Statistical Research Methods
This statistics program provides comprehensive, cross-disciplinary support for researchers who require a foundation or refresher in statistical methods. The program addresses topics such as data visualization, statistical inference, reproducibility of results, and linear regression. A concise pre-program diagnostic tool will provide the user with tailored, signposted material dependent on their learning level, with the opportunity to strengthen core skills and extend understanding.
This program adopts a modular format and offers flexibility alongside application through practical activities, video demonstrations, and downloadable portfolio pods to allow learners to consolidate and refresh their statistical understanding.
Advantages of using Statistical Research Methods
- Provides key support for researchers to evaluate and interpret statistics, and report on sound research findings
- A concise pre-program diagnostic tool will highlight learner needs and offer a tailored approach at all levels, granting time efficiency and flexibility.
- Improve core skills and further statistical understanding of topical issues such as data Integrity, open data, and inflation bias and reproducibility.
- Addresses the current statistics context, taking into consideration the interdisciplinary research environment, in which researchers often work across several subject areas.
- Diverse learning approaches: learners can expect to interact with real-world research examples, video demonstrations and practical activities.
- Downloadable data sets and portfolio pods allow learners to save activities and notes to revisit in the future to practice, refresh and test their skills.
This program is for:
Graduate and doctoral students, postdoctoral researchers, and early-career researchers.
- Western Sydney University
- James Cook University
- Victoria University
- Heidelberg University
- Brunel University
- University of Ulster
- University College London
- London South Bank University
SRM Core
- Utilise statistics to enable and enhance your research
- Storytelling with data visualisation and exploratory analysis with descriptive statistics
- Probability distributions as approximating models of reality
- Sampling, estimation and generalisation of results
- Evidence-based statistical inference
- False positives? False negatives? The need for replicability of results
- Categorical predictors with analysis of variance (ANOVA)
- Explaining the world of variation through linear modelling
SRM Companion
- Fundamentals of data management
- Data integrity, research ethics, and open data sources
- Mark Andrews, Associate Professor of Statistical Methods in the Department of Psychology, Nottingham Trent University
- Nady Belensky, Senior Lecturer within the Public Health Programme, University of Greenwich
- Alessandro Di Bucchianico, Associate Professor of Statistics, Eindhoven
- Darsy Darssan, Accredited Professional Statistician and a Fellow of Higher Education Academy, University of Queensland
- Andy Jones, Senior Lecturer in the School of Psychology, Liverpool John Moore’s University
- Matthias Mittner, Pofessor and leader of the research group for cognitive neuroscience, Artic University
- Dr James Abdey, Associate Professorial Lecturer in Statistics at the London School of Economics and Political Science.