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
Education
Q:
36 In a chi-square test the expected frequency is
a) the frequency you would expect if the null hypothesis were false.
b) the frequency you actually obtained.
c) the frequency you would expect if the null hypothesis were true.
d) Expected frequency has nothing to do with the null hypothesis.
Q:
35 We can only use a chi-square test with frequency data if
a) the observations are independent.
b) the observations are sorted into categories.
c) the sample size is not very small.
d) all of these apply
Q:
34+ If we want to test proportions, one wrong way to do so is to
a) put the proportions themselves directly into the chi-square formula.
b) convert proportions to frequencies.
c) take the difference in proportions and turn it into a z score.
d) You can"t do any of these things.
Q:
33 If we want to use the chi-square test to test the difference between two proportions, we should
a) convert the proportions to means.
b) convert the proportions to frequencies.
c) run a z test.
d) either b or c
Q:
32+ If we want to use the chi-square test to test the difference between means we should
a) convert the means to standard scores.
b) convert the means to totals.
c) run a different test instead.
d) be sure the means are not too small.
Q:
31 The chi-square test can run into trouble if
a) the observations are not independent.
b) the expected frequencies are too low.
c) the total number of subjects is very small.
d) all of the above
Q:
30 The difference between the chi-square test for a 22 table and one for a larger table is
a) we must use a different formula.
b) a 22 is a goodness-of-fit chi-square.
c) you can"t handle a contingency table larger than a 23.
d) There is no difference other than the number of cells we include.
Q:
29+ The correction for continuity is known as
a) Fisher's correction.
b) Pearson's correction.
c) Howell's correction.
d) Yates' correction.
Q:
28+ The text mentions a correction for continuity and suggests that
a) you always use it with a 22 table.
b) you always use it with any contingency table.
c) it is not needed.
d) you only use it with a goodness-of-fit test.
Q:
27+ In the contingency table shown above, the degrees of freedom would equal
a) 1.
b) 2.
c) 3.
d) 4.
Q:
26 With a two-way contingency table the degrees of freedom area) RC - 1.b) C - 1.c) RC.d) (R - 1)(C - 1).
Q:
25+ In the table above, the expected frequency in the Male/Disagree cell is closest to
a) 11.
b) 5.
c) 28.
d) 9.
Q:
24 Which of the following are the cell totals?
a) 25, 36, 61, 21
b) 25, 12, 36, 9
c) 61, 21, 37, 45
d) 82
Q:
23 Which of the following are marginal totals?
a) 25 and 36
b) 12 and 36
c) 25, 9, and 82
d) 61 and 21
Q:
22 A contingency table involves
a) one category of classification.
b) more than one variable on which subjects are classified.
c) no more than two levels of classification.
d) a substantially different computational approach.
Q:
21 With several categories in a goodness-of-fit test, a significant result means
a) the categories increase in frequency from left to right.
b) the categories decrease in frequency from left to right.
c) the categories are equally frequent.
d) The test doesn"t pay any attention to which category is larger than which other categories.
Q:
20 The multicategory goodness-of-fit case for chi-square is
a) a simple extension of the two-category case.
b) a way of comparing more that two categories.
c) a common situation.
d) all of the above
Q:
19+ The null hypothesis for the previous example which used a chi-square is
a) men and women do not differ on computer science lab report scores.
b) cats and dogs eat the same number of meals.
c) political affiliation and voting behavior on drunk driving are independent variables.
d) children and adults do not show different deviations in visual acuity.
Q:
18+ An example of data that would be analyzed with a chi-square is
a) the mean scores received by men and women on a computer science lab report.
b) the average number of meals eaten by cats and dogs.
c) the numbers of Republican and Democrats who voted for and against stricter drunk-driving laws.
d) the deviations from the median shown in the visual acuity levels of children and adults.
Q:
17 When using the chi-square tables, we reject the null hypothesis when
a) chi-square is larger than the tabled value.
b) chi-square is smaller than the tabled value.
c) chi-square is far from the tabled value in either direction.
d) It depends.
Q:
16+ The most common level of when running a goodness-of-fit chi-square isa) .05b) .01c) .05/cd) .95
Q:
15 For a goodness-of-fit chi-square test, the degrees of freedom are equal toa) N - 1, where N is the number of observations.b) C - 1, where C is the number of categories.c) NC - 1.d) (N - 1)(C - 1).
Q:
14+ The critical value of a) decreases as we increase the degrees of freedom.b) increases as we increase the degrees of freedom.c) increases as we increase the number of observations (N).d) varies only as a function of , not as a function of the degrees of freedom.
Q:
13 The chi-square distribution is
a) a sampling distribution.
b) the distribution against which we evaluate chi-square values.
c) a distribution whose shape varies with the number of degrees of freedom.
d) all of the above
Q:
12 The denominator in chi-square is there to
a) keep the resulting answer in perspective relative to the total frequency.
b) keep the result in perspective relative to the size of the expected frequency.
c) keep people honest.
d) control the probability of a Type II error.
Q:
11+ You should be careful about using a chi-square test when
a) the expected frequencies are quite small.
b) the obtained frequencies are quite small.
c) the expected frequencies are different across the categories.
d) both a and c
Q:
10+ Which of the following is the formula for a standard chi-square test?
a) b) c) d) none of the above
Q:
9 A goodness-of-fit test is
a) only used when we want to test the hypothesis that the categories are equally represented.
b) used when we want to test the hypothesis that some categories are more frequent that others.
c) used to test the null hypothesis that the data are distributed in a way that would be predicted by a theory.
d) probably the most common statistical test we have.
Q:
8 A significant result with a goodness-of-fit test might suggest to us that
a) observations are not distributed in line with the null hypothesis.
b) the category means are different.
c) some assumption was likely to have been violated.
d) we have a very flat distribution.
Q:
7+ A typical null hypothesis with a goodness-of-fit test as presented in the text might be
a) the hypothesis that the means increase evenly across categories.
b) the hypothesis that the representation in each category is equal.
c) the hypothesis that subjects are normally distributed.
d) the hypothesis that the expected values are uniformly large.
Q:
6 A goodness-of-fit test is used with
a) a contingency table.
b) normally distributed variables.
c) a one-way categorization.
d) a test of linearity.
Q:
5 With categorical data, the primary piece of data is
a) a measurement.
b) a cell frequency.
c) a mean.
d) both b and c
Q:
4+ When our emphasis is on sorting outcomes into categories of data, we are concerned with
a) frequency data.
b) categorical data.
c) bean counting.
d) both a and b
Q:
3 If we run a chi-square test on a one-way classification, a significant result tells us that
a) science has triumphed over evil.
b) the categories are evenly represented in the data.
c) the frequencies differ by category.
d) we have made an error.
Q:
2+ When we sort subjects only into those who improved their performance over time, worsened their performance over time, and stayed the same, we have
a) a one-way classification.
b) a two-way classification.
c) a contingency table.
d) a set of ordered data.
Q:
1 The chi-square test is used when we have
a) measurement data.
b) ratio data.
c) interval data.
d) categorical data.
Q:
62 A researcher examined reaction time in 12 people across 3 conditions: regular cola, caffeine free cola, and water. The overall F was significant, so she performed multiple comparisons to understand which conditions differed. Interpret the following multiple comparisons at the .05 level.cola = 2.43s, caffeine free = 2.52s, water = 2.53s. tcola/caffeinefree = 2.80; tcola/water = 2.17;twater/caffeinefree = 0.38
Q:
61 Given the following data from a repeated-measures design, what is the value for SSerror?
SSsubjects = 950, SStime = 1500, SStotal = 3100
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