Question 1

When one or both variables are measured on an ordinal level that seriously departs from interval level characteristics,Pearson correlation is the test of choice.

Accepted Answer

When one or both variables are measured on an ordinal level that seriously departs from interval level characteristics, Pearson correlation is not the test of choice as it assumes interval level data. Spearman's rank correlation coefficient is a more appropriate test for ordinal data.

Question 2

The observed distribution may bias one's decision about a parameter to focus on.

Accepted Answer

The observed distribution may not represent the true underlying distribution of the population, so it may lead to an incorrect decision about a parameter to focus on.

Question 3

Given two between-subject variables that are measured on a level that at least approximates interval characteristics,the most common method of analysis is the chi square test.

Accepted Answer

The chi square test is typically used for categorical variables, not interval variables. For interval variables, a t-test or ANOVA would be more appropriate.

Question 4

If the dependent variable is measured on an ordinal level that seriously departs from interval level characteristics,a parametric test is always the optimal test.

Accepted Answer

If the dependent variable is measured on an ordinal level that seriously departs from interval level characteristics, a non-parametric test would be more appropriate. Parametric tests assume normally distributed and interval level data, which is not the case with ordinal data.

Question 5

The use of Case II statistics is appropriate when a qualitative variable has many values and there are insufficient number of cases within each level to justify the analysis of means,medians,and the like.

Accepted Answer

The answer of The use of Case II statistics is...

Question 6

An investigator should never focus on multiple parameters (means,medians,ranks).

Accepted Answer

An investigator should consider multiple parameters to get a comprehensive understanding of the data and draw accurate conclusions. Focusing on only one parameter may lead to biased results.

Question 7

A Two-way ANOVA is used when there are two independent variables.

Accepted Answer

A Two-way ANOVA is used when there are two independent variables. It allows for the examination of the main effects of each independent variable and their interaction effect on the dependent variable.

Question 8

A non-parametric test should be pursued if there is reason to believe that the distributional assumptions of the corresponding parametric test have been violated to the extent that the parametric test is not robust.

Accepted Answer

A non-parametric test is a good alternative when the assumptions of normality, homogeneity of variance, or linearity are not met in the data. Non-parametric tests do not rely on specific distributional assumptions and are generally considered more robust than parametric tests.

Question 9

With only two or three values on the dependent variable,there will be a large number of ties in ranks,and numerous ties can create serious problems for ranked-based approaches.

Accepted Answer

When there are only two or three values on the dependent variable, there is a higher likelihood of ties in ranks which can lead to problems in ranked-based approaches. Therefore, the statement is true.

Question 10

Generally speaking,parametric methods are not conducive to analyzing qualitative variables.

Accepted Answer

Parametric methods rely on the assumption of normally distributed data and equal variances, which is not applicable to qualitative variables. Therefore, parametric methods are not suitable for analyzing qualitative variables.

Question 11

If means (or linear relationships)are the focus,then a nonparametric test is the analysis of choice.

Accepted Answer

Nonparametric tests are used when the data does not meet the assumptions of parametric tests, such as normal distribution and homogeneity of variance. They do not necessarily focus on means or linear relationships.

Question 12

The appropriate statistical methods for Case III situations are techniques called logistic regression and polychotomous logistic regression.

Accepted Answer

Logistic regression and polychotomous logistic regression are commonly used for Case III situations where the outcome variable is categorical with more than two levels. They allow for the analysis of the relationship between predictor variables and the likelihood of belonging to one of multiple categories.

Question 13

When both variables under study are qualitative and between-subjects in nature,the appropriate method of data analysis is the chi square test as discussed in Chapter 15.

Accepted Answer

When both variables under study are qualitative and between-subjects in nature, the appropriate method of data analysis is the chi square test as it is a non-parametric test used to analyze the relationship between two categorical variables.

Question 14

If ranks are the focus,then parametric methods should be pursued.

Accepted Answer

If ranks are the focus, then non-parametric methods should be pursued as they do not assume a particular distribution of data and are based on the order of values rather than their actual numerical values. Parametric methods, on the other hand, assume a specific distribution of data and are based on numerical values.

Question 15

The inference of the relationship is indexed by eta-squared for all statistical test.

Accepted Answer

Eta-squared is a measure of effect size used primarily in the context of ANOVA (Analysis of Variance), not all statistical tests.

Question 16

The choice of which distributional parameter to compare across groups is often dictated by theory or the substantive question of interest.

Accepted Answer

The choice of which distributional parameter to compare across groups is often based on theoretical considerations or the research question at hand. This means that theory or the substantive question of interest often guides the decision-making process when selecting which parameters to compare.

Question 17

A dependent variable can still be normally distributed,even if it has only two or three values and,hence,the assumption of nonparametric tests will be violated.

Accepted Answer

If a dependent variable has only two or three values, it cannot be normally distributed. Additionally, if the dependent variable violates the assumption of nonparametric tests, then parametric tests should be used instead.

Question 18

In situations where the dependent measure has only two or three values,most researchers will not use a parametric test.

Accepted Answer

When the dependent measure has only two or three values, it is often categorical or nominal in nature, and does not meet the assumptions of normality and homogeneity of variance required for a parametric test. Therefore, non-parametric tests such as chi-square, Fisher's exact test or Mann-Whitney U test are more appropriate for analyzing such data.

Question 19

The major factor to be weighed when deciding whether to use a parametric or a nonparametric test is the decision about what aspect of the distribution that you want to compare groups on.

Accepted Answer

This is because parametric tests assume that the data follows a specific distribution (usually normal distribution) and require specific assumptions to be met, such as equal variances and independence of observations. On the other hand, nonparametric tests do not assume any specific distribution and can be used when those assumptions are not met. Therefore, the decision about which test to use depends on the distribution of the data and the level of measurement of the variables.

Question 20

If both variables are qualitative,but one or both are within-subjects in nature,the appropriate method of data analysis is the chi square test as discussed in Chapter 15..

Accepted Answer

The chi-square test is used for analyzing the relationship between two categorical variables, but it is not suitable for within-subjects designs. For within-subjects designs, methods like repeated measures ANOVA or McNemar's test are more appropriate.

Quiz 17: Overview and Extension: Statistical Tests for More Complex Designs