INTRODUCTION TO STATISTICS AND DATA ANALYSIS - 2020/1
Module code: PSY1020
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.
Prior to registering online, you must read this general information and all relevant additional programme specific information. By completing online registration, you acknowledge that you have read such content, and accept all such changes.
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 good introduction and grounding in statistical methods in psychology.
ASKEW Chris (Psychology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 4
JACs code: G300
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
Basic numerical skills (GCSE mathematics)
The weekly lectures and workshops will cover theory and practice related to 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 and regression
|Assessment type||Unit of assessment||Weighting|
|Coursework||QUANTITATIVE ASSIGNMENT (4 PAGES)||25|
|Examination||MULTIPLE CHOICE EXAM (1.5 HOURS)||75|
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 SPSS, 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 SPSS 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: 25%
2) MCQ exam (60 questions in 90 minutes) Weighting: 75%
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.
|1||Be able to understand hypothesis testing, and it's relation to statistical parameters such as the p-value, effect size, power and significance||K|
|2||Be familiar with descriptions of data, categorisation of data and basic statistical terminology||K|
|3||Understand basic research design, methods and the role statistical analysis has herein||KP|
|4||Be able to calculate descriptive statistics, understand the limitations and assumptions of these, and how to report them most effectively||KC|
|5||Be able to conceptually understand basic statistical inferential analyses, their equational basis, run them in SPSS and report the results in APA format||K|
|6||Be able to interpret results from a variety of statistical procedures.||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 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.
Reading list for INTRODUCTION TO STATISTICS AND DATA ANALYSIS : http://aspire.surrey.ac.uk/modules/psy1020
Programmes this module appears in
|Psychology BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required 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.