Statistics II

Objectives

The course(*) learning objectives are the mastering and practical use of some advanced multivariate data analysis techniques, including exploratory, inferential and causal modeling. This curricular unit (CU) is organized in a set of sequential topics, which will allow students to perform descriptive, exploratory, inferential and causal modeling data analysis with specific software including SPSS Statistics and SPSS AMOS.

By the end of the CU the student should have developed the following specific skills and be able to:

1. Identify the usefulness of the main multivariate statistical analysis techniques.

2. Evaluate the relevance of a variety of multivariate methods and the study of multivariate relationships between variables.

3. Perform multivariate analysis with statistical software.

4. Critically analyze the assumptions of the learned data analysis techniques and the software reports.

5. Report, according to the APA guidelines, the results obtained.

(*2023-2024. The updated course’s syllabus will be available to students at the beginning of each academic term)


General characterization

Code

6305

Credits

3,5

Responsible teacher

TBA

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Available soon

Prerequisites

n/a 


Bibliography

Hair, J. F.; Black, W. C.; Babin, B.J.; Anderson, R.E. (2010). Multivariate Data Analysis. 7th. Ed. Upper Saddle River, NI:

Prentice Hall.

Kline, R. B. (2010) Principles and Practice of Structural Equation Modeling (4th Edition). The Guilford Press

Maroco, J. (2014). Analise Estatistica com o SPSS Statistics. 68 Edition. ReportNumber. Pero Pinheiro.

Maroco, J. (2014). Analise de Equaqoes Estruturais: Fundamentos teóricos, Software e Aplicações. 2a Edição.

ReportNumber. Pero Pinheiro.

Tabachnick, B. G., and Fidell, L. S. (2013). Using Multivariate Statistics, 6th ed. Boston: Allyn and Bacon. 

Teaching method

The teaching methodologies include theoretical lectures aided by audiovisual methods and problem solving hands-on practical examples with SPSS and AMOS software. 

Evaluation method

The grading scale used is typically ranging from 0 to 20 (10 points is considered the threshold pass grade). A qualitative Pass/Fail grading can also be used.


Subject matter

0. A brief introduction to multivariate data analysis

1. Principal Component Analysis

2. Exploratory Factor Analysis

3. Cluster Analysis

4. Structural Equation Modelling


Programs

Programs where the course is taught: