STATISTICS AND DATA ANALYSIS FOR THE MSC IN PSYCHOLOGY (CONVERSION) - 2022/3
Module code: PSYM094
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
The module involves a combination of 2-hour lectures and 2-hour workshops. Each week, lectures cover a specific statistical topic or concept. The theoretical and mathematical basis is explored using research examples and other types of illustrative example. Technological aids will also be incorporated to further student-centred interaction. The workshops focus on 'practising' the theoretical content of the preceding lecture. Datasets are explored and analysed using jamovi in a student-led approach that is guided by tutors. Together, lectures and workshops provide knowledge of statistical analyses from both a theoretical and practical perspective, resulting in a good introduction and grounding in statistical methods in psychology.
ASKEW Chris (Psychology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G300
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 26
Independent Learning Hours: 76
Lecture Hours: 22
Guided Learning: 13
Captured Content: 13
Prerequisites / Co-requisites
Basic numerical skills (GCSE mathematics)
The weekly lectures and workshops will cover theory and practice related to and including the following indicative topic areas:
- Introduction to research methods
- Hypothesis testing and research designs
- Descriptive statistics & significance testing
- z-scores and normal distribution
- An introduction to statistical testing, including t-test, correlation, ANOVA and regression
|Assessment type||Unit of assessment||Weighting|
|Coursework||QUANTITATIVE ASSIGNMENT (5 PAGES)||100|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
The first assignment (quantitative assignment) has been designed to assess competence in test selection in relation to data evaluation, use of jamovi, and writing results in the formal style used in psychology. This will assess all learning outcomes, but with more emphasis on calculation and reporting statistics, use of statistical terminology and understanding key concepts of p-value, effect size and significance.
The second assessment (MCQ exam) is designed to test all learning outcomes with emphasis on knowledge of statistical concepts and procedures, their assumptions and limitations, interpretation of jamovi outputs and a range of statistical procedures and test selection.
Thus, the summative assessment for this module consists of:
1) Quantitative assignment (4 pages) Weighting: 50%
2) MCQ exam (60 questions in 90 minutes) Weighting: 50%
Formative assessment and feedback
- MCQ questions in lectures
- Verbal feedback in lectures
- Tutor verbal feedback on exercises in workshops
- Feedback/answer sheet for workshops exercises
- Written feedback for coursework
- Correctly analyse experimental data using basic statistical procedures and understand the assumptions and limitations of these.
- Correctly interpret statistical results and understand the correct formats used to present data and results.
|001||To understand hypothesis testing, and it's relation to statistical parameters such as the p-value, effect size, power and significance||K|
|002||Be familiar with descriptions of data, categorisation of data and basic statistical terminology||K|
|003||Understand basic research design, methods and the role statistical analysis has herein|
|004||Be able to calculate descriptive statistics, understand the limitations and assumptions of these, and how to report them most effectively||KC|
|005||Be able to conceptually understand basic statistical inferential analyses, their equational basis, run them in jamovi and report the results in APA format||K|
|006||Be able to interpret results from a variety of statistical procedures, relate the findings to the broader context of psychological phenomena and behaviours observed in everyday life||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching methods include:
- Two-hour lectures with weekly two-hour workshops.
- Lectures cover statistical theory and how to use jamovi to run statistical analyses. Multiple choice and open-ended questions are used so that students get direct feedback on their learning.
- Workshops consist of exercises to be completed independently, in pairs, or small groups by students. Exercises involve the investigation and analysis of data sets that draw attention to specific statistical considerations or practices. Workshop tutors guide students and a written feedback sheet provides solutions to the exercises.
- There is a dedicated VLE site where handouts from the lectures and workshop materials are available. Readings are set each week from the core text book.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: PSYM094
Programmes this module appears in
|Psychology (Conversion) MSc(CORE)||1||Core||Each unit of assessment must be passed at 50% to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.