Longitudinal Data Analysis

Code:

PDCD007

Acronym:

MTI

Keywords
Classification	Keyword
OFICIAL	Sports Sciences

Instance: 2017/2018 - 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	2	Plano em vigor a partir de 2009	1	-	5	45	135

Teaching language

Suitable for English-speaking students

Objectives

1º Have a clear understanding of problems arising from longitudinal designs and analysis, namely from Linda Collins (2008) approach.

2º Have a precise understanding of the relevancy of longitudinal data analysis and its historical importance in Sport Sciences context.

3º Understand the significance and value of intra-individual change and inter-individual differences based on the notions of tracking and prediction.

4º Understand the General Linear Model importance when dealing with longitudinal data.

5º Have consistent knowledge and self-regulating working capacity when dealing with SPSS, LDA, TIMEPATH and HLM softwares.

Learning outcomes and competences

The syllabus contents are coherent with curricular unit aims, as they enable students to understand and appraise the relevancy of Longitudinal Data Analysis in their future work in other curricular units as well as in their final thesis. Starting from a theoretical basis concerning research questions linked to longitudinal designs, students with gain sufficient knowledge in diverse existent research problems. We also aim students with plentiful autonomous knowledge in SPSS, LDA, TIMEPATH and HLM use. Syllabus contents will give students opportunities to be exposed to FADE published papers with a longitudinal design, allowing for their critical assessment.

Working method

Presencial

Program

1. Historical aspects of longitudinal research in Sport Sciences arena.

2. Research designs: purely longitudinal, mixed-longitudinal and panel.

3. Fundamental issues: main hypothesis; sample size; time-age-cohort effects; attrition; data quality control; instrumentation, variables and measurement time effects.

4. Tracking and prediction: concepts; auto-correlations, Cohen´s Kappa, Foulkes & Davies =, Goldstein consistency index; Rao and Carter & Young linear and nonlinear models. Practical examples in software specificities: SPSS, LDA, and TIMEPATH.

5. General Linear Model: main problems and information levels for continuous and binary data; balanced and non-balanced designs; data reshaping; time metric, linear and nonlinear models; fixed and dynamic predictors; model testing. Practical examples in HLM.

Mandatory literature

Catrien CJH et al (eds); Long. data analysis. Designs, models and methods., Sage, 1998
Kinnear PR, Gray; SPSS 15 made simple, Psy Press, 2008
Maia J, et al; Tracking of Phys Fit. During Adolescence [...]. , Med Sci in Sport and Ex., 2001 ((5):765-771)
Maia J et al; Modeling Stability and Change in Strength Development, 2003 (Nº4:579- 591)
Maia J et al; A growth curve to model changes in sport participation in adolescent boys, 2010 (20(4):679-85)
O’Connel AA, McCoach DB; Multilevel modeling of educational data, 2008
Pedhazur Elazar J.; Measurement, design and analysis. ISBN: 0-8058-1063-3
Singer JD, Willet JB; Appl long. data analysis. Modeling change and event occurrence, Oxford U Press, 2003
Tabachnick BG, Fidell LS; Using multivariate statistics, 5th ed. Pearson Int Ed, 2007
Twisk JWR; Applied longitudinal data analysis for epidemiology. A practical guide., Cambridge U Press, 2003

Teaching methods and learning activities

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

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Trabalho escrito	100,00
Total:	100,00

Calculation formula of final grade

Evaluation will be done with two types of task. The first one relates to the presentation of a written paper by groups of two students with read 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 task weights 60%.

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