Undergraduate 

Psychology BSc/MA/MA(SocSci)

Statistical and Scientific Models 3H PSYCH4037

  • Academic Session: 2020-21
  • School: School of Psychology
  • Credits: 20
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Runs Throughout Semesters 1 and 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This course provides an overview of basic statistical modelling for the analysis of psychological data. Drawing conclusions from data requires the application of scientific models. The latter will teach students how theory impacts on analytical goals, which in turn influence the design and analysis of a study, and ultimately, what kind of model to apply. The course will be taught by various lecturers using examples from their own research activities.

Timetable

20 lectures over a twenty week period.

Excluded Courses

None

Assessment

Formal written examination 60%

Practical skills assessments 40%: two timed one hour take-home assignments taking place at the start of weeks 5 and 8.

Main Assessment In: December

Course Aims

To provide an understanding of basic statistical modelling approaches to the analysis of psychological data. To provide an understanding of the relation between scientific theory and analytical models, analytical goals and decision-making in scientific research and to be able to reflect on modelling criteria in relation to analytical goals and theoretical considerations.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Integrate knowledge about study design and statistics to formulate and estimate the General Linear Model (GLM) appropriate to the various types of study designs encountered in psychology, especially studies with repeated observations.

■ Visualise and interpret various effects (including interactions) in multi-way designs.

■ Estimate linear mixed-effects models and describe their relation to traditional techniques such as ANOVA and multiple regression.

■ Perform logistic regression and explain and interpret the statistical output.

■ Create reproducible data analysis scripts and reports within the R statistical programming environment.

■ Understand the relationship between theoretically motivated research aims and data analysis

■ Distinguish between different analytical goals in data modelling (e.g. confirmatory vs. exploratory).

■ Understand how analytical goals affect decision-making (and related criteria) in an analysis

■ Reflect on potential trade-offs in the analytical decision-making process (e.g. internal validity vs. generalisability, parsimony vs. theoretical soundness, accuracy vs. coverage).

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.