Applied Statistics II

Code:

C208

Acronym:

EST II

Keywords
Classification	Keyword
OFICIAL	Statistics

Instance: 2022/2023 - 1S (since 12-09-2022 to 16-12-2022)

Active?	Yes
Web Page:	https://moodle.up.pt/course/view.php?id=1088
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	52	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

Chapter I - Data screening Chapter II - Sample size 1. Representativeness 2. Statistical and other determinants 3. Statistical Power 4. Rules of thumb 5. Specific software Chapter III - Associations between two variables 1. Introduction 2.Associations between categorical variables, risk measures, independence tests, odds ratio, and association measures 3. Quantitative variables and correlations (Pearson and Spearman). Partial correlation Chapter IV - Differences between two groups 1. Introduction 2. Differences between means of 2 independent groups (samples) – independent samples t test and U Mann-Whitney tests. 3. Differences between means of 2 paired groups (samples) – parametric and non-parametric tests. 4. Differences between proportions of two independent and paired (matched) groups. Chapter V - Differences between more than two groups 1. Introduction 2. Differences between independent groups – parametric approach and non-parametric approach 3. Differences between paired groups – parametric approach and non-parametric approach Chapter VI - Linear regression modelling 1. Introduction 2. Basic linear regression modelling 3. Multiple linear regression modelling 4. Measures of model fit for linear regression models 5. Verification of assumptions in regression modelling (linearity, normality, homoscedasticity, independence, absence of multicollinearity) and identification of likely influent cases. Chapter VII - Categorical regressions 1. Introduction 2. Types of categorical regressions 3. Logistic regression

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

Comments from the literature

Additional material to support the monitoring of classes and individual study is available on the Moodle page of this course.

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

R
IBM SPSS Statistics
JASP

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

n.a.

Calculation formula of final grade

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

FE = Final exam

W = Distributed evaluation assignment

NOTE:

Regardless of whether of the evalution 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.

The work should be prepared during the semester and submitted at the Moodle till the dead-line: December 02, 2022 (23:59).


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, ...)

Taking a new written exam (EF).

The final classification in Applied Statistics II is calculated by applying the formula:

0.60xEF + 0.40xT.

(The grade obtained in the distributed assessment written work continues to be considered)

Classification improvement

Taking a new written exam (EFM).

In case of improvement, the final classification in Applied Statistics II is calculated by applying the formula:

0.60xEFM + 0.40xT.

(The grade obtained in the distributed assessment written work continues to be considered)

Observations

There is a page at the Moodle platform for this course. The Moodle page is permanently updated, with relevant information and study material, available as it is explored in classes.