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: 2020/1
Email: h.joseph@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 and have 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:
- Read, understand and critically evaluate quantitative methods reported in the research literature at a high level
- Summarise, represent graphically, and analyse data sets
- Use SPSS software to carry out statistical tests and interpret the results
Outline content:
Illustrative content of the module
Week 1: Introduction to quantitative methods
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:
Every week, students will have a one hour lecture presenting a new topic. This will be followed by a 1.5 hour practical class in which they will carry out exercises on paper or using computer software (SPSS).
| Autumn | Spring | Summer | |
| Lectures | 15 | ||
| Practicals classes and workshops | 10 | ||
| Guided independent study: | |||
| Wider reading (independent) | 60 | ||
| Wider reading (directed) | 60 | ||
| Exam revision/preparation | 30 | ||
| Completion of formative assessment tasks | 25 | ||
| Total hours by term | 0 | 0 | |
| Total hours for module | 200 |
| 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. 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.
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.Students will also have the opportunity to complete weekly tasks at home to support their learning (this is optional).
Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx
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):
Last updated: 14 April 2020
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.