Techniques of Statistical Analysis II second group
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1 Techniques of Statistical Analysis II second group Bruno Arpino Office: ; Building: Jaume I bruno.arpino@upf.edu 1. Overview The course is aimed to provide advanced statistical knowledge for the analysis of quantitative data in empirical social science research. The course is problem-based, that is the theory is always presented in the context of a practical problem needing solution. In order to have an applied background required for each professional on social sciences, the use of the statistical software Stata will support all the introduced concepts. 2. Prerrequisites Participants are supposed to have basic knowledge of regression modelling (OLS, probit, logit) and STATA. 3. Competencies Generals: Instrumental skills - Participants will develop their ability to: Analyze and synthesize Effectively plan and manage time Apply computer skills and use the statistical software Stata Interpersonal skills - Participants will develop their ability to: Be constructively critical and self critical Productively work as a team member. Work within an interdisciplinary team Communicate with people that are not experts in the subject Systemic skills - Participants will develop their ability to:
2 Specifics: Do research Work autonomously Generate new ideas (creativity) Design and manage projects Basic statistical competences involve the following components: Awareness about data limitations and strengths Interpretation skills (ability to describe what the results mean in a specific context) Communication skills (being able to explain the results to someone else) Understanding the assumptions underlying statistical analyses 4. Contents The course will be organized in two blocks or modules: 1. Multilevel and longitudinal models. In Political and social sciences, research often concerns relationships between individuals and the contexts to which they belong (family, area of residence, country, historical period). As a consequence, often data used by social scientists show a hierarchical structure, with individuals nested within groups. To analyze such hierarchical structures, we need multilevel modeling, which allows us to study the relationships between variables observed at different levels in the hierarchical structure. Multilevel modeling can also be used to analyze longitudinal data. This has several advantages compared to more classical approaches. In addition, multilevel models have been generalized to cover situations where data do not have a simple hierarchical structure, such as cross-classified data or multiplemembership models. This block is intended as a basic introduction to multilevel analysis and covers the following topics: - Hierarchical and non hierarchical structures and the benefit of multilevel models. Motivating examples from social science research. - The basic two-level linear model - Random versus fixed effects - Random slope and cross-level interactions - Multilevel models for binary data - Extensions to higher level models and non hierarchical structures - Longitudinal data analysis and time-series cross-section models
3 - Introduction to survival data analysis 2. Causal inference and Policy evaluation. Most studies in the social sciences are motivated by questions that are causal in nature. However, in these areas experiments are very rare because of ethical or practical reasons and the estimation of causal effects has to rely on observational studies. The validity of inference will then strictly depend on the plausibility of the assumptions underlying the employed statistical techniques. In the second part of the course, special emphasis will be placed on the language used in formulating those assumptions and on some of the statistical methods that have been developed for the assessment of causal claims. In particular, the following topics will be covered: - Introduction to the concept of causal inference: the potential outcome framework - Assumptions needed to identify causal effects. Randomized experiments versus observational studies. - Propensity score techniques; alternative estimators - Sensitivity analyses - Instrumental variables - Regression Discontinuity Design - Causal inference with multilevel and longitudinal data Suggested readings: (For the first block) Sophia Rabe-Hesketh and Anders Skrondal (2008) Multilevel and Longitudinal Modeling Using Stata. 2nd Edition, Stata Press. [It combines the basic elements of multilevel modelling with detailed illustration of the Stata code] Skrondal, A. and Rabe-Hesketh, S. (2008). Multilevel and related models for longitudinal data. In J. de Leeuw, E. Meijer (eds.), Handbook of Multilevel Analysis, Springer Available at: c7.pdf?SGWID= p [For longitudinal models] (For the second block) Shahidur R. Khandker, Gayatri B. Koolwal and Hussain A. Samad (2010) Handbook on Impact Evaluation. Quantitative Methods and Practices, The World Bank, Washington D.C. Available at _ /Rendered/PDF/520990PUB0EPI1101Official0Us e0only1.pdf [This handbook gives a (relatively) non technical overview of all the topics covered in block 2. It also offer Stata hints and exercises]
4 Slides and other teaching materials will be provided during the course trough the Aula Global Additional readings: Snijders, Tom A.B., and Bosker, Roel J. (1999) Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling. Sage Publishers. [This book is a good intermediate-level introduction to multilevel models] Holland, P. (1986) "Statistics and Causal Inference", with discussion and rejoinder. Journal of the American Statistical Association, 81, [This paper discusses the concept of causality and introduces the framework of causal inference] Nichols, A. Causal inference with observational data. Available at: [It includes a brief discussion of the topics covered in block 2 plus examples in Stata] Wooldridge, J Econometric Analysis of Cross Section and Panel Data. MIT Press. [A general textbook including most of the topics covered during the course] Web resources: (Multilevel and longitudinal models) (Multilevel and longitudinal models) (survival analyses) 5. Teaching Methodology The course presents a set of tools with an applied perspective, providing the methodological knowledge that is necessary to conduct an empirical research project with a fair level of competence and with the ability to use relatively more advances statistical techniques. The aim is to provide an overview of the most popular techniques in Political and Social Sciences. The focus will be on the practical implementation of the techniques, on understating their main advantages and pitfalls, and on the interpretation of results more than on the mathematical details underlying each technique. All sessions will be held in a computer lab. This will allow to combine traditional lectures providing motivation, methods and examples relevant for social science students with applied workshops using the software STATA in which students will actively participate. 6. Assessment
5 A continuous assessment will be implemented accordingly to the following criteria: Two homework assignments throughout the course. They will be a mixture of conceptual and applied activities. Assignments will be individual. Each homework will contribute to the final grade with a weight of 15%. An applied research project. Students are required to design an applied research project that can be completed using secondary data, using one or more of the techniques introduced in this course. The project should include the research goals, the interpretation of the results and conclusions. This project can be conducted in small groups. At the beginning of the course, working groups will be established. Details on the research project will be provided in a separate handout. The research project will contribute to the final grade with a weight of 70%.
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