ANTHROPOLOGICAL STATISTICS II

Transcription

1 ANTHROPOLOGICAL STATISTICS II ANTH 604 THE UNIVERSITY OF TENNESSEE Instructor: Benjamin Auerbach, Ph.D. Contact information: Office: 229 South Stadium Hall Office hours: Wednesdays, 1 3 P.M. (or by sign-up appointment) auerbach@utk.edu Class Time: Mondays and Wednesdays (8:00 A.M. 9:30 A.M.) Class Location: 253 South Stadium Hall Course description: Anthropological Statistics II builds on the statistical foundations developed in Anthropological Statistics I (ANTH 504). ANTH 504 introduced parameters and their statistical estimates, and common methods for examining their relationships, from t-tests & χ 2 to MANOVA & multiple linear regression methods. Students enrolled in ANTH 604 are expected to have a firm understanding of these methods their theoretical underpinnings and practical application. Like Anthropological Statistics I, the main goal of ANTH 604 is to introduce analytical methods and demonstrate the practical applications of statistics to anthropological inquiry. Unlike ANTH 504, however, this course is not focused on a single statistical model (the general linear model) or school of statistics (frequentist). Instead, it is intended to introduce an assortment of additional statistical methods that have utility in various anthropological settings. These techniques include frequentist methods for data reduction and association, as well as methods associated with maximum likelihood, categorical data analysis, and Bayesian methods. Some of these methods (such as finite mixture and transition analysis) are still in nascent stages of adoption within anthropology, but it is important for students to be aware of them given their potential utility in addressing a variety of scenarios. Students enrolled in this course should approach it as a survey of research design and the appropriation of quantitative methods to those analyses. That is, none of the statistical methods encountered this semester are exhaustively covered. Some methods will be introduced with the intention of exposure to basic concepts and application, though students seeking proficiency should seek out specialty courses or be prepared to undertake rigorous independent study. With the knowledge gained in ANTH 604, however, students will have the basic tools with which to venture into these additional studies. Most importantly, the focus of this course is on the ability to develop and execute a research project. The ability to develop a research design to assess a central question, followed by identifying the appropriate statistical methods for evaluating hypotheses generated to test that question, is the core of this project. All students are encouraged to tie these projects into dissertation or thesis research, and, furthermore, should consider producing research that may be turned into a peerreviewed publication. 1

2 Course objectives: By the end of the course, you should: have a clear understanding of the variety of statistical methods available beyond the general linear model, including their limitations and assumptions; developed an appreciation and comprehension of the logic (and mathematics) underlying the statistical methods introduced; be able to independently determine which statistical methods are most appropriately applied to your data; and learn the critical evaluation skills necessary to judge the statistical veracity of your interpretations and those made by others. Course prerequisites: Students enrolled in ANTH 604 must have completed ANTH 504 with at least a B, or have completed comparable courses (e.g., STAT 537 & 538) with a B. Course structure: Unlike Anthropological Statistics I, this course has a more informal daily class structure. The first three meetings consist of an interactive linear algebra boot camp, where students will have an opportunity to develop a basic understanding of matrices and their mathematical manipulations. In all subsequent sessions, Dr. Auerbach will discuss basic principles of all of the methods, though students are encouraged to add input to the lectures and bring up questions and observations throughout the class meetings. Two statistical packages will be used ad hoc in class to demonstrate computer methods for analyzing data: SPSS Statistics (versions 19 21) and R (version 2.1+). As R requires some basic programming ability and, ideally, prior experience, students are not expected to develop proficiency in its use during this course. However, some methods (namely maximum likelihood and Bayesian methods) are generally not available in any standard statistical packages except for R. SPSS Statistics does have modules that use R as an analytical extension. While direct use of R is encouraged, use of these modules may prove useful for some students. As in ANTH 504, students are strongly encouraged to bring their own data to Dr. Auerbach s attention for use in class examples. Students are very strongly encouraged to use their own data, if possible, in completing the course term paper. Other data are available from the Internet, including Howell s and the Goldman data sets from Dr. Auerbach s web site ( and lithic data from the PIDBA database ( among other resources. Course web site: All course materials, including readings and supplemental resources, will be available online from the course Blackboard site (online.utk.edu). Please do not submit any assignments to Dr. Auerbach via Blackboard. All assignments should be turned into Dr. Auerbach via . 2

3 Evaluation: This course is concentrated toward independent research and critical thinking. Therefore, the entirety of the grade will be reflected by your ability to be critical of your own application of statistical methods and those employed by other researchers. Moreover, students should focus on creating a reasonable but potentially publishable independent research paper, applying statistical knowledge to develop a thorough research design. Projects associated with doctoral or M.A. research are strongly encouraged, though any independent research project is acceptable. In addition to this term paper, students will be evaluated based on daily class participation and two critiques of peer-reviewed anthropology papers. DAILY CLASS PARTICIPATION (10% of total grade): Students should come to class fully prepared. This means that all of the readings provided must be read before coming into class, so that useful conversations about the nuances of the methods application may be more fully explored. Dr. Auerbach will spend the first portion of each class summarizing the methods presented, but interactive conversation and argumentation are goals expected of all students. CRITIQUES (15% of total grade each): Students are required to independently select a peer-reviewed, anthropologically relevant journal paper and critique the research design and the use of statistics by the author(s). This includes an ability to consider alternative statistical methods, when appropriate, to those employed in critiqued papers. Completed critiques, along with a copy of the paper reviewed, must be submitted via to Dr. Auerbach on 22 February and 22 March by 6:00 PM. LATE CRITIQUES WILL NOT BE ACCEPTED. In each critique, students should briefly summarize the hypotheses and goals of the study, explain the kinds of data collected by the authors, describe the statistical methods used, and argue the efficacy of the approach and analysis. Please see the critique grading scale guidelines provided as a separate document on Blackboard. Remember that constructive criticism involves weighing both the merits and shortcomings of an argument, including the validity of interpretations based on the evidence provided by the analyses performed. Dr. Auerbach is available to help should students have difficulty identifying an appropriate paper for review, or have questions about any paper. TERM PAPER (60% of total grade): Students should spend the semester developing a full research hypothesis that may be tested using any of their own data or data obtained from reputable sources. Prepare a paper (25 page maximum, not including citations, 12-point double-spaced) that provides a background and full research design, which includes: the justification of the research design based on the background; outlines of the main research question and its subsidiary hypothesis; descriptions and justifications of data utilized in testing the hypothesis; the statistical methods you decide are appropriate. The paper should then provide the results of the statistical analyses and a full interpretation of those results. The background need not be extensive, but it should demonstrate 3

4 the relevance of hypothesis to other studies previously performed. Term papers must be e- mailed to Dr. Auerbach by 5:00 on 1 May. Students must also include a copy of all statistical software output used in constructing the analysis. Abstracts for the term paper must be submitted to Dr. Auerbach on the 15 th of February by 5:00 P.M. The abstract may only be up to 350 words, and should explain the research question or questions that will be explored, the data used to address those questions, and a preliminary plan of analytical methods (i.e., statistics). Results are not expected to be included in the abstract. Dr. Auerbach is pleased to discuss various research options as students make preparations for term papers. Don t wait to get started on this paper. The earlier you start collecting and analyzing data, the better off you will be. This paper is an assessment of your ability to design a research question, execute appropriate data acquisition, and manipulate and analyze data. Moreover, it will demonstrate your ability to choose and use the most appropriate statistics. Also remember that more complicated does not equal better. So, don t overreach with your analysis! Do not turn in drafts of papers previously submitted for publication, or copies of your master s thesis; this paper should reflect relatively new work. MORE IMPORTANTLY, YOUR PAPER MUST REPRESENT YOUR INDEPENDENT WORK. DO NOT COLLABORATE WITH YOUR CLASSMATES OR OTHERS ON YOUR TERM PAPER. (However, of course, Dr. Auerbach is always available via and in his office to discuss your paper and address questions or problems you may be encountering.) Tips for getting the most out of the course: Like most statistics courses, a great amount of your comprehension of the methods discussed will depend on independent study and work. One crucial part of this is keeping up with the reading before class meetings. Know, however, that this course seeks to help by giving hands-on examples via in-class exercises with real data and the critical evaluation of other researchers analyses by which to practice the statistical methods discussed. Be sure to give a fair amount of time ahead of the due date of assignments in which to fully explore and reflect on the analytical methods used. In other words, don t try to write the papers at the last minute; it will not help you gain a depth of knowledge in really comprehending the methods of analysis covered in the course. Students with special needs: If you require accommodation because of special needs in learning, please contact the Office of Disability Services at 2227 Dunford Hall ( ). Please also contact Dr. Auerbach immediately via after you register with the Office of Disability Services. Arrangements will be made to adjust the course to fit your needs. Course texts: All course readings will be provided on Blackboard as scanned copies. There is no single textbook that covers the variety of statistics addressed in ANTH 604. However, Jerrold Zar s Biostatistical Analysis (5 th edition), which was the textbook used in ANTH 504, will remain an essential 4

5 reference. Additionally, Rebecca Warner s Applied Statistics (2 nd edition) is used heavily in the first third of the course. A bibliography of all texts listed in the course schedule is provided below. Students may look into acquiring a copy of the following books, especially if they may be found used or at reduced prices: Strang s Introduction to Linear Algebra, Warner s Applied Statistics, and Buck et al. s Bayesian Approach to Interpreting Archaeological Data. Full citations of these are below. Some may find Andy Field s Discovering Statistics Using SPSS (3 rd edition) a useful reference text for implementing most statistical methods in SPSS Statistics, and Field et al. s Discovering Statistics Using R a good introduction to the learning curve that is the R program. BOOK REFERENCES Albert J Bayesian Computation with R. Second edition. New York: Springer. (ISBN: ) Aldenderfer MS and Blashfield RK Cluster Analysis. Quantitative Applications in the Social Sciences No. 44. Los Angeles: SAGE Publications. (ISBN ) Bolstad WM Introduction to Bayesian Statistics. Second edition. New York: Wiley- Interscience. (ISBN: ) Buck CE, Cavanagh WG, and Litton CD Bayesian Approach to Interpreting Archaeological Data. New York: John Wiley & Sons. (ISBN: ) Doncaster CP and Davey AJH Analysis of Variance and Covariance: How to Choose and Construct Models for the Life Sciences. Cambridge: Cambridge University Press. (ISBN: ) Eliason SR Maximum Likelihood Estimation: Logic and Practice. Quantitative Applications in the Social Sciences No. 96. Los Angeles: SAGE Publications. (ISBN: ) Field A Discovering Statistics Using SPSS. Third edition. Los Angeles: SAGE Publications. (ISBN: ) Field A, Miles J, and Field Z Discovering Statistics Using R. Los Angeles: SAGE Publications. (ISBN: ) Hair JF Jr., Black WC, Babin BJ, and Anderson RE Multivariate Data Analysis. Seventh edition. New York: Prentice Hall. (ISBN: ) Kim J and Mueller CW Introduction to Factor Analysis: What It Is and How to Do It. Quantitative Applications in the Social Sciences No. 13. Los Angeles: SAGE Publications. (ISBN: ) Kleinbaum DG and Klein M Survival Analysis: A Self-Learning Text. Third edition. New York: Springer. (ISBN: ) 5

6 Namboodiri K Matrix Algebra: An Introduction. Quantitative Applications in the Social Sciences No. 34. Los Angeles: SAGE Publications. (ISBN: ) Quinn GP and Keough MJ Experimental Design and Data Analysis for Biologists. Cambridge: Cambridge University Press. (ISBN: ) Sokal RR and Rohlf FJ Biometry. Fourth edition. New York: WH Freeman. (ISBN: ) Strang G Introduction to Linear Algebra. Fourth edition. Wellesley, MA: Wellesley Cambridge Press. (ISBN: ) Warner RM Applied Statistics: From Bivariate through Multivariate Techniques. Second edition. Los Angeles: SAGE Publications. (ISBN: ) Zar JH Biostatistical Analysis. Fifth edition. New York: Prentice Hall. (ISBN: ) STUDENTS MAY CONSIDER INVESTING IN INDIVIDUAL TEXTS FROM THE SAGE QUANTITATIVE APPLIACTIONS IN THE SOCIAL SCIENCES SERIES, SOME OF WHICH ARE INCLUDED ABOVE. 6

7 COURSE SCHEDULE: ANTH 604 (Anthropological Statistics II) See Book References in course syllabus for full volume citations DATE TOPICS MAIN READING SUPPLEMENTARY READING / SOURCE WINTER BREAK How to use R FIELD ET AL: Chapters 3 & January 14 January 16 January 23 January 28 January Course introduction Introduction to linear algebra: vectors, matrices, and linear functions Matrix operations: multiplication, elimination, pivots, identity, and Ax = b Further matrix concepts: determinants, inversion, transposition, eigenvalues & eigenvectors, traces Review of frequentist models: NHST, least squares, and the general linear model Nested ANOVAs (and factorial ANOVAs reviewed) WARNER: pp ; STRANG: selections from Chapters 1-3, 5, and 6 WARNER: pp , 81-85, DONCASTER & DAVEY: pp ; ZAR: Chapter 15 NAMBOODIRI (full text) FIELD ET AL: Chapter 2 WARNER: Chapter 3 30 January MANOVAs, revisited WARNER: pp ZAR: Chapter 16 4 February Discriminant Analysis, revisited Distances (including Mahalnobis D 2 ) WARNER: pp FIELD: pp QUINN & KEOUGH: pp RIGHTMIRE (1969) HAIR ET AL: Chapter 5 6 February Factor analysis & PCA, part I WARNER: pp ; FIELD: pp February Factor analysis & PCA, part II; path analysis (supplement with pp ) KIM & MUELLER 13 February Cluster analysis HAIR ET AL: pp ALDENDERFER & BALSHFIELD 18 February Resampling methods (including Mantel tests) SOKAL & ROHLF: 7.13, 18.4 & February CLASS CANCELLED AAFS Meeting (1 st Critique due on 22 February) FIELD ET AL: pp. 202, ,

8 25 February An introduction to maximum likelihood ELIASON ZAR: Ch February Advanced categorical methods: odds ratio, follow-ups to χ 2, Fisher s exact test ZAR: selections from Chapter 24 FIELD ET AL: pp March Logistic regression, part I QUINN: Chapter 13 FIELD ET AL: pp March Logistic regression, part II WARNER: pp FIELD: pp March Loglinear analysis FIELD: pp FIELD ET AL: pp March Introduction to survival analysis KLEINBAUM & KLEIN: Chapter 1 QUINN: Ch March Survival analysis KLEINBAUM & KLEIN: Chapter 4 KLEINBAUM & KLEIN: Appendix 20 March Bayesian inference I BOLSTAD: pp. 1-8; Chapter 4 BOLSTAD: pp March SPRING BREAK (2 nd Critique due on 22 March) 1 April Bayesian inference II BUCK: Chapter 7 BOLSTAD: Chapters 6 & 7 3 April Bayesian applications I: Spatial analysis BUCK: Chapter 10 LITTON & BUCK (1995) 8 April Bayesian applications II: Transition analysis BOLDSEN ET AL. (2002); PRINCE ET AL. (2008) 10 April CLASS CANCELLED (AAPA Meeting IN KNOXVILLE!!!) 15 April Bayesian applications III: Finite-mixture analysis KRAMER & KONIGSBERG (1999) 17 April What to do when you don t have enough data Handouts 22 April 24 April NO CLASS WORK ON TERM PAPERS! 4 May Turn in your term paper no later than 8:00 P.M. via to Dr. Auerbach 8