The University of Southampton

ISVR6143 Research Methods

Module Overview

This module provides students with skills necessary for research in sound and vibration, in order to prepare them both for their projects and for future research activities. It recognises that students come from diverse backgrounds and provides opportunities for students to improve their skills in: • Scientific programming • Reading and comprehension of technical presentations and articles • Statistical analysis of experimental results • Practical measurement skills

Aims and Objectives

Module Aims

The aims of this module are to develop research skills specific to the discipline of Acoustical Engineering that will complement the generic skills taught as part of the dissertation module. The methods fall into four classes: 1. Computational methods 2. Measurement methods 3. Statistical experiment design methods 4. Comprehension

Learning Outcomes

Disciplinary Specific Learning Outcomes

Having successfully completed this module you will be able to:

  • Ability in the use of Python to solve common problems in sound and vibration.
  • Competence to select and use standard instrumentation for the measurement of sound and vibration
  • Ability to display and analyse experimental results statistically.
  • Ability to design experiments so as to have sufficient statistical power
  • Ability to critically summarise technical articles and presentations in the field of sound and vibration
  • Appreciation of the health and safety issues particularly associated with acoustical engineering.


Computation • Generic programming • Working with large amounts of numerical data • Storing and retrieving data to/from text and binary files • Visualising data • Linear algebra (solving linear systems, eigenvalues, etc) • Interpolating data, solving differential equations • Signal processing Measurement Methods • Standard instrumentation and techniques for sound and vibration measurements • Dual channel analysis of signals arising from sound or vibration measurements and transfer function estimation. Statistics • Experimental design • Descriptive and inferential statistics • Hypothesis testing • Effect sizes and statistical power Comprehension • Structure and layout of technical articles • Critical review of technical article • Writing technical reports • Presentation skills • Constructive feedback to peers • Work effectively in a small group • Literature search

Special Features


Learning and Teaching

Teaching and learning methods

This is a two-semester course, with one double lecture per week for both semesters. Some of these will be held in CLS workstation rooms to facilitate computer tutorial sessions, others will take the form of group discussions. Computation: Tutorial based classes in computer rooms Measurement: This will be taught in two practical lab classes, one on sound and vibration measurement (semester 1, formative) and one on dual channel analysis (semester 2, summative). Preparatory study material will be provided for both. Statistics: A series of interactive lectures and computer-based exercises with online self-paced quizzes and tests, covering • Experimental design • Descriptive and inferential statistics • Hypothesis testing • Effect sizes and statistical power Comprehension: This will be taught in a collaborative group setting. Students will be asked to attend seminars in the ISVR Engineering Research Seminar series and these will subsequently be discussed in class. Similarly a mixture of classic and recent research articles in sound and vibration journals will be read and discussed in class with students providing feedback to each other on the effectiveness of their summaries. Students will also make peer-assessments of their fellow students’ summaries. Independent study - as required to study the online material on Python and its applications, to prepare for both labs and write up the second one and to complete the rest of the coursework up to 100 hours.

Wider reading or practice6
Completion of assessment task60
Practical classes and workshops6
Preparation for scheduled sessions6
Follow-up work6
Total study time150

Resources & Reading list

The Study Skills Toolkit on Blackboard. 

Basic introduction to Python via the University’s subscription to (for students with little or no computing experience. 

Software requirements. Python software is available on university workstations and can be downloaded by students for use on their own computers.


Assessment Strategy





MethodPercentage contribution
Assignment 20%
Assignment 60%
Laboratory 20%


MethodPercentage contribution
Assignment 20%
Assignment 60%
Laboratory 20%

Repeat Information

Repeat type: Internal & External

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