PYMADR: Analysing Data Using R

Module code: PYMADR

Module provider: Psychology; School of Psych and Clin Lang Sci

Credits: 20

Level: Postgraduate Masters

When you'll be taught: Semester 1

Module convenor: Dr Anthony Haffey, email: anthony.haffey@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 23 May 2024

Overview

Module aims and purpose

The aim of the module is to provide students with in-depth knowledge of strategies of data analysis and their applications to psychological research using R. The module reviews statistical knowledge that would have been seen at Undergraduate level, gradually incorporating theoretical and practical knowledge of data analysis, from basic statistical concepts to applied general linear models. By the end of the course students will be able to analyse data for their dissertations or PhD work using R scripts, as they will have done this for both of the assessments. This transferable skill is of course applicable to other domains.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Explain the purpose, conceptual basis, assumptions and limitations of core statistical methods;
2. Describe and apply two strategies that underlie most statistical techniques in psychological science, namely general linear modelling and data reduction;
3. Compare, select and apply appropriate techniques to test hypotheses on datasets;
4. Confidently use R to implement and interpret statistical results.

Module content

Principles and practice of core statistical analysis, including descriptive statistics, data transformation, correlation, regression, t-test, hypothesis testing, effect size, confidence intervals, statistical power, as well as the general linear model framework, analysis of variance and permutation analysis.

Structure

Teaching and learning methods

Core content consists of lecture videos, as this is a mathematical module and student feedback has suggested they find it very helpful to be able to pause and rewind videos to make sure they understand a concept before proceeding. Each week, Students also complete worksheets in timetabled workshops, in which they use R to analyse data using the statistical concepts that they learned about in the lecture video that week. The lecturer and teaching assistants are present to help with any questions the students may have about the worksheets, and to check that the answers the students have come up with by the end of the workshop are correct (and to offer guidance if anything looks incorrect). The assessments will require participants to use what they’ve learned in the workshops and lectures to analyse data and write it up in a report that includes R codes and explanations of the analyses.

Study hours

At least 33 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Students complete weekly workshops with worksheets to analyse data. Answer sheets with the correct ways to analyse the data will be released at the end of each week, which will give feedback about whether their approach was correct. Students are also encouraged to ask questions about the worksheet as they complete it, and ask staff to check their answers before leaving the workshop.

Reassessment

Additional costs