Accounting
Anthropology
Archaeology
Art History
Banking
Biology & Life Science
Business
Business Communication
Business Development
Business Ethics
Business Law
Chemistry
Communication
Computer Science
Counseling
Criminal Law
Curriculum & Instruction
Design
Earth Science
Economic
Education
Engineering
Finance
History & Theory
Humanities
Human Resource
International Business
Investments & Securities
Journalism
Law
Management
Marketing
Medicine
Medicine & Health Science
Nursing
Philosophy
Physic
Psychology
Real Estate
Science
Social Science
Sociology
Special Education
Speech
Visual Arts
Counseling
Q:
60 Explain why SSsubjects is removed from SSerror in repeated-measures designs.
Q:
59 A researcher collected data from behaviorally disturbed youth to see if introducing a token economy would reduce their disruptive behavior. He collected 3 weeks of data at baseline, treatment, and withdrawal respectively. Identify three meaningful multiple comparisons you could calculate based on this data. Explain your answers.
Q:
58 Calculate and interpret F based on the following data.
Q:
57 On the following computer output, the significance of F varies depending on which test you look at. a) Why is this the case?
b) Which F value should be reported? Explain your answer.
Q:
56 Calculate and interpret F for the following example.Source df SSSubjects 15 850.77Time 4 512.5Error 60 780.35Total 79 2143.62
Q:
55 Answer the following questions based on the summary table below.Source SS Df MS FSubjects 850 13 Time 204 2 102 3.40Error 780 26 30 a) How many people were in the sample?b) How many times was the dependent variable measured?c) Was there a difference in the dependent variable over time? Explain.
Q:
54 Give an example in which counterbalancing might be important for a repeated-measures design.
Q:
53 Give two examples in which you might use a repeated-measures design.
Q:
52 In a repeated-measures design, SSerror is calculated the same as it is in a between-subjects design.
Q:
51 The Greenhouse-Geisser correction is used in repeated-measures ANOVAs when there is limited power due to restricted sample size.
Q:
50 Counter-balancing is an appropriate strategy to deal with practice or carry-over effects in repeated-measures designs.
Q:
49 An assumption underlying repeated-measures ANOVAs is that pairs of levels of the repeated factor are uncorrelated.
Q:
48 In a repeated-measures design, the error term is equivalent to the interaction between subjects and the repeated-measures factor.
Q:
47 If reaction time data were collected from the same 10 people, 4 times, the df for time in a repeated-measures ANOVA based on that data would be 3.
Q:
46 If reaction time data were collected from the same 10 people, 4 times, the total df in a repeated-measures ANOVA based on that data would be 40.
Q:
45 If the t for related means = 4, then the F for repeated measures based on the same data would = 16.
Q:
44 Measuring the height of the same group of children every year for three years is an example of a between-subjects design.
Q:
43 In a repeated-measures design, each subject receives all levels of at least one independent variable.
Q:
42 If we ran a repeated-measures analysis of variance to track changes in patients' distorted thoughts over 6 weeks of therapy, we would most likely want to report the effect size in terms of
a) eta-squared.
b) omega-squared.
c) computed on the difference between adjacent trials.
d) computed on the difference between the initial trial and the last trial.
Q:
41 You want to run a study examining the effects of poverty on the development of antisocial behavior. You randomly select a large group of normal 12- year-old children and sort them into three groups on the basis of family income. You meet with them yearly until they are 25 years old, using a standard assessment of antisocial behavior. What test should you run to analyze this data?
a) independent samples ANOVA
b) one-way ANOVA
c) repeated-measures ANOVA
d) chi-square
Q:
40 In a repeated-measures ANOVA, tests to correct the degrees of freedom, such as Greenhouse-Gelsser and Huynh-Feldt, should be used if
a) you violate the assumptions of constant correlations.
b) you do not violate the assumptions of constant correlations.
c) you forget the assumptions of constant correlations.
d) you do not have an assumption of constant correlations.
Q:
39 A _______ design is one in which subjects are measured repeatedly over time.
a) between-subjects
b) factorial
c) repeated-measures
d) matched groups
Q:
38 The typical way to control sequence effects is called
a) block randomization.
b) cross-sectional experimentation.
c) asymmetrical transfer.
d) counterbalancing.
Q:
37 A design in which each subject receives all levels of an independent variable is called a(n)
a) independent samples t-test.
b) repeated-measures design.
c) between-subjects design.
d) correlation.
Q:
36 If the assumption of constant correlations in a repeated-measures ANOVA is violated, which of the following is true?
a) The F-value is incorrect.
b) The degrees of freedom should be adjusted.
c) The sample cannot be valid.
d) The manipulation checks failed.
Q:
35+ A researcher wanted to see how watching movies influenced subjects' IQ scores. She gave IQ tests to subjects following each of two movies. Half of the subjects first saw Titanic followed by Schindler's List, while the other half first saw Schindler's List and then Titanic. Varying the movie order is an example of
a) counterbalancing.
b) random sampling.
c) selection bias.
d) practice effects.
Q:
34+ In a learning study using repeated measures, the correlation between early and later times will likely be low. Analyzing fewer levels of the independent variable would help to avoid violating the assumption of
a) normality.
b) homogeneity of variance.
c) constant correlations among pairs of levels of the repeated variables.
d) MSerroris an unbiased estimate of the magnitude of effect of the predictor variable in a regression analysis.
Q:
33 In an example in the text, an independent samples analysis of variance example from a previous chapter was converted to be used in a repeated-measures analysis of variance. Recalculating the F value with a repeated-measures analysis of variance yields an F value that is
a) less than the F value yielded by the independent measures ANOVA.
b) greater than the F value yielded by the independent measures ANOVA.
c) the same as the F value yielded by the independent measures ANOVA.
d) not predictably different from the F value yielded by the independent measures ANOVA.
Q:
32 In the printout of results for a repeated-measures analysis of variance, an F score for "mean" or "constant" sometimes appears. Why is this statistic often not interesting even if it is significant?
a) It shows differences between time sessions which are not important.
b) It is a randomly generated number.
c) It shows that the population mean is or is not equal to zero which is often of no interest.
d) It is redundant information given the F score for the time variable.
Q:
31 Which of the following demonstrates the similarities of a repeated-measures analysis of variance for two trials and a t test for related means?
a) F = t2
b) F2 = t
c) F = t
d) F = 2t
Q:
30+ In a typical learning experiment, a carry-over effect is
a) something to be avoided at all cost.
b) a necessary evil.
c) unlikely to be present.
d) what you are actually studying.
Q:
29 If any reasonable person would expect that with 4 trials the last trial is almost certain to be significantly different from the first, then Fisher's LSD test
a) has more protection against a high familywise error rate.
b) has a smaller degree of protection against a high familywise error rate.
c) will lead to a very high familywise error rate.
d) Error rates have nothing to do with the issue.
Q:
28 If a repeated-measures analysis of variance usually has an error term that is smaller that the error term in the corresponding between-subjects design, then we can assume that
a) repeated-measures designs have less power.
b) repeated-measures designs have greater power.
c) there are no differences due to power between the two kinds of designs.
d) neither design has very much power.
Q:
27 By shifting the data around the way the author did at the end of the repeated measures chapter, he was able to show that
a) the means became less different.
b) differences due to subjects were literally subtracted from the error term.
c) the F decreased.
d) all of the above
Q:
26+ The text used an example in which the author rearranged the data points to look as if they came from a repeated-measures design. In real life we would not move our data points around so that we could analyze them as repeated measures. Why not?
a) It wouldn"t work.
b) God would strike you dead.
c) It would be unethical to alter data in that way.
d) It is too awkward to do.
Q:
25 If both the Greenhouse and Geisser and the Huynh and Feldt corrections lead to significant results we should
a) not worry too much about our assumptions.
b) try to find a different way to analyze the data.
c) declare the uncorrected analysis to come have come to faulty conclusions.
d) decide that we should not believe our data.
Q:
24+ A Greenhouse and Geisser correction is a correction applied to
a) the mean.
b) the variance.
c) the degrees of freedom.
d) the interpretation.
Q:
23 Some summary tables include a term labeled "mean" or "constant," with a corresponding F test. This tests the hypothesis that
a) the mean is equal to each of the population means.
b) the mean of the scores is equal to the population mean.
c) the population mean is 0.00.
d) subjects are all equal.
Q:
22 Counterbalancing is a technique to
a) lower the weight of subjects.
b) distribute carry-over effects evenly across the data.
c) increase the power of an experiment.
d) reduce the likelihood of reasonable conclusions.
Q:
21+ The major disadvantage with repeated-measures designs is that they
a) require too many subjects.
b) are less powerful than between-subjects designs.
c) have a funny looking summary table.
d) are subject to the influence of carry-over effects.
Q:
20 The major advantage of repeated-measures designs is that
a) they allow you to use more subjects.
b) they allow you to remove subject differences from the error term.
c) they are easier to analyze.
d) they have a higher level of ï¢.
Q:
19 If you are concerned that you have violated the assumptions behind a repeated-measures design, you can
a) limit your analysis to only those levels of the repeated measure that meet the assumption.
b) use an adjustment to your degrees of freedom.
c) pretend you didn"t notice that there was a problem.
d) both a and b
Q:
18+ The assumptions behind the analysis of repeated-measures designs include
a) the assumption that all levels of the repeated measure are equally correlated with each other.
b) the assumption of normality.
c) the assumption of homogeneity of variance.
d) all of the above
Q:
17 In running multiple comparisons in a repeated-measures design we can use procedures that we would use with independent groups designs because
a) the error term is corrected for any lack of independence in the data.
b) the error term is a conservative one.
c) the multiple comparison procedures we use have nothing to do with the question of independence or lack of it.
d) we create our own adjusted error term for multiple comparisons.
Q:
16+ If we compared Time 1 (baseline) against the next time (Time 2) and then against the last time (follow-up), we would run the Bonferroni at
Q:
15 If we used a Bonferroni test to run multiple comparisons in the above example, the error term that we would use would be
a) the multiple comparison adjusted error term.
b) the MSerror from the table above.
c) the MSsubjects term.
d) MSerror/5.
Q:
14 If we wanted to run a set of multiple comparisons on the data analyzed in the summary table above, we could use
a) Fisher's LSD test.
b) the Repeated-Measures MC test.
c) the Bonferroni t test.
d) both a and c
Q:
13 The error term in this analysis could also be thought of asa) the within cells term.b) the between cells term.c) the Subject x Trials interaction.d) the variance.
Q:
12+ How many trials were there in this experiment?
a) 2
b) 4
c) 5
d) 8
Q:
11+ The results of this study tell us that
a) there is a change over trials.
b) there is no change over trials.
c) subjects are different.
d) There is too much noise in the system to draw any conclusions.
Q:
10 The MSerror = 30.68 tells us that
a) the average variance in this study is 30.68 units.
b) the average variance within the set of scores for a given subject is 30.68 units.
c) the average difference due to time is 30.68 units.
d) We cannot tell from what is given.
Q:
9+ We don"t have an F test on Subjects. What harm does that do?
a) It diminishes the value of the study.
b) It distorts the meaning of a significant trials effect.
c) It alters SStotal.
d) It does no particular harm.
Q:
8+ How much has the error sums of squares been reduced over what it would have been in a comparable between-subjects design?
a) It has not been reduced at all.
b) 30.68 units
c) 723.5 units
d) 1687.3 units
Q:
7+ How many subjects were involved in this study?
a) 14
b) 15
c) 74
d) 75
Q:
6 If, in the example of a headache study used in the text, all subjects had been studied over the same period of time, differences that might be caused by stressful times (such as Christmas) woulda) contaminate the results.b) add to the usefulness of the results.c) improve the generalizability of the study.d) reduce the variability in the scores.The next few questions are based on the following summary table. SourcedfSSMSFSubjects14723.5 Trials41687.3421.8213.74*Error561718.530.68 Total744129.3 *p<.05
Q:
5 If we have a repeated-measures design with subjects receiving four levels of a treatment, we assume that
a) the correlations among the levels will be zero.
b) the correlations among the levels will be 1.0
c) the correlations among the levels will be random.
d) the correlations among the levels will all be about the same.
Q:
4 For repeated-measures designs with one independent variable (Time),
a) subjects served under all levels of the independent variable.
b) subjects served under only some of the levels of the independent variable.
c) subjects served under only one level of the independent variable.
d) none of the above
Q:
3 All other things being equal, a repeated-measures design is _______ than the corresponding between-subjects design.
a) less powerful.
b) more powerful.
c) The two designs have comparable power.
d) We cannot predict this.
Q:
2+ All other things equal, the MSerror in a repeated-measures design is _______ than the corresponding MSerror in a between-subjects design.
a) smaller.
b) larger
c) the same size.
d) We have no way to predict this.
Q:
1+ A repeated-measures analysis of variance differs from a one-way and a factorial design because
a) the measures in a repeated-measures design are correlated.
b) the measures in a standard factorial are independent.
c) the measures in a repeated-measures design are not independent from time to time.
d) all of the above
Q:
63 The mean difference in GPA based on gender and year in school is a main effect.
Q:
62 The mean difference in GPA based on gender is an interaction effect.
Q:
61 A 2 X 4 factorial involves two independent variables. One has two levels and the other has four.
Q:
60 A factorial analysis of variance involves more than one dependent variable.
Q:
59 To calculate the F for a simple effect you
a) use the mean square for the main effect as the denominator in F.
b) first divide the mean square for the simple effect by its degrees of freedom.
c) use the same error term you use for main effects.
d) none of the above
Q:
58 Using the example in the text of a participant receiving therapy while sitting in a bath of ice water, what would be the best denominator for calculating ?
a) the square root of MSerror
b) the square root of MSerror after removing effects due to the Ice condition and its interaction with treatment
c) the standard deviation of participants in the non-ice condition
d) the standard deviation of difference scores
Q:
57 When we say that a measure is "not of theoretical interest" we mean that
a) variability attributable to it should be removed from the denominator.
b) we can simply ignore that variable.
c) variability attributable to it should be included in the denominator.
d) there is no significant effect for that variable.
Q:
56 When we compute an effect size measure such as for a factorial ANOVA we have to decide
a) what effects should be included in estimating the denominator.
b) what are our variables of theoretical interest.
c) whether we want an r-family measure or a d-family measure.
d) all of the above
Q:
55 What type of design is the above study?
a) 2 x 2 x 2
b) 2 x 2
c) 2 x 3
d) 3 x 3 x 3
Q:
54 In the Spilich et al. study of the effects of smoking that was discussed in the text, active smokers were found to do better than nonsmokers on a driving task but did worse than nonsmokers on a cognitive task. However, over all three tasks (the third was pattern recognition and the groups were not different on that) active smokers did not differ from nonsmokers on performance. The results suggest
a) an interaction between smoke group and task, and a main effect for smoke group.
b) an interaction between smoke group and task, but no main effect for smoke group.
c) a main effect for smoke group, but no interaction between smoke group and task.
d) a main effect for task only.
Q:
53 Dr. Gates looked at the effects of frustration on the use of profanity by males and females. Males and females were asked to write a lab report on computers in a lab, but half the computers were set up to crash during the session while half of the computers were not set up to crash. Three observers recorded the use of profanity by the participants during the task. What is the design of this study?
a) 2 x 2 factorial
b) 1 x 2 factorial
c) 2 x 2 x 3 factorial
d) 2 x 2 correlational
Q:
52 The overall effect of an independent variable is called a(n)
a) main effect.
b) simple effect.
c) interaction.
d) manipulation.
Q:
51 This graph represents a/an a) repeated-measures design
b) one-way ANOVA
c) 2 X 5 factorial
d) significant alpha
Q:
50 When you compare the effect of one variable at one level of another variable you are examining
a) a main effect
b) a simple effect
c) a correlational effect
d) both a and c
Q:
49 When comparing differences in an experiment with two or more independent variables we should use a(n)
a) one way ANOVA.
b) paired sample t-test.
c) R squared comparison.
d) factorial design.
Q:
48 In this graph we can see that there is a) a significant difference between Location 1 at time 1 and Location 2 at time 1.
b) an interaction between location and time.
c) a significant post hoc analysis.
d) a quadratic significant correlation.
Q:
47 In a factorial design involving the sex of the participant and the sex of the experimenter's confederate
a) there were multiple subjects in each cell of the design.
b) there were different subjects in each cell of the design.
c) all possible combinations of sex of subject and sex of confederate were represented.
d) all of the above
Q:
46 A factorial design has at least
a) one dependent variable and one independent variable.
b) two dependent variables and one independent variable.
c) two independent variables and one dependent variable.
d) The number of independent variables is not important.
Q:
45 The finding that women eat less in the company of men then when they are in the company of other women is a(n)
a) simple effect.
b) interaction.
c) factorial effect.
d) main effect.
Q:
44 Pliner and Chaiken (1990) wanted to investigate whether the amount of food eaten depended on the gender of the participant and the gender of the confederate. It was observed that women eat less than men overall and that women eat less in the company of men than they do when in the company of other women. The finding that women eat less than men across all conditions is a(n)
a) simple effect.
b) interaction.
c) factorial effect.
d) main effect.