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Statistics for the Behavioral Sciences by Frederick J. Gravetter, Larry B. Wallnau ISBN 10: 1305504917 ISBN 13: 9781305504912

Statistics is one of the most practical and essential courses that you will take, and a primary goal of this popular text is to make the task of learning statistics as simple as possible. Straightforward instruction, built-in learning aids, and real-world examples have made STATISTICS FOR THE BEHAVIORAL SCIENCES, 10th Edition the text selected most often by instructors for their students in the behavioral and social sciences. The authors provide a conceptual context that makes it easier to learn formulas and procedures, explaining why procedures were developed and when they should be used. This text will also instill the basic principles of objectivity and logic that are essential for science and valuable in everyday life, making it a useful reference long after you complete the course.

Statistics is one of the most practical and essential courses that you will take, and a primary goal of this popular text is to make the task of learning statistics as simple as possible. Straightforward instruction, built-in learning aids, and real-world examples have made STATISTICS FOR THE BEHAVIORAL SCIENCES, 10th Edition the text selected most often by instructors for their students in the behavioral and social sciences. The authors provide a conceptual context that makes it easier to learn formulas and procedures, explaining why procedures were developed and when they should be used. This text will also instill the basic principles of objectivity and logic that are essential for science and valuable in everyday life, making it a useful reference long after you complete the course.

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Overview: Three Basic Data Structures 703

■ Category 2: A Single Group of Participants with Two Variables

Measured for Each Participant

These research studies are specifically intended to examine relationships between variables.

Note that different variables are being measured, so each participant has two or more

scores, each representing a different variable. Typically, there is no attempt to manipulate

or control the variables; they are simply observed and recorded as they exist naturally.

Although several variables may be measured, researchers usually select pairs of variables

to evaluate specific relationships. Therefore, we present examples showing pairs of

variables and focus on statistics that evaluate relationships between two variables. Table 2

presents four examples of data in this category. Once again, the four data sets differ in

terms of the scales of measurement that are used. The first set of data (a) shows numerical

scores for each set of measurements. For the second set (b) we have ranked the scores from

the first set and show the resulting ranks. The third data set (c) shows numerical scores for

one variable and nominal scores for the second variable. In the fourth set (d), both scores

are measured on a nominal scale of measurement. The appropriate statistical analyses for

these data are discussed in Section II.

TABLE 2

Examples of data with

two scores for each

participant for one group

of participants.

(a) SAT Score (X) and College

Freshman GPA (Y)

(b) Ranks for the Scores

in Set (a)

X Y X Y

620 3.90 7 8

540 3.12 3 2

590 3.45 6 5

480 2.75 1 1

510 3.20 2 3

660 3.85 8 7

570 3.50 5 6

560 3.24 4 4

(c) Age (X ) and Wrist Watch

Preference (Y )

(d) Gender (X ) and Academic

Major (Y )

X Y X Y

27 digital M Sciences

43 analogue M Humanities

19 digital F Arts

34 digital M Professions

37 digital F Professions

49 analogue F Humanities

22 digital F Arts

65 analogue M Sciences

46 digital F Humanities

■ Category 3: Two or More Groups of Scores with Each

Score a Measurement of the Same Variable

A second method for examining relationships between variables is to use the categories of

one variable to define different groups and then measure a second variable to obtain a set

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