Question 1

The sole effect of one IV in a factorial design is called a(n)

interaction.

main effect.

one-way effect.

correlated-groups effect.

Question 2

Suppose a researcher is interested in examining the effects of sex of participant, sex of confederate, and self-esteem of participant (high, low) on the participants' reactions to negative feedback. How many separate groups are involved in this between-groups study?

2

4

6

8

Question 3

Which of the following is an impossible factorial design?

2 x 3

2 x 6

1 x 3

2 x 3 x 4 x 6

Question 4

Kyle is interested in examining how sex of participant (male, female) and self-esteem (high, low) affect perseverance on a novel task. What is the dependent variable in this study?

perseverance on a novel task

sex of participant

self-esteem

the experimenter Kyle

Question 5

Lola conducts a study to examine aggression. She has both men and women come to a laboratory room that is either 70 degrees or 90 degrees. Suppose she finds that overall, participants exhibit more aggression in the 90 degree room than the 70 degree room. This finding illustrates a(n)

main effect of sex of participant.

main effect of temperature.

interaction between sex of participant and temperature.

all of the above

Question 6

A significant interaction means that

there is a significant main effect of one variable and a significant main effect of another IV.

one main effect is significant and one main effect is not significant.

the effects of one IV depend on the particular level of another IV.

the independent variables operate independently from each other.

Question 7

Sandy conducts a study with two independent variables: one is an independent groups IV and the other is a repeated measures IV. Sandy has used _________ assignment.

variance

factorial

mixed

interaction

Question 8

When the effect of one independent variable on your dependent variable changes over levels of a second independent variable, a(n) ________ is present.

Group of answer choices

simple main effect

compound main effect

main effect

interaction

Question 9

Suppose you design an experiment that had one IV with two levels, one IV with three levels, and another IV with four levels. What is the shorthand notation for this design?

Group of answer choices

1 x 9

1 x 24

2 x 3 x 4

1 x 2 x 3

Question 10Question 10

Suppose you were interested in conducting an experiment with two independent variables. Why should you use a factorial design instead of two separate experiments?

The factorial design has greater reliability than two separate experiments.

The factorial design has a decreased chance of experimenter bias affecting the outcome.

The factorial design allows you to test for main effects.

The factorial design allows you to test for interactions

Question 11

Tests of Between-Subjects Effects

Dependent Variable:Int_Politics

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model Intercept	5525.200a 138816.600	5 1	1105.040 138816.600	61.190 7686.727	.000 .000
Gender Edu_Level Gender * Edu_Level	29.400 5328.100 167.700	1 2 2	29.400 2664.050 83.850	1.628 147.517 4.643	.207 .000 .014
Error Total Corrected Total	975.200 145317.000 6500.400	54 60 59	18.059

a. R Squared = .850(Adjusted R Squared = .836)

What is the appropriate write-up for the gender main effect?

There was a significant main effect of gender, F(1, 54) = 1.628, p < .05

There was a significant main effect of gender, F(1, 60) = 1.628, p < .05

There was no significant main effect of gender, F(1, 54) = 1.628, p > .05

There was no significant main effect of gender, F(2, 54) = 1.628, p > .05

Question 12

Tests of Between-Subjects Effects

Dependent Variable:Int_Politics

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model Intercept	5525.200a 138816.600	5 1	1105.040 138816.600	61.190 7686.727	.000 .000
Gender Edu_Level Gender * Edu_Level	29.400 5328.100 167.700	1 2 2	29.400 2664.050 83.850	1.628 147.517 4.643	.207 .000 .014
Error Total Corrected Total	975.200 145317.000 6500.400	54 60 59	18.059

a. R Squared = .850(Adjusted R Squared = .836)

Based on this source table, which kind of design did the researchers use? (Hint: don t forget to look at the df for the main effects! The df is always k - 1)

2 X 2 design

2 X 3 design

2 X 2 X 2 design

2 X 2 X 3 design

Question 13

Tests of Between-Subjects Effects

Dependent Variable:Int_Politics

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model Intercept	5525.200a 138816.600	5 1	1105.040 138816.600	61.190 7686.727	.000 .000
Gender Edu_Level Gender * Edu_Level	29.400 5328.100 167.700	1 2 2	29.400 2664.050 83.850	1.628 147.517 4.643	.207 .000 .014
Error Total Corrected Total	975.200 145317.000 6500.400	54 60 59	18.059

a. R Squared = .850(Adjusted R Squared = .836)

What is the best way to describe this data?

There is one significant main effect and no interactions

There are two significant main effects and no interactions

There is one significant main effect and one interaction

There are two significant main effects and no interactions

Question 14

What is another name for a regression line?

Line of best fit

Scatter plot line

Line graph

Line of the estimate

Question 15

When using two predictor variables, these variables should be ____________.

Correlated

Related

Independent

Dependent

Question 16

If our slope is 3 and our intercept is 14, how many months would a convict with 11 priors expect to receive___________?

45

46

47

48

Question 17

If you are trying to predict Y scores from X scores, X is referred to as the __________.

Nominal variable

Dependent variable

Predictor variable

Criterion variable

Question 18

If variables change in the opposite direction, what type of correlation is this called?

Positive correlation

Negative correlation

Positive causation

Negative causation

Question 191 pts

If the correlation between X and Y is equal to -1.0, what do we know about the prediction of Y by X?

It's weak

It's positive

It's perfect

Question 201 pts

What statistical technique is used to make predictions of future outcomes based on present data?

Analysis of variance

Repeated measures

Linear regression

Correlational analysis