Quantitative Data Analysis I

Code:

SOCI001

Acronym:

ADQ1

Keywords
Classification	Keyword
OFICIAL	Sociology

Instance: 2015/2016 - 2S

Active?	Yes
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	69	SOCI - Study Plan	1	-	6	65	162

Teaching language

Portuguese

Objectives

Learn the fundamental concepts and tools of descriptive statistics applied to sociological research. Explore basic features of data analysis using the SPSS statistical software package, such as editing and transforming data and describing variables.

Learning outcomes and competences

By the end of the semester, students should demonstrate knowledge of basic techniques of quantitative univariate data analysis applied to sociology 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 to research practice.

Working method

Presencial

Program

A – Theoretical foundations 1. Introduction to statistics 1.1. Main concepts and problems 1.2. Data sources 1.3. Descriptive statistics and statistical inference 1.4. Defining variables 1.5. Introduction to data gathering techniques 1.6. Statistical applications in social sciences 2. Exploring unidimensional variables 2.1. Frequency tables 2.2. Graphing data 2.3. Measuring central tendency 2.4. Measuring dispersion 2.5. Measuring concentration 2.6. Assimetry and kurtosis B – Introduction to SPSS analysis 1. Introduction to the software 2. Creating a data base 2.1. Creating variables 2.2. Transforming variables 3. Exploring unidimensional variables 3.1. Frequency tables and charts 3.2. Descriptive statistics.

Mandatory literature

Bryman, A. e D. Cramer ; Análise de Dados em Ciências Sociais: Introdução às Técnicas Utilizando o SPSS para Windows, 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

Complementary Bibliography

Ferreira, A. e L. Martines; Análise de Dados com SPSS : Primeiros Passos, Escolar Editora, 2007
Sampaio, E., M. Ramos e M. Barroso ; Exercícios de Estatística Descritiva para as Ciências Sociais, Edições Sílabo, 2003
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 which 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 (powerpoints, texts, assignments, previous exams’ examples) are available to all students registered in the discipline at SIGARRA.

Software

SPSS 18.0

keywords

Social sciences > Sociology

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	75,00
Participação presencial	25,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	75,00
Frequência das aulas	67,00
Trabalho de investigação	47,00
Total:	189,00

Eligibility for exams

Students are required to attend to a minimum of 75% of all classes (lectures, lab sessions and tutorial sessions). Students have also to conclude all the components of 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 (75%) and the classification obtained in the work developed in the tutorial sessions (25%).

Examinations or Special Assignments

Does not apply.

Special assessment (TE, DA, ...)

According to FLUP's regulations.

Classification improvement

Students can only improve the classification of the exam component. The final mark corresponds to the weighted average of the exam classification (75%) and the classification obtained in the work developed in the tutorial sessions (25%).