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1353337 - McGraw-Hill Higher Education (US) ©

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LEARNING OBJECTIVES Contrast the three ways of describing results: comparing group percentages, correlating scores, and comparing group means. Describe a frequency distribution, including the various ways to display a frequency distribution. Describe the measures of central tendency and variability. Define a correlation coefficient. Define effect size. Describe the use of a regression equation and a multiple correlation to predict behavior. Discuss how a partial correlation addresses the third-variable problem. Summarize the purpose of structural equation models.

1353337 - McGraw-Hill Higher Education (US) ©

STATISTICS HELP US UNDERSTAND DATA COLLECTED IN RESEARCH INVESTIGATIONS IN TWO WAYS: FIRST, STATISTICS ARE USED TO DESCRIBE THE DATA. Second, statistics are used to make inferences and draw conclusions, on the basis of sample data, about a population. We examine descriptive statistics and correlation in this chapter; inferential statistics are discussed in Chapter 13. This chapter will focus on the underlying logic and general procedures for making statistical decisions. Specific calculations for a variety of statistics are provided in Appendix C.

SCALES OF MEASUREMENT: A REVIEW Before looking at any statistics, we need to review the concept of scales of measurement. Whenever a variable is studied, the researcher must create an operational definition of the variable and devise two or more levels of the variable. Recall from Chapter 5 that the levels of the variable can be described using one of four scales of measurement: nominal, ordinal, interval, and ratio. The scale used determines the types of statistics that are appropriate when the results of a study are analyzed. Also recall that the meaning of a particular score on a variable depends on which type of scale was used when the variable was measured or manipulated.

The levels of nominal scale variables have no numerical, quantitative properties. The levels are simply different categories or groups. Most independent variables in experiments are nominal, for example, as in an experiment that compares behavioral and cognitive therapies for depression.