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Magnitude
an indication of the strength of the relationship between two variables
scatterplot
A figure that graphically represents the relationship between two variables
Causality
The false assumption that a correlation indicates a causal relationship between the two variables.
Directionality
The mistaken inference made with respect to the direction of a causal relationship between two variables
third variable problem
the problem of a correlation between two variables being dependent on another (third) variable
partial correlation
A correlation technique that involves measuring three variables and then statistically removing the effect of the third variable from the correlation of the remaining two variables
restrictive range
A variable that is truncated and has limited variability
person-who argument
Arguing that a well-established statistical trend is invalid because we know "a person who" went against the trend
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. As a general rule of thumb when calculating a correlation coefficient, we should have at least 10 participants per variable. Formula: r = (Σ Zx*Zy) ÷ N
Coefficient of Determination (r2)
A measure of the proportion of the variance in one variable that is accounted by another variable; calculated by squaring the correlation coefficient.
Spearman's Rank-order correlation coefficient
The correlation coefficient used when one (or more) of the variables is measured on an ordinal (ranking) scale
Point-biserial correlation coefficient
The correlation coefficient used when on of the variables is measured on a dichotomous nominal scale, and the other is measured on an interval or ratio scale.
Phi coefficient
The correlation coefficient used when both measured variables are dichotomous and nominal.
Regression analysis
A procedure that allows us to predict an individual's score on one variable based on knowing one or more other variables.
Regression line
The best-fitting straight line drawn through the center of a scatterplot that indicates the relationship between variables. Formula = Y' = bX + a where Y' = predicted value on the y variable, b = slope of the line, X = individual score on x variable, a = y intercept. To get b; b = r * (σy ÷ σx) where σ is standard deviation. To get a; a = (Mean of Y) - b *(Mean of x)
multiple regression analysis
Multiple regression analysis involves combining several predictor variables in a single regression analysis. Helps to assess the effects of multiple predictor variables on the dependent measure.
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