null hypothesis
Question
1. Which of the following is a null hypothesis for the averages of three groups in a one-way,
between-subject ANOVA?
1. H0: Average1 ≠ Average2 = Average3
2. H0: Average1 ≠ Average2 ≠ Average3
3. H0: Average1 = Average2 = Average3
4. H0: Average1 = Average2 ≠ Average3
1. If the null hypothesis is rejected in a one-way ANOVA with three groups, we can infer that:
a. There is a significant difference in means for each of the three possible group pairings.
b. There is no significant difference in means for each of the three possible group pairings.
c. There is probably at least one significant difference in means among the three possible group pairings.
d. There is a significant difference in means for at least two of the three possible pairings.
2. The major difference between t-tests and ANOVAs is that ANOVAs:
a. Deal with multiple groups.
b. Compare group variances.
c. Make different assumptions about the populations from which we sample.
d. None of the above.
3. An important assumption in a one-way ANOVA is that:
a. Observations are random.
b. Observations are independent.
c. Subjects are related.
d. There are equal numbers of observations in each group. Chapter 13: Factorial Analysis of Variance
5. Which is true regarding factorial ANOVA ?
a. A problem with this analysis is that the error term (SSwithin) is larger compared to oneway ANOVA.
b. A major advantage with this analysis is the ability to test for the interaction between treatment groups.
c. As in a one-way ANOVA, there is only one null hypothesis to be tested.
d. Factorial ANOVA cannot be used in nonexperimental research situations.
6. A fixed factor in a factorial ANOVA is one in which:
a. Levels of the factor represent a small percentage of all possible levels of the factor in the real world.
b. The experimenter is interested in only naturally occurring factors such as gender, not treatment factors.
c. The researcher includes all levels of a factor that are of interest in the study.
d. The treatment group variance is held constant.
7. An example of a random factor in a factorial ANOVA is:
a. Studying the hydration effect of tea, coffee, juice, and cola because they are readily available, while other forms of hydration (energy drinks, beer, etc.) are not considered.
b. Not holding treatment variance constant across the levels of a factor.
c. Randomly assigning subjects to one of three antidepressant treatment groups.
d. Assigning groups based upon political party preference.
8. When the lines in a graph of the cell means are roughly parallel in factorial ANOVA, we can conclude that:
a. Neither the main effect for factor A nor factor B is statistically significant.
b. Main effects for factor A and factor B are statistically significant.
c. The interaction effect of factor A and factor B is statistically significant.
d. The interaction effect of factor A and factor B is not statistically significant.Chapter 17: Analysis of Covariance
9. The primary goal of analysis of covariance (ANCOVA) is to:
a. Control for differences to determine if participant characteristics are statistically different.
b. Determine differences among groups while controlling for or removing the effects of some characteristics.
c. Increase the statistical power of experimental designs by increasing the error variance.
d. Measure the differences in some participant characteristics after the experiment has been conducted.
10. Which of the following would be an example of a valid covariate in an ANCOVA analysis?
a. IQ scores on students measured before implementing different reading comprehension programs.
b. Anxiety scores on subjects after implementing drug treatment interventions for
depression.
c. Racial bias of subjects prior to implementing a blood pressure reduction study.
d. Motivation level of subjects after implementing a smoking cessation intervention.
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