Page Title

PSY 305Module 1

Variables used in experimentation

• Variable– Any characteristic that can vary

• Qualitative– Categories

• Gender, eye color• Quantitative

– Numbers• Weight, height, depression score

Variables used in experimentation

• Independent variable (IV)– Cause– Manipulated variable

• Dependent variable (DV)– Effect– Measured variable

Ways to create variation in independent variable

• Presence vs. absence• Amount of variable• Type of variable

Defining independent variables

• Must translate IV into operational terms– Easy with some IVs

• Drugs, time– Difficult with other IVs

• Aggression, Delay of gratification

Number of independent variables

• More than one provides additional information

• Theoretically no limit• Practically, there is a limit

The dependent variable

• What dependent variable should you select?• How to insure that participants are responding seriously to

the measure?• How many dependent variables do you use?

Research ScenariosSelect the IV and DV

Asch (1952) conducted an experiment in which he wanted to determine if a person’s impressions of another individual is influenced more by information received immediately after being introduced or by information received later in the conversation.

Asch presented a series of positive and negative adjectives to two groups of individuals. One group received positive adjectives first while the other received negative adjectives first. After reading the lists, each group gave their impressions of the hypothetical person.

Answer

• Independent variable– Adjectives (positive or negative)

• Dependent variable– Impressions

Research ScenariosSelect the IV and DV

A study was conducted to determine if men think that women in a bar get more attractive as closing time approaches. This was a field study in which the researcher asked patrons of the bar to evaluate the attractiveness of various women in the bar at four different times in the evening, with the last evaluation being 10 minutes before closing.

Answer

• Independent variable– Time (4 different times)

• Dependent variable– attractiveness

Research ScenariosSelect the IV and DV

Benbow and Stanley (1980) wanted to find out if gender could be used in trying to differentiate mathematical ability, so they compared the test scores on the mathematics portion of the SAT of 9,927 eighth grade boys and girls. In this study, they used only the scores of the boys and girls who had taken the same number of math courses. When they compared the SAT scores, they found that boys scored significantly higher than girls.

Answer

• Independent variable– gender

• Dependent variable– SAT scores

Experimental Research Design

• Research Design—the outline, plan, or strategy used to answer the research question

• Purpose of research design– Control for unwanted variation– Suggests how data will be statistically analyzed

Designs with threat to internal validity

• One-Group Posttest-Only Design

Treatment Response

X Y

Designs with threat to internal validity

• One-Group Pretest-Posttest Design

Pretest treatment Posttest

X Y Y

Designs with threat to internal validity

• Nonequivalent Posttest-Only Design

Treatment Posttest

Experimental Group

X Y

Control Group Y

Requirements for experiment

• Answers research question• Control for extraneous variables• Allow generalizability of results

Pretesting of participants

• To test for a ceiling effect• To test for initial position• To insure initial comparability• To obtain evidence of change

Types of designs

• Between• Within• Mixed

Example of Between Design

Participants are assigned to study only positive words, only negative words, or only neutral words

positivewords

negativewords

neutralwords

Random Assignment

• Random assignment—– randomly assigning participants to treatment

groups

Experimental research designs

• Posttest-Only Design

Treatment Response

Experimental Group

X Y

Control Group Y

Simple Randomized Design

Treatment Response

Group I Y

Group II X1 Y

Group III X2 Y

Group IV X3 Y

Factorial Design

• Independent Variable A1 A2 A3

• Independent B1 • Variable B B2

B3

Components of a factorial design

• Cell—a treatment combination of two or more IV’s• Main effect—the effect of one IV• Interaction effect—when the effect of one IV depends on

the level of another IV

Classic Interaction Effect

15 30 45

45 30 15

Column 30 30 30Means

30

Row30 Means

A1 A2 A3

B1

B2

Classic Interaction Effect

05

101520253035404550

A1 A2 A3

B1B2

Advantages of factorial designs

• Can manipulate more than one IV• Can control potential extraneous variable by building

it into the design• Provides greater precision when add more than one

IV• Can test the effect of interactions

Difficulties with factorial designs

• Increases the number of research participants• Difficulty in manipulating more than one IV• Difficulty in interpreting higher-order interactions

Within subjects designs

Treatment A1 A2 A3

• Same 20 P1 P1 P1

• Participants in . . .• Each Treatment . . .• Condition P20 P20 P20

Example of Within subjects designs

Positive Words

Negative words

Neutral words

The same group of participants studies all 3 types of words that are mixed together on the same study list

Within subjects designs

• Advantages of within-participants design– Equivalence of research participants– Requires fewer participants than between-

participants design• Disadvantage of within-participants design

– Sequencing, carryover, practice effects

Counterbalancing

• Solution to sequence and carryover effects• Counterbalance order of treatment presentation

Factorial design based on a fixed model

• Characteristic is that it has a between and a within component– At least one IV requires different participants for

each level of variation– At least one IV requires the same participants in

each level of variation– Participants randomly assigned to between

component

Choice of research design

• Will it give an answer to the research question?• Will it provide control for extraneous variables?• Should you use a between or within design?

Control

• Identify causal relationships• Internal validity

– Requires control

Ways to achieve control

• Design of the experiment• Statistical adjustments• Incorporate control techniques into the research

design