2-10

Question 1

within-subjects design

Answer 1

An experimental design in which each subject is exposed to all levels of an independent variable.

data is averaged for analysis

also called repeated measures

Question 2

An experimental design in which each subject is exposed to all levels of an independent variable.

data is averaged for analysis

Answer 2

within-subjects design

also called repeated measures

Question 3

between-subjects design

Answer 3

An experimental design in which different groups of subjects are exposed to the various levels of the independent variable.

data is averaged for analysis

Question 4

An experimental design in which different groups of subjects are exposed to the various levels of the independent variable.

data is averaged for analysis

Answer 4

between-subjects design

Question 5

Pros/cons of and when to use within-subjects?

Answer 5

PROS: reduces error variance

CONS: • more demanding on subjects, especially in complex designs
• Subject attrition is a problem
• Carryover effects

WHEN TO USE: • Subject variables are correlated with the DV
• It is important to economize on participants or subjects
• You want to assess the effects of increasing exposure on behavior
• Track over time

Question 6

Advantages of matching?

Answer 6

Control over subject variables that can mask effects of your
independent variable

Increases sensitivity to effects of your independent variable

Question 7

What are main effects and how do we determine them?

Answer 7

The independent effect of one independent variable in a factorial design on the dependent variable. There are as many main effects as there are independent variables.

determine them:

Question 8

The independent effect of one independent variable in a factorial design on the dependent variable.

Answer 8

main effects

Question 9

interaction effects

Answer 9

When the effect of one independent variable on the dependent variable in a factorial design changes over the levels of another independent variable.

Question 10

When the effect of one independent variable on the dependent variable in a factorial design changes over the levels of another independent variable.

Answer 10

interaction effects

Question 11

error variance

Answer 11

Variability in the value of the dependent variable that is related to extraneous variables and not to the variability in the independent variable.

Question 12

Variability in the value of the dependent variable that is related to extraneous variables and not to the variability in the independent variable.

Answer 12

error variance

Question 13

How do we reduce error variance?

Answer 13

Hold extraneous variables constant by treating participants as
similarly as possible

Match participants on crucial characteristics

Question 14

How do we know whether our results are due to error variance or our manipulation (IV)?

Answer 14

You can estimate the probability that observed differences are caused by error variance by using inferential statistics

Question 15

If you have one IV, with multiple levels, you have a _____________ design. Multiple IVs with multiple levels is _________ design.

Answer 15

single-factor; factorial

Question 16

carryover effects

Answer 16

Exposure to a previous treatment affects performance in a subsequent treatment

Question 17

Exposure to a previous treatment affects performance in a subsequent treatment

Answer 17

carryover effects

Question 18

What are sources of carryover effect?

Answer 18

learning
fatigue
habituation (repeated exposure to a stimulus)
sensitization
contrast
adaptation

Question 19

How to reduce carryover effect?

Answer 19

counterbalancing: treatments presented in a different order for different subjects

partially-counter balancing: like counterbalancing except each treatment appears equally often in each position

Question 20

confounding

Answer 20

Two variables that vary together in such a way that the effects of one cannot be separated from the effects of the other.

Question 21

Two variables that vary together in such a way that the effects of one cannot be separated from the effects of the other.

Answer 21

confounding

Question 22

Sources of confounding?

Answer 22

experimenter bias

treatment conditions are not carefully conceived and unintended variables are introduced

Question 23

How do we deal with confounding variables?

Answer 23

plan carefully how your independent variables are to be executed.

Careful evaluation of your experimental design and variables