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Advanced Quantitative Data Analysis

Code: PDCD005     Acronym: MTI

Keywords
Classification Keyword
OFICIAL Sports Sciences

Instance: 2019/2020 - SP (since 11-03-2019 to 31-07-2020)

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 18 Plano em vigor a partir de 2009 1 - 5 45 135

Teaching language

Suitable for English-speaking students

Objectives


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

  2. 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).

  3. 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.

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

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

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 thesis. 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

Pedhazur Elazar J.; Measurement, design and analysis. ISBN: 0-8058-1063-3
Kinnear PR, Gray CD; SPSS 15 made simple, Psychology Press, 2008
O’Connel AA, McCoach DB; Multilevel modeling of educational data, Information Age Publishing, Inc., 2008
Pestana Maria Helena; Análise de dados para ciências sociais. ISBN: 978-972-618-498-0
Sheskin DJ; Handbook of parametric and nonparametric statistical procedures, CRC Press - Taylor & Francis Group, 2011
Stevens James P.; Applied multivariate statistics for the social sciences. ISBN: 0-8058-3777-9
Tabachnick BG, Fidell LS; Using multivariate statistics, Pearson International Edition, 2007
Wilcox Rand; Modern statistics for the social and behavioral sciences. ISBN: 978-1-4398-3456-5

Teaching methods and learning activities

Classes have a double format. Theoretical presentation and discussion of syllabus contents will be firstly addressed. Secondly, practical sessions include the use of SPSS, STATA, SuperMix and HLM dealing with problem solving.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

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

Amount of time allocated to each course unit

designation Time (hours)
Estudo autónomo 65,00
Frequência das aulas 70,00
Total: 135,00

Eligibility for exams

Students are expected to be present in 75% of all classes.

Calculation formula of final grade

The final mark is the simple mean of all four exams, such that
Final Mark= (Exam1+Exam2+Exam3+Exam4)/4.
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