Registry
Module Specifications
Archived Version 2020 - 2021
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Description This module will provide students with an understanding of advanced qualitative, quantitative and mixed research methods appropriate to psychological and behavioural sciences research. Through this module, students will acquire practical data analytic skills, with a focus on both qualitative and quantitative data. In relation to quantitative data analysis, the module will cover when and how to use a range of univariate and multivariate statistical procedures, how to report the results of these analyses, and how to interpret the findings. Students will also gain valuable research communication and dissemination skills - including oral and written formats. | |||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Demonstrate critical awareness of key qualitative, quantitative and mixed research methods 2. Demonstrate skilled analysis of data (both qualitative and quantitative) and interpretation of statistical tests. 3. Evaluate advances and current trends in research design and analysis. 4. Evaluate and review empirical research. 5. Clearly disseminate research findings in written and oral formats. | |||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and
Learning Activities Indicative content• Advanced information literacy skills • Research methods and design (quantitative, qualitative, mixed methods) • Qualitative methodology (e.g. action research, phenomenology, grounded theory, case study) • Approaches to qualitative data collection (e.g. interview, focus groups, observation) • Approaches to qualitative analysis (e.g. content and thematic analysis, IPA, narrative analysis) • Experimental and correlational design • Designing and evaluating interventions • Univariate and bivariate techniques (e.g. Within- and between-group t-tests and ANOVA, non-parametric equivalents of each, factorial ANOVA; correlation and simple regression analysis) • Preparing for multivariate analysis (e.g. examining your data, missing data analysis, outliers, assumptions, data transformation) • Dependence techniques (e.g. multiple regression analysis, logistic regression, multivariate analysis of variance) • Interdependence techniques (e.g. factor analysis) • Secondary data analysis • Knowledge translation • Ethical and professional issues • Advanced research reporting and communication skills. • Attendance at specific workshops held during the Wellbeing Spring School | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||
Programme or List of Programmes | |||||||||||||||||||||||||||||||||||||
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