Personnel, Work and Organizational Psychology

The modular part of the course will cover the following topics: - Preliminary data analysis: normality, extreme cases, outliers (Settembre/Ottobre) - Simple and multiple linear regression(Ottobre) - Exploratory Factor analysis (Ottobre/Novembre) - Structural equation models (Novembre) - Analysis of variance (Dicembre) - Cluster analysis (Dicembre)

To follow the lessons in a productive way it is essential that the students have a knowledge of the descriptive statistics (position indexes, central trend indexes, dispersion indices, correlation), the basics of the inferential statis-tics (hypothesis testing, random variables, main tests statistics), psychometric models (classical test theory, IRT), the validity and reliability of psychological measures. It is also important that students know the main tools for the collection of psychological data (questionnaires, tests, interviews) and the main psychological research techniques (experimental, quasi-experimental, correlational research).

The texts for the program are unique to the module and to the laboratory a) Barbaranelli, C. (2007). Analisi dei dati. II edizione. Milano: Led. (ch. 1, 2, 3,4, app. 1 e 2). This text describes the theoretical topics related to: - Preliminary data analysis: normality, extreme cases, etc. - Simple and multiple linear regression - Exploratory Factor Analysis - Analysis of variance b) Barbaranelli, C. (2006). Analisi dei dati con SPSS: Le analisi multivariate. Milano: Led. (ch. 1, 2 e 3). c) Barbaranelli, C. e D'Olimpio, F. (2007). Analisi dei dati con SPSS: Le analisi di base. Milano: Led. (ch. 1, 2, 3, 4 e 6). The two texts are useful for applications through the SPSS program of the main data analysis techniques examined in the theoretical part. d) Slides (pdf) and additional material presented in class. This material will be available on the elear-ning2.uniroma.it website. This is useful material to follow the lessons and mandatory for the exam.

The topics of the course will be presented through lectures, urging an active role on the part of the students. At the end of each topic presented in the lectures will be conducted exercises in order to consolidate the learning of the topics covered in lectures.

Frequency is optional. However, even in the light of the many practical exercises planned during the cour-se, it is strongly recommended

The assessment test is unique for the module and for the laboratory. The objective of the final test is to evaluate the knowledge of the theoretical models of data analysis and applications through software. There are no intermediate evaluation tests during the course. The intermediate tests envisaged, in fact, are not evaluative, but rather they aim at verifying "in itinere" the learning process. There will be a test (exam) at the end of the course, and 4 "recovery" tests during the current academic year, according to the academic calendar prepared by the course of study. The evaluation test (exam) includes a written test consisting of two parts: First part: 15 open-ended questions (2 points for each correct answer) Second part (all the exercises have open answers): 3 exercises on programming in MPLUS language (for a maximum of 12 points) 4 exercises on the output interpretation of the MPLUS program (for a maxi-mum of 4 points) 15 exercises on programming in SPSS language (for a maximum of 15 points) The student has 90 minutes to complete the test. It is possible an oral integration of the writing (at the request of the teacher or the student) which will deepen what emerged from the correction of the written part. The final grade (expressed in thirtieths) is equal to the average of the grades obtained in the 2 parts of the test described in previously. In the case of oral examination, the final grade obtained in writing can be increased or decreased according to the performance highlighted by the student.

General textbooks Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley. Hoyle, R. H. (2012) , Handbook of Structural Equation Modeling. New York: The Guilford Press. Kline, R. B. (2015) Principles and Practice of Structural Equation Modeling (3rd Edition). The Guilford Press Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Second Edition). Mahwah, NJ: Lawrence Erlbaum Associates. Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York: Routledge. Textbooks on Mplus: Byrne, B. (2012). Structural Equation Modeling with Mplus. Basic Concepts, Applications, and Programming. Routledge Geiser, C. (2010). Data Analysis with Mplus. NY: The Guilford Press Wang, J. and Wang, X. (2012). Introduction, in Structural Equation Modeling: Applications Using Mplus. John Wiley & Sons, Ltd, Chichester, UK.

The topics of the course will be presented through lectures, urging an active role on the part of the students. At the end of each topic presented in the lectures will be conducted exercises in order to consolidate the learning of the topics covered in lectures.