2024
Quantitative Data Analysis in Psychology
Name: Quantitative Data Analysis in Psychology
Code: PSI11137M
3 ECTS
Duration: 15 weeks/78 hours
Scientific Area:
Psychology
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Sustainable Development Goals
Learning Goals
General
Demonstrates to recognize the potential of the instrumentation of testing structural equation models (SEM) for both theoretical thinking and empirical research in psychology.
Demonstrates competence to tailor the test of SEM to single-sample cross-sectional correlational designs.
Specific
Demonstrates competence to identify SEM with observed and latent variables.
Demonstrates competence to generate SEM in LISREL according to the metric nature (continuous or discrete) of the observed variables.
Demonstrates competence to analyze the results of the test of SEM with latent variables (LV) denoted by discrete observed variables in LISREL.
Demonstrates to recognize the potential of the instrumentation of testing structural equation models (SEM) for both theoretical thinking and empirical research in psychology.
Demonstrates competence to tailor the test of SEM to single-sample cross-sectional correlational designs.
Specific
Demonstrates competence to identify SEM with observed and latent variables.
Demonstrates competence to generate SEM in LISREL according to the metric nature (continuous or discrete) of the observed variables.
Demonstrates competence to analyze the results of the test of SEM with latent variables (LV) denoted by discrete observed variables in LISREL.
Contents
1. Epistemological framework.
1.1. Instrumentation of multivariate data analysis (IMDA) of first (e.g., SPSS) vs. second generation (e.g., LISREL).
1.2. Advantages of second generation IMDA and SEM types.
1.2.1. Uni and multifactorial SEM (oblique, orthogonal and hierarchical), comparison between alternative models, structural validity examination, and computation of factor scores.
1.2.2. SEM of multiple causes and multiple indicators.
1.2.3. SEM of multiple and multivariate regression with LV.
1.2.4. SEM of trajectories with LV and analysis of mediation effects.
2. Analysis of SEM with PRELIS/LISREL.
2.1. Infrastructure with PRELIS: sample size and type II error, correlation vs. covariance matrices, admissibility diagnosis and estimation methods.
2.2. Testing with LISREL-SIMPLIS: specification, identification, estimation, admissibility diagnosis, goodness of fit and substantive evaluation of SEM.
1.1. Instrumentation of multivariate data analysis (IMDA) of first (e.g., SPSS) vs. second generation (e.g., LISREL).
1.2. Advantages of second generation IMDA and SEM types.
1.2.1. Uni and multifactorial SEM (oblique, orthogonal and hierarchical), comparison between alternative models, structural validity examination, and computation of factor scores.
1.2.2. SEM of multiple causes and multiple indicators.
1.2.3. SEM of multiple and multivariate regression with LV.
1.2.4. SEM of trajectories with LV and analysis of mediation effects.
2. Analysis of SEM with PRELIS/LISREL.
2.1. Infrastructure with PRELIS: sample size and type II error, correlation vs. covariance matrices, admissibility diagnosis and estimation methods.
2.2. Testing with LISREL-SIMPLIS: specification, identification, estimation, admissibility diagnosis, goodness of fit and substantive evaluation of SEM.
Teaching Methods
The analytical decomposition of the contents will enable students to make an integrative synthesis for the generation and interpretation of SEM test results. This is assessed through an individual open-book test of acquired knowledge, about topics of the syllabus points 1. and 2., and about PRELIS/LISREL outputs.
75% of the classes are mandatory.
Continuous and Final Evaluations
Test (20 values; minimum 9.5)
75% of the classes are mandatory.
Continuous and Final Evaluations
Test (20 values; minimum 9.5)
Teaching Staff (2023/2024 )
- António Augusto Pinto Moreira Diniz [responsible]
