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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23

Department: Management and Marketing

BUSI4T215: Advanced Quantitative Data Analysis

Type Tied Level 4 Credits 15 Availability Available in 2021/22

Prerequisites

  • None

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To facilitate students’ in-depth engagement with a range of advanced approaches to quantitative data analysis.
  • To develop students’ critical understanding of the logic of hypothesis testing and making causal claims.
  • To provide students with hands-on experience in advanced analysis with state-of-the-science software tools.
  • To facilitate students’ doctoral-level interpretation and writing skills for advanced quantitative data analysis.
  • To develop students’ critical understanding of ethical implications when conducting quantitative research.

Content

  • Hypothesis testing and causal inference in the context of advanced quantitative techniques.
  • Quantitative data management and data quality (e.g., missing responses).
  • Multiple regression for testing simple and complex moderation and mediation models
  • Advanced approaches to data analysis (confirmatory factor analysis, structural equation modelling, multilevel modelling, time series)
  • Software tools for advanced data analysis (e.g., MPlus, R)
  • Writing up and presenting quantitative research

Learning Outcomes

Subject-specific Knowledge:
  • Critical understanding of statistical principles of data analysis
  • Critical understanding of advanced quantitative data analysis approaches
  • How to ensure data quality
  • Critical understanding of ethics and open science practice in quantitative research
Subject-specific Skills:
  • Ability to select relevant data analytical approaches
  • Ability to interpret the results of advanced data analysis
  • Ability to communicate quantitative research results, verbally and in writing
Key Skills:
  • Conducting advanced data analysis
  • Using state-of-the-science software packages
  • Interpreting advanced research results
  • Writing and communicating advanced research results

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

  • The module will be delivered in a blended format, including lecture-type delivery, combined with tutor supported lab work (e.g., data analysis).
  • The summative assessment (group component) and formative assessment are designed for students to learn from each other, strengthen the building of a doctoral community, and develop their teamwork skills for collaborative research.
  • The summative assessment (individual) of the module is designed to facilitate students’ advanced quantitative data analysis and interpretation skills.
  • Comprehensive reading and self-study materials will be provided online.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures (online and classroom) 10 Weekly 2 hours 20
Tutor supported lab work (classroom) 3 As required 4 hours 12
Preparation and Reading 118
Total 150

Summative Assessment

Component: Written Assignment Component Weighting: 50%
Element Length / duration Element Weighting Resit Opportunity
Report (Group) 2000 words 100% Same
Component: Written Assignment Component Weighting: 50%
Element Length / duration Element Weighting Resit Opportunity
Learning Log (Individual) 4 entries, 500 words each 2000 words 100% Same

Formative Assessment:

Presentation of small group work related to the analysis and interpretation of data, using techniques covered in the module.


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