Module Code - Title:
PX6181
-
ADVANCED QUANTITATIVE DATA ANALYSIS (ISCTE-IUL)
Year Last Offered:
2025/6
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
Rationale and Purpose of the Module:
This module covers advanced statistics methods, such as moderation, mediation, and various regressions models. In addition, it gives students skills in interpreting the results of such analytical methods, and an opportunity to exercise the writing of reports of these methods for scientific purposes.
Syllabus:
The following topics are covered by this module:
1. Linear correlation & Multiple Linear Regression with Hierarchical steps
2. Moderator and mediated models
2.1. Moderated effect: interaction effect
2.2. Mediated effect: chain of effects
3. Modelling moderation using OLS regression
3.1. OLS with main effects and interaction effect
3.2. Quantitative moderator
3.3. Categorical moderator (dummy variable)
3.4. Post-Hoc probing conditional effects in moderation models
3.5. Applying software (SPSS and macro PROCESS)
3.6. Presenting results in a paper
4. Modelling mediation by OLS regression
4.1. Modelling with quantitative mediator and dummy mediator
4.2. Modelling by parametric methods (Baron & Kenny steps and Sobel test) and by nonparametric method (bootstrapping)
4.3. Partial and complete mediation
4.4. Applying software statistics (SPSS and macro PROCESS)
4.5. Presentation of results in manuscripts
5. Modelling by Logistic Regression
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
- identify and distinguish moderation and mediation effects
- design moderated and mediated models
- evaluate the adequacy of Linear Regression and Logistic Regression for testing moderated and mediated models
- evaluate the assumptions of Multiple Linear Regression and Logistic Regression
- demonstrated enhanced knowledge of linear regression models to test moderation and mediation models
- demonstrate knowledge of binary logistic regression
- apply multiple linear regression and binary logistic regression models to test moderation and mediation models
- analyze and interpret the results of different models
- present the results in a report or in a scientific paper
Affective (Attitudes and Values)
na
Psychomotor (Physical Skills)
na
How the Module will be Taught and what will be the Learning Experiences of the Students:
The teaching methodologies are linked with the learning goals in the following manner:
1. Theory practice lessons: knowledge and skills are developed
2. In the laboratory lessons: the skills are developed and practiced.
Students will study individually by reading the suggested bibliography. They will also be guided and supported in their learning by lessons of different kinds:
1. Theory practice lessons: clarification of the conceptual approach of methods; various examples are also presented and discussed by using empirical articles and reports with data analysis¿
2. Laboratory lessons: these sessions allow students to apply the different methods they have learned in class (using SPSS and its macros as well as ModGraph). Students conduct an analysis and describe the results in a paper format.
Evaluation contains individual and group coursework.
Research Findings Incorporated in to the Syllabus (If Relevant):
Prime Texts:
Pampel, F. (2000)
Logistic Regression
, Sage Publications
Other Relevant Texts:
Programme(s) in which this Module is Offered:
MAPSGMTFA - PSYCHOLOGY OF GLOBAL MOBILITY, INCLUSION AND DIVERSITY IN SOCIETY
Semester(s) Module is Offered:
Autumn
Module Leader:
sarah.jay@ul.ie