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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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Research suggests that romantic background music

increases the likelihood that a woman will give her

phone number to a man she has just met (Guéguen,

Jacob, & Lamy, 2010). The participants in the study

were female undergraduate students, 18–20 years old,

who were recruited to take part in research on product

evaluation. Each woman was taken to a waiting room

with background music playing. For some of the women,

the music was a popular love song and for the others, the

music was a neutral song. After 3 minutes the participant

was moved to another room in which a 20-year-old man

was already waiting. The men were part of the study and

were selected because they previously had been rated as

average in attractiveness. The participant and the confederate

were instructed to eat two cookies, one organic

and one without organic ingredients, and then talk about

the differences between the two for a few minutes. After

5 minutes, the experimenter returned to end the study

and asked the pair to wait alone for a few minutes.

During this time, the man used a scripted line to ask

the woman for her phone number.

You should recognize this study as an example of an

experiment. The researchers manipulated the type of

music to create two treatment conditions and randomly

assigned women to conditions to control extraneous

variables. They recorded whether each woman gave her

number to obtain a set of scores in each treatment and

then compared the two sets of scores.

Table 17.1 shows the structure of this design represented

by a matrix with the independent variable (different

music) determining the rows of the matrix and

the two categories for the dependent variable (yes/no)

determining the columns. The number in each cell of

the matrix is the frequency count showing how many

participants are classified in that category. For example,

of the 44 women who heard the romantic music, there

were 23 (52.3%) who gave their phone numbers. By comparison,

only 12 of the 43 women (27.9%) who heard

the neutral music gave their numbers. The researchers

would like to use these data to support the argument that

romantic background music increases the likelihood that

a woman will give her number to a man she has just met.

Although the Guéguen et al. study involves an independent

variable (the type of music) and a dependent

variable (did she give her number), you should realize

that this study is different from any experiment we have

considered in the past. Specifically, the study does not

produce a numerical score for each participant. Instead,

each participant is simply classified into one of two

categories (yes or no). The data consist of frequencies

or proportions describing how many individuals are in

each category. You should also note that Guéguen et al.

want to use a hypothesis test to evaluate the data. The

null hypothesis would state that the type of background

music has no effect on whether a woman will give her

phone number. The hypothesis test would determine

whether the sample data provide enough evidence to

reject this null hypothesis.

Because there are no numerical scores, it is impossible

to compute a mean or a variance for the sample

data. Therefore, it is impossible to use any of the familiar

hypothesis tests (such as a t test or analysis of variance

(ANOVA) to determine whether there is a significant

difference between the treatment conditions. What is

needed is a new hypothesis testing procedure that can be

used with non-numerical data.

In this chapter we introduce two hypothesis tests

based on the chi-square statistic. Unlike earlier tests

that require numerical scores (X values), the chi-square

tests use sample frequencies and proportions to test

hypothesis about the corresponding population values.

TABLE 17.1

A frequency distribution table

showing the number of participants

who answered either yes or

no when asked for their phone

numbers. One group listened to

romantic music while in a waiting

room and second group listened to

neutral music.

Type of

Music

Gave Phone Number?

Yes No

Romantic 23 21

Neutral 12 31

560

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