2023/24 Taught Postgraduate Module Catalogue

PSYC5901M Advanced Research Methods

20 creditsClass Size: 90

Module manager: Dr Carlo Campagnoli
Email: c.campagnoli@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24

Pre-requisite qualifications

At least an upper second class honours degree in Psychology or a discipline containing a substantial amount of psychology, research methods and statistics training.

Note: Additional support sessions for statistical methods training are offered as part of the module to all students, irrespective of their entry level skills in this area. However, there is a particular expectation that students with limited-to-no previous experience of using statistical software* will engage fully with these support sessions, in order to ensure they sufficiently support their learning.

*e.g. students whose undergraduate research-methods training may not have involved training in the use of software to undertake statistical analysis or who used used substantially different packages from the ones taught on this course.

Module replaces

PSYC5310M Advanced Research Methods (15 credits)

This module is not approved as an Elective

Module summary

This module expands on the knowledge and skills that students bring with them from their undergraduate degree or other relevant experience.It aims to:1. Advance their ability to understand, evaluate and apply advanced research methods and statistical techniques used in both qualitative and quantitative psychological enquiry.2. Build their confidence in independently using these techniques to design studies that investigate their own novel research questions3. Ensure they have the capacity to present their research and its findings in a professional, publishable standard format.The module is intended to build a student’s confidence in independently planning studies of various design (namely: observational and experimental quantitative, and "big Q" qualitative methodologies), giving them the skills to justify the specific design choices they have deemed most appropriate to investigate a given research question.In addition to this the module develops in students the ability to appropriately apply and interpret a range of advanced statistical and qualitative techniques used for psychological enquiry.

Objectives

The module is intended to build a student’s confidence in independently planning studies of various design (namely: observational and experimental qualitative, and qualitative methodologies), giving them the skills to justify the specific design choices they have deemed most appropriate to investigate a given research question.

In addition to this the module develops in students an ability to appropriately apply and interpret a range of advanced statistical and qualitative analysis techniques used for psychological enquiry.

Learning outcomes
On completion of this module, students should be able to:

1. Critically evaluate quantitative designs for research, and apply this knowledge to their own experimental work.
2. Apply principles of scaling appropriately to their own work.
3. Recognise and evaluate issues in relation to validity and reliability in psychological measurement.
4. Demonstrate expertise in understanding the constraints of experimental, non-experimental and quasi-experimental approaches, and when these techniques should be applied.
5. Identify the value and importance of qualitative methods in psychological enquiry and when to apply such techniques.
6. Show critical evaluation of the strengths and weaknesses of various advanced statistical techniques in research.
7. Use appropriate statistical software to organise and analyse data and to interpret the output of these processes correctly.
8. Independently select, interpret and present a range of univariate and multivariate statistical analyses and provide appropriate interpretation thereof.
9. Using learning from the aforementioned outcomes to relate a research question to appropriate methodology and apply this knowledge appropriately.

Skills outcomes
Ability to apply multiple perspectives to psychological issues, recognising that psychology involves a range of research methods, theories, evidence and applications.
Ability to generate and explore hypotheses and research questions.
Ability to carry out empirical studies involving a variety of methods of data collection, including experiments, observation, psychometric tests, questionnaires and field studies.
Ability to analyse data using both quantitative and qualitative methods.
Ability to present and evaluate research findings.
Ability to employ evidence-based reasoning and examine practical, theoretical and ethical issues associated with the use of different methodologies, paradigms and methods of analysis in psychology.
Ability to use a variety of psychological tools, including specialist software and psychometric instruments.

Syllabus

Module content can be subdivided into five main components.

Four of these are covered in interactive 'lecture + seminar' formats (listed below, delivered over 8 weeks). These involve short presentations interspersed with small-group learning activities, designed to develop students’ knowledge, understanding and ability to independently apply the skills being taught in these sessions.

1. The fundamentals of research design and the measurement and analysis of psychological functioning (week 1-2)
- Principles of the scientific method and defining quantitative measurement
- Reliability and Validity in psychological measurement
- Statistical methodologies for analysing data and testing hypothesis

2. Quantitative Observational Methods for Research (weeks 3 and 5)
- Testing for associations in the naturally variation between outcomes
- Advanced Tests of natural variation and Multivariate Techniques

3. Qualitative Methods for Research (mini-lectures weeks 1-8; seminars weeks 4 and 8)
- Introduction to the "big Q" qualitative paradigm
- Overview of epistemology/ontology
- Interpretative Phenomenological Analysis
- Discourse Analysis

4. Quantitative Experimental Methods for Research (weeks 7-8)
- Testing for the impact of experimental manipulation, within- and between-participants
- Advanced Tests of experimental manipulations and Multivariate Techniques

The fifth component focusses on applied statistical skills in (i) cleaning and validating newly collected data and (2) running and interpreting statistical analysis on such data. This component runs in parallel with the others, with teaching for it delivered in a weekly “flipped classroom” style tutorial (see teaching and learning methods section later for more details).

Also, to prepare for the coursework assessment of the first four components students attend two workshop sessions in weeks 9 and 10, where they are supported by teaching staff to develop and then debate and discuss ideas for their own independent project proposals.

Teaching methods

Delivery type	Number	Length hours	Student hours
Workshop	2	2.50	5.00
Lecture	8	2.00	16.00
Practical	11	1.00	11.00
Tutorial	8	2.50	20.00
Independent online learning hours			20.00
Private study hours			128.00
Total Contact hours			52.00
Total hours (100hr per 10 credits)			200.00

Private study

Independent online learning: For their statistical methods practicals students will be set a series of exercises 1 week in advance of a forthcoming tutorial, which is expected to take them approximately 2 hours to complete (excluding addition recommended reading, see below). Supported by an online discussion board (monitored by teaching staff) students are expected to attempt these exercises prior to attending the tutorial, where solutions and further discussion of the exercises will take place, led by the teaching staff. Following a brief introductory practical in week 1, where students are simply introduced to the software(s) they’ll be using and how to access these remotely (e.g. via AppsAnywhere), the first set of exercises will be realised (i.e. Friday of week 1) to be reviewed in the practical a week later (i.e. Friday of week 2). There will be one assignment per week up to week 10, for week 11. An additional stats practical will also be held in week 11 (with no preceding assignment) that will be used to advise students on how to prepare for their statistics assignment, submitted in January.
Private Study - Students will have 128 total private study hours. It is envisaged that this time will be spent as follows:
Recommended reading supporting each statistics tutorial (10 x 1hours) = 10 hours*
Preparation for Week 9 & 10 project proposal workshops = 7 hours
Completion of Week 11 Summative assessment (Brief Project Proposal) = 74 hours
Complete of Week 12 Summative assessment (Quantitative: Statistics Assignment) = 37 hours
*There is an additional statistics tutorial in Week 11 (i.e. 2 that week) which specifically covers the statistics assignment, which has no recommended reading associated with it

Opportunities for Formative Feedback

Student will receive group-based formative feedback in the following ways:
Through the first half of the semester, the weekly `Lecture + Seminar’ sessions will be foreshadowed by online pre-class exercises. These will ask students to post answers/opinions to online discussion boards. Feedback on student’s contributions will then be provided in the lecture/seminar by teaching staff. Within each of the lecture/seminar sessions students will also participate in several small group problem solving and critical discussion exercises per week. These will be overseen by the teaching team, who will provide summation and feedback at the conclusion of each activity.
Students will receive personalised formative feedback in the following ways:
In the weekly statistical practicals students are expected to complete, as part of their weekly exercises, a `self-test’ question prior to attending the session. This self-test question will be discussed, graded and peer reviewed in the class, with lecturer and postgraduate teaching assistants will also providing feedback directly to students in these sessions.
Students also complete two summatively assessed coursework assignments (submitted from mid-December through to early January):
• Development of a sensible research line, demonstrating understanding of both quantitative and qualitative methodologies (Project proposal, word limit 1300 words, submitted week 11)
• Analysis and Interpretation of Datasets (statistical research methods, word limit 1500 words, submitted January)
For each of these pieces of coursework students will receive detailed written feedback on how they could further improve their work. They will also have the opportunity, after receiving this feedback, to meet and discuss further these written comments with teaching staff (e.g. to obtain additional clarification).
Additionally, in weeks 9 and 10, two weeks prior to the deadline for submitting their project proposal, students will participate in a project proposal workshop in which they will be expected to verbally pitch an idea for a research project. Peers and teaching staff will critically evaluate the strengths and weaknesses of proposed projects and suggest improvements to their design, to help prepare students for producing a written (1300 word) version of their proposal, to be submitted in week 11. In week 10’s stats practical power-calculations will also be reviewed, in preparations for students including a power-calculation as part of their project proposal

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Analysis and interpretation of data files (Short Answer Questions)	33.33
Assignment	Assignment project proposal (1300 words)	66.66
Total percentage (Assessment Coursework)		99.99

Students overall grade will then be determined by a weighted average of these two assignments (project proposal: 66.66%; data & statistics: 33.33%). The weighting of each assessment in determining the overall grade reflect the equal importance of the three components of the overall teaching (i.e. qualitative methods, quantitative methods, statistical analysis), necessary for meeting the overall learning objectives of the module. To pass the module a student’s average grade across these two assignments must be >50.

Reading list

The reading list is available from the Library website

Last updated: 16/02/2024

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