Quantitative Methods and Statistics
Code: 104244 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| 2503710 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
There are no prerequisites
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
- 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.
- KM40 (Knowledge) Introduce the main sources of scientific information and documentation related to territorial and environmental processes in a study.
- SM34 (Skill) Correctly apply basic and multivariate statistical methods in a practical case.
- SM35 (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 essential 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
4.4 Inference in contingency tables and regression
Block 5. Quantitative methods for the analysis of the spatial dimension
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 | CM26, KM40, SM34, SM35 |
| Type: Supervised | |||
| Completion of practices in the computer lab | 22 | 0.88 | CM26, KM40, SM34, SM35 |
| Tutorials | 3 | 0.12 | CM26 |
| Type: Autonomous | |||
| Completion of the course practices | 60 | 2.4 | CM26, SM34, SM35 |
| Personal study, preparation tests | 15 | 0.6 | CM26, KM40, SM35 |
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.
According to the schedule, the instructor will reserve about 15 minutes of a session for students to complete the teaching and course evaluation surveys.
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, SM34, SM35 |
| Participation and attendance | 10% | 0 | 0 | CM26, SM34 |
| Regular LAB exercices | 10% | 0 | 0 | CM26, KM40, SM34, SM35 |
| Written exam | 50% | 3 | 0.12 | CM26, KM40, SM34, SM35 |
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 3.5 in the objective test and an average course grade of 5 to pass the course.
- Students who have only completed 1/3 of the evaluable activities will be graded as "Not evaluable."
- Activities not submitted or completed on the indicated date will be graded as "Not Submitted."
- If the student engages in any irregularity that could lead to a significant variation in the grade of an evaluation activity, this activity will be graded with 0, regardless of any disciplinary process that may be initiated. If several irregularities occur in the evaluation activities of the same course, the final grade for that course will be 0.
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. The procedure will be through email. The student will motivate their review request in their message.
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.
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).
Language list
| 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 |