Author Question: A researcher designs a study to measure whether receiving a free season pass to a beautiful ski ... (Read 124 times)

magmichele12 · **on:** Jul 8, 2018

A researcher designs a study to measure whether receiving a free season pass to a beautiful ski resort near Bountiful, Utah, increases the number of days a subject who has lived in the area for 1 to 3 years skis during the season.

An independent samples t-test of correlation is used to measure the effect of receiving a season pass upon number of ski days. If, instead, the researcher decided to measure the subjects' responses by asking them to check one of these four replies on a survey, Much more often than last year, More often than last year, Just about the same as last year, or Less often than last year, why would the independent samples t-test have to be abandoned for a Mann-Whitney U test instead? (Select all that apply.)
a. The variable of skiing is measured at the nominal instead of the ratio level.
b. The variable of skiing is now measured at the ordinal instead of ratio level.
c. The variable amount skied is now measured at the nominal level instead of the ratio level.
d. The variable amount skied is now measured at the ordinal level instead of the ratio level.
e. Both variables' levels have changed.
f. The independent samples t-test used demands ratio-level or interval-level data.
g. The Mann-Whitney U test allows for ordinal level values, but not nominal.

Question 2

Which of the following tests are parametric statistics that may be used to measure, clarify, or evaluate causation? (Select all that apply.)

a. t-test for independent samples
b. Mann-Whitney U test
c. t-test for dependent samples
d. Wilcoxon signed-rank test
e. ANOVA
f. Tukey Honestly Significant Difference (HSD)
g. Kruskal-Wallis test
h. Chi-square

6ana001 · **Reply #1 on:** Jul 8, 2018

Answer to Question 1

ANS: D, F, G
The parametric statistics used to determine differences that are covered in this chapter are the independent samples t-test, paired or dependent samples t-test, and analysis of variance (ANOVA). If the assumptions for parametric analyses are not achieved or if study data are at the ordinal level, then the nonparametric analyses of Mann-Whitney U, Wilcoxon signed-ranks test, and Kruskal-Wallis H are appropriate techniques to use to test the researcher's hypotheses. If the data do not meet the assumptions involving normality and/or equal variances for an independent samples t-test, then the nonparametric alternative is the Mann-Whitney U test. The Mann-Whitney U calculations involve converting the data to ranks, thereby discarding any variance or normality issues associated with the original values. In some studies, the data collected is ordinal level and the Mann-Whitney U test is appropriate for analysis of the data.

Answer to Question 2

ANS: A, C, E, F
The parametric statistics used to determine differences that are covered in this chapter are the independent samples t-test, paired or dependent samples t-test, and analysis of variance (ANOVA). In addition, the Tukey Honestly Significant Difference test is a post hoc test used to clarify which variables are related, after an ANOVA shows a significant difference.