Relationship Stats

Question 1

Pearson Correlation

Answer 1

-r= +1(positive) to -1 (negative)
-how close to the line of best fit
-interval/ratio data, methological
-is there a relationship and how strong is it?
-nothing to do with slope or agreement or cause/effect

Question 2

Pearson Correlation Interpretation

Answer 2

.1 small effect
.3 medium
.5 large effect

-must be significant in SPSS

Question 3

r^2

Answer 3

-coefficient of determination
-how much the variability in one variable can be redicted by the other

Question 4

r^pb

Answer 4

-point-biseral correlation
-correclation between 2 level of a categorical variable
-run the same as pearson
-must be significant

Question 5

Spearman Coefficiet

Answer 5

-non-parametric equivalent to pearson’s
-ordinal data
-rank order
-1 to 1
-D^2= larger the difference the smaller the association

Question 6

Internal Consistency

Answer 6

-how closely related the items in an outcome measure are as a group

Question 7

Crohbach’s a

Answer 7

-calculated by averaging all possible pairwise correlations between items
-0-1
-.7 acceptable
-.8 good
-.9 excellent

Question 8

Standard Error of Mean (SEM)

Answer 8

-value that describes the diff between the sample mean and true pop mean
-always smaller than SD
-smaller=less sampling error

-sample SD/Square root (n)

Question 9

Standard Error of Measurement

Answer 9

-related to test reliability (test-retest)
-how much error should be expected

Question 10

Intraclass Correlation Coefficient

Answer 10

-ICC
-reliability coefficient
-measure agreeemnt or association between 3 raters
-0-1

Question 11

ICC Model

Answer 11

Model 1: one way random, each participant is assessed differently, rare

Model 2: two-way random, each participant assessed by each rater, raters are radomized, common

Model 3: two way mixed, each participant assessed by each rater, raters are only of interest

Question 12

ICC Form

Answer 12

1: single measurement

2: average of 2 measurements

3: average of 3 measurements

Question 13

G-Theory

Answer 13

-generalizability
-sophisticated way to measure reliability
-relative contribution of error
-of the amount of error, what is contributing the most
-higher G= higher reliability
-generalizability coefficient in articles

Question 14

Regression

Answer 14

-preficts an outcome
-interval/ratio/nominal
-more data than pearson

Linear, Multiple Linear, Logistic, Multinominal logistic, ordinal logistic

Question 15

Linear

Answer 15

-one predictor and one outcome
ex: does GRE predict GPA

SPSS:
-ANOVA, will show significance for entire model but not where
-Model Summary: Adjusted R square, Significant F change, Durbin-Watson
-Coefficients: Unstandardized B

Question 16

Unstandardized B

Answer 16

-coefficients for regression that show where it was significant
-has constant and predictor value

Question 17

Linear Regression Equation

Answer 17

Outcome= Constant + (Predictor Score x Unstandardized B of Predictor)

Question 18

Multiple Linear

Answer 18

-multiple predictors and 1 outcome
ex: GPA and GRE predict PT GPA

-R square change for how much above and beyond
-sig F change for significance

Question 19

Logistic

Answer 19

-1 or more predictors and one categorical, 2 levels
ex: Does TUG predict fallers and non fallers
Catergory: Falling
Level: fall, non falls

-uses logarithmic curve

Question 20

Multinominal Logistic

Answer 20

3 or more predictors and one categorical, 2+ levels

Question 21

Ordinal Logistic

Answer 21

-1 or more predictors and 1 ordinal, 2+ levels

Question 22

Regression Assumptions

Answer 22

-Must be linear (scatterplot)
-Normality (Shapiro-Wilk Test if violates)
-Homoscedasticity
-Free of outliers (cook’s distance)
-Data Independence (durbin-Watson test)

Multiple Regression:
-Multicollinearity

Question 23

Shapiro-Wilk Test

Answer 23

-normality in regression
-dont want significance

Question 24

Cook’s Distance

Answer 24

-checks for outliers
->1 is bad

Question 25

Durbin-Watson Test

Answer 25

-check for independence of observation in
-0-4 and 2 is perfect

Question 26

Adjusted R Square

Answer 26

-generalize results to population

Question 27

Multicollinearity

Answer 27

-multiple regression assumption
-too much correlation of predictors
-do not want this
-<0.9 correlation, VIF should be <10, Tollerance should be >0.1