Owen has a background in Mathematics and Statistics with previous experience in social research, data analysis and computer programming.
Owen is currently employed as a Senior Lecturer and his role incorporates teaching, research and research support.
Owen teaches research methods and data analysis to undergraduate and postgraduate students.
Owen is engaged in research across a wide range of fields through collaborations with academic colleagues and clinicians.
This module introduces the principles and methods needed to analyse biological data using statistical software.
Mae¿r modiwl yn cyflwyno'r egwyddorion a'r dulliau sydd eu hangen i ddadansoddi data biolegol drwy ddefnyddio meddalwedd ystadegol.
The aim of this module is to provide a capstone experience to students¿ learning, through participating in their own enquiry-based research project, with guidance from an academic supervisor. The project may be laboratory or non-laboratory based, but it will always involve a research question that is drawn from the literature, and focused on a topic relevant to the life sciences. It will ask a research question and involve the critical analysis of research findings. Students will refine their oral and written communication skills to a graduate level through an oral presentation and dissertation on their research findings and conclusions.
This module aims to equip students with with the skills they will need to write their final year dissertation. We will give students the opportunity to apply the basic data analysis techniques they learned in level 5 and introduce the skills necessary to produce high quality scientific writing, with a particular emphasis on quantitative research reports and/or journal articles.
This module builds on the foundations introduced in the PM- 238 module by introducing students to some more advanced statistical methods, specifically those relevant to medical research. This module will involve more statistical theory and mathematics than the second year Biostatistics module.
This module introduces the topic of linked health data analysis at an introductory to intermediate level. It fills a gap in research training opportunities by combining the principles of health care epidemiology with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding in the classroom on each topic, followed by a training session on the corresponding computing solutions. Students use anonymised linked health data files in the hands-on exercises. The computing component of the module assumes a basic familiarity with computing syntax used in programs such as SPSS and/or R and methods of basic statistical analysis.
This module explores the concepts, principles and applications of various approaches to data analysis and interpretation for both qualitative (including thematic content analysis) and quantitative methods (including analysis of difference, variance and relationship).
This module explores the concepts, principles and applications of various approaches to data analysis and interpretation for both qualitative (including thematic content analysis) and quantitative methods (including analysis of difference, variance and relationship).
