Statistical Methods: Fundamentals

Recommended Prerequisites

Numeracy, reasoning, problem solving skills of mathematics (Secondary level). Knowledge of Portuguese language: Independent user (B1).

Teaching Methods

To enable student learning, both teacher-centered and student-centered approaches are used. In theoretical teaching, the expository method is predominantly used, complemented, when appropriate, by the use of multimedia presentations (e.g., videos, images, statistical apps). In PL classes, demonstration and active methods are used, appealing to the collaborative work of students (dyads / triads).

Learning Outcomes

This course introduces the fundamental concepts of statistical analysis in the social and behavioral sciences and, in addition, introduces ideas related to the reform of statistics in the behavioral sciences. Specifically, it aims to contribute to the development of three key competences: statistical reasoning (concepts, language, critical vision), persistence (resilience in the face of difficulties and desire to continue to apply statistics throughout life) and the development of positive attitudes about it data analysis (value and usefulness of statistics). Students learn to describe and explore data and to go beyond the tests of statistical significance to appreciate the complementary value of producing estimates of the effects and confidence intervals. They also acquire practical data analysis skills, namely, using standard software tools (e.g., SPSS).

Work Placement(s)

No

Syllabus

1. Statistical Methods for Psychology: Fundamental Concepts of Quantitative Research

2. Descriptive Analysis: Exploratory Data Analysis, Frequency Tables and Graphical Displays

3. Making Sense of Data: Measures of Central Tendency and Variability

4. Z scores, Normal Distribution and Probability

5. Extracting Conclusion from Data: Sampling, Confidence Intervals and Null Hypothesis Significance Tests

6. t Tests for Independent and Dependent Samples

7. Comparing Three or More Groups: Introducing the One-Way ANOVA

8. Multiple Comparisons: Logic, Computation and Interpretation

9. Identifying Data Patterns: Simple Linear Correlation

10. Making Predictions: Simple Linear Regression

11. Power and Effect Size.

Head Lecturer(s)

José Manuel Tomás Silva

Assessment Methods

Assessment
Exam: 100.0%

Bibliography

Cohen, B. (2008). Explaining psychological statistics. Hoboken, NJ: Wiley.

Cumming, G. & Calin-Jagman, R (2017). Introduction to the new statistics. Estimation, Open Science, and Beyond. New York, NY: Routledge.

Field, A. (2017). Discovering statistics using IBM SPSS. London: SAGE.

Guéguen, N. (1999). Manual de estatística para psicólogos. Lisboa: CLIMEPSI.

Howell, D. (2002). Statistical methods for psychology. Belmont, CA: Duxbury Press.

Howitt, D. & Cramer, D. (2017). Understanding statistics in psychology. Harlow, UK: Pearson.

Kline, R. (2013). Beyond significance testing: Statistics reform in the behavioral sciences. Washington, DC: APA.

Marôco, J. (2018). Análise Estatística com o SPSS Statistics. ReportNumber: Pêro Pinheiro.

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

Pallant, J. (2016). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS. London: Open University Press.