U10a1 Project 2 Part 3 Project 2 part 3 Part 3 will comp
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U10a1 Project 2 Part 3 Project 2 part 3 Part 3 will complete the method component of Project 2. In this assignment you w
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U10a1 Project 2 Part 3 Project 2 part 3 Part 3 will complete the method component of Project 2. In this assignment you will propose three types of multivariate statistical analyses, including a discussion of underlying assumptions, the role of each analysis in explaining the theory, and an evaluation for using each statistical analysis. This assignment will complete Project Two. REQUIREMENTS: Part 3 consists of three deliverables: (a) propose an analysis using two categorical variables; (b) propose a multiple regression analysis; (c) propose one of the following: log-linear, discriminant, or logistic regression analysis. You will submit a single document, organized by deliverables 1 through 3, covering the following components: â€¢ (a) Categorical analysis falls under some form of frequency analysis. That is, the data are frequencies rather than scores. When developing thought to a categorical analysis direct your attention to variables measured on a nominal or ordinal level. o Deliverable 1: Propose an analysis of two categorical variables. Next, represent the analysis as a 2-way contingency table (also known as cross-tabulation). Explain the purpose of this analysis with respect to your proposal and how it fits into the explanatory model. State the research hypothesis, which will be your best guess, based on your knowledge of the theory and literature you have read, about the statistical outcome. That is, how are these variables related? Include a statement of assumptions under the analysis of a 2-way contingency table (Chi-Square). An example of a 2-way contingency table can be found here:http://www.stat.berkeley.edu/~stark/SticiGui or http://davidmlane.com/hyperstat/ â€¢ (b) Regression analysis is used to explain the variability in the dependent variable as a linear combination of independent variables. The level of measurement appropriate for linear regression must be interval or ratio. While methods exist to use nominal or ordinal level variables (for example, dummy variables), these designs are beyond the scope of this course. o Deliverable 2: Propose a linear regression of two or more independent variables on one dependent variable. Identify the independent and dependent variables and explain their inclusion in the context of the theory. State the assumptions under linear regression. Evaluate the costs and benefits of regression analysis as it pertains to your proposal. State the research hypothesis and defend linear multiple regression is appropriate to test it. â€¢ (c) There are numerous multivariate models beyond regression. Log-linear analysis is an extension of a two-way contingency table. Think about a slice of bread as a two-way contingency table. You would use Chi-Square to determine whether the two variables are related. With log-linear analysis you are dealing with a loaf of bread with numerous two-way tables. The goal of log-linear analysis is to detect which slices of bread are sufficient to describe the entire loaf. Discriminant analysis is analogous to regression. Rather than the dependent variable being interval or ratio it is nominal or ordinal. The goal is to weight the independent variables in such a way as to differentiate each category of the dependent variable. Logistic regression is analogous to regression, the major difference is that the dependent variable is binary (0,1). This type of analysis is used to predict of the probability of occurrence of an event (for example, 0=it didnâ€™t happen; 1=it happened). o Deliverable 3: Propose one of the following multivariate statistical analyses: (a) log-linear (p. 200â€“202), (b) discriminant analysis? (p. 202; 210), or (c) logistic regression (p. 204â€“209). Identify the independent and dependent variables and explain their inclusion in the context of the theory. State the assumption under the selected multivariate analysis. Evaluate the costs and benefits of the statistical analysis as it pertains to your proposal. State the research hypothesis and define the selected multivariate analysis is appropriate to test it. If appropriate, also detail follow-up tests for additional hypotheses testing. There are numerous resources on the Internet that describe these designs. â€¢ Maximum length is 3 pages, in APA format. The Web sites, located in the Resources section, will help you to complete this assignment. Prior to submitting, read the scoring guide for this assignment, to ensure you have met the scoring criteria Resources to use http://www.stat.berkeley.edu/~stark/SticiGui/ http://davidmlane.com/hyperstat/ here is the scoring guide Criteria Non-performance Basic Proficient Distinguished Comments Complete a categorical analysis, in which variables are appropriate, research hypothesis present, and assumptions present. (33%) Does not complete a categorical analysis. Completes a categorical analysis, and includes appropriate variable type and level selection, in addition, research hypothesis present. Completes a categorical analysis, in which variables are appropriate, research hypothesis present, and assumptions present. Completes a categorical analysis, in which variables are appropriate, research hypothesis present, and assumptions present, and there is a clear connection between research hypothesis and theory. In addition, graphic representation of contingency table is presented, as well as Cost benefits of categorical analysis. Complete a regression analysis and appropriate variables. Include research hypothesis and assumptions. (33%) Does not complete a regression analysis. Completes a regression analysis which includes appropriate variable type and level selection. In addition, research hypothesis is present. Completes a regression analysis, and variables are appropriate. It also includes a research hypothesis and assumptions. Completes a regression analysis, and variables are appropriate. It also includes a research hypothesis and assumptions, in addition, has a clear connection between research hypothesis and theory, an example of regression equation mode, and a cost benefits of regression analysis. Include other multivariate analysis, appropriate variables, research hypothesis, and assumptions. (34%) Does not include other multivariate analysis. Includes other multivariate analysis, appropriate variable type and level selection, and research hypothesis. Includes other multivariate analysis, appropriate variables, research hypothesis, and assumptions. Includes other multivariate analysis, appropriate variables, research hypothesis, and assumptions, in addition, has a clear connection between research hypothesis and theory, example of multivariate equation model, and cost benefits of other multivariate analysis.
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