Applied Statistics II

Code:

C208

Acronym:

EST II

Keywords
Classification	Keyword
OFICIAL	Statistics

Instance: 2019/2020 - 1S (since 16-09-2019 to 20-12-2019)

Active?	Yes
Web Page:	https://moodle.up.pt/course/view.php?id=51
Course/CS Responsible:	Criminology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
C	42	Oficial Study Plan LC	2	-	6	-

Teaching language

Suitable for English-speaking students

Objectives

This curricular unit complements the knowledge transmitted in Applied Statistics I.

With this curricular unit, it is intended that students solidify and deepen their knowledge of statistics and can understand and interpret well the results of quantitative analysis present in the scientific literature. It is also intended that students be able, in their own research, to select the most appropriate statistical tools for data analysis problems they need to solve. Finally, students are also expected to able to use extensively statistical analysis software, such as IBM SPSS Statistics, JASP, and R.

Learning outcomes and competences

With this curricular unit it is specifically intended that the student be able to:

1) To interpret and carry out a quantitative exploration of available data to explain interesting phenomena in the field of Criminology;

2) Become proficient at the use of intermediate level statistical techniques in social sciences and especially in Criminology;

3) Implement quantitative analysis by sing extensively statistical software (e.g., SPSS, JASP, and R);

4) Present the results of the statistical analysis carried out, in scientific publications and technical reports, regarding scientific writing standards (eg APA).

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Despite not being obligatory, students should had taken classes on Applied Statistics I before.

Program

1) Inference and sample size: 1.1. Data screening and description of the sample; 1.2. Inferential operations; 1.3. Sample size.

2) Relationship between two variables: 2.1. Pearson correlation; 2.2. Spearman correlation; 2.3. Chi-square test; 2.4. Odds ratio; 2.5. Other association measures between two variables; 2.6. Criminology applied examples with extensive use of software.

3) Linear regression model: 3.1. Basics of regression modelling; 3.2. Extensions to the basic regression model and software applications.

4) Diferences between two groups: 4.1. Independent groups (samples) 4.2. Dependent (matched) groups (samples).

5) Diferences between more than two groups: 5.1. Independent groups (samples) 5.2. Dependent groups (samples).

6) Applied Statistics II complements: 6.1. Logistic regression 6.2. Principal Component Analysis 6.3. Factorial Analysis (SPSS applications in each chapter).

Mandatory literature

Field Andy; Discovering statistics using IBM SPSS Statistics and sex and drugs and rock.n.roll. ISBN: 978-14462-4918-5
Marôco João; Análise estatística com o SPSS Statistics. ISBN: 978-989-96763-2-9
Weisburd David; Statistics in criminal justice. ISBN: 978-0-387-34112-5
Bachman Ronet; Statistical for criminology and criminal justice. ISBN: 978-0-07-312924-2

Complementary Bibliography

Bachman Ronet; The^practice of research in criminology and criminal justice. ISBN: 978-1-4129-5032-9
Bushway Shawn 340; Quantitative methods in criminology. ISBN: 0-7546-2446-3
Pestana Maria Helena; Análise de dados para ciências sociais. ISBN: 972-618-297-2
Howell, david; statistical methods for psychology. ISBN: 0495012874
Imai, Kosuke; Quantitative Social Science - An Introduction, Princeton University Press, 2017. ISBN: 978-0-691-16703-9

Teaching methods and learning activities

Theoretical-practical classes of practical-laboratory teaching. At each point of the program, the contents are presented in theoretical terms focusing, at first, their usefulness in the quantitative analysis of the object of study. Students are then faced with a more practical approach, solving case studies, whenever possible, using specific software.

Tutorial classes are available to follow the work of distributed evaluation.

Moodle platform of the University of Porto is extensively used, allowing a greater interaction with the students.

Software

JASP
R
IBM SPSS Statistics

keywords

Physical sciences > Mathematics > Statistics
Social sciences > Criminology

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	60,00
Trabalho de campo	40,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	52,00
Frequência das aulas	55,00
Trabalho escrito	40,00
Trabalho laboratorial	15,00
Total:	162,00

Eligibility for exams

- The student must fulfill the attendance condition, which means that he / she cannot exceed the limit number of absences corresponding to 25% of the classes taught in this course.
- Students who, by law, are exempt from class attendance must submit a very short essay on the subject and at a date to be set by the teacher at the beginning of the classes.

Calculation formula of final grade

Final Grade = 0,60xFE + 0,40xW

FE = Final exam

W = Distributed evaluation assignment

NOTE:

Regardless of whether the student is assessed in the normal or appeal period, to complete successfully this course, students are required to:
a) Take the written Final Exam (EF) and complete an Written Work (W);
b) Obtain a minimum classification of 8/20 points in each evaluation component /EF and W);
c) Obtain a Final grade of no less than 9,5 points in a twenty points scale.

Examinations or Special Assignments

The evaluation in the Applied Statistics II curricular unit is comprised of the compulsory accomplishment of a written work. Such work should be submitted by the end of the semester, and it is timely scheduled by the end of September 2019.

At the beginning of the semester, the formal rules, the methodology and the deadlines to be met by the students in carrying out the applied work are disclosed.

The accomplishment of the work is COMPULSORY so the classification obtained in this distributed component is considered in the determination of the final evaluation classification in the two evaluation periods (normal season and resource season).

Internship work/project

n.a.

Special assessment (TE, DA, ...)

Students who, by law, are exempt from class attendance must submit a very short essay on the subject and a date to be set by the teacher at the beginning of the classes.

Classification improvement

By written final exam, without distributed assessment component.

Observations

All relevant UC-related information not available on this sheet is available from Moodle.