For an experiment comparing more than two treatment conditions you should use analysis of variance rather than separate t-tests because:
You are less likely to make a mistake in the computations of ANOVA
A test based on variance is more sensitive than a test based on means
ANOVA has less risk of a Type I error because several means are compared in one test
ANOVA has less risk of a Type II error because several means are compared in one test
The null hypothesis for an ANOVA states that
There are no differences between any of the population means
At least one of the population means is different from the others
All the population means are different from each other
None of the other 3 choices are correct
When the null hypothesis is true for ANOVA, what is the expected value for the F-ratio?
In analysis of variance, the F-ratio is a ratio of:
Two (or more) sample means
Sample means divided by sample variances
In an ANOVA, which of the following is most likely to produce a large value for he F-ratio?
Large mean differences and small sample variances
Large mean differences and large sample variances
Small mean differences and small sample variances
Small mean differences and large sample variances
For an independent-measures experiment comparing two treatment conditions with a sample of n= 10 in each treatment, the F-ratio would have to have df equal to:
A researcher uses analysis of variance to test for mean differences among four treatments with a sample of n=6 in each treatment. The F-ratio for this analysis would have what df values?
df= 4, 24
A researcher reports an F-ratio with df= 3,36 for an independent-measures experiment. How many treatment conditions were compared in this experiment?
A researcher reports an F-ratio with df= 3,36 for an independent-measures experiment. How many individual subjects participated in the experiment?
Cannot be determined from the information given
A factor experiment means that the experimental design includes:
Two independent variables
Two dependent variables
An interaction between the independent and the dependent variable
Exactly two separate groups of subjects
In a two-factor analysis of variance a main effect is defined as:
The mean differences among the levels of one factor
The mean differences among all treatment conditions
The mean differences between the two factors
The difference between the largest treatment mean and the smallest treatment mean
The results from a 2-way ANOVA show that both main effects are significant. We can conclude:
That the interaction also must be significant
That the interaction cannot be significant
There must be an interaction but it may not be statistically significant
You can make no conclusions about the significance of the interaction
Prior to introducing a new cake mix to the public, a food company wishes to determine the combination of baking temperature and baking time that will result in the best tasting cake. In their experiment, cakes made from the new cake mix are baked at 325 degrees F, 350 degrees F, and 400 degrees F for 50 minutes, 60 minutes, 70 minutes and 80 minutes. Taste is rated for each combination of baking temperature and baking time. How many treatments are in the experiment?
In two way ANOVA what should you always look at first?
The significance of factor 1
The significance of factor 2
The interaction between factors 1 and 2
None of the above
To test for an interaction effect the statistic should be:
One way ANOVA
Two way ANOVA
Two way chi-squared
A researcher wants to test the differences between three treatment conditions (cognitive-behavioural therapy, aversion therapy and behavioural therapy) by assigning individuals randomly to one of the three conditions. The researcher would need to perform which of the following tests to analyse the results?
Repeated measures ANOVA
Fisher's exact probability test
One-way between groups ANOVA
THe one way ANOVA is used to test hypothesis concerning:
ANOVA assumes that the data to be analysed:
Has a normal distribution
That the data is interval
The variances in the groups are similar
All of the above
One way ANOVA should be used when:
We want to compare the means of 1 variable from 2 groups
We want to compare the means of 2 variables within 2 groups
We want to compare 1 variable from more than 2 groups
None of these options are correction
Which of the following is incorrect in relation to ANOVA:
ANOVA is relatively robust to small numbers of participants
ANOVA is relatively robust to unequal numbers of scores in different conditions
ANOVA relatively robust to skewed data
ANOVA evaluates how likely it is that any differences in conditions is due to:
Homogeneity of variance
In a study which investigates the differences in contents among 3 different brands of cigarettes an ANOVA was performed which yielded a small p value. Which of the following is correct:
Because the p value is small there is evidence that all the brands differ from each other in the mean amount of tar present.
Because the p value is small, there is no evidence than any of the brands differ in the mean tar content from the other brands.
Because the p value us small, there is evidence that at least one brand has a different mean tar content from the other brands.
Because the p value is small, there is evidence that all brands have the same men tar content.
A large F ratio means:
more likelihood the variance is causes by the IV
Less likelihood the variance is caused by the IV
The sample size used was small
The error variance was larger than the IV variance
B and D
For ANOVA the null hypothesis states:
For the individuals in the sample there is no consistent difference between treatments
For the individuals in the population there is no consistent difference between the treatments
For the individuals in the sample there is a consistent difference between the treatments
For the individuals in the sample there is a consistent difference between treatments
If the null hypothesis is true you expect:
F to have a value closer to 1
F to have a value closer to 0
p to have a value > .05
p to have a value < .05
The degrees of freedom for the F test in a one way ANOVA are:
(n-c) and (c-1)
(c-1) and (n-c)
(c-n) and (n-1)
(n-1) and (c-n)
In a one way ANOVA:
An interaction is present
An interaction can be tested
There is no interaction
When carrying out an analysis, if the variance due to the IV is greater than the error variance, what does this mean?
An error in calculation was made
There should have been additional controls in the experiment
That there is a consistent difference between treatments
That there is no consistent difference between treatments
What do we call the overall effect of an IV on a DV:
When the between-groups variance is a lot larger than the within-groups varience, the F-value is _____ and the likelihood of such a result occurring by sampling error _____.