Statistics and Data Analysis 1 2017/2018
- 5 ECTS
- Taught in Portuguese
- Continuous Assessment
- relevant skillset
By the end of this curricular unit, the student should be able to:
-Distinguish the continuous or discrete nature of the variables;
-Acknowledge and interpret case count and distribution results;
-Identify, apply and interpret results from central tendency and dispersion measures;
-Identify, apply and interpret results from 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, association, simple linear regression and simple logistic regression analyses.
It doesn't apply.
Classes will include theoretical and practical components to foster the continuous consolidation of the lectured statistical concepts and operations with practical applications in the IBM SPSS software. Whenever possible, theoretical and practical classes will 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: Mode, median and mean
2.3 Dispersion measures: Frequencies, inter-quartile range and standard deviation
3 Inferential statistics
3.1 Association tests: Chi-square test, Pearson, Spearman and Point Bisserial correlation coefficients
3.2 Simple linear regression
4 IBM SPSS Interface
4.1 Data management and recoding
4.2 Descriptive statistics: Tables and graphical representations
4.3 Inferential statistics: Statistical assumptions, conduction of analyses and interpretation of association tests, simple linear regression and simple logistic regression outputs
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.
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.
Week 1. Students’ institutional welcoming. Presentation and evaluation of the curricular unit.
Week 2. Basic statistics and data analysis concepts: The scientific method. Creation of data files in SPSS and variable codification.
Week 3. Basic statistics and data analysis concepts: Variables, measures and research hypotheses (TP). Observed, latent and recoded variables in SPSS.
Week 4. Case count and distribution – normal distribution. Case count and distribution – normal distribution in SPSS.
Week 5. Central tendency measures. Central tendency measures in SPSS.
Week 6. Exercises for supervised problem solving.
Weeks 7 and 8. Statistical decision trees: Applications to descriptive and inferential statistical tests.
Week 9. Chi-square test. Chi-square test in SPSS.
Week 10. Spearman correlation coefficient, Pearson correlation coefficient and normality of distribution assumption. Spearman and Pearson correlation coefficients in SPSS.
Week 11. Point Bisserial correlation coefficient. Point biserial correlation coefficient in SPSS.
Week 10. Statistical assumptions: Outliers and independence of observations. Monitoring of group assignments.
Week 11. Simple linear regression and its assumptions. Simple linear regression in SPSS.
Week 12. Synthesis of the covered regression analyses: Decisions and procedures.
Week 13. Supervision of group assignments. Critical analysis of the statistical topics included in scientific papers.
Week 14. Scientific writing of statistical results. Demonstration, exercises and doubts regarding the scientific writing of statistical results.
Week 15. Delivery of written assignments and presentation. Synthesis of the contents lectured during the semester.
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 and simple linear 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’ future work in Psychology research. 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 skill||improved?||assessed?|
|Achieving practical application of theoretical knowledge||Yes||Yes|
|Analytical and synthetic skills||Yes||Yes|
|Balanced decision making||Yes||Yes|
|Commitment to quality||Yes||Yes|
|Foreign language proficiency||Yes|
|Information and learning management||Yes|
|IT and technology proficiency||Yes||Yes|
|Problem Analysis and Assessment||Yes||Yes|
|Relating to others||Yes|
|Written and verbal communications skills||Yes||Yes|