Module SOCI43615: Intermediate Statistics for Social Science Research

Department: Sociology

SOCI43615: Intermediate Statistics for Social Science Research

Prerequisites

• None.

Corequisites

• SOCI59215 Statistical Exploration and Reasoning

Excluded Combination of Modules

• None.

Aims

• To offer more advanced quantitative research methods training in SASS and, if possible, beyond, on the basis of the introductory module Statistical Exploration and Reasoning;
• To introduce some useful and relatively more advanced statistical methods for analysing real large-scale sample survey data to students of all Master’s programmes;
• To help students understand and tackle some complicated issues in analysing quantitative data with statistical methods.

Content

• Introduction: some important principles for conducting statistical analysis professionally in social science research, structure of the module, administration.
• Data preparation: checking data quality, examining distributions, selecting variables, selecting cases, transforming existing variables, creating new variables, etc.
• The effect of data collection on data analysis: sampling schemes, weights, missing values and their imputations.
• Relationship between two categorical variables: chi-square and g-square tests, studying residuals, odds ratios.
• Relationship structure between three or more categorical variables: Breslow-Day test and CMH test.
• Using multiple regression models properly: assumption of the response variable, selecting explanatory variables, regression diagnostics.
• Introduction to logistic regression models: simple logistic regression models.
• More logistic regression models: adding categorical explanatory variable, assessing model performance, extending to multi-category logistic regression models.
• Analysing a set of variables: reliability analysis and factor analysis.
• Review, Q&A.

Learning Outcomes

• Understanding the logic and the specific principles of a range of statistical methods and tools;
• Detailed knowledge and critical understanding of basic but important principles for using statistical methods in social science research;
• Understanding why preparation of data is important and how it affects subsequent analyses of data;
• Knowledge of different types of regression models and when to use a particular one in a specific situation;
• Understanding the meaning of reliability and the logic of measuring it;
• Knowledge of a latent variable and its statistical representation.

• Capabilities for managing research, including collecting and analysing data, conducting and disseminating research in such a way that is consistent with both professional practice and principles of research ethics and risk assessment;
• Interpretation of statistics derived from a particular regression model;
• Ability to prepare large-scale sample survey data for missing values, errors, unusual distribution, etc.;
• Ability to check basic information and assumptions of variables for subsequent analyses;
• Ability to produce statistics useful for analysing two or more categorical variables;
• Ability to use multiple regression models properly;
• Ability to use logistic regression models appropriately;
• Ability to study reliability and latent factor for a set of variables;
• Ability to use SPSS for completing the above tasks.

• KS1 – The ability to evaluate and synthesise information obtained from a variety of sources (written, electronic, oral, visual); to communicate relevant information in a variety of ways and to select the most appropriate means of communication relative to the specific task. Students will also be able to communicate their own formulations in a clear and accessible way; they will be able to respond effectively to others and to reflect on and monitor the use of their communication skills;
• KS2 – The ability to read and interpret complex statistical tables, graphs and charts; to organize, classify and interpret numerical data; to make inferences from sets of data; to use advanced techniques of data analysis; and to appreciate the scope and applicability of numerical data;
• KS3 – Competence in using information technology to use the SPSS computer software package effectively; to use effective information storage and retrieval; and to use web-based resources;
• KS5 – Effective time-management, working to prescribed deadlines;
• KS6 – The ability to engage in different forms of learning, to seek and to use feedback from both peers and academic staff, and to monitor and critically reflect on the learning process.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures: the lecturer will explain the idea, the logic and the procedure of using each particular method.
• Computer practical sessions: the lecturer will demonstrate how to produce relevant statistics by using particular functions in SPSS.
• Summative assessment: students will be asked to produce a 3000-word report on a specific issue of their choice by analysing a real large-scale sample survey dataset with the method learnt in this module.
• Formative Assessment: selected exercises at the end of each chapter of the main textbook will be used as formative assessment; feedbacks to submitted answers will be provided to help students complete summative submissions.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

selected exercises at the end of each chapter of the main textbook will be used as formative assessment; feedbacks to submitted answers will be provided to help students complete summative submissions.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University