bivariate correlation

bivariate correlation:An association that involves exactly two variables. Also called bivariate as

mean:An arithmetic average; a measure of central tendency computed from the sum of all the scores in a set of data, divided by the total number of scores.sociation.

Effect size:The magnitude, or strength, of a relationship between two or more variables.

Statistically significant: In NHST, the conclusion assigned when when p < .05; that is, when it is unlikely the result came from the null hypothesis population.

Replication:The process of conducting a study again to test whether the result is consistent.

Outlier:A score that stands out as either much higher or much lower than most of the other scores in a sample.

Restriction of range:In a bivariate correlation, the absence of a full range of possible scores on one of the variables, so the relationship from the sample underestimates the true correlation.

Curvilinear association:An association between two variables that is not a straight line; instead, as one variable increases, the level of the other variable increases and then decreases (or vice versa). See also negative association, positive association, zero association.

Directionality problem: In a correlational study, the occurrence of both variables being measured around the same time, making it unclear which variable in the association came first. See also temporal precedence.

Reverse causation: In a study that finds a relationship between variables A and B, the plausible inference that either A could cause B or that B could cause A.

Third variable problem: In a correlational study, the existence of a plausible alternative explanation for the association between two variables. See also internal validity.

Spurious association:A bivariate association that is attributable only to systematic mean differences on subgroups within the sample; the original association is not present within the subgroups.

Moderator:A variable that, depending on its level, changes the relationship between two other variables.

Multivariate design:A study designed to test an association involving more than two measured variables.

Longitudinal design:A study in which the same variables are measured in the same people at different points in time.

Cross sectional correlation:In a longitudinal design, a correlation between two variables that are measured at the same time.

Autocorrelation:In a longitudinal design, the correlation of one variable with itself, measured at two different times.

Cross lag sectional:In a longitudinal design, a correlation between an earlier measure of one variable and a later measure of another variable.

Multiple regression:A statistical technique that computes the relationship between a predictor variable and a criterion variable, controlling for other predictor variables. Also called multivariate regression.

Control for: Holding a potential third variable at a constant level (statistically or experimentally) while investigating the association between two other variables. See also control variable, multiple regression.

Criterion variable: The variable in a multiple-regression analysis that the researchers are most interested in understanding or predicting. Also called dependent variable.

Predictor variable: A variable in multiple-regression analysis that is used to explain variance in the criterion variable. Also called independent variable.

Parisomny: The degree to which a theory provides the simplest explanation of some phenomenon. In the context of investigating a claim, the simplest explanation of a pattern of data; the best explanation that requires making the fewest exceptions or qualifications.

Mediator:A variable that helps explain the relationship between two other variables. Also called mediating variable.