INTRODUCTION TO STATISTICS AND DATA ANALYSIS - 2023/4

Module code: PSY1020

Module Overview

The module introduces students to basic statistical theory. Teaching involves a combination of theory lectures and practical workshops with real world research examples. Each week, lectures cover a specific statistical topic. This is followed by a workshop that typically focuses on applying and practicing the theoretical content of the preceding lecture. Together, lectures and workshops provide knowledge of statistical analysis from both a theoretical and practical perspective, resulting in a good introduction and grounding in statistical research methods. This knowledge is extended and built on in the second semester module ‘Further Statistics and Critical Thinking (PSY1032)’.

Psychology

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

Workshop Hours: 20

Independent Learning Hours: 75

Lecture Hours: 22

Guided Learning: 11

Captured Content: 22

Semester 1

None.

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-tests and correlation

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework QUANTITATIVE ASSIGNMENT (4 PAGES) 100

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate understanding of both critical thinking and quantitative data analysis.

Thus the summative assessment consists of:

Quantitative coursework (4 pages worth 100% of the module mark)

This 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.

Assesses LO1, LO2, LO3, LO4 and LO5

Formative assessment and feedback

In statistics lectures, students regularly complete multiple choice  questions and receive immediate feedback to enable them to check their understanding of the module content. Weekly statistics workshops consist of a worksheet and data sets to analyse. Students receive feedback on their progress via a feedback sheet of worked solutions. In addition, workshop tutors provide individualised verbal guidance and feedback during the sessions. Workshop formative practice and feedback develop students' independence, digital capabilities, resourcefulness and resilience.

Module aims

• Enable students to correctly analyse experimental data using basic statistical procedures and understand the assumptions and limitations of these.
• Enable students to correctly interpret statistical results and understand the correct formats used to present data and results.

Learning outcomes

 Attributes Developed 003 Be familiar with descriptions of data, categorisation of data and basic statistical terminology K 004 Be able to calculate descriptive statistics, understand the limitations and assumptions of these, and how to report them most effectively KC 002 Be able to understand hypothesis testing and its relation to statistical parameters such as the p-value, effect size, power and significance K 005 Demonstrate in workshops and coursework the ability to conceptually understand basic statistical inferential analyses, run them in jamovi and report the results in APA format KCPT 006 Demonstrate in workshops and coursework the ability to interpret results from a variety of basic statistical procedures KCPT 001

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

There will be 11 weeks of 2-hour statistics lectures. In addition, there will be 10 weeks of 2-hour statistics workshops.

Lectures cover statistical theory as well as how to use jamovi (data analysis software) to run the statistical analyses discussed in lectures. Students then gain practice applying these techniques using jamovi in their workshops. This is to enable students to not only understand the theory behind statistical testing but also how to actually select and conduct the statistical tests using jamovi and interpret the results. These skills will also be essential for other modules; particularly the 'with Research Methods' modules and the final dissertation project.

Lectures and workshops are interactive. Students will be asked questions about the lecture content and have the opportunity to ask questions. They will work alone or in small groups in their workshop to complete the weekly worksheet.  Students receive written feedback sheets and can show their work to their workshop tutors to receive formative verbal feedback. Readings are set each week from the core text book. Students are expected to have done the reading and prepared for the computer (jamovi) workshops.

Students are taught in lectures and workshops to use jamovi software, an open-source statistical analysis software package. They are given the opportunity and support to develop their digital literacy and acquire transferable skills, including critical thinking and evaluation, creating and using data spreadsheets, and data analysis. Thus the module contributes to building student independence, employability and digital capabilities.

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: PSY1020

Other information

The School of Psychology is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to contribute to this in the following ways:

Employability

This module contributes to graduate employability by giving students the opportunity to develop and practice transferable skills that will be attractive to employers. These skills and capabilities include designing research, data analysis and interpretation, and using a statistical software package. Students will be familiar with descriptions of data, categorisation of data and basic statistical terminology, and will be able to demonstrate understanding of basic research design, research methodology, and interpretation of findings. A freely available, open source statistical package is used on the module so that students can continue to use it for work after their studies.

Digital capabilities

Students have multiple opportunities to develop their digital capabilities, including using the university of Surrey Virtual Learning Environment (SurreyLearn) to access lecture materials, worksheets, data, videos and quizzes, as well as completing workshop worksheets and assignments using statistical analysis software. In particular, the weekly computer lab workshops offer an excellent opportunity to develop digital literacy by providing a supportive learning environment to develop and practice data analysis skills using jamovi statistical software.

Resourcefulness and resilience

The weekly workshops and assessments play an important role in developing students’ resourcefulness and resilience. In statistics workshops/assessment, students are given example studies and data sets and are required to determine the most appropriate method of statistical analysis to address the research aims and hypotheses.The supported completion of weekly worksheets gives students better understanding and confidence with statistics, leading to increased independence and resilience.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
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 2023/4 academic year.