Degree | Type | Year | Semester |
---|---|---|---|
2500257 Criminology | OB | 2 | 1 |
• No previous training in statistics is required. However, knowledge in mathematics will help students to effectively learn the course contents.
Quantitative Research Methods is a course designed to introduce students to statistical data analysis as a tool for criminological research.
The Degree in Criminology trains students to use criminological methods and techniques to analize data about conflict, crime, and social control. To that end, the course objectives are:
1) Learning the basic statistical concepts of descriptive statistics.
2) Acquiring autonomy in the use of software for quantitative data analysis and its application in criminology.
3) Performing quantitative data analyses, both descriptive and inferential.
4) Identifying and knowing how to apply these concepts in criminological research projects.
This course is part of the degree's research methods itinerary. On the one hand, it is a continuation of the courses "Scientific research in criminology", and "Criminological Data Sources", in the first year,in which the logic of the research process in social sciences and criminological data is presented. On the other hand, this course has continuity in "Data Analysis", taught in the second semester, which deals with multivariate analysis.
PART I. THE DATA ANALYSIS SOFTWARE
Unit 0: The data, its treatment and the program
0.1. The graphical user interface
0.2. How the code is structured in the R language
0.3. Interpreting and understanding warnings and error messages
0.4. Objects and classes
0.5. The structure of functions
0.6. Doubts, documentation, and how to solve problems
PART II. DATA PROCESSING
0.7. Structure and dimensionality
0.8. Reading data
0.9. Selecting subsets
0.10. Transformations, recodifications
0.11. Calculating variables
0.12. Logical operations
PART III. DESCRIPTIVE ANALYSIS
Unit 1. Univariate descriptive statistics
1.1. What is descriptive analysis
1.2. Fundamentals of descriptive statistics
1.2.1. The concept of measurement and the levels of measurement
1.2.2. The data and the data set
1.2.3. Observations and variables
1.3. Frequency distribution tables
1.3.1. Absolute, relative and cumulative frequencies
1.3.2. Bar charts and sector diagrams
1.4. Summary measures of the distribution of a variable
1.4.1. Central tendency measures: mode, median and average
1.4.2. Position measures: quantiles
1.4.3. Dispersion measures: range, variance, standard deviation
1.4.4. The histogram
1.4.5. The boxplot
1.5. The normal distribution
Unit 2. Descriptive bivariate analysis
2.1. The analysis of contingency tables
2.1.1. Presentation and nomenclature
2.1.2. The different parts of a contingency table
2.1.3. Stacked bar charts
2.2. Comparing means
2.2.1. Descriptive statistics by groups
2.2.2. Grouped boxplots
2.3. Correlation
2.3.1. Concept and calculation
2.3.2. Scatterplots
PART IV. FUNDAMENTALS OF INFERENTIAL STATISTICS
Unit 3. Statistical sampling
3.1. Sample, sampling frame and population
3.1.1.Types of sampling
3.1.2. Sample size and sample error
Unit 4. Hypothesis testing
4.1. The logic of hypothesis testing. Null and alternative hypothesis
4.2. The conditions of application of a test
4.3. The Chi2 test for contingency tables
Statement
Teaching will be mixed: lectures will be online and seminars face-to-face.
Teaching and assessment methods may be submitted to change in case health authorities impose restrictions to access to campus.
Before the beginning of the course, a detailed schedule of the weekly activities will be published in the Virtual Campus. The activities performed during the course are the following:
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Exam | 5 | 0.2 | 2, 5, 1, 7 |
Lectures | 19.5 | 0.78 | 2, 5, 1 |
Workshops | 19.5 | 0.78 | 2, 5, 1 |
Type: Autonomous | |||
Exam preparation | 30 | 1.2 | 2, 5, 1, 7 |
Exercices and reading | 46 | 1.84 | 2, 3, 5, 1, 7 |
Group paper | 30 | 1.2 | 2, 5, 1 |
Assessment activities
A) Weekly exercises (10%):
B) Assessment and follow-up activities in the classroom (15%):
C) Paper (30%):
D) Exam (45%)
Conditions to be assessed:
Fraudulent conducts
Conduct during the course:
UAB fosters a diverse and inclusive environment for students, faculty, and staff. Acts of intolerance, discrimination, or harassment due to age, ancestry, disability, gender, gender identity, national origin, religious belief or sexual orientation will not be tolerated under any circumstance, nor will behaviours that create a hostile environment. These will be reported under the university harassment prevention policy.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exam | 45% | 0 | 0 | 3, 5, 1, 7 |
Exercices | 10% | 0 | 0 | 2, 3, 5, 1 |
Ongoing assesment | 15% | 0 | 0 | 5, 1, 7 |
Paper (groups) | 30% | 0 | 0 | 2, 3, 5, 4, 1, 6, 7 |
Handbooks
The following publications are the basic reference handbooks for the course. Although it is not mandatory, their use is recommended.
Boccardo, Giorgio; Ruiz, Felipe (2019). RStudio para Estadística Descriptiva en Ciencias Sociales.
Available at: https://bookdown.org/gboccardo/manual-ED-UCH/uso-basico-de-rstudio.html#que-es-rstudio-una-interfaz-para-usar-r
López-Roldán, Pedro; Fachelli, Sandra. (2015). Metodología de la investigación social cuantitativa. Bellaterra (Cerdanyola del Vallès): Universitat Autònoma de Barcelona.
Available at: https://ddd.uab.cat/record/129382
Additional complementary materials will be made available in the course Moodle.
Other references
Bardina, Xavier; Farré, Mercè; López-Roldán, Pedro. (2005). Estadística: un curs introductori per a estudiants de ciències socials i humanes. Volum 2: Descriptiva i exploratòria bivariant. Bellaterra (Barcelona): Universitat Autònoma de Barcelona.
Cea D’ancona, Mª Ángeles. (1998) Metodología cuantitativa. Estrategias y técnicas de investigación social. Madrid: Síntesis.
Farré, Mercè. (2005). Estadística: un curs introductori per a estudiants de ciències socials i humanes. Volum 1: Descriptiva i exploratòria univariant. Bellaterra (Barcelona): Universitat Autònoma de Barcelona.
Fox, James A.; Levin, Jack; Forde, David R. (2009). Elementary Statistics in Criminal Justice Research. Boston: Pearson.
Maxfield, Michael G.; Babbie, Earl R. (2005). Research Methods for Criminal Justice and Criminology. Belmont, CA: Thomson Wadsworth.
Walker, Jeffery; Maddan, Sean. (2009). Statistics in Criminology and Social Justice: Analysis and Interpretation. Boston: Jonesand Bartlett Pubs.