Overview

This 10-credit module is the first of four research methods modules you will follow in your degree, each building on the other and developing your skills as a researcher.

This module will give you hands-on experience in scientific research design, data collection, and descriptive and inferential statistical analyses. In weekly practical sessions, graduate instructors will assist you whilst you use a powerful statistical software platform to explore, visualise, and analyse various datasets. You will also have the opportunity to develop your writing skills whilst learning how to communicate results from scientific research in written reports. Crucially, this module will equip you with foundational skills that will be essential for your dissertation project. Overall, you will learn how to apply scientific thinking to evaluate data, a highly relevant skill that will enhance your employability.

Students will cover topics including: The Scientific Method, Ethics, Research Design, Descriptive Statistics, Inferential Statistics, Visualising and Interpreting Findings, Reporting Results.

There are two components to your module. Each week during the semester there will be a lecture to introduce a topic. To support this lecture, there will be a practical session where you will gain hands-on experience of scientific research. The sessions involve discussions about a range of issues in experimental methods and statistics along with the opportunity to collect and manipulate data. Some of the activities will include designing research, undertaking data collection and analyses, using computer software to describe and analyse data, and learning how to communicate the results of research in written reports. There will also be an optional weekly drop-in session, which students can attend for additional help.

Each student should expect to complete 100 hours of work for this module across the semester (including scheduled classes, independent research, and completing assignments).

Assessment Strategy

Threshold: D- to D+ - A basic knowledge and understanding of topic areas presented. - A description of research presented, with some sections covered clearly. - Some structural issues and only some of the relevant information within the field covered. - Little demonstration of independent research from academic sources outside of lectures and recommended readings. - Few logically presented and factually correct arguments, mostly undefended by sound scientific evidence. - Little original interpretation applied to problems - Weak presentation of material with little adherence to APA standards and lack of clear communication of scientific content.

Good: C- to B+ - A good knowledge and understanding of subject areas presented. - A scientific description of research presented, with most sections covered clearly & succinctly. - Well-structured and mostly-relevant coverage of the current field. - A demonstration of some independent research from academic sources outside of lectures and recommended readings. - Mostly logically presented and factually correct arguments, often defended by sound scientific evidence. - Some original interpretation applied to problems - Good presentation of material, mostly adhering to APA standards and communicating scientific content well

Excellent: A- to A* - A comprehensive knowledge and detailed understanding of topic areas presented. - A highly scientific description of research presented, with all sections covered clearly & succinctly. - Well-structured & highly relevant coverage of the current field. - A clear demonstration of independent research from academic sources outside of lectures and recommended readings. - Logically presented and factually correct arguments, defended by sound scientific evidence. - Original interpretation applied to problems. - Excellent written presentation of material, adhering to APA standards and clearly communicating scientific content.

Learning Outcomes

• Become proficient in using the statistical package SPSS: correctly inputting data, running basic statistical analyses, and interpreting the output.

• Demonstrate a basic understanding of data collection and statistics as used in research.

• Demonstrate a basic understanding of measurement, observation and experimental design as methodological techniques in empirical research.