### df between

An APA ANOVA result is presented thus: F (X, 12) = 19.43, p < .05 . What does X represent?

### one column

When arranging data in SPSS for an independent samples ANOVA, are the observations for different groups entered in separate columns for each group, or are they arranged with all observations in one column and group assignment in a separate column? [separate columns/ one column]

### has two or more treatment conditions with the same group of individuals tested in all the conditions

A repeated-measures ANOVA is used when the study either 1) ____________, or 2) observes the same group of participants at two or more different times.

### observes the same group of participants at two or more different times

A repeated-measures ANOVA is used when the study either 1) has two or more treatment conditions with the same group of individuals tested in all the conditions, or 2) ____________.

### factorial

The design of a research study that involves more than one factor is called a ________ design

### 2

In an experiment that looks at the effect of three different temperatures and two different levels of humidity on the level of human comfort, there are ____ factors.

### 2, 3

In an experiment that looks at the effect of three different temperatures and two different levels of humidity on the level of human comfort, the design would be considered a ___x____ factorial design

### 6

In an experiment that looks at the effect of three different temperatures and two different levels of humidity on the level of human comfort, there would be ____ groups.

### 2

In an experiment that looks at the effect of 5 different personalities and two different genders on depression, there are ____ factors.

### 5, 2

In an experiment that looks at the effect of five different personalities and two different genders on depression, the design would be considered a ___ x ___ factorial design.

### 10

In an experiment that looks at the effect of 5 different personalities and two different genders on depression, there would be ____ groups.

### personality on depression, gender on depression, interaction effect

In an experiment that looks at the effect of 5 different personalities and two different genders on depression, the three questions answered by a two-factor ANOVA are 1) the effect of ________, 2) the effect of __________, and 3) _________.

### Interaction effect

In an experiment that looks at the effect of 5 different personalities and two different genders on depression, the effect of specific combinations of personality and gender on depression is called the _______.

### Interaction effect

When we examine whether the effect of one factor on the dependent variable depends on the different levels of the other factor, we are examining the ________.

### total, between treatments, between factors

When calculating a two-factor ANOVA, what are the three variances we have to calculate?

### reject

In a two-factor ANOVA, when the F-obtained is > then F-critical, then we _______ the null hypothesis.

### independent

To analyze data from a study that compared men and women on their extraversion, would you use an independent samples t-test, or related samples t-test? [independent/related]

### independent

To analyze data from a study that compared students taught in-class with students taught on-line, would you use an independent samples t-test, or related samples t-test? [independent/related].

### related

To analyze data from a study that measured depression before and after treatment, would you use an independent samples t-test, or related samples t-test?

### related

To analyze data from a study that measured depression from participants that had been matched for certain variables, would you use an independent samples t-test, or related samples t-test?

### D

In a two-factor analysis of variance a main effect is defined as: A) the mean difference between the two factors. B) the mean differences among all treatment conditions. C) the difference between the largest treatment mean and the smallest treatment mean. D) the mean differences among the levels of one factor.

### C

The results from a two-factor analysis of variance show a significant main effect for factor A and a significant main effect for factor B. Based on this information, you can conclude that: A) there probably is a significant interaction B) the interaction cannot be significant. C) you cannot make any conclusion about the significance of the interaction. D) there must be a significant interaction.

### D

A two-factor experimental study means that the study has: A) two dependent variables. B) exactly two groups of participants. C) an interaction between two variables. D) two independent variables.

### D

For an experiment involving 2 levels of factor A and 3 levels of factor B, with a sample of n = 10 in each treatment condition, what are the df values for the F-ratio for the AxB interaction? A) 3, 54 B) 1, 54 C) 5, 54 D) 2, 54.

### A

The results of a two-factor analysis of variance produce df = 1, 30 for the F-ratio for factor A and df = 2, 30 for the F-ratio for factor B. What are the df values for the AxB interaction? A) 2, 30 B) 3, 30 C) 2, 60 D) 1, 30.

### A

A two-factor, independent-measures research study is evaluated using an analysis of variance. The F-ratio for factor A has df = 2, 36 and the F-ratio for factor B has df = 3, 36. Based on this information, what are the df values for the AxB interaction? A) df = 6, 36 B) df = 5, 72 C) df = 5, 36 D) df = 6, 72.

### A

The analysis of variance for a two-factor experiment produces: A) three separate F-ratios. B) one overall F-ratio followed by a series of required post hoc tests. C) four separate F-ratios. D) two separate F-ratios.

### B

In a two-factor analysis of variance, the F-ratios for factor A, factor B, and the AxB interaction: A) may have different df values and may have different denominators. B) may have different df values but they all have the same denominator. C) all have the same df values and they all have the same denominator. D) all have the same df values but they may have different denominators.

### B

If the results of a two-factor experiment are presented in a line graph, then an interaction can be seen whenever: A) there is a space separating the lines. B) the lines move toward each other or cross. C) the lines are parallel. D) the lines in the graph are not straight (bent).

### A

A two factor analysis of variance produces SSA = 20, SSB = 40 and SSAxB = 90. For this analysis, what is the value for SSbetween treatments ? A) 150 B) 30 C) Cannot be determined without additional information. D) 60.

### B (check this answer)

A two factor analysis of variance produces SSA = 120, SSbetween treatments = 240, and SSAxB = 90. For this analysis, what is the value for SSB? A) 150 B) 30 C) Cannot be determined without additional information. D) 60.

### correlation

A statistical technique that is used to measure and describe the relationship between two variables in a study where the variables are observed and no effort is made to control or manipulate them.

### Pearson's r

Measure of correlation that measures the degree and direction of the linear relationship between two variables.

### increase

When there is a positive correlation between x and y, it means that as X increases, Y tends to ______

### decrease

When there is a positive correlation between x and y, it means that as X decreases, Y tends to ______

### decrease

When there is a negative correlation between x and y, it means that as X increases, Y tends to ______

### increase

When there is a negative correlation between x and y, it means that as X decreases, Y tends to ______

### direction, strength

Pearson's r measure of correlation indicates what three characteristics of the relationship between two variables? 1) ________, 2) form (usually linear), 3) _________

### -0.60

Which Pearson's r indicates a stronger relationship between the variables X and Y? r=-0.60 or r=+0.30

### theory verification (theories make specific predictions about the relationships between variables)

Uses for the Pearson's correlation: 1) prediction (use one variable to make predictions about the other), 2) validity (correlate a new IQ test with an old one), 3) reliability (how consistent, how stable is a test) - correlate different administration of the test to the same participants, 4) ________.

### prediction (use one variable to make predictions about the other)

Uses for the Pearson's correlation: 1) ________, 2) validity (correlate a new IQ test with an old one), 3) reliability (how consistent, how stable is a test) - correlate different administration of the test to the same participants, 4) theory verification (theories make specific predictions about the relationships between variables).

### validity (correlate a new IQ test with an old one)

Uses for the Pearson's correlation: 1) prediction (use one variable to make predictions about the other), 2) ________, 3) reliability (how consistent or stable is a test - correlate different administration of the test to the same participants), 4) theory verification (theories make specific predictions about the relationships between variables).

### reliability (how consistent or stable is a test - correlate different administration of the test to the same participants)

Uses for the Pearson's correlation: 1) prediction (use one variable to make predictions about the other), 2) validity (correlate a new IQ test with an old one), 3) ________, 4) theory verification (theories make specific predictions about the relationships between variables).

### 25 (use r-squared for predictions: 0.5 squared is 0.25 = 25%)

If a correlation between X and Y yields r = 0.5, we can conclude that ____% of the variability of X is predicted by Y.

### -0.6 (use r-squared for predictions: [-0.60] x [-0.60] = 0.36 = 36%)

Which Pearson's r indicates a stronger relationship between the variables X and Y? r=-0.60 or r=+0.30

### correlation does not explain WHY variables are related (correlation is not causation)

Four cautions for using correlations: 1) _________, 2) range restriction (r value is affected by the range of scores), 3) strong outlier affect, 4) use r-squared (not r) for predictions.

### range restriction (r value is affected by the range of scores)

Four cautions for using correlations: 1) correlation does not explain WHY variables are related (correlation is not causation), 2) _________, 3) strong outlier affect, 4) use r-squared (not r) for predictions.

### strong outlier affect

Four cautions for using correlations: 1) correlation does not explain WHY variables are related (correlation is not causation), 2) range restriction (r value is affected by the range of scores), 3) _________, 4) use r-squared (not r) for predictions.