PHVIT9200 Qualitative Methods Course description

This course is based on PHVIT9100, Health Sciences II: Philosophy of Science, Research Ethics and Research Methodology. The course takes a critical perspective of key methodological traditions in qualitative research, focusing particularly on phenomenology, hermeneutics, and discourse analysis. Topics covered include research design, research interviews and different forms of interviewing methods, and observation as a research method. Emphasis is placed on the application of advanced strategies for analysing complex data material within the respective research traditions.

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On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate:

• is at the forefront of knowledge in selected qualitative research designs and their theoretical basis, and related methodological considerations
• has in-depth knowledge and understanding of interviews and observation as methodological research tools in the phenomenological, hermeneutic, and discourse-analytic research traditions
• can evaluate the usefulness of different forms of analysis, interpretation, and documentation within the relevant traditions

Skills

The PhD candidate can:

• plan a health science research project with relevant qualitative designs and methods
• analyse, interpret, and disseminate the results of qualitative research
• address complex scientific issues and challenge established knowledge and practice in qualitative methodology

General competence

The PhD candidate can:

• argue in favour of particular qualitative approaches on the basis of scientific theory
• identify relevant ethical issues and conduct research based on qualitative methodology with professional integrity

participate in discussions on qualitative methodology

Work and teaching methods consist of lectures, seminars, self-study, practical exercises in pilot interviews, and analysis of authentic interviews and observation notes. The outcomes of the seminars are presented and discussed in plenary sessions.

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The course will introduce students to basic bioinformatics including best practice when setting up and managing bioinformatics projects. The course covers introduction to high throughput sequencing technologies and will give students hands-on experience with the analysis of data from various sequencing platforms. Applications that are included in the practical part are processing of raw data reads, control of quantity and quality of data (FASTQC), expression analysis of small RNA sequencing data (miRNA) and transcriptome sequencing/microarray (mRNA-seq, cDNA) data, and detection of variation (e.g. SNPs) after resequencing (variant calling).

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate

• is able to conduct bioinformatics analysis projects in agreement with best practice (transparency and reproducibility) in the field of bioinformatic science's philosophy
• is in the forefront of knowledge about the current high throughput sequencing (HTS) technologies and understands the differences, benefits and drawbacks of these HTS technologies
• can evaluate and make sound decisions on which platform and bioinformatic approach to use for different HTS projects.
• Can contribute to development of new knowledge and interpret results from various HTS applications

Skills

The PhD candidate can

• Plan a HTS research project and choose optimal sequencing platform
• Carry out the relevant bioinformatic analyses both on the command-line (unix) and R-studio, and utilize web-based resources like Galaxy server and Genbank E-utilities.
• Interpret the results of bioinformatics analysis of HTS (e.g. reliability, sensitivity and specificity) and judge their value for answering biological questions
• Disseminate the results of HTS based research

General competence

The PhD candidate can

• argue in favour of particular HTS technologies or bioinformatic approaches on the basis of current knowledge
• argue in favour of the kind of materials and the number of samples to select/include in different kinds of HTS projects
• can participate in discussions on HTS methodology

The course consists of three weeks consecutive work that includes the following teaching methods: self-study including exercises/questionaries related to background theory (one week), lectures and seminars (one week), and practical exercises in the use of different software programmes for analysis of HTS data (one week). The outcomes of the practical exercises in last week are discussed in plenary sessions.

It is required that students complete all the obligatory practical exercises.

All obligatory exercises must be completed to take the final exam. The final exam is a written examination with invigilation, 4 hours. One internal and one external examiner will assess the answer papers submitted by all candidates.