QUANTITATIVE RESEARCH METHODS - 2023/4

Module Overview

This course is a lab-based/practical course intended to get students to use the freely available Jamovi software for data analysis and to understand what they are doing when they use it.

Module provider

Psychology

Module Leader

WONG Alan (Psychology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

None.

Module content

1. Refresher of basic statistics concepts, choosing statistical analyses, & introduction to Jamovi

2. Enhancing Data Quality, e.g., power analysis, data screening, reliability

3. Making Predictions, e.g., regression

4. Significance of Group Differences, e.g., ANOVA & its variants

5. Structure underlying Variables, e.g., factor analysis

6. Alternative to Null Hypothesis Testing, e.g., Bayesian statistics

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

i) Knowledge of common bi-variate and multivariate statistical procedures used in psychology

ii) the ability to use Jamovi to conduct statistical analysis

Thus, the summative assessment for this module consists of:

• School-Timetabled Sum. Examination/Test: Unseen open-book MCQ Class Test


• School-Timetabled Sum. Examination/Test: Unseen open-book Practical Data-analylsis Exam

Module aims

• This module aims to provide students with an understanding of the philosophy underlying psychological research using quantitative methods

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to give students the opportunity to gains hands-on experience of using Jamovi to solve real research based statistical questions in psychology. There will be a combination of lectures and computer-based exercises and class-based problem sheets with in-class formative feedback on their learning

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: PSYM065

Other information

The School of Psychology is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module contributes to the development of the following capabilities:

Employability: This module will equip students with the ability to evaluate and conduct research with quantitative methods, which will prepare them for jobs in different sectors requiring a research and development element.

Digital Capabilities: This module will enhance and develop students’ capabilities in digital data analysis and result presentation. Students will use SurreyLearn and statistical software such as Jamovi and R to facilitate learning.

Global and Cultural Capabilities: The module will use examples of statistical problem solving from all over the world to demonstrate the universality of the need for quantitative analyses in different settings. Further, many of the class exercises are group-based, which offers a chance for students from different cultural backgrounds to learn from each other, thus building such competencies as cross-cultural understanding and tolerance.

Sustainability: Many of the examples used in the class involves social, economic, and environmental problems, and students will get to appreciate the power of quantitative analysis tools in promoting sustainability.

Resourcefulness and Resilience: Students will use a range of sources to help understand statistical concepts, make decisions in analyses, and present the results of their analyses. Working in groups during class exercise will help build teamwork skills. Working to deadlines will make students more resourceful in terms of more effective allocation of time and effort. Finding solutions for real-life statistical problems will require students to excel in resourcefulness and resilience.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.