Display Commands in Log Instructions

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• Select Options .
• A dialog box will open. Click on the Viewer tab.
• Click the check box next to Display commands in the log .

Lesson 19, questions 1-6:

The data for exercises 1 and 2 are in the data set named Lesson 19 Exercise File 1 on the Web at http://www.prenhall.com/greensalkind The data are from the following research problem:

Marion collected questionnaire data from 12 students concerning their attitudes toward math. The students responded to three attitudinal items on a 5-point scale, with 1 = totally disagree to 5 = totally agree. Table 8 shows three items.

Exercise 1 :

Reverse-scale the values for item3 (1 = 5, 2 =4, 3 = 3, 4 = 2, 5 =1).

Exercise 2 :

Compute a scale called mathatt by taking the mean across the three attitudinal items with nonmissing values and multiplying that mean score by 3 to get a total-like score. Compute this score so that if any case has more than one missing value, mathatt will show a missing value for that case.

Table 8:

Variable Definition

Item 1 I like my mathematics classes.

Item 2 I find math to be a positive challenge.

Item 3 I am fearful of math.

The data for Exercises 3 through 5 are in the data named Lesson 19 Exercise File 2 on the Web at http://www.prenhall.com/greensalkind The data are from the following research problem:

Susan is interested in how much coffee people drink at work. To answer this question, she has10 individuals record the number of cups of coffee they drink daily at work for four days. She also collects in index of job stress and each person’s age. The data file contains four variables concerning coffee drinking (day 1 through day 4), the job stress variables (jobstress), and the age variable (age). There are no missing data in this data set.

Exercise 3 :

Create a categorical age variable named agecat with three age categories, 20-29, 30-39, and 40 and beyond.

Exercise 4 :

Create a categorical job stress variable named jobcat with two categories, 1 (jobstress score less than or equal to 21) and 2 (jobstress score greater than 21).

Exercise 5 :

Create a new variable called group with four categories based on low and high amounts of coffee drinking and low and high degree of job stress. Split the sample into low and high amounts of coffee drinking based on the total amount of coffee drinking, such that individuals who drink less than 5 cups of coffee are considered low and those who drink 5 or more cups are considered high.

Exercise 6 :

The data for the Exercises 6-8 (I only need #6) are in the data set named Lesson 19 Exercise File 3 on the Web at http://www.prehnall.com/greensalkind The data are from the following research problem.

Matt is interested in negative peer interaction in preschoolers. He collects data on 20 preschoolers. He has three measures: (1) a teacher rating of prosocial behavior with peers, (2) behavioral observations of the number of aggressive acts against peers per hour, and (3) peer reports of aggression. He wants to combine his three measures into a single measure of negative peer interaction. There are no missing data in this dataset.

Exercise 6 : Transform the scores for the three variables into z scores.

Lesson 20, questions 1 and 4:

Exercises 1 through 4 are based on the following research problem. The data for these exercises can be found in the data file named Lesson 20 Exercise File 1 on the Web at http://www.prenhall.com/greensalkind

Ann wants to describe the demographic characteristics of a sample of 25 individuals who completed a large-scale survey. She has demographic data on the participants’ gender (two categories), educational level (four categories), marital status (three categories), and community population size (eight categories).

Exercise 1 :

Conduct a frequency analysis on the gender and marital status variables. From the output, identify the following:

a. Percent of men

b. Mode for marital status

c. Frequency of divorced people in the sample

Exercise 4 :

Write a Participants section describing the participants in Ann’s sample.

Lesson 21, questions 6-8:

The data for the exercises 6 through 8 are in the data set named Lesson 21 Exercise File 2 on the Web at http://www.prenhall.com/greensalkind That data are from the following research problem.

Michelle collects questionnaire data from 40 college students to assess whether they have a positive attitude toward political campaigns. She asks them to rate five statements about campaigns on a 5-point Likert scale (1 = disagree to 5 = agree). She also has information on respondents’ political party affiliation (politic with 1 = Republican and 2 = Democrat). Table 14 shows the items on the scale.

Table 14

Political Campaign Attitude Items

Variables Definition

att1 Use of inappropriate tactics in campaign strategies.

att2 Candidates address the issues.

att3 Accurate presentation of candidate’s political agenda.

att4 Focus on issues relevant to the average citizen.

att5 Honesty in making campaign promises.

Exercise 6 :

Compute total attitude scores from the scores for the five attitudinal items. The total attitude scores should reflect whether students have a general positive attitude toward campaigning.

Exercise 7 :

Compute means on the total attitude scores for the two political parties.

Exercise 8 :

Create boxplots showing the distributions of the total attitude scores for the two political parties.

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