Research and Multivariate Analysis in Work, Organizational and Personnel Psychology

Recommended Prerequisites

Knowledge of univariate and bivariate descriptive statistics and the Fundamental Principles of Statistical Inference (concepts of significance and p-value). Estimation and tests of hypotheses about the average value of a normally distributed population. Comparison of the average value of two populations (large and small samples).

Teaching Methods

Guided by the principle of "learning by doing", this course focuses on theoretical and practical classes: diversified strategies and activities will be developed, lectures, lectures which include dialogue, group work and discussion, problem-solving and data analysis (using statistical software) and critical analysis of applications of the different techniques studied in class.

Learning Outcomes

Main aim: to expand and improve skills to carry out research in the field of Work, Organisational and Personnel Psychology (PTORH), providing students with more knowledge, reviewing and introducing new concepts and techniques that will boost research development and data analysis.

 

Competencies

• To understand and recognize the specificities of research in PTORH and the most commonly used types of research in the field;

• To choose between different strategies, according to the research objectives and questions;

• To define and design a research strategy, recognizing the steps that it should take;

• To use the statistical language clearly and correctly;

• To understand data analysis when reading scientific articles;

• To understand the basic concepts, models, assumptions, advantages and limitations of multivariate data analysis;

• To use reference tools to analyze data with the techniques discussed in class;

• To interpret the results obtained with the application of these techniq

Work Placement(s)

No

Syllabus

1. Introduction to the processes of research in PTORH: Planning, gathering data and interpreting results.

2. Introduction to multivariate analysis in PTORH: Concepts, Types of methods, Generalized Linear Model, methods often used in PTORH.

3. Multiple linear regression. Model and assumptions. Adjustment of the linear model. Stepwise and multilevel regression. Interaction. PASW applications.

4. Multivariate analysis of variance: assumptions and applications.

5. Exploratory factor analysis. Model and assumptions. Extraction of factors. Rotation. Scores of factors. SPSS applications.

6. Confirmatory factor analysis. Model and assumptions. Specification. Identification. Estimation. Assessment of the adequacy of the model. Applications with AMOS. Structural equation models. Regression analysis and path-analysis. Models with observed and latent variables. Model specification. Assessment of the adequacy of the model. Applications with AMOS.

Head Lecturer(s)

Bruno Cecílio de Sousa

Assessment Methods

Assessment
Pratical work: 10.0%
Critical analysis, public presentation and discussion: 40.0%
Frequency: 50.0%

Bibliography

Aliaga, M. and Gunderson, B. (2006). Interactive Statistics, 3rd Edition, Prentice Hall.

Brown, T. (2006). Confirmatory factor analysis for applied research. NY : The Guilford Press.

Byrne, B. (2010). Structural equation modeling with Amos: Basic concepts, applications and programming (2nd ed.). New Jersey: Lawrence Erlbaum Associates.

Field, A. (2009). Discovering statistics using SPSS (3th ed.). London: Sage Publications.

Hair, J., Black, W., Babin, B., Anderson, R. & Tatham, R. (2006). Multivariate data analysis (6th ed.). New Jersey: Pearson Prentice Hall.

Howell, D. C. (2006). Fundamental Statistics for the Behavioral Sciences (6th ed.). Canada:Thomson Wadsworth.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: TheGuilford Press.

Landy, F., & Conte, J (2010). Work in the 21 st Century – An Introduction to Industrial and Organizational Psychology (3rd ed.). John Wiley & Sons.