For this assignment, identify the most appropriate bivariate | Complete Solution

For this assignment, identify the most appropriate bivariate or multivariate statistical test or procedure from the bivariate or multivariate statistical tests or procedures list below that you would use for each of the items in the research situations and questions list that follows, and explain your reasoning.

• Use a Microsoft Word document to capture your work.
• Clearly demonstrate how you arrived at your conclusions.

Example

Situation: To what extent do hours worked and years of experience predict income after adjusting for years of education?

• Appropriate statistical method: Multiple regression.

Explanation: One dependent variable (monthly income) is measured on a quantitative scale, and three independent variables (hours worked, experience, years of education) are measured on a quantitative scale. The nature of the problem is to find predictors of income based on hours of work and years of experience after adjustment for years of education, or the problem is to examine whether hours worked and years of experience are independent predictors of income after adjusting for years of education.

Tests and Procedures

Below are commonly used bivariate or multivariate statistical tests or procedures:

• Bivariate correlation.
• Bivariate regression.
• Discriminant analysis.
• Factor analysis.
• Factorial MANOVA.
• Factorial MANCOVA.
• One-way ANOVA.
• Logistic regression.
• Two-Way ANOVA.
• Structural Equation Model.
• One-way MANCOVA.
• Multiple regression.
• Independent sample t-test.

Research Situations and Questions

Identify the most appropriate bivariate or multivariate statistical test or procedure from the bivariate or multivariate statistical tests or procedures listed below that you would use for each of these items, and explain your reasoning:

1. To examine the relationship between family income and test scores among 12th graders in Minnesota.
2. To determine the extent to which family income predicts test scores among 12th graders in Minnesota.
3. To determine whether SAT scores differ significantly between students from private and public schools in Minnesota.
4. To determine whether SAT scores differ among students from low, middle, and high socioeconomic status (SES) families in the state of Minnesota.
5. To determine among freshmen college students in Minnesota the extent to which exercise activity (low, moderate, high) affects HDL (good cholesterol), LDL (bad cholesterol), and total cholesterol levels.
6. To examine the causal effects among a number of risk factors including caloric intake, frequency of exercise, weight, and family history of heart disease on total cholesterol levels among college students in Minnesota.
7. To discriminate between students who dropped out of high school and those who did not drop out, you studied student risk behaviors including failure to do homework, drug use, sexual activity, and family SES.
8. To determine which of the following combination of factors (family income, caloric intake, exercise activity, and mother's educational level) best predict total cholesterol levels among college freshmen.
9. To determine whether type of work (academia, government, industry, self-employed) significantly affects income level and stress level among families in Minnesota after adjusting family-type (single-family, married-family).
10. To determine how family-type (single-family, married-family) and work-type (academia, government, industry, self-employed) impact income and stress level in Minnesota?
11. To examine the extent to which factors (illegal drug use, sexual activity, age, gender) predict the odds of drop out among high school students.
12. To determine how gender and level of exercise activity (low, moderate, high) impact LDL cholesterol, HDL cholesterol, and total cholesterol, after adjusting for family income, among adolescents in Hennepin County, Minnesota.
13. To examine whether any underlying structure exists among the following variables: caloric intake, weight, educational level, income level, LDL cholesterol, and total cholesterol.