Chapter 8

Question 1

Factor means

Answer 1

an independent variable

Question 2

Factorial means

Answer 2

More than one independent variable

Question 3

Factorial Design

Answer 3

An experiment that contains more than one IV.

Question 4

Factorial Notation

Answer 4

A factorial is described with a numbering system that simultaneously identifies the # of IVs and the # of levels of each IV.

Question 5

The # of digits =

Answer 5

The number of IVs.

Question 6

The value of digits =

Answer 6

The # of levels of that IV.

Question 7

In order to determine the number of conditions or cells,

Answer 7

we multiply the numbers in the notation.

Question 8

In factorial designs, levels and conditions

Answer 8

cannot be used interchangeably, they now refer to different things.

Question 9

A cell matrix helps

Answer 9

us figure out what the study is about.

Question 10

The inferential statistic to be used in a single-factor two-level design is

Answer 10

A t-test.

Question 11

The inferential statistic to be used in a single-factor multi-level design is

Answer 11

The one-way analysis of variance (ANOVA), is followed by a post-hoc test if the ANOVA is significant.

Question 12

The ANOVA is performed and within it,

Answer 12

The inferential statistics are performed on the main effects and the interaction effects.

Question 13

Main Effect

Answer 13

Refers to the overall effect of one particular IV.

Question 14

The number of main effects =

Answer 14

The number of IVs.

Question 15

To determine the main effect,

Answer 15

we combine the data over all levels of the other factor.

Question 16

We find the mean in the cell matrix by

Answer 16

Adding the values of the two levels and dividing them by the number of IVs.

Question 17

Interaction Effects

Answer 17

Occurs when the effect of one IV depends on the level of another IV.

Question 18

Interaction effects take precedence over

Answer 18

Main effects

Question 19

The advantage of factorials over single-factor designs is that

Answer 19

It is possible to detect interactions that provide more accurate results.

Question 20

In factorial designs, which is better, a line graph, a bar graph, or a histogram?

Answer 20

A line graph

Question 21

An easier, more efficient way of determining whether an interaction effect is present is by

Answer 21

Looking at the data that is plotted in a line graph. If the lines are not parallel, there is an interaction.

Question 22

The interaction is considered to be more important than the main effect because it

Answer 22

Provides a more detailed account of how the variables are related.

Question 23

The independent variable will always be on the x-axis or the y-axis?

Answer 23

The y-axis.

Question 24

If we have a study with 2 IVs and want to determine if the main and interaction effects are statistically significant, we will perform

Answer 24

An ANOVA test.

Question 25

An ANOVA is an

Answer 25

umbrella term for inferential statistics that you perform on each possible effect.

Question 26

Each inferential test is called an

Answer 26

F-test

Question 27

If a study has more than one dependent variable, we will have to draw

Answer 27

a separate graph for each dependent variable.

Question 28

Ceiling Effect

Answer 28

When the mean of 2 or more conditions are near the maximum it gives the impression that no difference exists between them.

Question 29

The floor effect is the exact opposite of a

Answer 29

Ceiling effect

Question 30

Mixed Factorial Design

Answer 30

Atleast one IV is a between-subject and another IV is a within-subject.

Question 31

Factorial designs can be

Answer 31

completely between-subject or completely within-subject.

Question 32

Between-subject

Answer 32

All IVs are between-s factors and each condition has a different set of Ss.

Question 33

Within-subject

Answer 33

All IVs are within-s factors and the same set of Ss is tested across all conditions.

Question 34

P x E

Answer 34

P = person (subject) E = environment (manipulated)

Question 35

In a Mixed Factorial,

Answer 35

at least one IV is a between-subject factor and one IV is a within-subject factor. Now we have two control problems. Creating equivalent groups for the between-subject and controlling for order effect for the within-subject.

Question 36

P x E Factorial Design

Answer 36

These between-subject designs have a manipulated IV and a subject IV.

Question 37

P represents

Answer 37

subject variable

Question 38

E represents

Answer 38

manipulated variable

Question 39

The P x E Factorial Design involves a

Answer 39

-Between-subject factor -Within-subject factor -Manipulated variable -Subject variable

Question 40

If the ANOVA indicates that the interaction effect is statistically significant, that gives us the green light to perform a

Answer 40

Simple effects analysis to determine the more detailed nature of that interaction and whether these comparisons are statistically significant.

Question 41

Simple Effects Analysis

Answer 41

Breaking down the interaction to further understand it. It examines the IV at each level of the other IV.

Question 42

We don't compare conditions in the matrix that are

Answer 42

Diagonal to one another.