PY0417 - Psychological Research Methodologies

APPLY NOW BOOK A VIRTUAL OPEN DAY Add to My Courses Register your interest / Course PDF

What will I learn on this module?

This module will provide you with an introduction to quantitative data analysis, i.e. statistics. You will gain an understanding of fundamental concepts and principles in statistics. These include levels of measurement; standardised effect size measures; sample distributions, standard errors and confidence intervals; and statistical significance testing and the problems it causes.

In addition to basic principles, you will learn about a range of frequently used techniques for data analysis using the programmes SPSS and ESCII. For each of the techniques you will learn to identify when it is suitable to use; how to run the analysis; how to report its results to experts and lay people; and how to use these result to inform your critical judgement about your own research and that of others. The techniques you will learn about include descriptive statistics; the estimation of standardised effect sizes; t-tests and their non-parametric alternatives; chi2 to analyse proportions; meta-analysis; correlation; linear regression; and ANOVA.

How will I learn on this module?

On this module you will learn through a combination of lectures, workshops and tutorials, along with independent study. The workshop components of the module will take place in our specialist IT labs using a variety of statistical software

During teaching weeks, you will have two hours of lectures alternating with two hours of workshops. The lectures introduce you to concepts and techniques, which you then learn to apply in your workshops. These will be recorded to aid understanding. Additional tutorials are available to clarify problematic issues, discuss how you can apply these techniques to the research you carry out in other modules, etc.

You will be provided with references to relevant literature for your idependent study. To further guide your independent study, a rich array of exercises will be available for you via the eLearning Portal. Online discussion groups encourage all students to form an interactive learning community.

How will I be supported academically on this module?

You have four hours of scheduled teaching during teaching weeks. Tutors are also available for you via email, the online discussion forum, or for tutorials. All lecture and workshop materials will be available online, including annotated lectures slides and video recordings of the lectures. These online materials will help you to catch up on sessions you might miss and to revise in order to deepen your understanding of the subject matter. Exercises with worked answers and other materials for guided learning will be available. Exam-like multiple choice questions will be available for you to practice the exams and to monitor your own progress. Your summative assessments will be carried out via online testing.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at:
(Reading List service online guide for academic staff this containing contact details for the Reading List team –

What will I be expected to achieve?

Knowledge & Understanding:
• MK1: By the end of the module you will understand fundamental concepts and principles in statistics.
• MK2: You will know and understand a number of techniques for statistical analysis of data.

Intellectual / Professional skills & abilities:
• MIP1: You will develop your research skills by being able to identify the proper analysis for a number of elementary research designs; be able to read and report the output from these analyses; and be able to draw appropriate conclusions

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):

How will I be assessed?

Formative assessment:
Mock multiple-choice exam questions will allow you to continuously monitor your progress on MK1; MK2; MIP1

Summative assessment:
Four 30 minute in-class multiple choice tests form a portfolio of your statistical knowledge. Each test constitutes 25% of the mark.
MK1; MK2; MIP1

Feedback will be provided via the eLearning Portal. Cohort mean, standard deviation and distribtuions of marks for the tests will be provided so that you can compare your performance with that of your cohort.





Module abstract

Psychology is an empirical science, i.e. it uses actual data to shape and test ideas about behaviour and the mind. Relevant data typically derive from measurements and are then in the form of numbers. To make a connection between our data and the ideas that inspired our research is far from trivial. In this module you will learn how to do that. These skills are crucial throughout all three years of your programme. You will need them to develop, analyse, and interpret your own research and in order to fully understand and evaluate the research of others. Your knowledge will be assessed incrementally as your knowledge of research methods progresses, ensuring you receive regular feedback on your performance.

A solid understanding of data is a skill valued by many employers. Many students approach this module with trepidation; but we know from experience that students who engage with the module perform well on it, even if they don’t have a strong background in maths. Moreover, most students enjoy learning about fundamentals that can then be applied in hands-on exercises.

Course info

UCAS Code C8M9

Credits 20

Level of Study Undergraduate

Mode of Study 3 years full time/4 years full time with optional study abroad year

Department Psychology

Location City Campus, Northumbria University

City Newcastle

Start September 2021

Fee Information

Module Information

Current, Relevant and Inspiring

We continuously review and improve course content in consultation with our students and employers. To make sure we can inform you of any changes to your course register for updates on the course page.

Your Learning Experience find out about our distinctive approach at

Admissions Terms and Conditions -
Fees and Funding -
Admissions Policy -
Admissions Complaints Policy -