar, right? What is different now?
TreatmentPosttest Measure
Experimental Group X O
Control Group OCompare
Posttest-Only Control Group Design
We could have more than 1 experimental group
TreatmentPosttest Measure
Control Group O
Experimental Group 1 X1 O
Experimental Group 2 X2 OCompare
Pretest-Posttest Control Group Design Simply add pretest to previous design What comparisons will we make?
PretestMeasure Treatment Posttest
Measure
Experimental Group O X O
Control Group O O
Benefits of Pretest
Ensure equivalency of groups
Detect ceiling and floor effects
Empirically demonstrate effect of treatment
See if initial position on DV is important
Within-Participants Designs A.k.a. repeated measures designs Most common: posttest-only P’s receive EVERY level of treatment Complete posttest after each exposure Will discuss counterbalancing next week There is no control group—each P is own
control*
Each participant experiences ALL conditions Example: impact of breakfast choice on test
performance
Pop-Tarts OEggs & toast
ONo
breakfastO
P1 P1 P1 P1 P1 P1
P2 P2 P2 P2 P2 P2
P3 P3 P3 P3 P3 P3
P4 P4 P4 P4 P4 P4
P5 P5 P5 P5 P5 P5
Day 1 Day 2 Day 3
Advantages of within-participants Each P is his/her own control group Requires fewer P’s
Disadvantages of within-participants Sequencing effects
Order of condition exposure may impact DVCounterbalancing helps
Requires more time for each participantFatigue, attrition
Factorial Designs So far: basic designs (one IV, one DV)
Now: more than one IV (still one DV)
2 x 3 Factorial Design
Independent Variable AA1 A2 A3
A1 B1Cell mean
A2 B1 A3 B1
A1 B2 A2 B2 A3 B2IV B
B1
B2
B1Marginalmean
B2Marginalmean
A1Marginal
mean
A3Marginal
mean
A2Marginal
mean
2 types of effects Main Effect - The influence of one
Independent variable in a factorial design
Interaction Effect - joint influence of two or more IVs on the DVThe effect of one IV depends on the level of
another IV.
Example
Study examining gender (M-F) and intervention to improve test-taking skills
3 IV levelscontrol (no intervention)reading material (instructional booklet)personalized tutoring
Intervention
Control Booklet Tutoring
A1 B1 A2 B1 A3 B1
A1 B2 A2 B2 A3 B2
Male
Female
What would a main effect of gender look like?
Control Reading Tutoring0
102030
405060
708090
100
MaleFemale
What would a main effect of intervention look like?
0
20
40
60
80
100
120
Control Reading Tutoring
MaleFemale
What would an interaction look like?
0
20
40
60
80
100
120
Control Reading Tutoring
MaleFemale
What should we interpret?
If one main effect - report it 2 main effects – report both BUT if there’s an interaction…
Only interpret/report the interactionBecause the effect of test-taking intervention
depends on gender
Interaction
0
20
40
60
80
100
120
Control Reading Tutoring
MaleFemale
Combining Between and Within Participant Designs
Factorial design based on a mixed model -or- mixed model design IVs can be either between-groups (e.g.,
gender) or within-groups (a.k.a. repeated)
Advantages of Factorial Designs Can test more than 1 hypothesis at a time Able to deal with extraneous variables
Build into design and test outright Increases precision b/c it evaluates more
variables at once Allows researcher to understand interactive
effects of variables
Disadvantages of Factorial Designs
Gets messy with more than 2 IVs
Requires more participants (N per cell)
More difficulty to simultaneously manipulate all IVs when you have more of them
Choosing a Research Design Depends on… Research question Nature of variables you are investigating We have discussed design building blocks
Page 255: guiding questions