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General information

Academic year:
2025
Description:
In a world of data, critical knowledge of the processing, analysis and interpretation of results is essential. The global environment for obtaining and analysing primary data is explored, with a focus on descriptive statistics, variable classification, database structure analysis, and univariate and bivariate statistical inference. New perspectives in data analysis are introduced using other methods such as quantitative text analysis and social network analysis. These different methods of analysis provide structured input for informed decision-making.
ECTS credits:
6
Course coordinator:
Marta Solans Margalef

Groups

Group EA

Duration:
One-semester, 2nd semester
Teaching staff:
Marta Solans Margalef
Language of the classes:
English (100%)

Syllabus

1. Module 1. Survey methodology

2. Module 2. Univariate descriptive statistics

3. Module 3. Univariate inferential statistics

4. Module 4. Bivariate descriptive and inferential analysis

5. Module 5. Introduction to the linear regression model

Activities

Activity type Hours with a teacher Hours without a teacher Virtual hours with a teacher Total
Analysis / case study 9,50 9,00 0 18,50
Assessment test 2,00 19,00 5,00 26,00
Solution of exercises 15,00 22,00 0 37,00
Theory class 19,00 28,00 0 47,00
Teamwork 8,00 8,50 5,00 21,50
Total 53,50 86,50 10,00 150

Bibliography

  • Groves, Robert (2009). Survey Methodology (2nd). Hoboken, N.J: Wiley. Catàleg
  • Danielle J. Navarro and David R (2025). Learning Statistics with jamovi: A Tutorial for Beginners in Statistical Analysis. Cambridge, UK: Open Book Publishers. Recuperat , a https://doi.org/10.11647/OBP.0333 Catàleg
  • The Jamovi project. Jamovi tutorial. Recuperat , a https://docs.jamovi.org/
  • Spiegelhalter D (2019). The art of statistics: Learning from data. UK: Penguin books.

Assessment and Grading

Assessment activities:

Description of the activity Assessment Activity % Remediable subject
Task 1. Univariate inferential statistsics Online evaluation task using Jamovi and a real dataset, in which students (in groups of 4) will have to perform and interprete univariate inferential analysis. 10 No
Task 2. Bivariate analysis Online evaluation task using Jamovi and a real dataset, in which students (in groups of 4) will have to perform and interprete descriptive and inferential bivariate analysis. 10 No
Task 3. Linear regression model Online evaluation task using Jamovi and a real dataset, in which students (in groups of 4) will have to perform and interprete a linear regression model. 10 No
Survey design project In groups of 4, students will have to define a research objective and hypotheses, select a population of study and sampling method, design a questionnaire, select a data collection method and deliver their questionnaire, curate data, and analyse it with Jamovi. The findings will be presented in an oral presentation. 25 No
Participation in the 'Global Week' activity Participation in the 'Global Week' activity related to Data Analysis. 5 No
Final exam Written final exam including topics from modules 1 to 5. 40 Yes

Grading

A minimum score of 4/10 in the final exam and an overall mark (based on a weighted average of all evaluation tasks) of minimum 5/10.

For those not reaching a 4/10 in the final exam or a 5/10 in the overall mark, there will be a resit examination. Attending the final exam is a requirement to take the resit examination.

Specific criteria for the "No show" grade:
Those students not attending the final exam will be graded as "Not Presented". Attending the final exam is a requierement to take the resit examination.

Single Assessment:
Overall mark based on the survey project (25% of the final mark) and a final exam (75% of the final mark).

The final exam will include a written part and a practical part (use of Jamovi). A minimum mark of 4/10 in the exam is required and there will be a resit examination. Attending the final exam is a requirement to take the resit examination.

Minimum requirements to pass:
A mininum score of 4/10 in the final exam and weighted average of all evaluation tasks of minimum 5/10.

Mentorship

In-person or online meetings can be arranged through direct email to the teacher of the subject.

Communication and interaction with students

All instructions to follow the lectures will be provided during classes and relevant information will be also posted in the subject's forum.

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