Essentials of survey design and analysis

SOST20022 Essentials of survey design and analysis 20 Semester 2 Undergraduate Level 2 Francesco Rampazzo Unit coordinator Myong Sook Kim Unit coordinator Social Statistics 1 FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 The unit aims to: 1. Provide an overview of the process of survey planning, design, management, estimation and analysis of complex survey data 2. Introduce the key decisions in survey design, including research ethics and the Total Survey Error Framework 3. Introduce methods of random sampling and calculating sample size 4. Present the design and testing of questionnaires 5. Introduce examples of inference, including calculating and using survey weights in statistical modelling 6. Introduce a range of UK and international social sciences surveys Lectures provide an overview of the survey design and analysis process. Following an introduction in the first week, four major topics in survey research are discussed: random sampling and design; measurement with focus on questionnaires; survey planning and implementation; survey weights; Total Survey Error; research ethics; and modelling of survey data. The last week provides a summary and a longer session for students to ask questions about the material. Tutorials follow lectures in each week and elaborate on practical aspects of the material covered. The tutorials have a thread to help students engage with the material on sample design and implementation. We   also have computing lab sessions on the analysis of survey data and inference in the program language R. This way, the students can simulate the main steps of survey planning, design and execution in a classroom project week by week and learn how to analyse complex survey data. This will create a strong link between lectures, tutorials and computing labs, increasing learning efficiency for students.  <figure class="table"><table aria-rowcount="12" border="1" data-tablelook="32" data-tablestyle="MsoNormalTable"><tbody><tr aria-rowindex="1" role="row"><td data-celllook="69905" role="columnheader">Week </td><td data-celllook="69905" role="columnheader">Lecture</td><td>Practical</td><td>Quiz</td><td>Activity</td></tr><tr aria-rowindex="2" role="row"><td data-celllook="4369">1   </td><td colspan="1" data-celllook="4369">Introduction: Overview of the course, main steps of doing surveys, basic concepts, UK and international examples, current (and future) relevance of social surveys</td><td> </td><td> </td><td>Activity 1</td></tr><tr aria-rowindex="4" role="row"><td data-celllook="4369">2 </td><td data-celllook="4369">Sampling I: Probability and non-probability samples, simple random sampling, sampling error, confidence intervals</td><td>Discussion I: Understanding Survey Designs</td><td> </td><td>Activity 2</td></tr><tr aria-rowindex="5" role="row"><td data-celllook="4369">3 </td><td data-celllook="4369">Sampling II: Stratified  Cluster Samples and multi-stage sampling</td><td>Discussion II: Planning a Survey</td><td> </td><td>Activity 3</td></tr><tr aria-rowindex="6" role="row"><td data-celllook="4369">4 </td><td data-celllook="4369">Sampling III:  Brief introduction to complex survey designs, survey weights </td><td>Discussion III: Choosing Data Collection Mode and Survey Design</td><td> </td><td>Activity 4</td></tr><tr aria-rowindex="7" role="row"><td data-celllook="4369">5 </td><td data-celllook="4369">Sampling IV: Comparing statistical inference between sample designs, a few simple statistical tests </td><td>Discussion IV: Questionnaire Design</td><td> </td><td>Activity 5</td></tr><tr aria-rowindex="8" role="row"><td data-celllook="4369">6 </td><td data-celllook="4369">Questionnaire Construction I: Basics of measurement, structure and content of questionnaires</td><td>Discussion V: Questionnaire Testing</td><td>X</td><td>Activity 6</td></tr><tr aria-rowindex="9" role="row"><td data-celllook="4369">7 </td><td data-celllook="4369">Questionnaire Construction II: Questionnaire testing, piloting and sources of bias</td><td>Computer Lab I: Introduction to R</td><td> </td><td>Activity 7</td></tr><tr aria-rowindex="10" role="row"><td data-celllook="4369">8 </td><td data-celllook="4369">Survey Design I: Quality, types of errors, nonresponse</td><td>Computer Lab II: Simple Statistical Tests in R</td><td>X</td><td>Activity 8</td></tr><tr aria-rowindex="11" role="row"><td data-celllook="4369">9 </td><td data-celllook="4369">Survey Design II: Survey planning and management, power and sample sizes, ethics, writing a survey report on methods</td><td>Computer Lab III: Inference under various survey designs in R</td><td> </td><td>Activity 9</td></tr><tr aria-rowindex="12" role="row"><td data-celllook="4369">10  </td><td data-celllook="4369">Revision, summary of course and main takeaways, discussion of final assessment </td><td>Computer Lab IV: Data Preparation, descriptive statistics in R</td><td>X</td><td>Activity 10</td></tr></tbody></table></figure> The unit aims to:  1. Provide an overview of the process of survey planning, design, management, estimation and        analysis of complex survey data  2. Introduce the key decisions in survey design, including research ethics  3. Introduce methods of random sampling and calculating sample size  4. Present the design and testing of questionnaires 5. Introduce examples of inference, including calculating and using survey weights in statistical       modelling 6. Introduce a range of UK and international social sciences surveys  <ul><li>On completion of this unit, successful students will be able to</li><li>Understand the main requirements and problems of planning, designing and implementing a sample survey (including sampling methods, drawing a random sample, designing questionnaires, sample size calculations) and apply these to specific research questions; </li><li>Describe a few major UK and international surveys from the past few decades;</li><li>Distinguish between different sampling designs;</li><li>Choose the sampling design that is most appropriate to a given research question;</li><li>Prepare survey data and apply basic statistical methods that are appropriate for different sampling designs;</li><li>Use the statistical software R effectively on social science datasets;</li><li>Identify the steps of questionnaire design, implementation and testing;</li><li>Assess the quality of a survey (considering errors, biases and nonresponse);</li><li>Discuss the key ethical issues in survey research;</li><li>Draft a survey report.  </li></ul> Other You will develop your skills in: Problem solving and data analysis; Statistical computing, data handling and data manipulation; Interpreting statistical analysis; Report writing. The course will involve: lectures, group work in tutorials and computing lab classes. Extensive use will be made of relevant on-line resources where students can learn about social science data. Please note the information in scheduled activity hours are for guidance only and may change. Blackboard resources will be used to enable students to access teaching data, data sources and supplementary learning material. The lecture component will provide a theoretical and methodological framework for learning about the design of surveys and analysis of survey data. Group work in the tutorials will give students hands on experience on the design and implementation of a survey. Practical sessions in the computing lab (using the R software package) will give students hands on experience in the basic exploratory and statistical analysis of complex survey data, data manipulation, interpretation of   results. Such skills are highly transferable. The emphasis on the use of real data to answer real questions is designed to engage students and enable students to consider using such approaches as part of their own dissertation research.   0 Other 100% Weekly online assessments (x10, 10% of the final mark),  Three quizzes (x3, 24% of the final mark), Final written exam (66% of the final mark All Social Statistics courses include both formative feedback - which lets you know how you're getting on and what you could do to improve - and summative feedback - which gives you a mark for your assessed work.   Y Specific readings are listed for each week.   A Part 1 of the module: Design of Surveys 1. Czaja, R. and Blair, J. (2013) Designing Surveys: a guide to decisions and procedures.   3rd ed. Sage, London. 2. De Vaus, D. (2002) Surveys in Social Research. 5th ed. Routledge, London.   3. Fowler, F. J. (2013) Survey Research Methods, 5th edition. Sage, California. 4. Groves, R M et al. (2009) Survey Methodology, 2nd Edition. J. Wiley, Hoboken.   5. Oppenheim, A.N. (2000) Questionnaire Design, Interviewing and Attitude Measurement   New Edition. Bloomsbury Academic, London.    B Part 2 of the module: Statistical analysis 1. I. Diamond and J. Jefferies (2001).  Beginning Statistics: an Introduction for Social Scientists, London: Sage.    2. A. Field (2013).  Discovering Statistics using SPSS, 4th Edition, London: Sage.   3. P. R. Kinnear and C. D. Gray (2006).  SPSS for Windows Made Simple: Release 12.0, Hove: Psychology Press. 4. L. Jaisingh (2005).  Statistics for the Utterly Confused, 2nd Edition. McGraw-Hill.  Lectures 20 Practical classes & workshops 10 0 170