# INTRODUCTION TO STATISTICS AND DATA ANALYSIS - 2018/9

Module code: PSY1020

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 good introduction and grounding in statistical methods in psychology.

Module provider

Psychology

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 4

JACs code: G300

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

Basic numerical skills (GCSE mathematics)

Module content

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 pattern

Assessment type Unit of assessment Weighting
Coursework QUANTITATIVE ASSIGNMENT (4 PAGES) 25
Examination MULTIPLE CHOICE EXAM (1.5 HOURS) 75

Alternative Assessment

N/A

Assessment Strategy

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
Written feedback for coursework

Module aims

• 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.

Learning outcomes

Attributes Developed
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

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

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.