Teaching code

4S007370

Credits

6

Coordinator

Margherita Pasini

Language

Italian

Scientific Disciplinary Sector (SSD)

M-PSI/03 - PSYCHOMETRICS

Courses Single

Authorized

Moodle Seminars 0

The teaching is organized as follows:

Learning objectives

GENERAL OBJECTIVES:
- Understand the main research methods, as well as data collection and analysis techniques (qualitative and quantitative) used in psychological and educational fields.
- Apply the main research methods, as well as data collection and analysis techniques (qualitative and quantitative) used in psychological and educational fields.

SPECIFIC OBJECTIVES:
The course offers the opportunity to acquire the necessary skills to conduct basic statistical analyses to support research in psychology (also through the use of open-source statistical packages). Topics such as the use of statistics for descriptive purposes, inferential analyses, and correlational models will be covered. Basic skills related to the design, execution, analysis, interpretation, and reporting of research on psychological topics, particularly in organizational contexts, will be acquired. The teaching aims to achieve the following specific training objectives:
- develop knowledge and the ability to use data collection, analysis, and interpretation techniques and tools, particularly quantitative ones, used in psychological and educational fields;
- develop knowledge and skills useful for evaluating the effectiveness of interventions;
- promote the ability to develop research paths, particularly in the fields of organizational psychology and educational psychology.

Prerequisites and basic notions

The knowledge acquired during the first year of the course, and those provided in the "APsyM workshop on quantitative data analysis", a not mandatory 3-credit lab proposed during the first year, second semester, are prerequisites.

Program

Module 1: Introduction
- The scientific method
- Variables and measurement
- Collect, organise and manage data
Module 2: Describe and synthesize data
- Descriptive statistics
- Central tendency
- Variability
- Central tendency and variability in research articles
Module 3: Introduction to Hypothesis Testing
- Core logic of hypothesis testing
- The hypothesis-testing process
- One-tailed and Two-tailed hypothesis tests
- Hypothesis tests in research article
Module 4: Sample differences for scores and for frequencies
- Independent sample t-test
- Paired sample t-test
- t-test in research articles
Module 5: Introduction to t tests and Analysis of Variance- Basic logic of analysis of variance
- Hypothesis testing with analysis of variance
- Analysis of variance in research articles
Module 6: Correlation and Regression
- The correlation coefficient
- Significance of correlation coefficient
- Correlation and causality
- Correlation in research article
- Simple linear regression analysis
- Multiple linear regression analysis
- Regression in research articles
Module 7: Advanced techniques (introduction)
- Effect size
- Meta-analysis
- Confidence intervals
- Power analysis
Suggested Handbooks:
Howitt, D. & Cramer, D. (2011). Introduction to Statistics in Psychology (7th Ed.). London: Pearson Education.
or
Welkowitz, J., Cohen, B. H., & Ewen, R. B. (2006). Introductory statistics for the behavioral sciences. John Wiley & Sons.

Bibliography

Didactic methods

Lessons will take place in the computer room.
Since the computer room has a limited number of places, the lessons will also be LIVE STREAMING, to allow students to participate also from other locations. The zoom link will be communicated on the moodle page of the course.
The exercises WILL NOT BE REGISTERED.
Teaching methods are expected to encourage proactive involvement of students and high level of participation, thus, shall involve interactive lectures and independent learning. The interactive learning component will consist of lecture sessions, classroom exercises, and discussions. The independent learning component, on the other hand, shall include such exercises as independent reading and doing individual assignments.
It is envisaged that students will be guided to read, think, solve problems and actively participate in the learning process.

Learning assessment procedures

A written assessment, using the computer, will be proposed. Students should be able to use a spreadsheets (Microsoft Excel, free for students, or Apache OpenOffice Calc) and JAMOVI, an open source statistical packages. Starting from a given dataset, students should perform some analysis in agreement with the program of the course, and discuss the results.
Open-ended or closed-ended questions aimed at verifying theoretical knowledge will also be possible.
Subject to agreement with students, the exam may be an orale exam.

Evaluation criteria

The score in the exercises/questions will consider theoretical skills, technical skills, and the ability to interpret the results.

Criteria for the composition of the final grade

The final evaluation will be the sum of the scores of each exercise/question.

Exam language

Italiano