New Semester
Started
Get
50% OFF
Study Help!
--h --m --s
Claim Now
Question Answers
Textbooks
Find textbooks, questions and answers
Oops, something went wrong!
Change your search query and then try again
S
Books
FREE
Study Help
Expert Questions
Accounting
General Management
Mathematics
Finance
Organizational Behaviour
Law
Physics
Operating System
Management Leadership
Sociology
Programming
Marketing
Database
Computer Network
Economics
Textbooks Solutions
Accounting
Managerial Accounting
Management Leadership
Cost Accounting
Statistics
Business Law
Corporate Finance
Finance
Economics
Auditing
Tutors
Online Tutors
Find a Tutor
Hire a Tutor
Become a Tutor
AI Tutor
AI Study Planner
NEW
Sell Books
Search
Search
Sign In
Register
study help
business
nonparametric statistical inference
Advanced And Multivariate Statistical Methods 7th Edition Craig A. Mertler, Rachel A. Vannatta, Kristina N. LaVenia - Solutions
Discuss similarities and differences between discriminant analysis, multiple regression, and principal components analysis.
Describe how discriminant analysis is often seen as the reverse of MANOVA.
How much variance in the original set of variables is accounted for by the components?
If reliable components are identified, how might we interpret those components?
How many reliable and interpretable components are there among items 26–46 from the Perceptions of School Leadership Survey?
Develop research questions appropriate for principal components analysis.
Distinguish between orthogonal and oblique rotations.
Discuss the process of rotation in terms of its benefit to an overall principal components analysis.
Reconcile the concepts of assessment of model fit and parsimony in a principal components analysis.
Describe how model fit is assessed in principal components analysis.
Define eigenvalue and its relationship to a scree plot.
Differentiate between factor analysis and principal components analysis.
Describe what is represented by communalities.
Explain what factor loadings actually measure.
Does the obtained regression equation resulting from a set of seven predictor variables allow us to reliably predict female life expectancy?
Which of the seven predictor variables (i.e., percent urban population, GDP, birth rate, number of hospital beds, number of doctors, number of computers, and number of cell phones) are most influential in predicting female life expectancy? Are there any predictor variables that do not contribute
Develop research questions appropriate for multiple regression analysis.
Explain why the assumptions associated with prediction errors are essential in obtaining the best linear estimates.
Compare and contrast forward selection, stepwise selection, and backward deletion as approaches to stepwise multiple regression.
Compare and contrast standard, sequential, and stepwise approaches to multiple regression.
Explain the relationship between multicollinearity and orthogonality.
Summarize the concept of a least-squares solution in a multiple regression analysis.
Describe what is meant by the terms regression line and centroid.
Explain differences between simple regression and multiple regression.
Previously in this chapter, we determined that the variable of transformational leadership was significantly affected by gender when controlling for education level.Transformational leadership consists of four subscales: idealized influence, inspirational motivation, intellectual stimulation, and
You are interested in evaluating the effect of job satisfaction (satjob2) and age category(agecat4) on the combined DV of hours worked per week (hrs1) and years of education(educ).a. Develop the appropriate research questions and/or hypotheses for interaction and main effects.b. Run Explore to
a. Does gender and job satisfaction significantly interact on hours worked, after removing the effect of age?b. Does hours worked differ by gender, after removing the effect of age?c. Does hours worked differ by job satisfaction, after removing the effect of age? If so, which age categories differ?
a. Does gender and job satisfaction significantly interact on income, after removing the effect of age?b. Does income differ by gender, after removing the effect of age?c. Does income differ by job satisfaction, after removing the effect of age? If so, which age categories differ?
a. Does gender and job satisfaction significantly interact on worker productivity (as measured by the combination of income and hours worked), after removing the effect of age?b. Does worker productivity (as measured by the combination of income and hours worked) differ by gender, after removing
a. Do gender and education level interact on appreciation among K-12 principals?b. Does appreciation differ by gender among K-12 principals?c. Does appreciation differ by education level among K-12 principals?
a. Do gender and education level interact on self-awareness among K-12 principals?b. Does self-awareness differ by gender among K-12 principals?c. Does self-awareness differ by education level among K-12 principals?
a. Do gender and education level interact on teachability among K-12 principals?b. Does teachability differ by gender among K-12 principals?c. Does teachability differ by education level among K-12 principals?
a. Do gender and education level interact on humility (teachability, self-awareness, and appreciation) among K-12 principals?b. Does humility (teachability, self-awareness, and appreciation) differ by gender among K-12 principals?c. Does humility (teachability, self-awareness, and appreciation)
Are there significant mean differences in hours worked for individuals of different ages?If so, which age categories differ?
Are there significant mean differences in income levels for individuals of different ages?If so, which age categories differ?
Are there significant mean differences in worker productivity (as measured by the combination of income and hours worked) for individuals of different ages?
Create an entire set of research questions appropriate for a multivariate analysis of variance.
Describe the logic behind the use of both MANOVAs and MANCOVAs.
Explain the essential assumptions and limitations in using both MANOVAs and MANCOVAs.
Explain the advantages of using multivariate ANOVAs over using univariate ANOVAs.
Compare and contrast ANOVA/ANCOVA with MANOVA/MANCOVA.
Conduct ANCOVA.a. Is factor interaction significant? Explain.b. Are main effects significant? Explain.c. Does the covariate significantly influence the DV? Explain.d. What can you conclude from the effect size for each main effect?e. Write a results statement.
Create a line plot of the factors. Do factors interact?
Test the assumptions of normality, linearity, homogeneity of regression slopes, and homogeneity of variance.a. Based upon the tests of normality, histograms, and boxplots, what is your conclusion regarding normality? Does a scatter plot support the assumption of linearity between the covariate and
Screen data for missing data and outliers. Keep in mind, in this data set, outliers for income (less than 5) have been eliminated. What steps, if any, are necessary for reducing missing data and outliers?
Develop the appropriate research questions and/or hypotheses for main effects and interaction.
Describe the logic behind the use of analysis of covariance
Develop research questions appropriate for both one-way and factorial ANOVAs.
Discuss possible reasons for limiting the number of covariates used in a single analysis of covariance.
Describe the three main purposes for the use of analysis of covariance.
Examine the differences between an analysis of variance and an analysis of covariance.
Discuss the process of partialing out the effects of a covariate.
Describe what is meant by the term concomitant variable.
Write the results summary for the previous problem. Include a table of descriptive statistics as well as the ANOVA summary table.
Using your research questions from Question 1, complete a factorial ANOVA analysis.The variables used were self-awareness (SelfAwareness) for individuals by principal gender (Psex) and level of education (Peduc2). Use the following questions to guide your interpretation of the output.a. Can we
The following table presents means for the self-awareness (SelfAwareness) for individuals by principal gender (Psex) and level of education (Peduc2). Using the table’s data, draw a line plot. Use the line plot to complete the following steps to estimate the factor main effects on the dependent
Describe the nature of the partitioning of sums of squares variability in a two-way ANOVA.
Develop research questions appropriate for both one-way and factorial ANOVAs.
Differentiate between ordinal and disordinal interactions.
Describe all of the possible hypotheses in a two-way analysis of variance.
Discuss the purpose of post hoc testing in ANOVAs.
Interpret the basic formula for the calculation of an F ratio.
Explain the generic form of the null hypothesis in a one-way ANOVA.
Compare and contrast a one-way ANOVA and a factorial ANOVA.
You are interested in studying predictors (HumilityMean and ConfidenceMean) of the overall leadership effectiveness (MLQMean).a. Examine missing data, outliers, and univariate normality for each variable. What are your conclusions about the distributions? What transformations should be conducted?b.
You are interested in investigating if the leadership attribute of humility (HumilityMean)differs by education level (Peduc2—have a master’s or post-master’s degree) among K-12 principals. Complete the necessary steps to examine univariate grouped data in order to respond to the following
Explain how the assumption of homoscedasticity is assessed and violations are determined.
Describe the use of residuals in evaluating violations to the assumption of linearity.
Discuss the purpose and use of data transformations.
Compare and contrast various forms of departure from normality in a variable’s (or set of variables’) distribution.
Interpret the proper application of Mahalanobis distance when evaluating potential outliers.
Distinguish between univariate and multivariate outliers.
Describe four main purposes for screening data.
Discuss the importance of screening data prior to any substantive data analysis.
What underlying structure exists among the following variables: amount of alcohol use, drug use, sexual activity, school misconduct, cumulative GPA, reading ability, and family income?
Do gender and education level significantly affect the degree of leader efficacy among K-12 principals?
To what extent do certain risk-taking behaviors (amount of alcohol use, drug use, and sexual activity, and the presence of violent behavior) increase the odds of a suicide attempt occurring?
Do ethnicity and learning preference significantly affect reading achievement, math achievement, and overall achievement among sixth grade students?
Do preschoolers of low, middle, and high socioeconomic status have different literacy test scores?
Which combination of risk-taking behaviors (amount of alcohol use, drug use, sexual activity, and violence) best predicts the amount of suicide behavior among adolescents?
Does ethnicity significantly affect reading achievement, math achievement, and overall achievement among sixth grade students after adjusting for family income?
Do adolescents from low, middle, and high socioeconomic status families have different literacy test scores after adjusting for family type?
Which leadership factors (humility, confidence, transformational leadership, and outcomes of leadership) best distinguish leader efficacy (low, moderate, high) among K-12 superintendents?
What is the relationship between SAT scores and college freshmen GPAs?
Do males and females have significantly different income levels?
Does ethnicity significantly affect reading achievement, math achievement, and overall achievement among sixth grade students?
To what degree do SAT scores predict college freshmen GPAs?
Accurately identify independent and dependent variables, as well as the appropriate number of independent variable categories, for a given research question.
Develop research questions, applicable to their respective fields of study, that are appropriate for each statistical technique described.
Describe techniques for determining the underlying structure of a set of variables, including factor analysis and principal components analysis.
Describe techniques used to predict group membership, including discriminant analysis and logistic regression.
Describe various techniques for testing the significance of group differences, including t tests, one-way ANOVAs and ANCOVAs, factorial ANOVAs and ANCOVAs, one-way MANOVAs and MANCOVAs, and factorial MANOVAs and MANCOVAs.
Describe various techniques for measuring the degree of relationships between variables, including bivariate correlation and regression, and multivariate regression.
Describe what is reported by effect size.
Discuss the relationship between α (alpha) and β (beta).
Evaluate the difference between Type I and Type II errors.
Explain the process of hypothesis testing and determining statistical significance, using appropriate terminology.
Distinguish between standard and sequential analyses.
Provide appropriate examples of orthogonal and nonorthogonal relationships between variables.
Explain differences between various data matrices.
Showing 2700 - 2800
of 5397
First
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Last
Step by Step Answers
Get 50% OFF
Claim Now
