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.
(*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