Exam 2 - Handout 6b - Bivariate Analysis & Comparing Groups - Practice Questions

Question 1

Which statistical test is used to compare means when there are more than two groups?

Answer 1

ANOVA

Question 2

What is the null hypothesis for a paired t-test comparing A1C at baseline and post-treatment

Answer 2

H0 = MeanB - MeanPT = 0

Question 3

Which nonparametric test is used for ordinal or heavily skewed continuous data when comparing two independent groups?

Answer 3

Wilcoxon-Mann-Whitney test

Question 4

What does the Pearson correlation coefficient (r) measure?

Answer 4

The strength and direction of the liner relationship between two continuous variables

Question 5

Which test is appropriate for comparing the proportion of patients at A1C goal between two groups

Answer 5

Chi-square test of homogeneity

Question 6

What is the two-step analysis process used in ANOVA when comparing means for more than two groups?

Answer 6

1. Calculate F-statistic and p-value to determine if any differences exist among group means
2. If p < 0.05, conduct post-hoc tests (eg. tukey’s/ HSD, Scheffe’s test) to determine which groups differ

Question 7

How does the sign test differ from other statistical tests in terms of data requirements and what is measures?

Answer 7

The sign test does NOT require continuous data, only the direction of change

It tests the null hypothesis that the number of patients w/ decreased values is equal to the number w/ increased values

Question 8

In the statistical test section flowchart, what are the 3 key factors to consider when choosing an appropriate test?

Answer 8

1. Independence of dependence of the comparison groups
2. Level of measurement of the dependent variable
3. Assumptions of parametric tests vs nonparametric tests

Question 8

What is the Spearman Rank Correlation Coefficient (rs) used for? What does it measure?

Answer 8

Used for ordinal or skewed data and measures the degree of correspondence between the ranks of two variable

Question 9

What is the main difference between parametric tests in terms of their assumptions about the data?

Answer 9

Parametric tests (eg. t-test, ANOVA) make assumptions about the population distribution

NONparametric tests (eg. Wilcoxon, kruskal-wallis) have fever assumptions about the underlying distribution of the data