ABSTRACT
Abscissa Kendall’s Coefcient of Concordance Point Biserial rpb Biserial r Linearity Positive Relationship Bivariate Lurking Variable Positively Accelerated Curve Bivariate Frequency Distribution Machine Formula Regression Coefcient of Determination Magnitude Scattergram Coefcient of Nondetermination Monotonic Relationship Spearman Rank Order Correlation Coefcient Negative Relationship Correlation Coef cient Correlation Ratio (ETA) Negatively Accelerated Curve Standard Error of Estimate Degrees of Freedom (df) Ordinate Underestimate Direction Pearson Product Moment Univariate Forced Dichotomy Correlation (r) Homoscedasticity Phi Coefcient Inter-Judge Reliability Phi Correlation
Thus far, we have been concerned with graphical and statistical techniques for describing the distribution of scores for a single variable and explored univariate graphical and statistical techniques. In this chapter, we will discuss graphical and statistical techniques for describing
bivariate frequency distributions. As the name implies, a bivariate frequency distribution describes simultaneously the distribution of scores on two variables. In behavioral research, one is often the independent variable (IV) and the other is the dependent variable, in this chapter called the dependent measure (DM).
