ADVANCED STATISTICS AND DATA ANALYSIS - 2020/1
Module code: PSY2017
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
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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 SPSS 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 broader understanding of advanced statistical methods in psychology.
NG-KNIGHT Terry (Psychology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: C832
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
PSY1020 and PSY1021: each of these modules must be completed prior to taking this module.
The weekly lectures and workshops will cover theory and practice related to and including the following indicative topic areas:
- Revision of statistical concepts and methods
- Advanced ANOVA-related techniques and their assumptions
- Simple and multiple regression techniques (including forced entry and hierarchical) and the assumptions of regression analysis
- Logistic regression
|Assessment type||Unit of assessment||Weighting|
|Coursework||QUANTITATIVE ASSIGNMENT (4 PAGES)||25|
|Examination||MCQ/SINGLE ANSWER EXAM (1.5 HOURS)||75|
The assessment strategy is designed to provide students with the opportunity to demonstrate each of the learning outcomes
Learning outcomes 1-6 are assessed by both the Exam and Quantitative assignment.
Thus, the summative assessment for this module consists of:
- Quantitative assignment, 4 page limit.
- MCQ/Single answer exam (exam period), 55 questions and length of 90 minutes.
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 advanced 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.
|1||Understanding of the circumstances in which it is appropriate to use advanced statistical procedures||KCPT|
|2||Understanding of how to run univariate and multivariate analyses in SPSS and the handling of large data sets||KPT|
|3||Understanding of evaluations of assumptions, robustness, power, strengths and limitations for each of the procedures covered.||KC|
|4||The ability to interpret and report the results of advanced statistical analyses appropriately.||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Workshop Hours: 20
Independent Study Hours: 108
Lecture Hours: 22
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Build upon material covered in modules PSY1020 and PSY1021 and develop skill in understanding multivariate statistics in preparation for the dissertation project and employment. A range of different tests are covered, but these are interlinked both in terms of interpretation and calculation. Knowledge is progressively attained with each class building upon previous classes.
The learning and teaching methods include:
- Two-hour lectures with weekly two-hour workshops.
- Lectures cover statistical theory and how to use SPSS 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: PSY2017
Programmes this module appears in
|Psychology BSc (Hons)(CORE)||1||Core||Each unit of assessment must be passed at 40% 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 2020/1 academic year.