MSc Research Methods with Planning and Environmental Management

This unit aims to provide a robust grounding in quantitative research, including its use in mixed methods work. The unit will allow you to implement research projects using experimental, questionnaire and survey data.  Data will be analyzed using the R statistical package with a substantial part of the course devoted to hands-on experience of analyzing real-world data. In addition, the course will introduce a range of specialized analytical techniques from several academic disciplines.  

This course provides a theoretically consistent approach to data analysis using modern methods and will enable participants to analyse and interpret complex models for continuous, categorical and count data using graphical displays
 

The aim of this unit is to introduce the use, application and possibilities of quantitative research. More specifically, the unit aims to:

• Foster an awareness of the principles of quantitative data collection, coding, data cleaning and analysis;
• Develop students’ understanding of undertaking statistical analysis and modelling;
• Develop students’ practical skills in analysing quantitative data in R;
• Enable students to utilise R to present effective quantitative data analysis visually;
• Inform the development of students quantitative-based research proposals;
• Enable students to review and critique quantitative and mixed-methods based research articles and gain awareness of a wide range of quantitative methods including cluster, factor and multilevel modelling.
   

•    Introduction and Installing R
•    Foundation: Key concepts and skills
•    Foundation: Descriptive statistics 
•    Foundation: Inferential statistics
•    GLM: Modelling with a continuous outcome 
•    GLM: Modelling with interactions
•    GLM: Categorical explanatory variables
•    GLM: Modelling with binary and count outcome
•    GLM: Modelling with a categorical outcome
•    GLM: Applying these skills to your data-set
•    Symposium on quant and mixed methods
•    Critical reading of quant and mixed methods

This unit presents a system of analysis that may be applied to a broad range of data collected using different methodologies.

This unit will involve a variety of lessons and learning methods such as interactive lectures, reflective seminars, a combination of these two, as well as online forums. Throughout the unit students will engage in tasks before, during and after the sessions. The pre-task is an individual reading and/or practical statistical exercise (e.g. using RStudio with Rcmdr).

Solutions are provided in class and through video demonstrations. Sessions contain tutor demonstration and exposition and student statistical analysis activities. The after-task involves follows up with additional expanded readings and/or analysis activities.

Initial weeks of the unit mainly involve interactive lectures as this is the time where students are getting familiar with key concepts in this unit and developing an understanding of their own research project. Nevertheless, the statistical analysis tasks mentioned above are still undertaken. Following this, the analytical emphasis increases and learners are given opportunities to develop, enhance and operationalise their skill as users of statistical tools..

The final week is group-work based.

The teaching is both synchronous and asynchronous as there is an extensive set of videos that mirror key aspects of session content as well as the range of resources available on blackboard. In addition students are encouraged to engage with the SEED PGR quantitative research training, either by attending in-person or by viewing the recorded PGR sessions asynchronously.

• develop and execute an appropriate statistical analysis and modelling of a dataset, including Generalised Linear Modelling (GLM).

• form appropriately worded research questions using the expected nomenclature in the form of hypotheses.
• develop and deploy a conceptual framework for analysis of a quantitative dataset to answer appropriate research questions.
• make informed decisions to support model selection.

• Develop a quantitative-based analysis plan.
• Use statistical software tools to analyse and visually present quantitative data.
   

• demonstrate autonomy by choosing the dataset and research focus.

Agresti, A. (2018).  Statistical Methods for the Social Sciences (5th edition). Pearson.
Crawley MJ. The R Book. Second edition. Wiley; 2012. doi:10.1002/9781118448908
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models. Sage Publications.
Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression (2nd edition). Sage Publications.
Glynn, M. (2019). Speaking data and telling stories: data verbalization for researchers. Routledge.
Hutcheson, G.D. & Sofroniou, N. (1999) The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models. 1st edition. London, SAGE Publications, Limited.
Hutcheson, G. and Sofroniou, N. (2010) Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models. 2nd edition. Sage Publications, Limited.
Hutcheson, G. and Moutinho, L.A.M. (2008) Statistical Modelling for Management. Sage Publications, Limited.
Urdinez, F. and Cruz, A. (2020) R for Political Data Science: A practical guide. Chapman and Hall/CRC. https://doi.org/10.1201/9781003010623 
Wheelan, C.J. (2013). Naked statistics: stripping the dread from the data. Charles Norton.
Catalano, M. (2015) Review of Naked Statistics: Stripping the Dread from Data by Charles Wheelan. Numeracy : advancing education in quantitative literacy. 8 (1), 13-. doi:10.5038/1936-4660.8.1.13.
Papers and books discussing critical approaches to quantitative research 
Angrist, J.D. and Pischke J. (2015). Mastering Metrics: The Path from Cause to Effect. Princeton University Press. 
Buch-Hansen, H. (2014) Social Network Analysis and Critical Realism. Journal for the theory of social behaviour. 44 (3), 306–325. doi:10.1111/jtsb.12044.
Hastings, C. (2021) A critical realist methodology in empirical research: foundations, process, and payoffs. Journal of Critical Realism. https://doi.org/10.1080/14767430.2021.1958440 
Kohler, U., Class, F., & Sawert, T. (2023). Control variable selection in applied quantitative sociology: a critical review. European Sociological Review. https://doi.org/10.1093/esr/jcac078 
Porpora, D. (2015). Do realists run regressions? In Reconstructing Sociology: The Critical Realist Approach, Cambridge University Press. 
Porpora, D. (2023) Do realists predict? Journal for the Theory of Social Behaviour. https://doi.org/10.1111/jtsb.12404 
Empirical papers that use quantitative approaches
Buil-Gil, D., Moretti, A. & Langton, S.H. (2022) The accuracy of crime statistics: assessing the impact of police data bias on geographic crime analysis. Journal of experimental criminology. 18 (3), 515–541. doi:10.1007/s11292-021-09457-y.
Dahab, R., Bécares, L. & Brown, M. (2020) Armed conflict as a determinant of children malnourishment: A cross-sectional study in the Sudan. BMC public health. 20 (1), 532–532. doi:10.1186/s12889-020-08665-x.
Olsen, W. Bridging to Action Requires Mixed Methods, Not Only Randomised Control Trials. European Journal Development Research 31, 139–162 (2019). https://doi.org/10.1057/s41287-019-00201-x   
Stopforth, S., Kapadia, D., Nazroo, J. & Bécares, L. (2023) Ethnic inequalities in health in later life, 1993–2017: the persistence of health disadvantage over more than two decades. Ageing and society. 43 (8), 1954–1982. doi:10.1017/S0144686X2100146X.
Taylor, H., Dawes, P., Kapadia, D., Shryane, N., & Norman, P. (2021). Investigating ethnic inequalities in hearing aid use in England and Wales: a cross-sectional study. International Journal of Audiology. https://doi.org/10.1080/14992027.2021.2009131
Torche, F. (2005). Unequal but fluid: social mobility in Chile in comparative perspective. American Sociological Review, 70(3), 422-450.