The University of Southampton
Courses

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

Module Aims

• 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

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

Syllabus

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). Week 18: Regression and Mediation Readings: Field, A. (2009). Discovering Statistics Using SPSS (3rd Ed.) Sage. Chapter 7. Preacher & Hayes (2004, 2008) Week 19: Introduction to AMOS and Path Analysis Readings: AMOS Part1: 1, 2 AMOS Part2: 1-4 Kline (4-6) Moeller and Crocker (2009) Week 20: Confirmatory Factor Analysis Reading: AMOS Part2: 8 Kline (7) Russell (2002) Sheard et al. (2009) Week 21: Structural Equation Models Reading: AMOS Part2: 5 Kline (8) Anderson & Gerbing (1988) Gramzow et al. (2000) Week 22: Multi-Group CFA/SEM Reading: AMOS Part2: 10-12, 24, 25 Byrne (7) Kline (11) Boudrias et al. (2004) Roth et al. (2008) Schmader et al. (2001) Week 23: Latent Mean Structures Reading: AMOS Part 2: 13, 15 Kline (10) Aikin et al. (1994) Cooke et al. (2001) Week 24: Bootstrapping, Non-Normality, & Missing Data Reading: AMOS Part 2: 17-21 Preacher & Hayes (2004, 2008) Week 25: Introduction to HLM Reading: Heck at al. (2) Easter Break Week 30: The 2-Level Model Reading: Heck et al. (3) Barth et al. (2004) Riketta & Sacramento (2008) Week 31: The 3-Level Model Reading: Heck et al. (4) Belanger & Eagles (2005) Romano et al. (2005) Wiggins et al. (2002) Week 32: Repeated Measures Designs (2-Level) Reading: Heck et al. (5) Campbell & Hedeger (2001) Curran et al. (1997) Elkin et al. (1995) Ong et al. (2002) Fals-Stewart (2003) Week 33: Multilevel Multivariate Models Reading: Heck et al. (7) Hoffman & Rovine (2007) Hauck & Street Launceau et al. (2005) Fraine et al. (2005) Raudenbush et al.

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.

TypeHours
Completion of assessment task104
Follow-up work36
Lecture24
Wider reading or practice36
Total study time200

Resources & Reading list

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

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

Supplemental chapters and articles will be available on Blackboard.. 

Statistical software. You will need to download and install AMOS 21. This is available through ISS – it is packaged with SPSS: https://www.software.soton.ac.uk/

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

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

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

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

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

Assessment

Summative

MethodPercentage contribution
Problem sets 100%

Referral

MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

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