Bivariate analysis

Assignment 2: Project In this assignment you will be developin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing the structure of your analysis plan for your project. You may use a topical outlin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ine to address each part of the assignment (except where you must draw dummy tables). Other than your tables, your discussions should not exceed 4 typed pages in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in Microsoft Word. Specifically: 1. Include a summary or overview of your analysis plan. Be sure to in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">include a basic discussion of the sequence of analyses you would undertake to develop the results from your survey data. 2. Identify the major dependent, criterion, or outcome variable(s) that are directly related to your origin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">inal research question. 3. List the in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">independent variables if you are in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">interested in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in lookin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing at relationships between or among those variables and your dependent variables. If your research does not call for assessin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing such relationships, list at least 3 socio-demographic variables that you thin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ink will be relevant to your research. For each of these (socio-demographics), briefly explain" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in a sentence or two why you thin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ink it relevant to your project. For example, you may thin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ink the tenure of a teacher is important to understandin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing teachers’ attitudes toward education (as you might thin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ink newer teachers would hold more optimism). 4. Design dummy tables A. Bivariate Analyses. These should be contin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ingency tables that relate at least two (categorical/nomin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">inal) dependent variables (or outcome variables) to an in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">independent variable (also nomin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">inal) and/or socio-demographic descriptor. B. Contin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">inuous Data. 1. Usin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing a table format based on ANOVA, show how you present data for an in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">interval dependent variable and categories or conditions of an in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">independent variable (so socio-demographic). 2. Present an ANCOVA table format, clearly labelin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing each variable. C. Identify the specific statistic you would use to assess the relationshipand the strength of association between each set of your variables (bivariate and contin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">inuous). D. Briefly discuss how you would thin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ink about conductin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing a multivariate analysis of your data. Which variables would you in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">include? What would be the benefit of doin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing a multivariate analysis of your data? And, what statistical technique would you thin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ink about usin" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">in" rel="nofollow">ing; why?