Introduction to Quantitative Methods

SOST70511 Introduction to Quantitative Methods 15 Semester 1 Postgraduate Taught Level 7 Mariana De Araujo Cunha Unit coordinator Social Statistics 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 This 15 credit course aims to equip graduate students with a basic grounding in the theory and methods of quantitative data analysis. It adopts a heavy emphasis on hands on learning, with a series of tutor supported lab classes that complement the core lectures. You will learn practical methods of analysis using  the statistical software package SPSS working on real survey datasets.  The course is taken by Masters and PhD students drawn from programmes across the social sciences and beyond. It is a compulsory component of a number of ESRC approved Research Training programmes (under the 1+3 PhD training model).  It is recognised that our students come from diverse disciplinary backgrounds, and that some will have very little experience or confidence working with quantitative data. The course thus works from first principles and includes a well developed system of student support. The course is an opportunity to acquire valuable quantitative research skills with hands on training and experience in the use of the software SPSS to analyse large scale social datasets.   In summary, the course moves sequentially through the following themes:  • We introduce the sample survey and its role in social research. We then consider the basic characteristics of a survey dataset and the techniques for getting to know survey data, whether data is collected yourself or from a large existing study. We then discuss the role of sampling in surveys, including the way sample data can be used to make inferences about the populations from which it is drawn.  • The course then turns to consider the approaches and techniques for data analysis. We start with methods for looking at relationships between categorical variables, which covers the techniques needed for completing Part 1 of the formal assignment before moving to look at techniques where data is measured on a continuous scale including correlation, and simple linear regression which form the basis of Part 2 of the formal assignment.  The module aims to equip students with a basic grounding in the theory and methods of quantitative data analysis, focussing on the social survey. It is an introductory level course aimed at graduate students who have no real background in quantitative methods.   The module aims to:  • Introduce you to the social survey as a key quantitative resource for Social Science research.  • Introduce you to survey data, with consideration of the process by which variables in a dataset are derived from the survey questionnaire.  • Introduce you to the role of random sampling in survey research - this will cover the   theory that allows us to generalise findings from sample data to the wider population  • Provide an understanding of different sampling designs, including their strengths and weaknesses  • Provide basic training in the data analysis software package, SPSS  • Provide basic training in the techniques of exploratory data analysis using SPSS to analyse 'real' social survey data.  • Provide the skills required to carry out, interpret and report a secondary data analysis On completion of this unit successful students should be able to demonstrate:  • Understanding of the way surveys are used in social research  • Knowledge and understanding of the derivation and attributes of survey data, including levels of measurement  • Understanding of the role of sampling in survey research and the underlying theory that enables generalisation from random samples  • Knowledge of different sample designs and how these can be applied in a practical context.  • Basic familiarity with a range of techniques for exploratory data analysis using SPSS  • An ability to interpret the output of secondary analysis accurately and critically   The course contains a mixture of independent study, recorded and live lectures, and practical exercises.  A typical week will involve the following 3 elements  1. Watch lecture videos that introduce that week's topic and material, and dip into the recommendations for reading. This is done in independent study time as preparation for the live lecture on Wednesday,  2. Attend the live lecture. We'll start each lecture with a revisit of the PREVIOUS weeks work to highlight and discuss key learning points from the practical exercise, and to answer any questions. We will then move on to discuss the current weeks topic drawing on the preparatory material of pre-recorded and lectures and readings  3. Attend the practical workshop - a chance to get hands-on, applying the techniques covered using real survey data, which we analysis in the software package SPSS (SPSS training is provided as part of the course) . The practical classes build up your skills week by week to the point where you will have had a chance to learn and apply all the techniques required for your data analysis for the main assignment.   0 Other 100% Completion of a two-part Assignment (each part contributes 50% of final mark). Both parts involve the write up of a secondary analysis of survey data in SPSS (each part uses a different dataset and different techniques of analysis). Written feedback available via Turnitin Y While lectures and workshops cover the key concepts and techniques needed for the course, your understanding and confidence in applying these will be improved with some background reading.  Please note that most methods text books include material that goes beyond the level required for this introductory module. However, we are aware that many students taking IQM may be going on to more advanced courses in quantitative methods, or using quantitative methods in their dissertations or PhD research, so the aim here is to highlight resources to meet the different current and future needs of all those taking the course.  Further recommendations including a range of on-line resources will also be highlighted as we progress through the course.  Some Recommendations ….  Blaikie, N. (2003) Analyzing Quantitative Data: From Description to Explanation  Bryman, A (2015) Social Research Methods Oxford 5th edition (or earlier editions) University Press, Oxford  De Vaus, David A. (2014) Surveys in Social Research, 6th edition (or earlier editions), London: Routledge  Diamond, I. and Jefferies J. (2001) Beginning statistics: an introduction for social scientists, London: Sage  Dilnot A and Blastland M (2008) The Tiger That Isn't: Seeing Through a World of Numbers   Elliott, J. and Marsh C. (2008) Exploring Data (2nd Edition) Polity Press   Field, A. (2017) Discovering statistics using SPSS for Windows, 5th edition (or earlier eds):   London: Sage  Fielding J. and Gilbert N. (2006) Understanding Social Statistics (2nd edition), London:   Sage.  Macinnes, J (2016) An introduction to secondary Data Analysis with IBM SPSS  Morgan, George A. (2013) IBM SPSS for introductory statistics: use and interpretation 4th   ed.   Lectures 11 Practical classes & workshops 9 0 130