Inferential Stats

Bivariate analysis

Examining the relationship between 2 variables

Contingency Tables

usually used when you have two nominal variables you want to compare

can use t-tables, and chi squared

t-tests

used to test whether group means are statistically significant different from each other

Correlations

Used for assessing the relationship between 2 variables

ANOVAs

used to determine whether 3 or more groups significantly vary in relation to each other

Bivariate Regression

over time things tend to regress toward the mean

2 variables

1 variable (IV) to predict another variable (DV)

Multivariate regression

same as bivariate, however includes 3+ variables

Pearson’s r

Two continuous measures (measures from -1 = perfect negative association to +1 = perfect positive association)

R-R

Phi Coefficient

measures the association between two dichotomies

N-N

Spearman’s Rank order Correlation (Rho)

measures the association between two rank ordered variables

O-O

Point-bisereal correlation

measures the association between a continuous variable and a dichotomous variable

N-R

Polyserial Correlation

Measures the association between a continuous variable and a theoretically continuous but not polytomized variable categorized in 3+ levels

N-R

Tetrachoric Correlation

measures the association between two dichotomous variables and estimates what the association would be had both variables been continuous

N-N

polychoric correlation coefficient

measures the association between ordinal variables with two or more levels

O-O