Exam 12: Quantitative Data Analysis: Using Statistics for Description and Inference

According to Box 12.3, "collinearity" refers to a perfect linear fit between the independent and dependent variables.

For the 2018 GSS, you regress number of hours of television watched on the average day (Y) on number of years of education completed (X) and obtain the following result: Y = 4.78 - .15X. How much change in hours of television watched is associated with a change of one year in a respondent's education?

Some data-processing activities can be programmed into computer-assisted interviewing.

Which of the following most accurately describes Singleton's study of alcohol consumption and academic performance?

Suppose a researcher finds a statistically significant relationship between salary and job satisfaction among a random sample of employees. From this information, she can

Refer to the figure. According to the theoretical model,

Using the 2018 GSS, we regressed the number of hours of television watched on the average day on three variables: years of education, age, and marital status. Marital status is a dummy variables with 1 = married. We get the following results for the unstandardized regression coefficients: TVhours = 3.38 - .13Educ + .03Age - .52Married. According to this equation,

Univariate analysis can determine whether to recode variable categories for further analysis.

Suppose two variables are negatively related. Which of the following regression equations might describe this relationship?

Describe the differences in the bivariate analysis of nominal/ordinal variables and interval/ratio variables. What descriptive and inferential statistics are used to describe each type of variable?

Examining a scatterplot can reveal whether a linear regression analysis is appropriate.

Using the 2018 GSS, we regressed the number of hours of television watched on the average day on three variables: years of education, age, and marital status. Marital status is a dummy variables with 1 = married. We get the following results for the standardized regression coefficients: TVhours = - .16Educ + .20Age - .10Married. According to this equation,

Using the 2018 GSS, we regressed the number of hours of television watched on the average day on three variables: years of education, age, and marital status. Marital status is a dummy variables with 1 = married. We get the following results for the unstandardized regression coefficients: TVhours = 3.38 - .13Educ + .03Age - .52Married. What is the predicted number of hours of television viewing for a 20-year old, unmarried person with 14 years of education?

What is the usual order of steps in processing completed survey interviews or questionnaires?

Refer to the following table. Table 1. Percentage of Respondents Expressing High Tolerance of Civil Liberties for Political Dissidents, by Gender and Religiosity Which variable(s) is (are) controlled, or held constant, in each partial table of Table 1?

What is the order of steps in the quantitative analysis of survey data?

The quality of data is affected at several stages of social research, including data processing. What techniques do survey researchers apply to avoid errors and enhance data quality during data processing? Are data processing errors unavoidable, like random sampling error? Explain.

Consider the following table from the 2018 GSS, which shows the relationship between race and whether someone favors or opposes "a law which would require a person to obtain a police permit before he or she could buy a gun." The data in this table suggest that (the answer may require some calculation)

According to Box 12.3, leaving out important variables from a model is called a "specification error."

Someone at your college conducts a survey on helping and voluntarism. Suppose you are consulted about how to analyze the data to test the hypothesis that students majoring in the arts are more likely to do volunteer work than students majoring in the sciences. (a) What questions would you ask about the data before you make your recommendations? (b) As you might point out, why is a bivariate analysis seldom, if ever, sufficient to test a hypothesis that one variable causes another? (c) As you might explain, how is multiple regression superior to elaboration?