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Q:
22 The Kruskal-Wallis test is based ona) thet distribution.b) the z distribution.c) the distribution.d) the F distribution.
Q:
21 The Kruskal-Wallis test is appropriate when
a) we have several independent groups.
b) we want to compare group medians rather than means.
c) we are worried about outliers.
d) all of the above
Q:
20 When using Wilcoxon's test for paired samples, subjects whose two scores are equal, yielding a difference score of zero, are usually
a) given a random sign.
b) omitted from the data.
c) punished.
d) given a plus sign for one score and a minus sign for another.
Q:
19 Wilcoxon's test for paired samples focuses on
a) the mean of the two samples.
b) the sign of the difference scores.
c) the relative magnitude of the difference scores.
d) both b and c
Q:
18 Distribution-free tests and parametric tests differ in their null hypotheses in that
a) distribution-free tests have a less specific null hypothesis.
b) distribution-free tests make very strong assumptions about the mean.
c) distribution-free tests don"t really have a null hypothesis.
d) parametric tests have rather loose null hypotheses.
Q:
17+ Tied scores often present a problem in distribution-free tests. The most common way to deal with them in a Mann-Whitney test is to
a) flip a coin.
b) use a random number table.
c) assign tied ranks.
d) throw out the tied data.
Q:
16+ In the previous example of the smoking cessation study we might be tempted to apply Friedman's test, because it can handle similar data. This would be a bad idea because
a) Friedman's test really won"t handle these data.
b) Friedman's test will not use some of the information inherent in the data.
c) Milton Friedman is a conservative economist, and we only like liberal psychologists.
d) We really should use Friedman's test.
Q:
15+ In the previous question, the most appropriate test would be
a) the Mann-Whitney test.
b) the Wilcoxon signed-ranks test.
c) Friedman's test.
d) the Kruskal-Wallis test.
Q:
14+ Assume that we asked 20 subjects to participate in a smoking cessation study. We recorded their craving for a cigarette before and after applying a nicotine patch. One reason we might want to use a distribution-free test is because
a) we have too few subjects for a parametric test.
b) there are some outliers that we don"t want to have undue influence on the results.
c) the data are normally distributed.
d) the subjects are all male.
Q:
13 When we have relatively large sample sizes, the distribution-free tests for comparing two groups or sets of data discussed in the text have
a) a chi-square approximation.
b) a normal approximation.
c) no solution.
d) problems.
Q:
12+ With at least some distribution-free tests, a two-tailed test
a) is not appropriate.
b) requires an additional calculation.
c) is more likely to be significant than a one-tailed test.
d) is automatic.
Q:
11+ One of the unusual things about distribution-free tests is that they are often set up so that
a) they only work with equal sample sizes.
b) the null hypothesis is never assumed to be true.
c) we reject the null hypothesis when our test statistic is too small, rather than too large.
d) they are all named after people.
Q:
10+ Each of the distribution-free tests that are covered in the book deal with
a) ranks.
b) means.
c) standard deviations.
d) raw data.
Q:
9 If the null hypothesis is true and we run the Mann-Whitney test on our data, the expectation is that
a) the difference scores will all be zero.
b) the test will be significant.
c) the sum of the ranks in the two groups will be approximately equal (assuming equal sample sizes).
d) the number of subjects in each group will be about the same.
Q:
8+ Which of the distribution-free tests is roughly equivalent to the t test for two independent means?
a) the Mann-Whitney test
b) Wilcoxon's signed-ranks matched-pairs test
c) Friedman's test
d) the Kruskal-Wallis test
Q:
7+ Distribution-free tests are
a) more sensitive to the effects of outliers than parametric tests.
b) more sensitive to the effects of scores near the mean than parametric tests.
c) largely unaffected by the presence of outliers.
d) both a and c
Q:
6 When the assumptions behind parametric tests are not met,
a) they are not useful tests.
b) they cannot even be computed.
c) distribution-free tests may have more power.
d) none of the above
Q:
5+ The major advantage of distribution-free tests is that
a) they do not rely on assumptions as severe as those for parametric tests.
b) they have more power.
c) they are substantially easier to run.
d) they are more common.
Q:
4 When we speak about rank-randomization tests we are talking about procedures that
a) deal with ranks.
b) ask how ranks would be distributed if the data were random.
c) form the basis of many distribution-free tests.
d) all of the above
Q:
3 Which of the following is a nonparametric procedure?
a) at test.
b) the analysis of variance.
c) the Mann-Whitney test.
d) Pearson's correlation coefficient (r).
Q:
2+ If the usual assumptions behind parametric tests are met (at least approximately),
a) distribution-free tests are more powerful than parametric tests.
b) distribution-free tests are somewhat less powerful than parametric tests.
c) the tests are indistinguishable.
d) you shouldn"t use a parametric test.
Q:
1 A distribution-free test
a) is almost synonymous with a nonparametric test.
b) is a test that makes few assumptions about the distribution from which the data were drawn.
c) usually has less power than a parametric test.
d) all of the above
Q:
56 Based on the previous example:
a) What are the df?
b) Calculate and interpret Chi-square.
Q:
55 A political science student did a survey to see if the political affiliation of voters was related to whether or not they would consider voting for a progressive candidate in the upcoming gubernatorial race. How would you calculate the marginals and expected frequencies for each cell.
Q:
54 Calculate and interpret the z score based on the proportion of youth who end up in prison using the data from the previous example.
Q:
53 Calculate and interpret Chi-square based on the contingency table you created.
Q:
52 A social worker has been asked to testify before her state legislature about the impact of long-term foster care on child outcomes and government spending. She knows that 60% of children who remained in the foster care system without being adopted ended up in prison. The figure for foster care children who were eventually adopted was 25%. These data were based on 500 children who remained in foster care and 800 children who were eventually adopted. Create the appropriate contingency table.
Q:
51 Calculate and interpret Chi-square for the previous example.
Q:
50 A researcher wants to be sure that her random assignment to groups has been working. She wants to be sure that socio-economic status and treatment group are independent. Ideally, given her particular sample, there would be an equal number of people in each category. Calculate the marginal totals and the expected frequencies for each cell. Experimental Group Control GroupBelow poverty line 22 28Above Poverty line 28 22
Q:
49 Assuming the student body in the previous example is 30% male and 70% female,
a) What are the expected values
b) Calculate and interpret Chi-square
Q:
48 A professor believes that a greater proportion of females than males have enrolled in her class. Assuming an equal number of males and females in the student body, calculate Chi-square, and evaluate her hypothesis based on the following data.Males Enrolled Females Enrolled10 20
Q:
47 Indicate whether or not the following Chi-square statistics are significant:
a) 2.75; k =2
b) 11.00; k =5
c) 12.40; df = 6
Q:
46 In a contingency table, the expected frequency of any given cell is represented by this formula:
Q:
45 A marginal total is the sum of the level of one variable across all of the levels of the other variable.
Q:
44 In a Chi-square including two variables, one with 4 categories, and the other with 3 categories, the df = 6.
Q:
43 In order for a Chi-square test to be valid, a general rule of thumb is that every cell have expected frequencies greater than or equal to 10.
Q:
42 It is appropriate to use Chi-square when data from the same subject are in multiple cells.
Q:
41 If a Chi-square is conducted based on data from 30 people who are categorized into one of 4 possible categories, the df = 26.
Q:
40 In a Chi-square, k refers to the number of categories.
Q:
39 Expected frequencies are the expected value for the number of observations in a cell if the alternative hypothesis is true.
Q:
38 A Goodness-of-fit test compares the observed number of frequencies with predicted frequencies.
Q:
37 Chi-square is used to analyze continuous data.
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