ADVANCED STATISTICS AND DATA ANALYSIS - 2017/8

Module code: PSY2017

Module provider

Psychology

Module Leader

NG-KNIGHT T Dr (Psychology)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 5

JACs code

G300

Module cap (Maximum number of students)

N/A

Module Availability

Semester 1

Overall student workload

Workshop Hours: 22

Independent Study Hours: 108

Lecture Hours: 22

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework QUANTITATIVE ASSIGNMENT (4 PAGES) 25
Examination MCQ/SINGLE ANSWER EXAM (1.5 HOURS) 75

Alternative Assessment

N/A

Prerequisites / Co-requisites

PSY1020 and PSY1021: each of these modules must be completed prior to taking this module.

Module overview

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.

Module aims

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.

Learning outcomes

Attributes Developed
Understanding of the circumstances in which it is appropriate to use advanced statistical procedures KCPT
Understanding of how to run univariate and multivariate analyses in SPSS and the handling of large data sets KPT
Understanding of evaluations of assumptions, robustness, power, strengths and limitations for each of the procedures covered. KC
The ability to interpret and report the results of advanced statistical analyses appropriately. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

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

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. 

Assessment Strategy

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), 75 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

Reading list

Reading list for ADVANCED STATISTICS AND DATA ANALYSIS : http://aspire.surrey.ac.uk/modules/psy2017

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Psychology BSc (Hons) 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 2017/8 academic year.