Chapter 11: Correlational Research

Differential Analysis

Technique of using moderator variables to form subgroups when examining the relationship between two other variables

Correlational research is primarily intended for determining

how variations in a sample's score on one measure are related to variations in their scores on one or more other variables

As a correlation coefficient increases in size, it means that

scores on one variable are better able to predict scores on the other variable

Scattergram/Scatterplot

A graph of the relationship between two variables

Multiple Regression

A mathematical technique that enables researchers to determine (1) how well the scores for each of a set of measured independent variables predict the scores on the measured dependent variable and (2) how well the combination of scores for all the measured independent variables predict the scores on the measured variables

Nesting

A situation in which a variable exists at several levels of an organizational structure

Canonical Correlation

A specialized type of multiple regression used when there are multiple dependent variables that can be viewed as different facets of an underlying factor

Tetrachoric Correlation Coefficient

A statistic used to describe the magnitude of the relationship between two variables, both of which yield scores that can be split into artificial dichotomies

Linear Correlation

A statistical approach for analyzing the distribution of a sample's scores on two measures, based on the assumption that as scores on one distribution increase, scores on the other distribution will also increase or decrease throughout the range of the score distribution

Prediction Research

A type of investigation that involves the use of data collected at one point in time to predict future behavior or events

Discriminant Analysis

A type of multiple regression that enables researchers to determine how well scores on several independent variables predict scores on a dependent variable when those scores are in the form of categories

Correlational Research

A type of quantitative investigation that seeks to discover the direction and degree of the relationship among variables, typically by the use of correlational statistics

Continuous Variable

A variable all of whose values have been measured and used by researchers; can assume different values between each point Ex: height

Discrete Variable

A variable with fixed values Ex: number of children

Negative Correlation

Any situation in which higher scores on a measure of one variable are linked to lower scores on a measure of another variable

Logistic Regression Analysis

Can be used for the same purpose as discriminate analysis but is more commonly used when the dependent variable is dichotomous

Dichotomous Variable

Can only have two values

True Dichotomy

Has only two possible variables in reality Ex: high school graduation (graduated or not graduated)

Artificial Dichotomy

Has only two values because they have been created by researchers or others

Effect Size

Helpful in determining the practical significance of statistical results

Bivariate Correlational Statistics

Indicates the magnitude of relationship between two, and only two, variables

Test of Statistical Significance

Indicates the likelihood that the statistical results obtained from the research sample are chance deviations from the results that would have been obtained if the researchers had studied the entire population that the sample represents

Multivariate Correlation

Involves a statistical analysis of the relationship between three or more variables

Line of Best Fit

Lines that allows for the best prediction of an individual's score on the y axis from knowing her score on the x axis

Correlation Coefficient

Mathematical expression that provides information about the direction and magnitude of the relationship between a sample's scores on measures of two or more variables; can range in value from -1.00 to +1.00

Criterion Variable

Measured at a subsequent point in time and is the outcome that researchers are trying to predict

Path Analysis/Structural Equation Modeling

Multivariate techniques for testing causal links among different variables that have been measured

Positive Correlation

Occurs when higher scores on a measure of one variable are associated with higher scores on a measure of the other variable

Independent Variable

Presumed cause in a causal relationship

Dependent Variable

Presumed effect

Moderator Variable

Presumed to affect the strength or direction, or both, of the correlation between two other variables

Factor Analysis

Purpose it to determine whether a set of variables reflects a smaller number of underlying factors

Correlation Matrix

Shows the correlation coefficient for all pairs of variables that were measured

Pearson Product-Moment Correlation Coefficient (r)

Statistic that indicates the direction and magnitude of a relationship between a sample's distribution of scores on two measures

Hierarchical Linear Modeling (HLM)

Statistical technique that enables researchers to determine how the correlation between two variables is affected by the different levels of nesting

Curvilinear Correlation

The line of best fit forms a curve, such that low values of variable A are associated with low values of variable B, medium values of variable A are associated with high values of variable B, and high values of variable A are associated with low values of variable B

Nonlinear Correlation

Variables are correlated with each other, but not in a linear manner

Predictor Variable

Variables that are measured in one point in time and then correlated with a criterion variable

Correlational research...

can be used to explore causal relationships

If researchers wish to determine whether the correlation between two variables is affected by different levels of "nesting," the appropriate statistical technique would be

hierarchical linear modeling

In their report of a correlational study, researchers should

identify and discuss flaws in the study

A correlational research design can include

multiple predictor variables

If researchers wish to determine the influence of multiple independent variables on a single dependent variable, the appropriate statistical technique would be

multiple regression

Inspection of a scattergram provides information about whether

the correlation between two variables is linear or nonlinear

A correlational coefficient provides information about

the magnitude and direction of the relationship between two variables but not its linearity

The null hypothesis in a typical correlational study states that

the true correlation coefficient in the population represented by the research sample is zero