Module Code:

PS923

Module Name:

Methods & Analysis in Behavioural Science

Module Credits (CATS):

15

Module Convener

Pete Trimmer

Module Teachers

Pete Trimmer, Mikhail Spektor

Module Aims

The purpose of the module is to introduce experimental design and statistical programming.

Behavioural scientists need statistical analysis of experimental data and of large data sets. This module covers these topics to allow students to understand how to test hypotheses, plan experimental design and perform statistical analysis using R.

Learning Outcomes

By the end of the module, students should be able to:

• Understand hypothesis testing and the relationship between hypotheses and experimental planning and design.
  
  
• Design and report studies in a manner that permits replication attempts.
  
  
• Program reproducible statistical analyses.

Assessed by:

• Modelling assignments, weekly brief assignments, presentation
  
  
• Modelling assignments, weekly brief assignments, presentation
  
  
• Modelling assignments, weekly brief assignments

Module Work Load

Module Length

10 weeks

Lectures

10 lectures of 2 hours each

Practical classes

10 practical classes of 2 hours each

Attendance

Attendance at lectures and practical classes is compulsory

Module Assessment

Assessed work:

Modelling Assignment 1 – based on modelling in R

Modelling Assignment 2 – based on modelling in R

Weekly brief assignments - 8 assignments in weeks 3-10 worth 2% each

Group presentation – based on weekly assignments

Weighting:

36%

36%

16%

12%

Module Programme

Topics covered on the course will likely include:

• Introduction to research methods, statistics, and R
• Modern R: tidyverse et al.
• Data visualization with ggplot2
• Simple Linear Regression
• Multiple Linear Regression
• One Categorical Covariate
• Interactions of Categorical Predictors
• More theory
• Repeated-measures and pooling

Module Reading List

Baguley, T. (2012). Serious stats: a guide to advanced statistics for the behavioral sciences. Houndmills, Basingstoke, Hampshire; New York: Palgrave Macmillan.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. New York: Routledge Academic.

Fox, J. (2015). Applied regression analysis and generalized linear models. Los Angeles: Sage.

Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage.

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press.

Grolemund, G. (2014). Hands-on programming with R. Sebastopol, CA: O’Reilly.

Howell, D. C. (2010). Statistical methods for psychology (7th ed.). Belmont, CA: Wadsworth.

Howell, D. C. (2013). Fundamental Statistics for the Behavioral Sciences, 8th Edition. Belmont, CA: Wadsworth Cengage Learning.

Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Sebastopol CA: O’Reilly.