1. Chapter 16 The Elaboration Model Key Terms

2. Descriptive statistics Statistical computations describing either the characteristics of a sample or the relationship among variables in a sample. Proportionate reduction of error (PRE) A logical model for assessing the strength of a relationship by asking how much knowing values on one variable would reduce our errors in guessing values on the other.

3. Regression analysis Method of data analysis in which the relationships among variables are represented in the form of an equation, called a regression equation. Linear regression analysis A form of statistical analysis that seeks the equation for the straight line that best describes the relationship between two ratio variables.

4. Multiple regression analysis A form of statistical analysis that seeks the equation representing the impact of two or more independent variables on a single dependent variable. Partial regression analysis A form of regression analysis in which the effects of one or more variables are held constant, similar to the logic of the elaboration model.

5. Curvilinear regression analysis A form of regression analysis that allows relationships among variables to be expressed with curved geometrical lines instead of straight lines. Path analysis A form of multivariate analysis in which the causal relationships among variables are presented in graphic format.

6. Time-series analysis An analysis of changes in a variable (e.g., crime rates) over time. Factor analysis A complex algebraic method for determining the general dimensions or factors that exist within a set of concrete observations.

7. Nonsampling error Those imperfections of data quality that are a result of factors other than sampling error. Statistical significance A general term referring to the likelihood that relationships observed in a sample could be attributed to sampling error alone.

8. Tests of statistical significance A class of statistical computations that indicate the likelihood that the relationship observed between variables in a sample can be attributed to sampling error only. Level of significance The degree of likelihood that an observed, empirical relationship could be attributable to sampling error.