Course description Analysis of Survey Data, 7.5 ECTS-credits, advanced level

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1 STOCKHOLM UNIVERSITY Department of Statistics Peter Lundquist preliminary Course description Analysis of Survey Data, 7.5 ECTS-credits, advanced level Analys av surveydata, 7,5 hp, avancerad nivå Course code: ST726A Autumn semester 2014 Contents The syllabus of the course is posted on the course website, see The course consists of one unit: 1. Analysis of Survey Data, 7.5 ECTS-credits The course covers how to use data collected in sample surveys for other purposes than purely descriptive ones. The course covers both simple and complex sample designs, for example, designs with varying selection probabilities and the usual statistical analyses, such as linear and logistic regression and multilevel models. The course also addresses how auxiliary information on the frame can be used in the analysis. The concepts of inference population and survey population are explained. With today's easy access to easy-to-use software, the use of survey data in complex analyses has become commonplace. The course provides skills that are often needed in order to exploit data fully, both in public debate and in research in the social and behavioural sciences. The course provides a basis for further advanced studies and research studies in statistics. The teaching consists of lectures and exercises. Course literature [1] Lohr, S. (2010), Sampling: Design and Analysis, 2nd edition, Boston: Brooks/Cole Selected chapters from the following books (Available as ebooks in the university library): o [2] Lehtonen, R., and Pahkinen, E. (2004). Practical Methods for Design and Analysis of Complex Surveys, 2nd ed. New York: Wiley. o [3] Goldstein, H. (2010). Multilevel Statistical Models, 4th Ed. London: Arnold. o [4] Särndal, C.E., and Lundström, S. (2005). Estimation in surveys with nonresponse, Hoboken, NJ: Wiley. Additional material distributed or specified during the course, i.e. lecture notes and other material. Analysis of survey data, Autumn

2 General information and documents pertaining to the course are continuously posted on the Mondo system, available to all students enrolled in the course; see Learning goals After completing the course students should be able to apply a variety of model-based and design-based methods to analyse survey data that has been collected with different sampling designs use frame information to enhance analyses understand the difference between inference population and survey population Schedule The schedule is posted on the course website, see There are nine scheduled lectures, each lasting approximately for three hours. Each occasion will typically consist of a lecture followed by a session of exercises or computer laboratories. Although attendance is voluntary, it is strongly recommended that students attend all training sessions in order to better achieve the learning goals. The tenth lecture F10 however, is mandatory and is reserved for the individual presentations of assignment 4. Depending on the number of students attending the course, an additional occasion devoted to presentations may be scheduled and notified in good time before. Students are assumed to have read through, before the lecture, announced chapters and articles. The course is equivalent to 7.5 credits, which in turn corresponds to 10 half-time weeks or 200 hours workload. A typical workload may be approximately allocated as follows: 38 hours of instructor-led teaching, 40 hours for home assignments, 60 hours to solve exercises and tutorials (by hand or computer) and 60 hours to read the literature. A preliminary outline of the contents and reading assignments for each lecture is given on the following page. Analysis of survey data, Autumn

3 Lectures L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 Date, Local Tue 4 Nov, Tue 11 Nov, Mon 17 Nov, Thu 20 Nov, B319 Mon 24 Nov, Thu 4 Dec, B319 Wed 10 Dec, Thu 18 Dec, Thu 8 Jan, Tue 13 Jan, B413 Topic Repetition survey sampling, design vs model-based inference and design effect Reading assignment and exercises* [1] Lohr: Ch 2-7 and 9 (Selected parts, just an overview!) Categorical data analysis [1] Lohr: Ch 10, [2] Lethonen & Pahkinen: Ch 7 and lecture exercise Examples of complex analysis, disclosure control etc. Categorical data analysis (cont.), Computer exercises Regression with complex survey data Regression with complex survey data (cont.), Computer exercises Articles announced in F2 See F2, material handed out and lecture exercise [1] Lohr: Ch 11 and articles announced See F5 and material handed out and lecture exercise Logistic regression, loglinear models etc. [1] Lohr: Ch 10-11, [2] L&P: Ch 8, articles announced and lecture exercise Multilevel models [3] Goldstein: Ch 1,2,10 Generalized regression estimation for population [4] Särndal & Lundström: totals and calibration Ch 4,6,7 Presentations of assigned papers - * Exercises announced during the course. Examination schedule 1 st date 2 nd date Assignment 1 Lecture F4 Thu 20 November Lecture F6 Thu 4 December Assignment 2 Lecture F6 Thu 4 December Lecture F8 Thu 18 December Assignment 3 Lecture F8 Thu 18 December Lecture F9 Thu 8 January Oral presentation Lecture F10 Tue 13 January After agreement with the examiner Coursework Lecture F7 Wed 10 December Sun 18 January Examination Students are assessed by examination of the learning outcomes of the student with respect to the learning goals. A detailed description, the grading criteria and weighting of each component are described in the section Examination criteria. The examination is based on: 1. Three individual home-assignments, handed in as written reports 2. One oral presentation of a scientific paper Analysis of survey data, Autumn

4 3. One coursework, a mini-paper of approximately 10 written pages Grades Grading is done according to a seven-point scale related to the specified learning outcomes: A. Excellent B. Very good C. Good D. Satisfactory E. Pass Fx. Insufficient F. Completely insufficient See Examination criteria below, to pass a minimum grade of E is required. To pass the course it is required that all components 1-3 that the examination is based on are completed and passed during the autumn semester That is, results on exercises, oral presentation and coursework are not transferable after end of semester. Transitional provisions Students can request examination in accordance with the course syllabus (available on the course website, see above) up to three times during a period of two years after the course is no longer given. Such a request must be in writing and sent to the head of the department. Here, the term examination refers to all compulsory elements of the course. Examination criteria (Draft version may be changed!) The examination consists of a portfolio of examination components: (1) three individual homeassignments to be handed in as written reports, (2) an oral presentation of a scientific paper and (3) a written course work delivered at the end of the course. Important To pass the course (grades A E) it is required that all three components are completed and passed during the same semester, i.e. results on the home-assignments, the oral presentation and the written course work are not transferable after end of semester. (1) Individual home-assignments Each of the three home-assignments consists of a number of exercises, some using SASprograming, which are to be solved individually and handed in as a written report according to the examination schedule. The following applies: The exercises should be solved and handed in no later than the given date in the examination schedule, The written report must be neat. If the report is submitted electronically, should just one document be sent, all editing must be done by the student. A sloppy report can give points deduction, Each assignment is worth maximum 25 points, To pass, at least 5 points per assignment and a total of at least 30 for all three Each assignment represents 12.5 % of the final grade; the total weight is 37.5 % Failure to hand in on time or less than 5 points? In agreement with the examiner may extra exercises be given, but maximum drops to 15 points! Analysis of survey data, Autumn

5 (2) Oral presentation A mandatory oral presentation of a scientific paper takes place January the 13 th. The following applies: Each student is assigned a scientific paper that is to be presented orally in class (last lecture L10), The presentation is graded with a maximum of 25 points, To pass, at least 10 points is required, 12.5 % of the final grade, 100 percent attendance is expected (otherwise there will be points deducted), Miss out or less than 10 points? New opportunity may be given by the examiner, but maximum drops to 15 points! (3) Course work A technical report, a mini-paper, of approximately 10 pages should be submitted by January 18 th. Instructions will be given no later than December the 4 th. The following applies: The work should be individual, The course work is graded with a maximum of 100 points, To pass, at least 50 points is required, 50 % of the final grade, Less than 50 points? Then, the paper must be completed in order to pass, The paper may require a revision in accordance to written or oral specifications given by the examiner Turnitin (The University s text-matching tool) will be used to check the mini-paper. General information, teacher The Department of Statistics is located on the 7th floor in the B-building (Södra Husen). General information about the department (office hours, phone numbers, schedules etc.) is posted on the department website, Teachers Reception hours Room Peter Lundquist Lecture days B768 Peter.Lundquist@stat.su.se Lecturer and examiner, course coordinator Michael Carlson - B733 Michael.Carlson@stat.su.se Guest lecturer Analysis of survey data, Autumn