Researching Mental health and Wellbeing in Education 2

EDUC71512 Researching Mental health and Wellbeing in Education 2 15 Semester 2 Postgraduate Taught Level 7 Charlotte Bagnall Unit coordinator Education 1 FHEQ level (Framework for Higher Education Qualifications) ' Undefined ' European Credit Transfer & Accumulation System Rating : 7.5 Building on the foundational knowledge and skills acquired in Researching Mental Health and Wellbeing in Education 1, this unit delves into the process of data analysis and reporting, covering essential skills from data management and diverse quantitative and qualitative analytical techniques to the effective integration, interpretation, and impactful dissemination of research findings for different audiences.    Building on the foundational knowledge and skills acquired in Researching Mental Health and Wellbeing in Education 1, this unit delves into the process of data analysis and reporting, covering essential skills from data management and diverse quantitative and qualitative analytical techniques to the effective integration, interpretation, and impactful dissemination of research findings for different audiences.    The unit aims to enable students to manage, analyse and report research data, with a continued focus on mental health and wellbeing in educational contexts.    <ul><li>Differentiate between a range of quantitative and qualitative data analytic methods.</li><li>Outline the steps undertaken to arrive at a credible set of findings in a chosen data analytic process. </li><li>Explain the implications of a given set of research findings for policy and/or practice in the context of mental health and wellbeing in education.</li></ul> <ul><li>Select and justify appropriate quantitative and qualitative analytical strategies to address a chosen research question(s)</li><li>Critically evaluate their own quantitative and qualitative research findings, including via comparison to the published literature </li></ul> <ul><li>Competently apply descriptive (e.g. central tendency and variability) and inferential statistical methods (e.g. multiple regression) using appropriate software.</li><li>Competently apply qualitative analytic methods (e.g., thematic analysis) using appropriate software.</li></ul>  <ul><li>Communicate complex analytical processes and research findings accurately and persuasively to non-specialist audiences.</li></ul>  Week 1: Working with Data (e.g., quantitative data cleaning and preparation; transcribing and managing qualitative data; data analysis tools; secondary data analysis)Weeks 2, 3, 4: Quantitative Analysis (e.g., descriptive statistics; data visualisation; inferential statistics; effect size)  Weeks 5, 6, 7: Qualitative Analysis (e.g., data familiarisation; inductive and deductive coding; content analysis; thematic analysis; interpretive phenomenological analysis; discourse analysis; grounded theory)Week 8: Integration and Interpretation (e.g., interpretation of quantitative and qualitative findings; meta-inference in mixed methods research)Week 9: Dissemination and Impact (e.g., reporting research for different audiences; pathways to impact)  Interactive lectures (13.5 hours total, whole class) will introduce core data analytic concepts and methods, with active student participation through Q&A, Menti polls, and small group discussion (e.g. case study of mixed methods findings for meta-inference).Hands-on seminars/workshops (9 hours total, small groups) will be used to develop practical skills and apply learning from the interactive lectures.  Each seminar/workshop will include guided exercises on, e.g. data visualisation, inferential statistics, analysing qualitative data excerpts using diverse analytic approaches.Asynchronous online learning will build on/extend learning from the seminars/workshops. This will include engagement with Canvas materials (e.g., multimedia learning resources, quizzes, discussions, self-assessments) for the course unit, with additional digital resources such pertaining to JAMOVI (for quantitative analysis) and NVIVO (for qualitative analysis). Finally, students will be expected to engage in both directed (e.g., pre- and/or post-session reading) and independent (e.g., searching the academic literature and identifying relevant sources) reading, and assignment preparation (127.5 hours total, individual).  3 Report 33% 7 Oral assessment/presentation 17% 9 Set exercise 50% Via the VLE, using standard marking rubric, within standard period following submission deadline  MSc Mental Health & Wellbeing MSc Mental Health & Wellbeing PGDT Taught Component Mandatory MSc Mental Health&WellbeingPT MSc Mental Health&Wellbeing PT PGDT Taught Component Mandatory N Navarro, D. & Foxcroft, D. R. (2025). Learning statistics with JAMOVI. Cambridge: Open Book Publishers.Mortelmans, D. (2025). Doing qualitative analysis with NVIVO. London: Springer.Steven, P. (ed.). (2022). Qualitative data analysis: key approaches. London: Sage.Gruia, M. (2024). The guide to communicating research. Sheffield: Research Retold Lectures 13.5 Seminars 9 127.5