Quantitative Data Analysis III

Code:

SOCI003

Acronym:

ADQ3

Keywords
Classification	Keyword
OFICIAL	Sociology

Instance: 2024/2025 - 2S

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	53	SOCI - Study Plan	2	-	6	41	162

Teaching language

Portuguese

Objectives

This course is intended for all those who already have some background on basic descriptive analysis of quantitative data. The main goal of the course is to introduce students to statistical inference and to outline the fundamental concepts and tools of inferencial statistics applied to the social sciences. Furthermore, this course explores the features of the software SPSS that can enhance inferencial analysis.

Learning outcomes and competences

By the end of the semester, the student should demonstrate: knowledge of basic techniques of inferencial analysis of quantitative data applied to sociology; familiarity with the computer software SPSS; critical and reflexive stand in data analysis.

Working method

B-learning

Program

1.Basic concepts of statistical inference: sampling and sampling distribution; the normal distribution

2.Estimation and confidence intervals

3.Hypotheses testing and parametric tests

3.1. Tests to compare means

3.1.1. One sample t-test

3.1.2. Paired samples t-test

3.1.3. Two independent samples t-test

3.1.4. One way analysis of variance    
3.1.5. Tests for proportions      

3.2. Nonparametric tests

3.2.1. Binomial test

3.2.2. Chi-square test
3.2.3. Wilcoxon sign test
3.2.4. McNemar test

3.2.5. Mann-Whitney U test

3.2.6. Median test

4.Sampling theory

4.1. Basic concepts in sampling

4.1.1.Random and non-random sampling plans

4.1.2.Statistical representativeness, sampling error and sample confidence level

          4.2.Definition of the dimension of a sample

Mandatory literature

Healey, Joseph F; Statistics. ISBN: 0-534-55785-6
Pestana, Maria Helena; Análise de dados para ciências sociais. ISBN: 972-618-220-4
Vicente, Paula; Sondagens. ISBN: 972-618-136-4

Complementary Bibliography

Blalock Jr., Hubert M.; Social statistics. ISBN: 0-07-005752-4
Elifson, Kirk W.; Fundamentals of social statistics. ISBN: 0-07-115690-9
Levin, Jack; Estatística aplicada a ciências humanas
Siegel, Sidney; Estatística nao-paramétrica para as ciências do comportamento

Comments from the literature

Students will be granted access to course materials in the e-learning platform.

Teaching methods and learning activities

 The contents in the syllabus are approached combining lectures on each topic with lab sessions where students are actively engaged in problem solving exercises and in applying what they have learned during the lectures. Some specific activities are planned with the goal of consolidating skills by mean of a hands-on approach. The course uses e-learning tools available in the software used by the University: moodle-UP.

Software

SPSS 29.0

keywords

Physical sciences > Mathematics > Statistics
Social sciences > Sociology > Socio-economic research

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Teste	20,00
Trabalho escrito	30,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	70,00
Frequência das aulas	39,00
Apresentação/discussão de um trabalho científico	3,00
Trabalho escrito	25,00
Trabalho laboratorial	25,00
Total:	162,00

Eligibility for exams

The assessment will comprise: - 1 written exam - portfolio of 5 exercises to be submitted through the e-learning platform - 1 group task in the e-learning platform Students that fail in the course can try a second assessment according to the school’s calendar of exams. The second round of assessment will comprise a final exam and the submission of a new portfolio of exercises. In order to qualify to the second round of assessment the student must have completed the e-learning group task and the portfolio of 5 exercices. 

Calculation formula of final grade

1 written exam - 50% - portfolio of 5 exercises to be submitted through the e-learning platform - 20% - 1 group task in the e-learning platform - 30%

Examinations or Special Assignments

Does not apply.

Special assessment (TE, DA, ...)

Students with a special status, namely those covered by the status of Association Leader, Student Athlete, Student Firefighter, Military Student, National Team Athlete, High Performance Sports Practitioner or Student Worker" (article 14, point 1) are obliged to carry out 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 assignment. This work can be done individually if the student wishes.
Students may waive the portfolio exercises if they wish, in which case the final exam will have a weighting of 70% in the final grade.

Classification improvement

According to the regulations of the school it only apllys to the exam component.