Multilevel and Longitudinal Analysis

SOST70053 Multilevel and Longitudinal Analysis 20 Postgraduate Taught Level 7 Diego Perez Ruiz Unit coordinator Social Statistics 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 10.0 This unit aims to extend student knowledge to complex survey designs and more intricate data structures in the social sciences. The unit will expand on the concepts, methods and models previously introduced and will further develop programming skills in R and RStudio. Specifically, the course will focus on models that can be used to analyse hierarchical data, such as cross-country data or longitudinal data. The unit will make use of real data originating from surveys of varying complexity to enable students to develop methodologically and statistically robust skills in tackling these complexities in practice.  This unit aims to extend student knowledge to complex survey designs and more intricate data structures in the social sciences. The unit will expand on the concepts, methods and models previously introduced and will further develop programming skills in R and RStudio. Specifically, the course will focus on models that can be used to analyse hierarchical data, such as cross-country data or longitudinal data. The unit will make use of real data originating from surveys of varying complexity to enable students to develop methodologically and statistically robust skills in tackling these complexities in practice.  The unit aims to: Expand student knowledge to more complex data structures and modes of analysis in the social sciences using multilevel and longitudinal modelling with R and RStudio.  · Recognise different types of nested data in the social sciences and how they impact modelling decisions;  · Specify how longitudinal data can be used to answer key questions in the social sciences;  · Distinguish between different methods for addressing nested and longitudinal data structures.  · Interpret statistical output from a variety of advanced quantitative methods;  · Appraise academic literature that uses nested and longitudinal data;  · Select appropriate statistical methods to account for different nested and longitudinal data structures;  · Defend methodological selection(s) using justified, evidence-based arguments.  · Develop an appropriate design, plausible model, and appropriate method of analysis of nested and longitudinal data;  · Utilise statistical computing software (R and RStudio) to implement various complex methods (e.g. multilevel model for change);  · Identify and implement suitable methods for interrogating complex data;  · Interpret results of a variety of methods suitable for nested and longitudinal data;  · Assess the quality, practical value, and limitations of estimated statistical models.  · Apply suitable statistical methods and models to gain valuable insights from complex data using R and RStudio;  · Formulate, organise, express, and communicate data-driven opinions effectively;  · Communicate statistical information using suitable and effective graphics.  The course unit will consist of asynchronous videos, interactive activities, and guided online readings. In addition, there will be frequent synchronous sessions such as seminars, tutorials and/or practicals. Students will consolidate their knowledge through formative weekly quizzes and other activities. There will be two summative assessments. The first will comprise of a mid-term test (open-ended questions) which will consolidate theoretical and practical knowledge obtained in the first half of the course. The formative activities and summative test will prepare students for the end-of-course assessment which will require the completion of a research project. Students will also be provided with additional materials such as assigned readings, video demonstrations, and other individual and group activities to supplement their learning.  0 Other 100% Short-answer Test (1500 words, 40%) Research Paper (2500 words, 60%) Weekly Quizzes x8 (10 questions, non-assessed)  Online feedback within 15 working days on assessed work Automatic online feedback on weekly quizzes.  N Long, J. D. (2011). Longitudinal Data Analysis for the Behavioural Sciences Using R. Thousand Oaks, Calif: SAGE Publications, Inc.  Newsom, J. T. (2015). Longitudinal Structural Equation Modelling: A Comprehensive Introduction. Routledge.  Singer, J., & Willett, J. (2003). Applied Longitudinal Data Analysis: Modelling Change and Event Occurrence. Oxford: Oxford University Press.  Snijders, T.A.B. & Bosker, R.J. (2012) Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling, 2nd edition. London: Sage. For Information and advice on Link2Lists reading list software, see: <a href="http://www.library.manchester.ac.uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/" rel="noreferrer noopener" target="_blank">http://www.library.manchester.ac.uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/</a>  0 0 173 There will be no on-campus contact hours and learning will take place fully online. Contact time – 27 hours