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Statistics and Data Analysis 1 2017/2018

  • 5 ECTS
  • Taught in Portuguese
  • Continuous Assessment


By the end of this curricular unit, the student should be able to:
-Acknowledge the continuous or discrete nature of the variables;
-Acknowledge, understand and interpret case count and distribution results;
-Acknowledge, understand and interpret central tendency and dispersion measures;
-Acknowledge, understand and interpret statistical tests of association, simple linear regression and simple logistic regression according to the variables’ scale of measurement;
-Acknowledge and use the IBM SPSS interface to insert data and conduct descriptive statistics, normality distribution, internal consistency, association, simple linear regression and simple logistic regression analyses.

Recommended Prerequisites

It doesn't apply.

Teaching Metodology

Classes will alternate theoretical and practical components to foster the continuous consolidation of the lectured statistical concepts and operations, as well as students’ experiential learning using the IBM SPSS software. Whenever possible, theoretical and practical classes will also include the critical analyses of published articles and supervised problem solving. Specific references will be recommended at each class for students to deepen their study of the lectured contents. A continuous evaluation methodology will be used, including two short-tests and a group assignment.

Body of Work

1 Basic statistics and data analysis concepts
2 Descriptive statistics
2.1 Counting and data distribution analyses
2.2 Central tendency measures
2.3 Dispersion measures
3 Inferential statistics
3.1 Association tests
3.2 Simple linear regression
3.3 Simple logistic regression
4 IBM SPSS Interface
4.1 Data management and transformation
4.2 Descriptive statistics: Tables and/or graphical representations
4.3 Inferential statistics: Statistical assumptions, conduction and interpretation of association tests, simple linear regression and simple logistic regression results

Recommended Bibliography

Marôco, J., & Bispo, R. (2006). Estatística aplicada às ciências sociais e humanas. Lisboa, Portugal: Climepsi Editores.
Martinez, L. F., & Ferreira, A. I. (2010). Análise de dados com SPSS: Primeiros passos. Lisboa, Portugal: Escolar Editora.
Martins, C. (2011). Manual de análise de dados quantitativos com recurso ao IBM SPSS: Saber decidir, fazer, interpretar e redigir. Braga, Portugal: Psiquilíbrios.
Pereira, A. (2008). SPSS: Guia prático de utilização análise de dados para ciências sociais e psicologia. Lisboa, Portugal: Edições Sílabo.

Complementary Bibliography

Coolican, H. (2014). Research methods and statistics in psychology (6ª ed.). NY: Psychology Press.
Dancey, C.P. & Reidy, J. (2011). Statistics withouth maths for psychology (5ª ed.). Essex: Pearson Education.
Field, A. (2009). Discovering Statistics Using SPSS. London, UK: Sage.
Greene, J., & d’Oliveira, M. (2011). Learning to use statistical tests in psychology. Berkshire: Open University Press.
Howell, D. C. (2010). Statistical methods for psychology. Belmont, CA: Cengage Wadsworth.
Pallant, J. (2016). SPSS Survival manual – a step by step guide to data analysis using IBM SPSS (6ª ed.). Berkshire: McGraw Hill Education.
Pestana, M.H. & Gageiro, J.N. (2005). Análise de dados para ciências sociais – A complementaridade do SPSS (4ª ed). Lisboa: Edições Silabo.

Weekly Planning

Week 1. Students’ institutional welcoming. Presentation and evaluation of the curricular unit.
Week 2. Basic statistics and data analysis concepts: The scientific method (TP). Creation of data files in SPSS and variable codification (P).
Week 3. Basic statistics and data analysis concepts: Variables, measures and research hypotheses (TP). Observed, latent and recoded variables in SPSS (P).
Week 4. Case count and distribution – normal distribution (TP). Case count and distribution – normal distribution in SPSS (P).
Week 5. Central tendency measures (TP). Central tendency measures in SPSS (P).
Week 6. Statistical decision trees(T).
Week 7. Chi-square test and Spearman correlation coefficient (TP). Chi-square test and Spearman correlation coefficient in SPSS (P).
Week 8. Pearson correlation coefficient and normality of distribution assumption (TP). Normality of distribution assumption and Pearson correlation coefficient in SPSS (P).
Week 9. Synthesis of the covered association tests: Decisions and procedures (TP). Supervision of group assignments (P).
Week 10. Simple linear regression and simple logistic regression: Introduction and statistical assumptions (TP). Analysis of statistical assumptions: Outliers and independence of observations in SPSS (P).
Week 11. Simple linear regression (TP). Simple linear regression in SPSS (P).
Week 12. Simple logistic regression (TP). Simple logistic regression in SPSS (P).
Week 13. Synthesis of the covered regression analyses: Decisions and procedures (TP).
Week 14. Scientific writing of statistical results (TP). Demonstration, exercises and doubts regarding the scientific writing of statistical results (P).
Week 15. Delivery of written assignments. Synthesis of the contents lectured during the semester (TP).

Demonstration of the syllabus coherence with the curricular unit's objectives

The coverage of basic statistics and data analysis concepts crosses all the curricular unit’s contents and sustains the attainment of the remaining learning goals. The descriptive statistics topics afford students the possibility to understand the continuous or discrete nature of variables, the normality of data distribution, and how these contents are applied in SPSS. The inferential statistics contents foster students’ understanding of statistical assumptions and adequacy of association tests, simple linear regression and simple logistic regression to different variables. All the contents will be practically applied in SPSS, thus stimulating students’ knowledge and use of its interface to solve problems.

Demonstration of the teaching methodologies coherence with the curricular unit's objectives

Contents will be lectured in a permanent articulation between theoretical exposition and practical application/interpretation to sustain students’ experiential learning and future work in literature reviews and Psychology research. The teaching methodology will rely on a permanent articulation between theoretical classes (during which concepts will be defined and clarified) and practical classes (during which contents will be explored, rehearsed and interpreted). For coherency with the teaching methodology, a continuous evaluation method is used to assess students’ theoretical comprehension and practical application of the covered contents, considering the interpretation of information and problem solving. The group assignment will focus on a critical analysis of a scientific manuscript published in Portuguese, taking the interpretation and discussion of the used statistical data analyses into account.

relevant generic skillimproved?assessed?
Achieving practical application of theoretical knowledgeYesYes
Analytical and synthetic skillsYesYes
Balanced decision makingYesYes
Commitment to qualityYesYes
Foreign language proficiencyYes 
Information and learning managementYes 
IT and technology proficiencyYesYes
Problem Analysis and AssessmentYesYes
Relating to othersYes 
Research skillsYesYes
Written and verbal communications skillsYesYes
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