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