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The University of Southampton

PSYC6046 Advanced Statistical Methods in Psychology

Module Overview

This module is divided into two components that focus on cutting-edge statistical techniques. The first half focuses on Structural Equation Models, covering Path Analysis, Confirmatory Factor Analysis, Structural Equation Modelling, Multigroup Models and Latent Mean Structures. The second half focuses on Hierarchical (Multilevel/Mixed) Linear Models, which is appropriate for nested data (e.g., certain repeated-measures designs, students nested within schools, romantic couples, or individuals within groups). In addition to the readings below, a Blackboard site will be maintained throughout the Semester, where lecture slides, additional readings, and datasets will be available. Software manuals Arbuckle, J. L. (2017). Amos 24: User’s guide. Chicago: SPSS. SEM Textbooks Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Hove, UK: Routledge. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). London: Guilford. Multilevel Textbooks: Bickel, R. (2007). Multilevel analysis for applied research: It's just regression. London: Guilford Press. Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal modeling with IBM SPSS. New York: Routledge. Hox, J. (2010). Multilevel analysis: Techniques and application. (2nd ed). Abingdon: Routledge.

Aims and Objectives

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • - appreciate the statistical underpinnings of various structural equation modelling and hierarchical linear modelling statistical techniques - be able to perform these techniques using appropriate software - be able to interpret output from such analyses


Regression and Mediation Introduction to AMOS and Path Analysis Confirmatory Factor Analysis Structural Equation Models Multi-Group CFA/SEM Latent Mean Structures Bootstrapping, Non-Normality, & Missing Data Introduction to HLM The 2-Level Model The 3-Level Model Repeated Measures Designs (2-Level) Multilevel Multivariate Models

Learning and Teaching

Teaching and learning methods

Each 2-hour weekly session is a combination of lecture and hands-on practical application. The practical exercises will focus on data analysis, primarily using SPSS and AMOS. In addition to providing students with direct practical experience in the application of relevant statistical techniques, these exercises will help students prepare their answers to the problem sets. Assessment is based on two problem sets completed throughout the semester (50% each). The problem sets are based on practical exercises that correspond to the material being covered at the time.

Independent Study126
Total study time150

Resources & Reading list

Arbuckle, J. L (2017). Amos 24: User’s guide. 

Hox, J. (2010). Multilevel analysis: Techniques and application. 

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. 

Bickel, R. (2007). Multilevel analysis for applied research: It's just regression. 

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal modeling with IBM SPSS. 

Statistical software. You will need to download and install AMOS 21. This is available through ISS – it is packaged with SPSS:

Supplemental chapters and articles will be available on Blackboard.. 

Kline, R. B. (2005). Principles and practice of structural equation modeling. 

Blackboard. In addition to the listed readings, a Blackboard site will be maintained throughout the semester.



MethodPercentage contribution
In-class activities 5%
Individual Presentation 30%
Written assignment 65%


MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

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