Introduction to Statistical Modelling

SOST70013 Introduction to Statistical Modelling 20 Semester 2 Postgraduate Taught Level 7 Myong Sook Kim 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 introduce students to complex quantitative data analysis in the social sciences. The course unit is designed to help students develop technical competence and robust foundations of the underlying principles of the statistical methods employed to be able to competently interpret analyses output. The unit will make use of real data from across the social sciences (e.g., politics, economics, psychology, sociology, criminology, etc.) to build students’ ability to conduct descriptive, exploratory, and inferential statistics.  This unit aims to introduce students to complex quantitative data analysis in the social sciences. The course unit is designed to help students develop technical competence and robust foundations of the underlying principles of the statistical methods employed to be able to competently interpret analyses output. The unit will make use of real data from across the social sciences (e.g., politics, economics, psychology, sociology, criminology, etc.) to build students’ ability to conduct descriptive, exploratory, and inferential statistics.  The unit aims to introduce students to data analysis and modelling using statistical programming (R and RStudio software). The course will cover exploratory data analysis, hypothesis testing and statistical modelling (e.g. regression). The course will embed real-world applications of the methods as well as critical evaluation of social analysis.  · Describe the concept of statistical inference and the relationship between population, samples, and uncertainty;  · Outline the assumptions of different statistical methods and recognise their practical value and limitations; · Discuss the challenges associated with causal inference in the social sciences;  · Recognise the importance of conceptual and theory-driven research in the social sciences;  · Formulate mathematical functions to represent statistical models.  · Interpret and assess statistical output from a variety of methods;  · Synthesize and critically evaluate academic literature;  · Derive evidence-based arguments for/against competing hypotheses;  · Develop a causal reasoning approach to hypothesis-driven research.  · Describe, summarise, and visualise different data types using R and RStudio;  · Utilise appropriate methods to quantify, model, and assess relationships between variables in R;  · Interpret results of a variety of exploratory and inferential statistical methods.  · Critically assess quantitative research;  · Utilise statistical programming software (R and RStudio) to interrogate data and draw valuable insights;· Formulate, organise, express, and communicate data-driven opinions effectively;  · Communicate statistical information using suitable and effective graphics and statistical models.  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.The course will include three summative assessments. Firstly, students will be required to critically evaluate published research. Secondly, they will apply the theoretical and practical knowledge acquired throughout the course unit to write a research paper. Finally, they will be assessed on their contributions to a discussion forum. In preparation for the summative assessments, students will also complete formative quizzes to consolidate their learning.Students will also be provided with additional materials such as assigned readings, video demonstrations, and various other individual and group activities, to supplement their learning.  0 Other 100% Discussion Forum Contributions (10%)Critical Evaluation of Research Analysis (1500 words, 35%)Research Paper (2500 words, 55%)Weekly Quizzes (x8, non-assessed)      Online feedback within 15 working days on assessed coursework. Automatic online feedback on short weekly quizzes.   N Agresti, A. (2018). Statistical Methods for the Social Sciences, Global Edition. Pearson. Arnold, J.B. (2018). Quantitative Social Science: The R Tidyverse Code [online]. <a href="https://jrnold.github.io/qss-tidy/" rel="noreferrer noopener" target="_blank">https://jrnold.github.io/qss-tidy/</a>  Imai, K. (2017). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.  Morgan and Winship, (2015). Counterfactual and Causal Inference: Methods and Principles for Social Science Research. Cambridge University Press. 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.  Online Contact Time – 27 hours