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Quantitative Methods and Statistics

Code: 104244 ECTS Credits: 6
2025/2026
Degree Type Year
Geography, Environmental Management and Spatial Planning OB 2

Contact

Name:
Antonio Lopez Gay
Email:
antonio.lopez.gay@uab.cat

Teaching groups languages

You can view this information at the end of this document.


Prerequisites

Language proficiency: To take this course, students must have a B2 level or higher in Catalan and/or Spanish.


Objectives and Contextualisation

Quantitative Methods and Statistics is taught the Second Course of the Degree in Geography, Environment and Planning.

The general objective of the course is to provide students with the fundamental tools and knowledge of statistics so they can apply quantitative techniques in the design and analysis of data related to Geography. This content will thus facilitate the understanding of data specific to the geographical discipline as well as decision-making based on quantitative analysis, preparing students to face professional and academic challenges.

The specific objectives of the course are:

- To provide students with the fundamental tools for data management: methods for the collection, organization, analysis, and presentation of data related to Geography.

- To familiarize students with statistical terminology.

- To equip students with the skills to use computational tools for basic statistical analysis.

- To introduce the fundamental concepts of descriptive and inferential statistics.

- Regarding descriptive statistics, to train students in the use of measures of central tendency and dispersion applied to geographical data, as well as to introduce methods of representation.

- Regarding inferential statistics, to introduce the concepts of correlation and regression, and to provide tools to interpret and analyze the relationship between variables using linear regression methods.

- To train students to decide which statistical method is appropriate based on the data and the objectives of the research.

- To introduce statistical methods to solve spatial issues, such as indicators of segregation, location, and others specific to spatial statistics.

- To prepare students to understand, interpret, and argue the results of quantitative and statistical analysis.


Learning Outcomes

  1. CM26 (Competence) Interpret the statistical results obtained in a study through data analysis in order to make judgements that include a reflection on relevant social, scientific or ethical issues.
  2. KM40 (Knowledge) Introduce the main sources of scientific information and documentation related to territorial and environmental processes in a study.
  3. SM33 (Skill) Correctly apply basic and multivariate statistical methods in a practical case.
  4. SM33 (Skill) Correctly apply basic and multivariate statistical methods in a practical case.
  5. SM34 (Skill) Use basic and instrumental statistical software for the input and identification of survey data, and for their transformation and statistical analysis.

Content

Block 1. Data sources, types of variables, and basic tools in Excel

Block 2. Univariate statistics

2.1 Measures of central tendency and dispersion

2.2 Variable transformations

Block 3. Bivariate statistics

3.1 Relationship between variables: correlation and linear regression

3.2 Relationship between variables: contingency tables

Block 4. Introduction to statistical inference

4.1 Basic concepts in inference

4.2 Confidence intervals

4.3 Hypothesis testing and applications to contingency tables and regression

Block 5. Quantitative spatial analysis: segregation, localization, and spatial autocorrelation


Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Directed      
Master classes and carrying out of directed practices in the computer lab 47 1.88
Type: Supervised      
Completion of practices in the computer lab 22 0.88
Tutorials 3 0.12
Type: Autonomous      
Completion of the course practices 60 2.4
Personal study, preparation tests 15 0.6

Types of activities

The course is structured around directed, supervised, and autonomous activities where students will be able to acquire the course content with the in-person support of the instructor at various levels.

- Managed activities: include theoretical sessions and the development of practical exercises, led by the instructor.

- Supervised activities: in-person supervision of practical sessions, where students will independently, but under supervision, develop various exercises.

- Autonomous activities: study of theoretical content and resolution of practical exercises.

Innovative teaching methodologies

Participatory and interactive dynamics are used during sessions to reinforce the content as it is taught. Throughout the course, students participate in the collaborative construction of a database based on contextual variables, which is later used to apply statistical analysis techniques covered in class.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Assessment

Continous Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Partial LAB assignments 30% 0 0 CM26, KM40, SM33, SM34
Participation and attendance 10% 0 0 CM26, KM40, SM33, SM34
Regular LAB exercices 10% 0 0 CM26, KM40, SM33, SM34
Written exam 50% 3 0.12 CM26, SM33

This subject does not incorporate single assessment.

Assessed activities:

- An objective knowledge test conducted through two written exams. Weighting factor: 50% of the final grade. Each exam represents 25% of the final grade.

- Partial LAB exercises (submission of more comprehensive practical dossiers reinforcing the course content). Weighting factor: 30% of the final grade.

- Regular LAB exercises (submission of practical work developed in the classroom). Weighting factor: 10% of the final grade.

- Participation and attendance. Both items will be measured through different activities, such as interactive activities like Kahoot. Weighting factor: 10% of the final grade.

Evaluation criteria:

- The final grade of the course will be the weighted average of all activities subject to evaluation.

- The final grade of the written test will be the average of the two partial exams.

- It is necessary to obtain a minimum of 4 in the objective test and an average course grade of 5 to pass the course.

- Activities not submitted or completed on the specified date will be marked as "Not presented" and graded with a zero.

- Students who have only completed 1/3 of the evaluable activities will be graded as "Not evaluable."

- The instructor reserves the right to include oral assessments, either general or specific, in order to verify the authorship and understanding of the contents of any assessable activity. These assessments will not alter the weighting of the corresponding activities, but they may affect the final grade if serious inconsistencies are detected.

- If a student commits any irregularity (such as plagiarism or the use of unauthorized tools during an exam) that may affect the grading of an assessment, that activity — or the entire course, if the irregularity is deemed sufficiently serious — will receive agrade of zero, regardless of any disciplinary procedure that may be initiated.

Review procedure:

All evaluated activities will be subject to grade review. Students will be informed via the Moodle classroom of the corresponding date in each case. 

Resit eximanation:

The resit eximanation will be done through a written test.

The grade of one of the partial practical exercises can be recovered, only if it has been submitted.

Regular practical exercises cannot be recovered, as they are considered exercises that track the course progress.

 

Use of Artificial Intelligence:

For this course, the use of artificial intelligence (AI) technologies is permitted exclusively as support for working with material covered in class or for solving statistical formulations in practical exercises. Under no circumstances may these tools be used to interpret results or analyze patterns derived from data. Students must clearly identify which parts were generated using AI tools, specify the tools used, and include a critical reflection on how these influenced the process and final outcome of the activity. Lack of transparency regarding the use of AI in an assessed activity will be considered academic dishonesty and may result in partial or total loss of the grade for the activity, or more serious penalties in severe cases.


Gender criteria: Data analysis and problem-solving will take into account, where applicable, social and gender differences. Students are encouraged to use non-sexist language. The UAB guidelines (see "Ten tips for non-sexist language use") can be helpful.


Bibliography

BARDINA, Xavier; FARRÉ, Mercè; LÓPEZ ROLDAN, Pedro. (2005). Estadística: un curs introductori per a estudiants de ciències socials i humanes. Volum 2 descriptiva exploratòria bivariant. Introducció a la inferència. Bellaterra: Servei de Publicacions Universitat Autònoma de Barcelona, Col·lecció Materials 166. (*)

BURT, James E; BERBER, Gerald. (1996). Elementary Statistics for Geographers. London: Guilford press.  (Cap.  3, 5, 7,8, 9).

EBDON, David. (1982). Estadística para geógrafos. Barcelona: Oikos Tau.( pp 18-23, 28-33, 51-68, 129-142, 168-175, 182-212, 240-249).

FARRÉ, Mercè. (2005). Estadística: un curs introductori per a estudiants de ciències socials i humanes. Volum 1 descriptiva i exploratòria univariant. Bellaterra: Servei de Publicacions Universitat Autònoma de Barcelona, Col·lecció Materials 162. (*)

FREEDMAN, David; PISANI, Robert; PURVES,Roger; ADHIKARI, Ani.  (1993). Estadística. Segunda edición. Barcelona: Antoni Bosch editor. (Partes:  II, III , VI ( cap. 23,  pp. 459-480) y VIII. (cap. 26, pp. 519-550)).(*)

ILLOWSKY, Barbara, DEAN,Susan  (2022) Introduccion a la estadística. OpenStax. Rice University (Cap. 6,8,9,10,12) https://openstax.org/details/books/introductory-statistics (*)

López-Roldán, Pedro.; Fachelli, Sandra. (2015). Metodología de la Investigación Social Cuantitativa. Bellaterra (Cerdanyola del Vallès): Dipòsit Digital de Documents, Universitat Autònoma de Barcelona. : http://ddd.uab.cat/record/129382.  (Parte II, Cap. 1; Parte III cap 3;Parte III cap 6, pp. 1-23; Parte III cap 4).(*)

MARQUÉS, Felicidad. (2009). Estadística descriptiva a través de EXCEL. México D.F.: Alfaomega grupo editor S.A.

MARSH, Catherine (1990). Exploring Data.An Introduction to Data Analysis for Social Scientists. Oxford: Polity Press.  Cap 1 i 2 i Part II.

PEÑA SANCHEZ DE RIVERA, David; ROMO URROZ, Juan José. (1997). Introducción a la estadística para las ciencias sociales. Madrid: McGraw-Hill Interamericana de España.

QUICK, Thomas (2021) Excel 2019 for Social Science Statistics. A guide to solving Practical Problems.Second Edition. Switzerland. Springer

 https://link-springer-com.are.uab.cat/book/10.1007/978-3-030-64333-1

RAJARETNAM, T (2016) Statistics for Social Sciences, Sage, NY  (cap 4,5,6,7,8,11) https://ebookcentral-proquest-com.are.uab.cat/lib/uab/detail.action?pq-origsite=primo&docID=5770011

RASO, José Maria; MARTÍN VIDE, J.I.; CLAVERO, Pedro. (1987). Estadística básica para Ciencias Sociales. Barcelona. Ariel.  Caps:  4 (pp. 77-92 ) i 6.

ROGERSON, Peter A. (2020). Statistical Methods for Geography. 5th Edition. Sage. London. (Cap 2.5.7 i 8).

SANTANA LEITHER, Andres (2017) Análisis cuantitativo: técnicas para describir y explicar en Ciencias Sociales. Barcelona: Editorial UOC. https://elibro.net/es/lc/uab/titulos/57723

 

 (*) Main references 

Gender issues have been taken into account in the list of references.


Software

Excel will be the software used throught the course (we do not have SPSS license).


Groups and Languages

Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.

Name Group Language Semester Turn
(PLAB) Practical laboratories 1 Catalan first semester morning-mixed
(PLAB) Practical laboratories 2 Catalan/Spanish first semester morning-mixed
(TE) Theory 1 Catalan first semester morning-mixed
(TE) Theory 2 Catalan/Spanish first semester morning-mixed