Postgraduate Programme and Module Handbook 2020-2021 (archived)

# Module SOCI43615: Intermediate Statistics for Social Science Research

## Department: Sociology

### SOCI43615: Intermediate Statistics for Social Science Research

Type | Open | Level | 4 | Credits | 15 | Availability | Available in 2020/21 | Module Cap | None. |
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#### Prerequisites

- None.

#### Corequisites

- SOCI59215 Statistical Exploration and Reasoning

#### Excluded Combination of Modules

- None.

#### Aims

- To offer more advanced quantitative research methods training in the Department of Sociology and students from other departments 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 Masterâ€™s programmes;
- To help students understand and tackle some complicated issues in analysing quantitative data with statistical methods;
- To train students to conduct replicable research.

#### Content

- Introduction: some important principles for conducting statistical analysis professionally in social science research, structure of the module, administration, introducing computer programmes.
- 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: graphs for showing the relationship between two categorical variables, chi-square and G-square tests, standardized residuals, odds ratios, etc.
- Relationship structure between three or more categorical variables.
- Using multiple regression models properly: assumption of the response variable, selecting explanatory variables, interaction and moderation, 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 exploratory factor analysis.
- Review, Q&A.

#### Learning Outcomes

Subject-specific Knowledge:

- 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.

Subject-specific Skills:

- 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 one statistical software package for completing the above tasks;
- Ability to conduct replicable research.

Key Skills:

- KS1 â€“ The ability to evaluate and synthesize 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 â€“ 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 â€“ Competence in using information technology to use a computer software package effectively; to use effective information storage and retrieval; and to use web-based resources;
- KS5 â€“ Effective time-management, working to prescribed deadlines;
- KS6 â€“ 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

- During periods of online teaching, for asynchronous lectures in particular, planned lecture hours may include activities that would normally have taken place within the lecture itself had it been taught face-to-face in a lecture room, and/or those necessary to adapt the teaching and learning materials effectively to online learning.
- 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 make research replicable and produce relevant statistics by using particular functions in one statistical software package.
- 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: A short proposal (500 words max) for completing the summative assessment, including questions to be answered, source of data, methods of data analysis, and expected results.

#### Teaching Methods and Learning Hours

Activity | Number | Frequency | Duration | Total/Hours | |
---|---|---|---|---|---|

Lectures | 10 | weekly | 1 hour | 10 | ■ |

Computer practical sessions | 10 | weekly | 1 hour | 10 | |

Preparation and Reading | 130 | ||||

Total | 150 |

#### Summative Assessment

Component: Assignment | Component Weighting: 100% | ||
---|---|---|---|

Element | Length / duration | Element Weighting | Resit Opportunity |

Individual Essay | 3000 words | 100% | Yes |

#### Formative Assessment:

A short proposal (500 words max) for completing the summative assessment, including questions to be answered, source of data, methods of data analysis, and expected results.

■ 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