Module PSYS3357: ADVANCED STATISTICAL APPROACHES

Department: Psychology (Applied Psychology) [Queen's Campus, Stockton]

PSYS3357: ADVANCED STATISTICAL APPROACHES

Prerequisites

• 100 credits from C817 Psychology (Applied) Level 2 modules OR PSYC2101 Statistics for Psychology

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• This module has two aims:
• First, for students to learn to apply advanced statistical techniques to data analysis on a variety of data sets with different characteristics
• Second, to introduce industry and business-related approaches to data analysis

Content

• Background: Introduction to advanced research methods and introduction to advanced statistical approaches including rationale and applications
• Two methods, Meta Analysis and Non-linear regression, are then studied in depth. Applications of these methods to increasingly complex data sets are gradually introduced, and their relevance to work-based analysis is discussed
• Meta Analysis will be applied in depth using: a) fixed effects; and b) random effects analyses
• Non-Linear Regression will be applied in depth using multiple models for regressing to different data formats, including quadratic, cubic, quartic and other non-linear forms. These forms are representative of and transferable to a number of scientific, clinical, industry and business-related analyses

Learning Outcomes

• Detailed knowledge of advanced statistical approaches for Psychology including current theory, evidence, and practice

• Ability to review critically and consolidate understanding of a coherent body of psychological knowledge and apply it appropriately
• Competent data analysis skills with the base programmes of SPSS and Microsoft Excel

• Good written communication skills
• Good IT skills in word processing, data manipulation, and data presentation
• Ability to work independently in scholarship and research within broad guidelines

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Knowledge and understanding of advanced statistics, and the ability to select appropriate methods in addressing a specific question are developed through the weekly lectures/practicals
• Skills in applying this knowledge using advanced computing packages are acquired during the weekly computing practical portion of lectures
• This knowledge will be assessed via a 90 minute examination with one seen question in which students must demonstrate practical skills in reporting and undertaking open-ended data analysis using the appropriate technique
• These abilities are also assessed via formative assessment throughout the practical classes
• Good IT and data handling skills are required for the formative and summative work in this module. Feedback is provided regarding the adequacy of these skills where necessary

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Continuous throughout the course

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University