26259 - Applied Statistics

Learning outcomes

By the end of the course, students will know and understand the main research methods and data analysis applied to work and organizational psychology. In particular, students will be able to identify appropriate measures, to use statistical tools for data analysis, and to present results.

Course contents

Foundations: The research process and ethics in research. Research design. Types of designs and internal validity. Sampling: Size and representativeness. Cultural and contextual issues. External validity.

Choosing, adapting, and/or developing the measurement instruments: Types of measures in recruitment, selection and performance. The role of culture and diversity.

Psychological measurement and levels of measurement or scales: nominal, ordinal, interval, and ratio scales.

Classical Test Theory: true score theory, sources of error in psychological testing and the concept of reliability.

Essential statistics: descriptive statistics, measures of central tendency, measures of variability, the normal curve model and non-normal distributions.

Inferential statistics and the logic of significance testing. Parametric tests and assumptions: General Linear Model and analysis of variance, correlation, multiple regression, mediation and moderation, factor analysis. Multicultural approach: introduction to measurement equivalence.

Non-parametric tests: chi-square, linear correlation and regression.

The validation process: test development and item analysis, validity and source of validity evidence. Statistical methods for validation studies: validity and reliability analyses. Essentials of norms and test score interpretation.

Readings/Bibliography

The course will be based on a textbook written by the teacher and provided to all the students via the course website.

Since the course will be mainly based on practical statistical applications, students might deepen their theoretical knowledge through the following references:

Bernstein, I.H. & Rowe, N. A. (2001) Statistical Data Analysis Using Your Personal Computer. Sage.

Cohen, J. & Cohen, P., West, S. G. & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences, 3rd ed. Hillsdale, NJ, LEA.

Coolican, H. (2014). Research methods and statistics in psychology. Psychology Press.

Hair, JF; Anderson, RE; Tatham, RL; Black, WC (2005). Multivariate data analysis. Prentice Hall International.

Howitt, D. & Cramer, D. (2005). Introduction to Statistics in Psychology. Pearson Education.

Pallant J (2001). SPSS survival manual. Philadelphia, Open University Press.

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

Todman, J., & Dugard, P. (2007). Approaching multivariate analysis. An introduction for Psychology. New York: Psychology Press.

Teaching methods

Teacher’s presentations and explanations about the involved methods. Discussion about research questions, research designs, hypothesis elaboration, and hypothesis testing.

Guided analyses performed individually or in small groups by 2-3 students in the laboratory using the statistical package IBM- SPSS. Interpretation of statistical outputs and discussion of research papers and case studies.

Homework for practicing the methods and techniques taught using data provided by the teacher.

The attendance of the classes is mandatory.

Assessment methods

A) Individual or group (2-3 students) written assignment based on hypothesis testing, analysis of a data set (provided by the teacher) and report of the results.

B) Individual oral exam consisting of a power presentation of the written assignment and its discussion in front of the class.

A total 0-30 grade will be attributed, which will be then averaged with the grade obtained in the other module of the same course.

The deadline for delivering the written assignment to the teacher and the date of the oral presentation of the results will be established during the course.

There is no need to register for the exam.

Teaching tools

Power point presentations, journal papers, SPSS databases.

Office hours

See the website of Paola Gremigni