Literature Review PaperLiterature Review Paper

• Use a summary narrative form

• Assignment sheet http://commfaculty.fullerton.edu/jreinard/sample_papers.htm

• Outline format essential

Agenda for This Unit

• Experimental Design

• Causal Reasoning in Experiments

• Notation

• Factorial Designs

• Main Effects

• Interaction Effects

Experimental Design

Defined: a study of the effects of variables manipulated by a researcher in a situation in which all other variables are controlled, completed for the purpose of establishing causal relationships

Experimental Design• Distinguishing Carefully Designed

Studies from Experiments

• Manipulation of Variables

• Cause-effect Conclusions

Finding True Causes in Experiments

• The Challenge of Control

• A Case Study

Notation

O -

observation

X -

experimental variable

R -

randomization

Designs

• Reading Designs

• Pre-Experimental vs. True Experimental Designs

Randomization

• controlling for extraneous variables

• random assignment

• random selection

Comparing Designs

Internal Validity

Defined: the degree to which the researcher can make an unequivocal statement of experimental effect

Sources

Mnemonic device:

He said my tush is sagging extra inches.

External Validity

Defined: the degree to which research findings can be generalized to other similar circumstances

Sources

Other Sources of Invalidity

• Law of the Instrument

• Experimenter Effects (Demand Characteristics)

• Ignoring Initial Differences between Control and Experimental Groups

Factorial Designs

Defined: experimental designs using more than one independent variable

One Factor DesignsX1- +

A Two Factor Design

A 2 x 2 Design

X1- +

- +

X2

A 3 x 2 DesignX1

- +

X2

LOW MO HI

A Three Factor Design

X1- +

- +

X2

- +

X3

The Relationship Between Factorial and Simple Designs

X1- +

- +

X2

The Relationship Between Factorial and Simple Designs

X1- +

- +

X2

R X OR OR X OR O

R X OR O

R X OR O

Identification of Offset Control Groups

X1- +

- +

X2

R X OR OR X OR O

R X OR O

R X OR O

Identification of Offset Control Groups

X1- +

- +

X2

R X O

R X OR O

R O

R X OR OR X OR O

Designs in Which Control Groups are Included

X1- +

- +

X2

R X O

R X OR O

R X OR OR X OR O

Main Effects

Defined: dependent variable effects from independent variables separately

A Main Effect Example

X1- +

- +

X2

5040

3020

Amount of Attitude Change Advocated

Source Character

A Main Effect Example

X1- +

- +

X2

5040

3020

30 40

Amount of Attitude Change Advocated

Source Character

A Main Effect Example

X1- +

- +

X2

5040

3020 25

45

30 40

Amount of Attitude Change Advocated

Source Character

Diagrams of Main Effects

20

15

10

5

Men Women

Variable 1

D.V.:touching

Diagrams of Main Effects

20

15

10

5

Men Women

Variable 1

D.V.:touching

Diagrams of Main Effects

50

40

30

20

Low High

Variable 1

Amount of Attitude Change Advocated

D.V.:AttitudeChange

Diagrams of Main Effects

50

40

30

20

Low High

Variable 1

Variable 2 (Low)

Amount of Attitude Change Advocated

Source Character

D.V.:AttitudeChange

Diagrams of Main Effects

50

40

30

20

Low High

Variable 1

Variable 2 (High)

Variable 2 (Low)

Amount of Attitude Change Advocated

Source Character

D.V.:AttitudeChange

Effects X1- +

- +

X2

19 9

2111

Effects X1- +

- +

X2

19 9

2111 16

14

Effects X1- +

- +

X2

19 9

2111 16

14

2010

Diagrams of Main Effects

20

15

10

5

Low High

Variable 1

Diagrams of Main Effects

20

15

10

5

Low High

Variable 1

Variable 2 (Low)

Diagrams of Main Effects

20

15

10

5

Low High

Variable 1

Variable 2 (Low)

Variable 2 (High)

Interaction EffectsDefined: dependent variable effects from

independent variables taken together

Forms: Ordinal

(in the same direction as the main effects of variables involved)

Disordinal

(not in the same direction as the main effects of

the variables involved)

An Interaction Effect ExampleX1- +

- +

X2

5020

2020

An Interaction Effect ExampleX1- +

- +

X2

5020

2020 20

35

An Interaction Effect ExampleX1- +

- +

X2

5020

2020 20

35

20 35

Diagram of the Interaction Effect

50

40

30

20

Low High

Variable 1

Diagram of the Interaction Effect

50

40

30

20

Low High

Variable 1

Variable 2 (Low)

Diagram of the Interaction Effect

50

40

30

20

Low High

Variable 1

Variable 2 (High)

Variable 2 (Low)

Another Interaction Effect Example

X1- +

- +

X2

2040

4020

Sex of Clinician

Male FemaleType of Stuttering: Clonic

Blocking

Another Interaction Effect Example

X1- +

- +

X2

2040

4020

Sex of Clinician

Male FemaleType of Stuttering: Clonic

Blocking

30 30

Another Interaction Effect Example

X1- +

- +

X2

2040

4020

Sex of Clinician

Male FemaleType of Stuttering: Clonic

Blocking

30

30

30 30

Diagram of the Interaction Effect

40

30

20

10

Low High

Variable 1 Male Female

Sex of Clinician

Diagram of the Interaction Effect

40

30

20

10

Low High

Variable 1

Variable 2 (Low)

Male Female

Sex of Clinician

Type of Stuttering:

Clonic

Diagram of the Interaction Effect

40

30

20

10

Low High

Variable 1

Variable 2 (Low)

Variable 2 (High)

Male Female

Sex of Clinician

Type of Stuttering:

Clonic

Blocking

Interpreting Ordinal Interactions

• acceptable to look at the independent variables separately

• permissible to interpret main effects for independent variables involved in the interaction

Interpreting Disordinal Interactions

• must look at both independent variables together

• not permissible to interpret main effects for independent variables involved in the interaction

OK to Interpret Main Effects

50

40

30

20

Low High

Variable 1

Variable 2 (High)

Variable 2 (Low)

Not OK to Interpret Main Effects

40

30

20

10

Low High

Variable 1

Variable 2 (Low)

Variable 2 (High)

Effects: Example 1

20

15

10

5

Low High

Variable 2

Effects: Example 1

20

15

10

5

Low High

Variable 2

Variable 1 (Low)

Effects: Example 1

20

15

10

5

Low High

Variable 2

Variable 1(High)

Variable 1 (Low)

20

15

10

5

Low High

Variable 2

Effects: Example 2

20

15

10

5

Low High

Variable 2

Effects: Example 2

Variable 1 (Low)

20

15

10

5

Low High

Variable 2

Effects: Example 2

Variable 1 (High)

Variable 1 (Low)