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58 Cards in this Set

  • Front
  • Back
Spearman correlation and Pearson chi-square are two examples of …
non-parametric statistics
Non-parametric statistics should be typically used when the dependent variable is ____
ordinal with 4 or fewer points; ranked data; nominal; highly skewed (>2.0) (4 marks)
Non-parametric tests are not as _____ as parametric tests.
strict
Non-parametric tests do not test hypotheses about …
mean differences.
Technically, in the context of group differences, the non-parametric statistic null hypothesis is that the data have been sampled from …
identical populations
Non-parametric statistics assume that the _____ of the distributions are homogeneous.

shapes

Kruskal-Wallis assumes _____ of variance.
homogeneity
In the context of non-parametric statistics, there is a _____version of Levene’s test of homogeneity of variance.
median-based
Technically, we should not apply parametric statistics on data scored on _____ scales.

Likert

If data are Likert in nature and have ______ points in the scale, it may be argued to be justifiable to apply parametric statistics to them.
5 or more
Non-parametric statistics should always be applied to ____ data.

ranked

Although simulation research shows that the accuracy of parametric statistics is fairly good in cases where the ordinal scale has 5 or more points, this research assumes that _____, which is ___ in practice.
all points in the scale are used; often not the case (2 marks)
The Kruskal-Wallis test is based on transforming the original dependent variable data into ______.
ranks.
Strictly speaking, the Kruskal-Wallis null hypothesis is that the groups have …
identical population distributions
Differences in group ______ may be implied by a statistically significant Kruskal-Wallis statistic.

mean ranks

The notion that a difference in group medians may be implied by a statistically significant Kruskal-Walliss statistic is based on …
strict assumptions being met.
In order to test the assumption of normally distributed data, many people use a statistic called …
Shapiro-Wilks.
The problem with statistics such as Shapiro-Wilks for testing distributions for “sufficient normality” is that they are …
excessively powerful
In order to get SPSS to conduct the median-based version of Levene’s test of homogeneity of variance, one must use the ____ utility and then select the _____ option.
Descriptives --> Explore; power estimation within the Plots window (2 marks)
Kruskal-Wallis is essentially based on ____ and then _____the summed ranks within each group.
summing; squaring (2 marks)
What effect size estimate does SPSS calculate for Kruskal-Wallis?
None
Effect size for Kruskal-Wallis can be calculated by…

dividing the chi-square value by N -1 (2 marks)

Like ANOVA, Kruskal-Wallis is an ______ statistic.

omnibus

Typically, if a researcher obtains a statistically significant Kruskal-Wallis, the researcher will follow-up with a series of …
Mann-Whitney tests.
The Mann-Whitney test should be ____, given the existence of _____.
dead; Kruskal-Wallis
The Mann-Whitney test is a non-parametric equivalent of the …
independent samples t-test.
The Mann-Whitney test tests the null hypothesis that the _____ are equal.
mean ranks
What is the Mann-Whitney value associated with the SPSS table below?
What is the Mann-Whitney value associated with the SPSS table below?
6.00
From what analysis is the below window in SPSS?
From what analysis is the below window in SPSS?
Mann-Whitney
From what analysis the below window in SPSS?
From what analysis the below window in SPSS?
Kruskal-Wallis
What estimate of effect size does SPSS calculate for Mann-Whitney test?

none.

What is the eta squared formula for the Mann-Whitney test?
Z squared divided by N-1 (2 marks)
An alternative to conducting a series of Mann-Whitney U tests as a follow-up to a significant Kruskal-Wallis is to conduct a …
Jonckheere-Terpstra test
The Jonckheere-Terpstra test is really just a…
trend analysis for mean ranks.
In the SPSS window below, what two tests are missing from the ‘Test Type’ box?
In the SPSS window below, what two tests are missing from the ‘Test Type’ box?
Kruskal-Wallis; Jonckheere-Terpstra (2 marks)
The effect size estimate reported by SPSS for the Jonckheere-Terpstra test is referred to as…
there is none.
An effect size statistic that can be calculated by hand based on the Jonckheere-Terpstra statistic is known as….
eta squared
The following formulate can be used to calculate effect size for the Jonchkeere-Terpstra statistic….
Z2/(N-1)
Friedman’s ANOVA does not assume ______ data.

normally distributed

Friedman’s ANOVA can be used on data that conform to a ____ factor.

within-subjects

One of the limitations of the Friedman ANOVA test is that there is no _____ version of it.

factorial

Given the existence of Friedman’s ANOVA, _____ should be dead.

Cochran's Q

Referring to Friedman’s ANVOA as an ANOVA is a …

misnomer.

The Friedman ANOVA statistic converts the dependent variable into …

ranks.

When you really get down to it, Friedman’s ANOVA may be considered a test of ...

consistency.

In the SPSS window below, what two tests are missing from the ‘Test Type’ box?
In the SPSS window below, what two tests are missing from the ‘Test Type’ box?
Friedman’s ANOVA; Cochran’s Q (2 marks)
The formula to estimate effect size in the Friedman ANVOA case is…
chi-square/((N)(k-1)) (2 marks)
In the context of conducting a Friedman’s ANOVA, the effect size estimate is known as ….
Kendall’s W
Given that _______ is a measure of effect size, and you can get exactly the same p value for this effect size in SPSS, there is really no need to conduct a …
Kendall’s W; Friedman’s ANOVA (1 mark; yes, 1 mark)
The ______ is a non-parametric equivalent of the dependent samples t-test.
Wilcoxon-Signed Rank Test
The ______ could be used as a post-hoc test in the Friedman omnibus statistic scenario.
Wilcoxon-Signed Rank Test
Given the existence of Cochran’s Q, ____ should be dead.
McNemar chi-square
Given the existence of Friedman’s ANOVA, _____ should be dead.
Cochran’s Q.
Many non-parametric statistics do in fact assume …
homogeneity of variance.
Non-parametric statistical analyses are often _____ than the corresponding parametric statistical analyses.
less powerful
One of the main limitations associated with non-parametric statistics is that you can’t use them to test …
interactions.
In practice, all too often, non-parametric statistics are not actually the solution to our …
statistical problems.
Between-groups ANOVA is to Kruskal-Wallis as repeated measures ANOVA is to…
Friedman ANOVA