Module 3

Question 1

Correlation does what

Answer 1

evaluates the relationship between variables
correlation does not mean causation

Question 2

positive correlation vs negative correlation

Answer 2

positive = increase and decrease together
negative = inversely related as one increases the other decreases

Question 3

How can correlations be looked at

Answer 3

scatterplot or correlation coefficient

Question 4

Variable in correlation studies

Answer 4

not an independent or dependent variable since you are simply looking at how they are related to one another

Question 5

positive linear relationship vs negative
How does it look on scatterplot

Answer 5

line goes up in positive
line goes down in negative

Question 6

correlation coefficient

Answer 6

describes the strength and direction of a relationship

Question 7

what is a -1 vs +1 in correlation coefficient

Answer 7

-1 perfect negative relationship
+1 perfect positive correlation

Question 8

Pearson Correlation Coefficient assumptions

Answer 8

independence
normality
variables are interval/ratio level data

Question 9

Pearson’s correlation coefficient null hypothesis

Answer 9

There will be no relationship between… and …

Question 10

Pearson’s output

Answer 10

r is correlation p is significance
r near 1 so near perfect positive correlation

Question 11

Spearman rank correlation coefficient assumptions

Answer 11

nonparametric test
assumptions - independence
DOESN’T require interval or ratio level data or normality

Question 12

spearman rank correlation - what type of data can be used

Answer 12

ordinal (like a Likert scale) agree, disagree, etc.

Question 13

Independent dependent variable in correlation coefficients?

Answer 13

none. You are testing the relationship so there is not a predictor or outcome variable

Question 14

Spearman CC output

Answer 14

Question 15

What does this mean

Answer 15

negative or inverse relationship between the tested groups. -0.392 not a strong correlation but significant since p value was <0.000.

Question 16

How do you read this table

Answer 16

numbers on column correspond to numbers on side. *** shows significance

Question 17

When you see using one variable to predict another what does that me

Answer 17

regression

Question 18

simple linear regression assumptions

Answer 18

normality, homogeneity of variances, DV interval/ratio

Question 19

Regression line equation =

Answer 19

Y = a + bX

Y = dependent variable
a constant from output
b slope from output
X = independent variable

Question 20

How to know if you can do a regression?

Answer 20

if the two things tested are correlated then can do regression analysis

Question 21

What is R in regression analysis
What is R squared

Answer 21

R = correlation coefficient anywhere from -1 to +1

R square = percent of variance that can be explained by the variable being tested 0.141 = 14.1%

Question 22

To get line regression equation in regression analysis, which column do you look at?

Answer 22

B column
constant row = a
independent variable label is b

Y = -9473.852 + 3349.145 X

You would input x using dependent variable to get Y

Question 23

When are nonparametric tests used

Answer 23

When assumptions are not met for parametric tests
normally distributed
homogeneity of variances

Can be used to examine nominal and ordinal level data
often do not analyze raw data
Less powerful than parametric tests (unless assumptions are not met then they are more powerful)

Question 24

When comparing 2 independent groups what test would you use for parametric and nonparametric

Answer 24

parametric - T test for independent samples (evaluates means)

nonparametric - Mann-Whitney U test
use with ordinal data, inhomogeneity of variances, normal distribution(evaluates medians)

Question 25

Mann-Whitney U tests what

Answer 25

whether the medians differ significantly between two groups

Question 26

Mann-Whitney U test assumptions

Answer 26

random samples
independence of variables
DV is ordinal, interval, or ratio

Question 27

Interpret the output

Answer 27

U = 0.500, p< 0.000
knowledge levels in the APRN group were significantly higher than knowledge levels in the brochure group

Question 28

Assumptions not met with paired t-test what would you use

Answer 28

Wilcoxon Signed-Rank test

Question 29

Wilcoxon signed rank test evaluates what

Answer 29

evaluates the rank for the difference between measurements

Rank from smallest difference to the highest rather than scores

Question 30

Wilcoxon signed rank test assumptions

Answer 30

random samples
DV interval or ratio level data
NOT INDEPENDENT GROUPS (pre and post test)

Question 31

Interpret output for wilcoxon signed rank test

Answer 31

Fail to reject the null
There was no statistically significant difference in back pain before and after yoga intervention
Z=-1.807, p = 0.071

Question 32

Comparing more than two groups nonparametric test what wouldn’t it be and what would you use instead

Answer 32

NOT ANOVA compares means
WOULD USE Kruskal-Wallis Test compares medians

Question 33

Kruskal-Wallis test assumptions

Answer 33

random samples
independence
DV is ordinal, interval/ratio level

Question 34

Interpret kruskall wallis output

Answer 34

H(2) = 2.317, p = 0.314
(2)= degrees of freedom which is # of groups minus 1

fail to reject the null hypothesis. there is no statistically significant difference

Question 35

what test would you use if you wanted to compare categorical variables (nominal or ordinal)

Answer 35

Chi-square test

Question 36

Chi-square assumptions

Answer 36

independence
expected counts are greater than 1 and no more than 20% of cells are less than 5

Question 37

If Chi-square assumptions aren’t meet what would you use

Answer 37

Fisher’s Exact Test

Question 38

Can you use chi-square for with this? expected counts are greater than 1 and no more than 20% of cells are less than 5

Answer 38

Yes because numbers in cells are not less than 5

Question 39

Interpret

Answer 39

You can use Chi-square because expected counts are greater than 1 and no more than 20% of cells are less than 5 which is displayed in b.

x2 (1) = 0.047, p = 0.828
fail to reject the null hypothesis and say there is not statistically significant relationship