1 Course Overview 2023 / 2024
Welcome to your Research Methods 1 course on the MSc Psychology Conversion programme! We will introduce you to how psychologists use quantitative research methods to answer their research questions. There are many components that go into understanding and applying the techniques you will learn about, so throughout the semester we will cover fundamental research methods and statistics concepts, data skills to work with quantitative psychology data, and research skills to develop your understanding of reading and writing quantitative psychology reports.
This book provides supporting information for the course such as the assignments you will complete, what content you will cover in the labs, and resources to support research and writing. Please take the time at the start of the course to read the following course overview and learn where your sources of support are. You will keep referring back to this book throughout the semester as you progress through the labs and you approach each assignment.
1.1 Intended Learning Outcomes
By the end of the course, you will be able to:
Understand and apply the principles of open and reproducible science;
Generate and explore hypotheses and research questions for experimental and observational research;
Select appropriate research designs and methodologies for different research questions;
Demonstrate critical awareness of the assumptions of these methods and analyses and the limitations associated with experimental and observational research designs;
Identify the ethical issues involved in experimental and observational research;
Work as a group to plan and execute a small-scale research project using quantitative research methods;
Demonstrate critical analysis, evaluation, and synthesis of ideas;
Use the programming language R to conduct a range of descriptive and inferential statistics.
1.2 Assessments
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MCQs (5%)
- Answer Multiple Choice Questions (MCQs) related to content covered in weeks 1-4 from Research Methods 1 including lectures, data skills sessions, and labs.
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Data skills portfolio (15%)
- Two data skills worksheets using R and R Studio, each worth 7.5%.
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Stage one group report (30%)
- Group submission of a planned introduction and method to answer a research question you develop.
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stage two individual report (50%)
- Individual submission of an abstract, results, and discussion that address the research question you developed in your group.
Please check the Assessment and Feedback Information Sheet chapters for detailed information and deadlines.
1.3 Course overview
Below is a provisional order of content in this semester. We will notify you of any changes in advance, but feel free to read ahead if you would like:
| Week | W/C | Lecture | Lab | Data skills | Research Skills | Assessment |
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| 0 | 11-9-2023 | Inductions | ||||
| 1 | 18-9-2023 | Introduction to quantitative research methods | Introduction to the course and assignments |
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| 2 | 25-9-2023 | Summarising data | Finding and reading journal articles |
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| 3 | 2-10-2023 | Statistics and probability | Introduction structure |
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Formative: Group work agreement |
| 4 | 9-10-2023 | Hypothesis testing | Academic writing |
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| 5 | 16-10-2023 | Correlation | Method structure |
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MCQ |
| 6 | 23-10-2023 | Reading week | Reading week | |||
| 7 | 30-10-2023 | Independent samples t-tests | Communicating correlation results |
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Data skills 1 |
| 8 | 6-11-2023 | One-sample and paired-sample t-tests (Tobias Thejll-Madsen) | Communicating t-test results |
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Stage one report |
| 9 | 13-11-2023 | Effect sizes and statistical power | Discussion structure |
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| 10 | 20-11-2023 | Decision making in data analysis | Abstract structure |
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Data skills 2 |
| 11 | 27-11-2023 | Writing and editing week | ||||
| 12 | 4-12-2023 | Writing and editing week | Stage two report |
1.4 Available Support
There is a lot of support in this course to help you build your knowledge and understanding of quantitative research methods and statistics. You do not have to use all the different sources of support, and some will work better for you than others will. Part of learning is about finding what helps you best. Below are a few of the main approaches we have on this course to help you and if in doubt, please just ask:
Weekly lab sessions with your tutor with time for questions in each lab;
GTA support sessions in-person and online;
Teams channel for discussion, questions, and support;
Student Office Hours (sometimes called Student Drop-in Hours) - just turn up and ask anything;
Assessment information sheets and common questions and answer documents to support assignments.
The best approach is to write down your questions when they come up, check the available material for answers, and if you are still unsure after that, use one of the approaches above.
However, please note we would ask that you do not send questions, either about a topic or an assignment, as a direct message on Teams to an individual staff member. While we always want to help, this approach is not sustainable and there is a highly likely chance your question will get missed and go unanswered. We would strongly encourage you to post the question on the course Team channel, as that way staff and students have the opportunity to answer your question, and other students can benefit from the answer. Alternatively, use the student office hours or your lab sessions to ask questions more privately.
1.5 TA data skills support sessions
Building your data skills primarily comes from self-directed learning as you work through the Fundamentals of Quantitative Research Methods book we have developed. These walk through learning R and applying data skills such as wrangling data, visualisation, and inferential tests. However, we appreciate you may have questions or encounter errors you cannot work out on your own. That is why we schedule weekly support sessions with our Teaching Assistants (TAs).
The TA support sessions are drop-in sessions, meaning they are not timetabled classes you must attend. They are there to go to if you have a problem, and you can come and ask questions. Our TAs may demonstrate techniques or how to fix problems, but they are not intended to be chapter walkthroughs. The idea is you engage in self-directed study, and then you can come to the support sessions if you encounter problems.
You are more than welcome to come to the support sessions with any data skills relevant question, but we often find - particularly early in the course - you might not know what questions you have at this point. That is why we include theme weeks so you know what chapters or tasks the TAs are supporting. The support themes are listed in the timetable below. They follow a lagged approach, meaning they will be supporting the week's chapter, one week later. For example, chapter 1 - programming basics is in the week 1 progress tracker, but it is the theme for week 2 in the support sessions. We expect you have completed the chapter by week 2, so you should be in a position to know whether you had any problems or questions.
Early in the semester, the support sessions have open invites. This means anyone can drop in and there might be a handful of students present and asking questions. However, as we approach key assessment periods, the sessions will have specific sign up slots to ensure the TAs can provide clearly defined equitable time to each sign-up.
| Week | W/C | Support Themes |
|---|---|---|
| 0 | 11-9-2023 | |
| 1 | 18-9-2023 |
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| 2 | 25-9-2023 |
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| 3 | 2-10-2023 |
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| 4 | 9-10-2023 |
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| 5 | 16-10-2023 |
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| 6 | 23-10-2023 | READING WEEK |
| 7 | 30-10-2023 |
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| 8 | 6-11-2023 |
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| 9 | 13-11-2023 |
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| 10 | 20-11-2023 |
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| 11 | 27-11-2023 |
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| 12 | 4-12-2023 |
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1.6 Individual work and group work
In this course, we will encourage you to make use of group work from time to time, and even put you into groups for the stage one report assignment. Working as a group is essential in science and collective knowledge is also incredibly beneficial for learning. That said, we will also remind you at times when an assignment or activity should be done by yourself to avoid contravening University plagiarism rules. Beyond that, individual assignments help you see what you have learned. The first rule of science is that we must not fool ourselves. If in doubt though as to what should be done together and alone, please just ask.
In brief:
You will work on the stage one report submission as a group. We will let you know when allocations are ready to view. All other assignments are individual assignments.
Use each other’s strengths and knowledge throughout the semester to help you learn about the topics we discuss and introduce.
Support each other through discussion and interaction on the Teams channel.
1.7 Trust Yourself and Work With Us!
This course, and the knowledge and skills we will help you learn, is new to everybody. It will take time and it will take trial and error. Academic writing, especially for reports, and learning data skills are both challenging. That said, there is nothing to say that you cannot do this and that you cannot achieve extremely highly in the skills you will learn here. Take your time. Trust yourself. Work with us and ask us questions to help clarify anything you are unsure of!