module specification

PC6068 - Coding for Psychology (2021/22)

Module specification Module approved to run in 2021/22
Module title Coding for Psychology
Module level Honours (06)
Credit rating for module 15
School School of Social Sciences and Professions
Total study hours 150
 
114 hours Guided independent study
36 hours Scheduled learning & teaching activities
Assessment components
Type Weighting Qualifying mark Description
Coursework 100%   Portfolio assessment
Running in 2021/22
Period Campus Day Time Module Leader
Autumn semester North Tuesday Morning

Module summary

This module will provide students with opportunities to venture into one of the very in-demand job markets – data science. As more and more data are being harvested, psychology plays an increasingly important role in data analysis. The module will introduce students to two programming languages used in psychology and data science as well as in wider professional communities: Python and R.

Python is a very powerful and accessible programming language. It is applied in psychology, data science, computing, artificial intelligent and is continuing to gain popularity in different industries (e.g. NASA, Google, New York Stock Exchange). Python also has a wide application in different branches of psychological research (e.g. experimental design and creation, data analysis, and data visualization).

The module will also introduce students to R. This aspect of the module will focus particularly on equipping students with the ability to conduct a range of widely used statistical analysis. The combined understanding of programming concepts in Python and statistical analysis using R will help students to gain experience and develop transferable skills that are in demand in psychology and in different areas of industry, thereby improving their employment potential and ability to undertake post-graduate training in different disciplines.

The module will be delivered via lectures, workshops and tutor-led practical sessions. Learning resources will be delivered using WebLearn.

Prior learning requirements

PC5001 Research Design and Data Analysis in Psychology

Syllabus

Python fundamentals: variables and common types; Loops and conditions; functions and classes; general purpose programming and data analysis; introduction to PsychoPy; coder and builder; extending trials into blocks; statistical analysis in R.

Learning Outcomes LO 1 - 4

Balance of independent study and scheduled teaching activity

The module is delivered through a variety of teaching and learning methods. A combination of group and individual activities will take place within a framework of lectures & workshops. There are numerous opportunities for problem solving to apply the knowledge students learn throughout the module. Summative assessment is designed to incorporate the similar problems that students completed in workshops, to further consolidate learning. Formative assessments and constructive feedback will be provided throughout the module to help students to prepare for the summative assessment.

Learning outcomes

Successful completion of the module will allow students to:

1. understand the fundamental skills of programming languages and packages
2. integrate skills and concepts in psychology and data science to address research questions and solve problems
3. develop and programme psychology experiments using appropriate platforms and languages
4. use R and Python to conduct different statistical analyses and accurately interpret and describe output

Assessment strategy

The module is assessed via a portfolio consisting of a developmental log and series of exercises in coding, which will be submitted at the end of the module. Formative assessments and constructive feedback will be provided throughout the module to help students to prepare for the summative assessment. A minimum grade of 40% is required to pass the module.

Bibliography

Peirce, J. & MacAskill, M. (2018). Building experiments in PsychoPy. London: Sage. [Core]

McKinney, W. (2017). Python for data analysis: Data wrangling with pandas, NumPy, and IPython (2nd Edition). Sebastpol, CA: O’Reilly Media.