Analysis and Data Processing

Recommended Prerequisites

Not applicable.

Teaching Methods

Given the nature of this curricular unit, its direct instruction plays an important role in the learning method. Firstly, the student is instructed in the use of specialized quantitative methods and techniques using the exemplification and real demonstration of the procedures required to obtain, for example, a certain statistics or analysis. Secondly, the student is asked to perform individually or in groups, a particular operation or test procedure, and in the end, all the issues raised by the learning situation are discussed (it is evaluated and criticized), using predominantly active methods.

Learning Outcomes

1. It is aimed that students develop skills of descriptive and inferential analysis of data using parametric and nonparametric simple methods through the use of advanced statistical analysis software (SPSS for Windows).

2. It is aimed that students develop the statistical reasoning and the ability to decide and select the most appropriate analysis methods to different practical and research problems, and are able to perform these procedures and interpret the information obtained from the outputs produced by the analytical software used.

3. It will be given particular attention to the preparation of reports analyzing the outputs from the software, based on the scientific publication standards that are generally accepted (ex.: norms of the American Psychological Association).

Work Placement(s)

No

Syllabus

1. Introduction to methods of Data Analysis / Statistics

2. Computerized data analysis: Introduction to SPSS

3. Descriptive analysis and graphical representation

4. Statistical Inference I: parametric and nonparametric univariate methods.

5. Statistical Inference II: MANOVA and linear simple and multiple Regression Models.

6. Items analysis methods based on classical test theory (accuracy and validity)

6. Advanced methods of data analysis I: exploratory and confirmatory factor analysis

7. Advanced methods of data analysis II: SEM models (structural equation models)

Head Lecturer(s)

José Manuel Tomás Silva

Assessment Methods

Evaluation
a typical research report of quantitative data analysis: 100.0%

Bibliography

Cohen, B. (2008). Explaining Psychological Statistics. Hoboken, NJ: Wiley.

Glass, G. V. & Hopkins, K. D. (1996). Statistical Methods in Education and Psychology (3rd ed.). Boston: Allyn & Bacon

Hair, J. Black, W., Babin, B., &Anderson, R. (2010)). Multivariate data analysis (6th ed.). New Jersey: Prentice-Hall

Howell, D. (1997). Statistical methods for psychology (4ª Ed.). Belmont, CA: Duxbury Press.

Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Press.

Maroco, J. (2007). Análise estatística – Com utilização do SPSS (3ª ed.). Lisboa: Edições Sílabo.

Norusis, M. (2012). IBM SPSS Statistics 19 Guide to Data Analysis. Upper Saddle River, NJ: Pearson.

Pestana, M. H. e Gageiro, J. N. (2008). Análise de Dados para Ciências Sociais - A complementaridade do SPSS (5ª ed.). Lisboa: Edições Sílabo.

Tabachnick, B. G., and Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). Boston: Allyn and Bacon.