Overview of project shown below. The reference textbook is Andrew Gelman’s Data Analysis Using Regression and Multilevel/Hierarchical Models (attached). The thesis is open ended, but I want to use the survey package in the analysis. http://cran.r-project.org/web/packages/survey/survey.pdf is the package in question. Please use models shown in the book as best you can, but as the description says, if you use other methods please describe it thoroughly. Please use library(survey), and use the apipop data to do whatever analysis.The due date is officially on the 21st of March, but I would like it done by Thursday March 13. I am willing to pay extra for the rush time if needed, so I can check the quality of the output. I have a significant budget, and am willing to work with a tutor who can provide/show experience in statistical projects/assignments. Functions to use include lmer, glm, etc. for multilevel/hierarchical modeling. Overview The project will be to undertake an analysis of data from the social sciences using methods covered in the lectures or other methods that are suggested by them. You can select any data-set you find interesting, but preferable related to your areas of current of future study. For undergraduate students, it is worth thinking about where you will go to after your degree. If it is to graduate school, choose data from the general area where you would like to go. Then you can mention the analysis on your grad school application or to potential employers. Choose an area of interest to you where there are questions that can be answered empirtically and are of interest (at least to you). For graduate students, it is natural to choose data related to your graduate work or thesis area. If could be data you are currently working on for your thesis, or data in areas you might go with your thesis work. If this is not yet specified, you can look in the literature for studies that address questions in you area and see if you can get access to the data. For all students, great places to browse for data are: I do not want a quick and routine analysis; a good project will show understanding of the problem and possible solutions and techniques to consider. The technical conclusions should be stated clearly. Your solution to the problem should be submitted in the form of a data analysis report. The report should be typed and at most 20 pages of 12 pt or greater type, including any appendices, tables, and figures. Please include margins of at least one inch on all sides of the paper. It should be submitted on the class website under the Homework section. The report should be self-contained and suitable for a non-statistician with a M.S. level of knowledge of statistics. Advanced modeling methods should be defined briefly in the text to the extent necessary for understanding of the results, along with a reference. By advanced I mean methods that were not prerequisite for the course. It is recommended that the following outline be followed in preparing the report: i. Abstract. This should consist of a brief statement of the results of your analysis. This should be like that of a research paper analyzing the data. ii. Introduction. Here include a clear statement of the scientific questions addressed by the your analysis of the data. The goal of the statistical analysis and the scientific context of the problem should be clear to all who read the introduction. iii. Analysis and Results. Describe your analysis and its results clearly and concisely. If necessary, use graphical displays and tables to convey the results. Describe methods used, approaches taken to examine the underlying assumptions and so on. Explain why the methodology is appropriate. Avoid using highly technical language. iv. Discussion. This section should describe the scientific and statistical issues raised by the results described in the previous section. Limitations of the study and of the analysis should be discussed here. There may be scientific issues that you would have liked to discuss with the investigator if this had been a real collaboration with a non- statistical scientist. If so, describe these issues and why they would be relevant to the analysis and/or interpretation of results. If appropriate, provide suggestions for further analysis or collection of additional data. Summarize your conclusions about the issues of scientific concern. v. References. List the key books and articles you consulted that are reflected in your report, as well as references that might be useful to the reader if they want to know more. vi. Tables and Figures. These should be included in the text, but can also be included in a separate section at the end. All should have clear titles/captions, and figures should have explanatory legends. vii. Appendices. There can be one or more appendices. You may include more technical discussion of your methodology or any theory developed to implement your models. Appendices are not required (in fact they are discouraged), and should only be included only if you feel they add something important to your report. You should spend the majority of your time thinking about the scientific and statistical issues and writing the report rather than spending all your time carrying out the statistical analysis. You may do a wonderful job of analysis, but it is of no use unless you can communicate the results to your audience. Evaluation Criteria used to judge performance will include the following three factors, each given equal weight: i. Statistical Appropriateness. Appropriateness of the analyses and models for the data and questions. Technical execution of the analysis. ii. Scientific appropriateness. Thoughtfulness and simplicity of your analysis. Does your analysis really answer the scientific questions of interest? iii. Quality of the written report. The report will be judged based upon its organization, clarity, and accuracy. Simple, concise sentences are preferred to sentences which are convoluted or otherwise confusing. Choice of your data set The data set should contain at least 20 cases, and at least four variables measured on each case. At least one of the variables should be an outcome variable that you want to predict or model via the other variables Do not merely use data from a textbook the world is an interesting place! All data sources must be cited, and described. If you find interesting data but are unsure if it fits the class requirements, see me after class or in my office hours to discuss it. Your statistical task is to model the structure in the data and describe it. To do this you should consider the various forms of social structure considered in the course. Here are some suggestions: Start by presenting visual summaries of the data, followed by numerical summary measures. Describe the impact of the covariates on the outcome variable(s) via simple models first. For any model be sure to consider diagnostics of the appropriateness of the model. Construct summaries of the overall quality of the fit, and a comparison between alter- natives to the model finally used. This is an open ended question, so feel free to experiment.
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