Advanced Quantitative Data Analysis

Code:

PDCD005

Acronym:

MTI

Keywords
Classification	Keyword
OFICIAL	Sports Sciences

Instance: 2013/2014 - SP

Active?	Yes
Course/CS Responsible:	Sport Sciences

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PDCD	20	Plano em vigor a partir de 2009	1	-	5	45	135

Teaching language

Suitable for English-speaking students

Objectives

1º It is expected that the relationship among working hypothesis, research design and data analysis be clearly understood.

2º Have a clear and rigorous understanding of essential aspects of multivariate exploratory data analysis, analysis of variance and covariance, multiple and logistic regression, as well as test theory (Classic and Generalizability).

3º Have a sufficient knowledge and independent working capacity when dealing with SPSS and SYSTAT software’s; commands and output interpretation are expected to be closely linked to students data analysis problems.

4º A multivariate look from MANOVA and Discriminant Function Analysis is expected to deal with complex data matrices.

5º It is hoped that students search for hierarchical data structures and understand the need for multilevel data analysis.

6º Independency and autonomy in using HLM software is expected from all students.

Learning outcomes and competences

The syllabus contents are coherent with curricular unit aims, as they enable students to understand and evaluate the relevancy of Advanced Quantitative Data Analysis in their future work in other curricular units as well as in their final dissertation. From a theoretical basis concerning multiple research questions and advanced data analysis, students will acquire sufficient knowledge in diverse research designs always context specific. We also aim to have students with ample autonomous knowledge in SPSS, SYSTAT and HLM use. Syllabus contents will give students opportunities to present published papers allowing for a critical analysis of their content given the knowledge they have.

Working method

Presencial

Program

1. Research process, hypothesis, design and analysis – a coherent framework.

2. Univariate, bivariate and multivariate exploratory data analysis: (i) fundamental ideas; (ii) graphical representations; (iii) relevant statistics; (iv) examples in SPSS and SYSTAT.

3. Hypothesis testing: t-tests, ANOVA I and II, ANCOVA and their non-parametric homologs.

4. Brief introduction to repeated measures analysis: t-tests and ANOVA

5. Bivariate, partial correlations and the bootstrap.

6. Multiple and logistic regression – model building, robustness, their tests and inference. Examples in SPSS and SYSTAT.

7. Classical and Generalizability theory applied to data quality control.

8. MANOVA and Discriminant function analysis. Examples in SPSS.

9. Hierarchical analysis and multilevel modeling of nested data. Basic and intermediate ideas, data structuring and the HLM software.

Mandatory literature

Pestana MH, Gageiro JN ; A análise de dados para ciências sociais. A complementaridade do SPSS, 2003
Tabachnick BG, Fidell LS ; Using multivariate statistics, 2007
Wilcox R ; Modern statistics for the social and behavioral sciences, 2012
Stevens JP ; Applied multivariate statistics for the social sciences, 2002

Complementary Bibliography

5. Sheskin DJ ; Handbook of parametric and nonparametric statistical procedures. , 2011

Teaching methods and learning activities

Classes have a triple format. Theoretical presentation and discussion of syllabus contents will be firstly addressed. Secondly, practical sessions include the use of SPSS, SYSTAT and HLM dealing with problem solving. Thirdly, all students will present a published paper of his/her interest, putting themselves in the “researcher skin”.

Evaluation will be done with two types of tasks. The first one relates to the presentation of a written paper by groups of two students with real data; the second one is a written exam concerning all syllabus contents. The final mark will be the weighted sum of the two marks, given that the second one weights 60%.

Evaluation Type

Evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	100,00
Total:	100,00

Calculation formula of final grade

Final mark= exam result