_________________________ are used to describe the degree of association between two or more variables (i.e., the degree to which two or more variables co-vary).

Correlational techniques.

The X (IV) is refered to as the _____________________, while the Y (DV) is the ___________________.

###
- Predictor
- Criterion

__________________ are used to summarize the degree of association between 2 variables.

Bivariate techniques.

The degree of association for 2 variables can be depicted in a __________________, in which the X (predictor) is placed on the horizontal axis, and the Y (criterion) is located on the vertical axis. When there is a narrow scatter of data points, this indicates a strong relationship.

Scattergram.

A ___________________ summarizes the degree of association between variables with a single number. Selection is based on the ________________ of the variables being correlated.

###
- Correlation coefficient
- Scale of measurement

______________________ (a.k.a. _____________ Product Moment Correlation Coefficient):

- Variable 1: Interval or ratio
- Variable 2: Interval or ratio
- Range: -1.0 to 1.0 (perfect negative to perfect positive correlation).

###
- Pearson r
- Pearson

____________________ (a.k.a. ______________ Rank-Order Correlation Coefficient):

- Variable 1: Rank-ordered
- Variable 2: Rank-ordered

###
- Spearman rho
- Spearman

________________:

- Variable 1: True dichotomy
- Variable 2: True dichotomy

Phi.

_____________________:

- Variable 1: Artificial Dichotomy
- Variable 2: Artificial Dichotomy

Tetrachoric.

______________________:

- Variable 1: Nominal
- Variable 2: Nominal

Contingency.

______________________:

- Variable 1: True dichotomy
- Variable 2: Interval or ratio

Point Biserial.

_______________:

- Variable 1: Artificial dichotomy
- Variable 2: Interval or ratio

Biserial.

______________: Used when the relationship between variables is nonlinear.

- Variable 1: Interval or ratio
- Variable 2: Interval or ratio

Eta.

Correlation coefficients require that ___ assumptions be met.

3.

__________________: The relationship between X and Y can be summarized by a straight line. If this is not true, the Pearson r will underestimate the degree of association.

Linearity.

______________________: The data have been collected from people who are heterogenous with regard to the characteristics measured by X and Y. If this is not met, the Pearson r is likely to be an underestimate.

Unrestricted range.

_____________________: The range of Y scores is about the same for all values of X. If this is not met, a coefficient may be produced that does not represent the full range of scores.

Homoscedasticity.

A correlation coefficient can be interpreted directly in terms of __________________; the closer to -1 or +1, the stronger the association; the closer to 0, the weaker the association.

Degree of association.

Whenever a correlation coefficient represents the degree of association between two different variables, it can be squared to obtain a ______________________, which provides a measure of shared variability.

e.g., If the correlation coefficient for two variables is .60, then 36% (0.6^{2} = 0.36) of the variability in one variable is accounted for by the other variable.

Coefficient of determination.

When a correlation coefficient is a reliability coefficient, it is ______________ squared.

Never.

___________________: The correlation coefficients can be evaluated to determine if they are statistically significant by comparing the obtained coefficient to the appropriate critical value. The magnitude of the critical value is determined by alpha and the sample size; the ___________ the sample, the _____________ the correlation coefficient must be to be statistically significant.

###
- Hypothesis testing
- Smaller
- Larger

______________________ is the technique that allows a predictor (X) to estimate performance on a criterion (Y).

Regression analysis.

The technique used to find the regression line in a scatterplot is referred to as the ____________________, which locates the line so that the amount of error in prediction is minimized.

Least squares criterion.