2025
Sociological Quantitative Data Analysis
Name: Sociological Quantitative Data Analysis
Code: SOC12051M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Sociology
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
The main objective of the course is to introduce the student to the main methods and techniques in depth
quantitative analysis of data. Its main objectives are: to provide students with a more in-depth training with a
focus on quantitative analysis from a social perspective; empower with practical knowledge from data and
tools for quantitative analysis in advance interdisciplinary research of contemporary societal problems, so that
may apply an approach for quantitative analysis in developing their dissertations or Master reports. Specific
learning objectives include: applying tools of statistical analysis to the study of societal problems; to
understand how to use empirical data; to know, to locate, to collect and process primary data sources (national
and international surveys households, ESS, Eurobarometer, among others); interpret statistical results, to
formulate and write an essay supported on the evidence
quantitative analysis of data. Its main objectives are: to provide students with a more in-depth training with a
focus on quantitative analysis from a social perspective; empower with practical knowledge from data and
tools for quantitative analysis in advance interdisciplinary research of contemporary societal problems, so that
may apply an approach for quantitative analysis in developing their dissertations or Master reports. Specific
learning objectives include: applying tools of statistical analysis to the study of societal problems; to
understand how to use empirical data; to know, to locate, to collect and process primary data sources (national
and international surveys households, ESS, Eurobarometer, among others); interpret statistical results, to
formulate and write an essay supported on the evidence
Contents
Sampling
* Basic notions on sampling and estimation.
* Main steps about planning a sampling design and selection of sampling units.
* Simple random sampling, systematic random sampling and stratified random sampling.
* Methods for data collection in survey sampling.
Multivariate Statistics
* Principal component analysis.
* Factor analysis.
* Discriminant analysis.
* Clusters analysis.
Categorical Data Analysis
* Contingency rables.
* Logistic regression.
* Basic notions on sampling and estimation.
* Main steps about planning a sampling design and selection of sampling units.
* Simple random sampling, systematic random sampling and stratified random sampling.
* Methods for data collection in survey sampling.
Multivariate Statistics
* Principal component analysis.
* Factor analysis.
* Discriminant analysis.
* Clusters analysis.
Categorical Data Analysis
* Contingency rables.
* Logistic regression.
Teaching Methods
A mixture of theoretical and practical lectures and with support of e-learning tools. Introductory concepts are
given using real examples of different areas of applications to show the relevance of programmatic contents.
Lectures: 3 h/week throughout parts of the semester. PC-exercises: 3 hours per week through parts of the
semester. Students will be asked to complete a set of 4 short assignments for the course (40%). These
assignments will be meant to introduce students to data, statistical analysis and quantitative techniques.
Students are welcome to work with one another on completing the assignments, but all final work turned in
should reflect the students own efforts. The evaluation will be based on participation according to the different
modules lectured and have a proportional weighting in calculating the final grade of the student to the number
of contact hours for each module. Final Exam: (2 h written exam) (60%). The final exam will cover all contents.
given using real examples of different areas of applications to show the relevance of programmatic contents.
Lectures: 3 h/week throughout parts of the semester. PC-exercises: 3 hours per week through parts of the
semester. Students will be asked to complete a set of 4 short assignments for the course (40%). These
assignments will be meant to introduce students to data, statistical analysis and quantitative techniques.
Students are welcome to work with one another on completing the assignments, but all final work turned in
should reflect the students own efforts. The evaluation will be based on participation according to the different
modules lectured and have a proportional weighting in calculating the final grade of the student to the number
of contact hours for each module. Final Exam: (2 h written exam) (60%). The final exam will cover all contents.
Teaching Staff
- Lidia Patricia Santos Amaral Tomé [responsible]
Certificações
