Module 3 Flashcards by Dennis McWhorter

Correlation does what

evaluates the relationship between variables
correlation does not mean causation

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positive correlation vs negative correlation

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

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How can correlations be looked at

scatterplot or correlation coefficient

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Variable in correlation studies

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

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positive linear relationship vs negative
How does it look on scatterplot

line goes up in positive
line goes down in negative

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correlation coefficient

describes the strength and direction of a relationship

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what is a -1 vs +1 in correlation coefficient

-1 perfect negative relationship
+1 perfect positive correlation

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Pearson Correlation Coefficient assumptions

independence
normality
variables are interval/ratio level data

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Pearson’s correlation coefficient null hypothesis

There will be no relationship between… and …

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Pearson’s output

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

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Spearman rank correlation coefficient assumptions

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

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spearman rank correlation - what type of data can be used

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

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Independent dependent variable in correlation coefficients?

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

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Spearman CC output

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What does this mean

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

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How do you read this table

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

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

regression

simple linear regression assumptions

normality, homogeneity of variances, DV interval/ratio

Regression line equation =

Y = a + bX

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

How to know if you can do a regression?

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

What is R in regression analysis
What is R squared

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%

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

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

When are nonparametric tests used

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)

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

parametric - T test for independent samples (evaluates means)

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

Mann-Whitney U tests what

whether the medians differ significantly between two groups

Mann-Whitney U test assumptions

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

Interpret the output

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

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

Wilcoxon Signed-Rank test

Wilcoxon signed rank test evaluates what

evaluates the rank for the difference between measurements Rank from smallest difference to the highest rather than scores

Wilcoxon signed rank test assumptions

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

Interpret output for wilcoxon signed rank test

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

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

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

Kruskal-Wallis test assumptions

random samples independence DV is ordinal, interval/ratio level

Interpret kruskall wallis output

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

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

Chi-square test

Chi-square assumptions

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

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

Fisher's Exact Test

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

Yes because numbers in cells are not less than 5

Interpret

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