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== First Year Introduction Sequences == | == First Year Introduction Sequences == | ||
A sequence is typically going to be a 2-3 quarter group of classes that will give you solid basis into statistics. These will all cover probability, introductory statistics, and statistical programming in R, Stata, SPSS, SAS, etc. They should each cover hypothesis testing and statistical inference, descriptive statistics, some visualization, and linear regression. They might go further or touch on other things as well. Many of these sequences will also cover more basic features of quantitative social scientific research like operationalization and measure construction, experiment design, etc. A sequence like is the foundation for quantitative analysis and statistics but it is ''not'' a complete training. In almost all cases, it will likely need to be supplemented with additional classes. All CDSC students should take a sequence during their first year. | A sequence is typically going to be a 2-3 quarter group of classes that will give you solid basis into statistics. These will all cover probability, introductory statistics, and statistical programming in R, Stata, SPSS, SAS, etc. They should each cover hypothesis testing and statistical inference, descriptive statistics, some visualization, and linear regression. They might go further or touch on other things as well. Many of these sequences will also cover more basic features of quantitative social scientific research like operationalization and measure construction, experiment design, etc. | ||
A sequence like is the foundation for quantitative analysis and statistics but it is ''not'' a complete training. In almost all cases, it will likely need to be supplemented with additional classes. All CDSC students should take a sequence during their first year. | |||
These are listed more or less in the order of recommendation although different courses will make sense for different students: | These are listed more or less in the order of recommendation although different courses will make sense for different students: | ||
'''Biostatitics (BIOST)''' Biostats. Applied Biostats II | '''Biostatitics (BIOST)''' Biostats. Applied Biostats II is an excellent course, well-taught, with a tight relationship between theory, method, and application. As of Summer '18, no one in the [[CDSC]] has taken Applied Biostats I, but it's this year's recommendation for a first class and we'll update this page when possible. | ||
'''Sociology (SOC):''' SOC504, SOC505, and SOC506. These are good courses but are quite applied. For [[CDSC]] members, this would be the easiest minimum option and would be slightly discouraged. | '''Sociology (SOC):''' SOC504, SOC505, and SOC506. These are good courses but are quite applied. For [[CDSC]] members, this would be the easiest minimum option and would be slightly discouraged. | ||
'''Communication (COM):''' COM520 + COM521. | '''Communication (COM):''' COM520 + COM521 offered in 2016-2017 and then, if all goes to plan, every other year after. Due to Mako's trip to CASBS in '18-19, the sequence will likely be taught again in 2019-2020. COM520 is more about quantitative research design and basic social scientific epistemology and design. COM521 is taught by Mako but it is a truly introductory stats class with a strong emphasis on application in GNU R. Like the SOC sequence, this would be discouraged for CDSC folks who would be encouraged to take a more technical course. | ||
POLS503/CS&SS503 is Advanced Quantitative Political Methodology and might be a good choice for a 2nd or 3rd quarter in statistics. It is a slightly mathematical applied statistics class which introduces regression and multi-variable techniques for developing causal arguments using statistics. The course stuck fairly closely to the two textbooks Real Stats and Mastering Metrics (an undergrad textbook) in Spr 2018 and the course sites from the last two years | '''Political Science (POLS):''' This sequence beings POLS500 in the autumn which is similar to COM520 and is a gentle introduction to quantitative research in the social sciences. POL503/CS&SS501 focuses on "testing theories with empirical evidence. Examines current topics in research methods and statistical analysis in political science. Content varies according to recent developments in the field and with interests of instructor." POLS503/CS&SS503 is Advanced Quantitative Political Methodology and might be a good choice for a 2nd or 3rd quarter in statistics. It is a slightly mathematical applied statistics class which introduces regression and multi-variable techniques for developing causal arguments using statistics. The course stuck fairly closely to the two textbooks Real Stats and Mastering Metrics (an undergrad textbook) in Spr 2018 and the course sites from the last two years are published on GitHub [LINK?], so take a look there if you want a preview of what will be covered. The class is sponsored by Political Science, so some of the content is influenced by their disciplinary norms. | ||
'''Economics/Stastics (ECON/STAT):''' If you already have good linear algebra and multivariate calculus, Taking ECON 580 and 581 is a good short-cut to getting a lot of methods covered in other classes. You could take SOC 504,505,506, CS&SS 503,504 560, and 564 or you could take ECON 580, 581 and read a few books. This is the ideal class for any CDSC folks although it will likely be a poor choice until you have a relatively strong mathematics background. Details on these classes are provided below. | '''Economics/Stastics (ECON/STAT):''' If you already have good linear algebra and multivariate calculus, Taking ECON 580 and 581 is a good short-cut to getting a lot of methods covered in other classes. You could take SOC 504,505,506, CS&SS 503,504 560, and 564 or you could take ECON 580, 581 and read a few books. This is the ideal class for any CDSC folks although it will likely be a poor choice until you have a relatively strong mathematics background. Details on these classes are provided below. | ||
'''Education Psychology (EDPSY):''' EDPSY490, EDPSY491 strong focus on psychometric techniques drawn from psychology. Should be relatively easier and very applied but will not provide a good training for research using non-experimental settings. Due to the rigor and the focus on experiments, ANOVA, and SPSS, this is discouraged for CDSC members | '''Education Psychology (EDPSY):''' EDPSY490, EDPSY491 strong focus on psychometric techniques drawn from psychology. Should be relatively easier and very applied but will not provide a good training for research using non-experimental settings. Due to the rigor and the focus on experiments, ANOVA, and SPSS, this is discouraged for CDSC members. | ||
== Other Courses == | |||
''' | '''CS&SS505, Review Of Mathematics For Social Scientists''' is an option for those who need to brush up on their mathematical foundations. | ||
== Advanced Statistics Courses == | == Advanced Statistics Courses == | ||
There are many useful courses offered by the Center for Statistics in the Social Sciences (CSSS). Most of CSSS classes are applied and will give you a chance to apply the methods that you learn to your own projects. Try to take advantage of these opportunities to make progress on your research. CSSS 509 is an exception, and will be discussed below under econometrics. | There are many useful courses offered by the Center for Statistics in the Social Sciences (CSSS). Most of CSSS classes are applied and will give you a chance to apply the methods that you learn to your own projects. Try to take advantage of these opportunities to make progress on your research. CSSS 509 is an exception, and will be discussed below under econometrics. | ||
'''CSSS566, Causal Inference''' CS&SS 566 is good. It covers experimental, instrumental variable, and quasi-experimental designs, structural equation modeling, and DAGs. It takes a relatively philosophical and theoretical approach to causality and shows that common assumptions about causal identification can be wrong (e.g adding a variable to your model ''can''introduce bias (in theory). | |||
'''CS&SS504, Applied Regression''' is an applied, but still technical course on regression. It may vary based on who is teaching the class. This will be the default option for [[CDSC]] students. | '''CS&SS504, Applied Regression''' is an applied, but still technical course on regression. It may vary based on who is teaching the class. This will be the default option for [[CDSC]] students. | ||
'''CS&SS 560, hierarchical modeling''' is important. Hierarchical models are the bread and butter for working with datasets that have community level variables and individual level variables, or that have longitudinal data. | '''CS&SS 560, hierarchical modeling''' is important. Hierarchical models are the bread and butter for working with datasets that have community level variables and individual level variables, or that have longitudinal data. | ||
== Mathematical Statistics and Econometrics == | == Mathematical Statistics and Econometrics == | ||
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'''ECON 580/CS&SS 509, Mathematical Statistics''' This class is great. It is a big class with quantitative methods folks from all over the social sciences. It is a "meta-methods class." The goal of the course is for you to understand in mathematical terms and notation how to derive statistical methods from probability theory. You work through proofs of various statistical methods. It covers probability theory, statistical tests, OLS, MLE, and Bayesian inference. There is some R programming where you use simulations to demonstrate theorems or analytical results. This class is very much not applied. There are pretty hard tests and homework assignments where you prove theorems and derive corollaries. To enjoy this class you should have at least 2 quarters of college calculus and an introductory stats sequence under your belt, or a strong math background (e.g. you were a math or physics major). | '''ECON 580/CS&SS 509, Mathematical Statistics''' This class is great. It is a big class with quantitative methods folks from all over the social sciences. It is a "meta-methods class." The goal of the course is for you to understand in mathematical terms and notation how to derive statistical methods from probability theory. You work through proofs of various statistical methods. It covers probability theory, statistical tests, OLS, MLE, and Bayesian inference. There is some R programming where you use simulations to demonstrate theorems or analytical results. This class is very much not applied. There are pretty hard tests and homework assignments where you prove theorems and derive corollaries. To enjoy this class you should have at least 2 quarters of college calculus and an introductory stats sequence under your belt, or a strong math background (e.g. you were a math or physics major). | ||
You can brush up on your calculus and stats to prepare for this class. Kaylea recommends Kahn Academy for calculus and [https://onlinecourses.science.psu.edu/stat414 PSU (414 and 415)] for statistics. | You can brush up on your calculus and stats to prepare for this class. Kaylea recommends Kahn Academy for calculus and [ https://onlinecourses.science.psu.edu/stat414 PSU (414 and 415)] for statistics. | ||
'''ECON 581, Econometrics''' The first few weeks of ECON581 generalize 580 into the multivariate case. The second part provides regression methods (instrumental variables, two stage least squares, GMM) for when OLS assumptions are violated. | '''ECON 581, Econometrics''' The first few weeks of ECON581 generalize 580 into the multivariate case. The second part provides regression methods (instrumental variables, two stage least squares, GMM) for when OLS assumptions are violated. | ||
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There are also 400 level introduction to machine learning classes in CSE and STAT, but STAT 588 looks better than either of these. | There are also 400 level introduction to machine learning classes in CSE and STAT, but STAT 588 looks better than either of these. | ||