EDM181-Quantitative Research Methods in Education

Module Provider: Institute of Education
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Daisy Powell
Email: d.a.powell@reading.ac.uk

Type of module:

Summary module description:

This module will introduce students to quantitative research methods used in Education research. It will cover basic principles of the scientific method, providing explanations of measuring variables can allow us to test hypotheses. Practical sessions each week will allow students to extend their conceptual understanding of the tests to a practical knowledge of how to use specialised software to analyse data.

Aims:

To develop an understanding of quantitative data analysis

To develop an ability to assess the research of others (in research papers)

To develop an ability to use quantitative methods in students’ own research

To develop an understanding of descriptive statistics

To develop an understanding of inferential statistics, including parametric and non-parametric tests at a standard required for an M level dissertation in Education

Assessable learning outcomes:

.

Additional outcomes:

Students should be able to report descriptive and inferential statistics using APA style andÌýhave a secure grounding in statistical skills to enable them to approach more advanced methods independently

By the end of the module students should be able to:

1. Read, understand and critically evaluate quantitative methods reported in the research literature at a high level


2. Summarise, represent graphically, and analyse data sets


3. Use SPSS software to carry out statistical tests and interpret the results

Outline content:

Illustrative content of the module

Week 1: Introduction to quantitative methodsÌý

Week 2: Describing your data and testing research questions

Week 3: An introduction to SPSS Statistics environment

Week 4: Exploring data with graphs

Week 5: Screening your data: outliers and distributions

Week 6: Non-parametric models

Week 7: Correlation

Week 8: Linear Regression and Multiple Regression

Week 9: Comparing two means (t-tests)

Week 10: In-class test

Brief description of teaching and learning methods:

This is an online module. All content and interactive workshop sessions will be delivered remotely on Teams. Students can choose whether to attend live sessions at 10am or 5pm.Ìý Each week students will access asynchronous material (typically, three videos of around 20 minutes each) on Blackboard before the live session in which they will work in action learning sets to complete a set practical activity using statistics software (SPSS), with guidance provided by module lecturers where nee ded.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		15
Practicals classes and workshops		10
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)		60
Ìý Ìý Wider reading (directed)		60
Ìý Ìý Exam revision/preparation		30
Ìý Ìý Completion of formative assessment tasks		25
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	200	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Report	60
Set exercise	40

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

There will be two types of summative assessment. First, there will be an in-class multiple choice test at the end of the module which will make up 40% of the final mark for the module. The test will cover all the main statistical analyses covered in the module (except for analysis of variance, because this is covered in the week prior to the class test). A multiple choice test is ideal for a module on Statistics because much of what students learn and need to know consists of very specific knowledge of ‘statistical facts’ which are essential for knowing how to select an appropriate statistical test correctly (related to Learning Outcome 1: Read, understand and critically evaluate quantitative methods reported in the research literature at a high level). Essays in a course such as this are inappropriate.Ìý

Students also need to be able to use their statistical knowledge to analyse data in SPSS and write up the results appropriately (Learning Outcomes 2 and 3: Summarise, represent graphically, and analyse data sets; and Use SPSS software to carry out statistical tests and interpret the results). To assess this, students will also be asked to write two 1000 word reports consisting of a Method and Results section for a data set. Students will be provided with an existing dataset, and the description of an experiment. They will then use appropriate statistical tests to analyse the data, and report descriptive and inferential statistics using APA style. Each written assignment will contribute to 30% of their final mark.

Formative assessment methods:

Students will carry out supervised exercises during the practical classes and will receive immediate formative feedback on their work.Ìý Students will also have the opportunity to complete weekly tasks at home to support their learning (this is optional).

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 'Penalties for late submission for Postgraduate Flexible programmes', which can be found here: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

50%

Reassessment arrangements:

Students who fail the assessment will be given the opportunity to re-sit any tests they failed, and to do the coursework assignment for a second time but their overall mark will be capped at 50%.

Additional Costs (specified where applicable):

1) Required text books:Ìý

2) Specialist equipment or materials:Ìý

3) Specialist clothing, footwear or headgear:Ìý

4) Printing and binding:Ìý

5) Computers and devices with a particular specification:Ìý

6) Travel, accommodation and subsistence:Ìý

Last updated: 30 March 2023