PSY 411 Chapter 5 Notes

**Causality**

The assumption that a correlation indicates a causal relationship between the two variable

**correlation coefficient**

A measure of the degree of relationship between two sets of scores. It can vary between -1.00 and + 1.00

**Directionality**

The inference made with respect to the direction of a relationship between two variables

**Magnitude**

An indication of the strength of the relationship between two variables

**negative correlation**

An inverse relationship between two variables in which an *increase* in one variable is related to a *decrease* in the other, and vice versa

**partial correlation**

A technique used to determine the effects of a third variable by removing the effect of the third variable from the correlation of the remaining two variables

**person-who argument**

Arguing that a well established statistical trend is invalid because we know a “person who” went against the trend

**positive correlation**

A relationship between two variables in which the variables move together—an *increase* in one is related to an *increase* in the other, and a *decrease* in one is related to a *decrease* in the other

**restrictive range**

A variable that is short and does not vary enough

**Scatterplot**

A figure that graphically represents the relationship between two variables

**third-variable problem**

The problem of a correlation between two variables being dependent on another (third) variable

**coefficient of determination (r2)**

A measure of how much one variable is accounted for in another variable; calculated by squaring the correlation coefficient.

**Pearson product-moment correlation coefficient (Pearson’s r)**

The most commonly used correlation coefficient when both variables are measured on an interval or ratio scale

**phi coefficient**

The correlation coefficient used when both measured variables are *constant* and *nomina*l.

**point-biserial correlation coefficient**

The correlation coefficient used when one of the variables is measured on a constant nominal scale and the other is measured on an interval or ratio scale

**Spearman’s rank-order correlation coefficient**

The correlation coefficient used when both of the variables are measured on an ordinal (ranking) scale

**Degrees of freedom**

The number of scores in a sample that are free to vary; need the df in order to calculate the statisical significance df = n- 2

**Alpha**

probability level: alpha (α)  set at .05 rcrit for a two-tailed test from r table

**Temporal ordering**

what comes first

Assumptions of correlational analysis

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1. Correlation focuses on linear relationships only
2. Correlation do not work well if the data range is "restricted"
3. Variability should be consistent at all values of x
4. Data are continuous

**Homoscedasticity**

when the standard deviations of a predicted variable, monitored over different values of an independent variable are *constant*

**Heteroscedasticity**

when the standard deviations of a predicted variable, monitored over different values of an independent variable are *non-constant*

**Statistical significance**

An observed difference between two descriptive statistics (such as means) that is unlikely to have occurred by chance.

**Describe the difference between strong, moderate, and weak correlation coefficients.**

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* **Strong: + .70 - 1.00**
* **Moderate: + .30 - .69**
* **Weak: + 00 - .29**