Quantitative Data Analysis II

Code:

SOCI002

Acronym:

ADQ2

Keywords
Classification	Keyword
OFICIAL	Sociology

Instance: 2024/2025 - 1S

Active?	Yes
E-learning page:	https://moodle.up.pt/
Responsible unit:	Department of Sociology
Course/CS Responsible:	Bachelor in Sociology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
SOCI	50	SOCI - Study Plan	2	-	6	41	162

Teaching language

Portuguese

Objectives

Learn the fundamental concepts and tools of multivariate analysis, focusing mainly on descriptive statistics measures. Apply correctly bivariate analysis techniques in sociological research.

Learning outcomes and competences

• By the end of the semester, students should be able to use multivariate analysis techniques in applied sociological research and develop a critical and reflexive stand-in data analysis. They should also be able to develop their oral and writing skills related to statistics and reinforce competencies associated with research practice.

Working method

Presencial

Program

A – Theoretical foundations
1. Bidimensional variables
1.1. Contingency tables
1.2. Percentaging contingency tables
1.3. Measures of association for qualitative variables
1.4. Regression and correlation
2. Índex  
2.1. Univariate indices
2.2. Complex indices
B –SPSS commands
1. Obtaining contingency tables
2. Measures of association for contingency tables
3. Regression and correlation

Mandatory literature

Bryman, A. e D. Cramer ; Análise de Dados em Ciências Sociais: Introdução às Técnicas Utilizando o SPSS para Windows, Oeiras , Celta Editora, 2003
Healey, J. ; Statistics: A Tool for Social Research, Cengage Learning, 2011
Pestana, M. H. e J. N. Gageiro; Análise de Dados para Ciências Sociais – A Complementaridade do SPSS, Edições Sílabo, 2000
Sampaio, E., M. Ramos e M. Barroso ; Exercícios de Estatística Descritiva para as Ciências Sociais, Edições Sílabo, 2003

Complementary Bibliography

Ferreira, A. e L. Martines; Análise de Dados com SPSS: Primeiros Passos, Escolar Editora, 2007
Maroco, J.; Análise Estatística com Utilização do SPSS, Edições Sílabo, 2007
Pallant, J. ; SPSS Survival Manual – A Step by Step Guide to Data Analysis Using SPSS for Windows , Open University Press, 2001

Teaching methods and learning activities

The learning process is based on theoretical-practical lessons (TP) and tutorial sessions (OT). TP lessons incorporate lectures on topics covered in the syllabus, illustrating with practical examples. They also comprise lab sessions that explore sociology-related exercises with the use of SPSS. OT sessions involve students in the learning process by proposing the resolution of a number of assignments focusing on the techniques approached in the course. Students also benefit from personal attending sessions, in which they can clarify eventual doubts in specific parts of the program. All course materials (e.g., powerpoints, texts, assignments, previous exams’ examples) are available to all students registered in the discipline at SIGARRA.

Software

SPSS 29.0

keywords

Social sciences > Sociology

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	70,00
Trabalho escrito	30,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	48,00
Frequência das aulas	39,00
Trabalho de investigação	75,00
Total:	162,00

Eligibility for exams

According to FLUP regulations. Students have to conclude all the components of the assessment with a minimum mark of 8 values.

Calculation formula of final grade

The final mark corresponds to the weighted average of the exam classification (70%) and the classification obtained in the work developed in the tutorial sessions (30%).

Special assessment (TE, DA, ...)

Students with a special status, namely those covered by the status of Association Manager, Student Athlete, Student Firefighter, Military Student, National Team Athlete, High Performance Sports Practitioner or Student Worker" (Article 14, point 1) are obliged to complete all the assessment components under the terms defined for students in general. They should contact the teacher of the curricular unit to set special deadlines for the distributed assessment component, i.e. for submitting the data analysis work.

Classification improvement

Students can only improve the classification of the exam component.