Kolegij
Studiji
PsihologijaKomunikologija
Povijest
Sestrinstvo
Sociologija
Sestrinstvo
Studijska godina
1ISVU ID
252565ECTS
6
Course Objectives:
This course delves into the practical aspects of applied statistics,
encompassing the formulation of research questions, hypothesis
generation, research design, and data analysis. Students will
acquire hands-on experience utilizing statistical software and the
correct application of various statistical tests. Furthermore, the
course emphasizes the significance of effectively communicating
research findings to diverse audiences, equipping students with
the necessary skills to present their results coherently.
By the conclusion of the course, students are expected to
independently design and execute experiments, analyze the
gathered data using suitable statistical
methodologies, and proficiently convey their findings to a
scientific audience. This fosters an active engagement with the
course material and encourages participation in discussions and
group tasks. In addition to attending lectures and seminars,
students are required to complete a data analysis project,
culminating in an oral seminar presentation. This project offers
students the opportunity to apply their acquired data analysis
skills to a real-world problem in the context of social science
research.
To successfully complete the course, students must obtain at least
70% of their grade through various class activities, including
midterm exams, oral presentations, and the seminar project. This
ensures consistent engagement with the course content and
promotes the ongoing development of mastery of the skills and
concepts explored throughout the course.
Course Content:
Foundations of Applied Statistics: Introduction to the key concepts
and principles of applied statistics in contemporary research
contexts.
Statistical Programming Essentials: Familiarization with
programming language for statistical analysis, including basic
syntax and functionality.
Statistical Concepts Refresher: A review of essential statistical
concepts, including probability theory, distributions, hypothesis
testing, and parametric and nonparametric methods.
Exploratory Data Analysis Techniques: In-depth exploration of
data visualization, descriptive statistics, and methods for detecting
patterns and relationships in data sets.
Confirmatory Data Analysis Approaches: A comprehensive study
of inferential statistical techniques, such as regression analysis,
ANOVA, and hypothesis testing, for validating research
hypotheses.
Empirical Research Project: Application of acquired statistical
knowledge and skills in designing, conducting, and analyzing a
real-world research problem, culminating in an oral seminar
presentation.
1. Develop research questions and hypotheses appropriate for
empirical research in social science. 2. Design research studies that
incorporate appropriate research methods and sampling techniques. 3.
Conduct statistical analyses using advanced statistical techniques to
answer research questions. 4. Use statistical software to effectively
manage and analyze data. 5. Evaluate statistical models for their
appropriateness in answering research questions. 6. Interpret
statistical results and conclude them in the context of the research
questions. 7. Communicate research findings effectively to different
audiences using appropriate visual aids. 8. Develop critical thinking
skills to evaluate the appropriateness of statistical analyses for
different research questions. 9. Design and conduct an independent
data analysis project and present the findings orally to the class.
Navarro, D. J. (2019). Learning Statistics with R: A tutorial for
psychology students and other beginners. Adelaide, Australia:
University of Adelaide Press.
Avaliable online: https://learningstatisticswithr.com/
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R.
London: SAGE Publications Ltd.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An
Introduction to Statistical Learning: with Applications in R. New York:
Springer.
Freedman, D. A. (2009). Statistical Models: Theory and Practice.
Cambridge: Cambridge University Press.
Everitt, B. S., & Hothorn, T. (2011). An Introduction to Applied
Multivariate Analysis with R. New York: Springer.
Tabachnick, B. G., & Fidell, L. S. (2018). Using Multivariate Statistics.
Boston: Pearson.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018).
Multivariate Data Analysis. London: Cengage Learning.
Attendance is crucial for success in this course, and students
are expected to attend at least 70% of lectures and seminar
sessions.
Class activities: Midterm exam (written), seminar
presentation (written and oral) and final exam
Final course grade is based on 100 points earned through
student’s continuous involvement in class activities:
Fair (2) – 50 to 64 points
Good (3) – 65 to 79 points
Very good (4) – 80 to 89 points
Excellent (5) – 90 to 100 points
Earning credits:
Class activities contribute to 70% of the grade:
Midterm exam – maximum 40 points
Seminar – maximum 20 points
Seminar presentation – maximum 10 points
Final exam contributes to 30% of the grade:
Final exam – maximum of 30 points
| VRSTA AKTIVNOSTI | ECTS bodovi - koeficijent opterećenja studenata | UDIO OCJENE (%) |
| Pohađanje nastave | 1.5 | 0 |
| Kolokvij-međuispit | 1.8 | 40 |
| Seminarski rad | 0.9 | 20 |
| Seminarsko izlaganje | 0.45 | 10 |
| Ukupno tijekom nastave | 4.65 | 70 |
| Završni ispit | 1.35 | 30 |
| UKUPNO BODOVA (nastava+zav.ispit) | 6 | 100 |
Course Objectives:
This course delves into the practical aspects of applied statistics,
encompassing the formulation of research questions, hypothesis
generation, research design, and data analysis. Students will
acquire hands-on experience utilizing statistical software and the
correct application of various statistical tests. Furthermore, the
course emphasizes the significance of effectively communicating
research findings to diverse audiences, equipping students with
the necessary skills to present their results coherently.
By the conclusion of the course, students are expected to
independently design and execute experiments, analyze the
gathered data using suitable statistical
methodologies, and proficiently convey their findings to a
scientific audience. This fosters an active engagement with the
course material and encourages participation in discussions and
group tasks. In addition to attending lectures and seminars,
students are required to complete a data analysis project,
culminating in an oral seminar presentation. This project offers
students the opportunity to apply their acquired data analysis
skills to a real-world problem in the context of social science
research.
To successfully complete the course, students must obtain at least
70% of their grade through various class activities, including
midterm exams, oral presentations, and the seminar project. This
ensures consistent engagement with the course content and
promotes the ongoing development of mastery of the skills and
concepts explored throughout the course.
Course Content:
Foundations of Applied Statistics: Introduction to the key concepts
and principles of applied statistics in contemporary research
contexts.
Statistical Programming Essentials: Familiarization with
programming language for statistical analysis, including basic
syntax and functionality.
Statistical Concepts Refresher: A review of essential statistical
concepts, including probability theory, distributions, hypothesis
testing, and parametric and nonparametric methods.
Exploratory Data Analysis Techniques: In-depth exploration of
data visualization, descriptive statistics, and methods for detecting
patterns and relationships in data sets.
Confirmatory Data Analysis Approaches: A comprehensive study
of inferential statistical techniques, such as regression analysis,
ANOVA, and hypothesis testing, for validating research
hypotheses.
Empirical Research Project: Application of acquired statistical
knowledge and skills in designing, conducting, and analyzing a
real-world research problem, culminating in an oral seminar
presentation.
1. Develop research questions and hypotheses appropriate for
empirical research in social science. 2. Design research studies that
incorporate appropriate research methods and sampling techniques. 3.
Conduct statistical analyses using advanced statistical techniques to
answer research questions. 4. Use statistical software to effectively
manage and analyze data. 5. Evaluate statistical models for their
appropriateness in answering research questions. 6. Interpret
statistical results and conclude them in the context of the research
questions. 7. Communicate research findings effectively to different
audiences using appropriate visual aids. 8. Develop critical thinking
skills to evaluate the appropriateness of statistical analyses for
different research questions. 9. Design and conduct an independent
data analysis project and present the findings orally to the class.
Navarro, D. J. (2019). Learning Statistics with R: A tutorial for
psychology students and other beginners. Adelaide, Australia:
University of Adelaide Press.
Avaliable online: https://learningstatisticswithr.com/
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R.
London: SAGE Publications Ltd.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An
Introduction to Statistical Learning: with Applications in R. New York:
Springer.
Freedman, D. A. (2009). Statistical Models: Theory and Practice.
Cambridge: Cambridge University Press.
Everitt, B. S., & Hothorn, T. (2011). An Introduction to Applied
Multivariate Analysis with R. New York: Springer.
Tabachnick, B. G., & Fidell, L. S. (2018). Using Multivariate Statistics.
Boston: Pearson.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018).
Multivariate Data Analysis. London: Cengage Learning.
Attendance is crucial for success in this course, and students
are expected to attend at least 70% of lectures and seminar
sessions.
Class activities: Midterm exam (written), seminar
presentation (written and oral) and final exam
Final course grade is based on 100 points earned through
student’s continuous involvement in class activities:
Fair (2) – 50 to 64 points
Good (3) – 65 to 79 points
Very good (4) – 80 to 89 points
Excellent (5) – 90 to 100 points
Earning credits:
Class activities contribute to 70% of the grade:
Midterm exam – maximum 40 points
Seminar – maximum 20 points
Seminar presentation – maximum 10 points
Final exam contributes to 30% of the grade:
Final exam – maximum of 30 points
| VRSTA AKTIVNOSTI | ECTS bodovi - koeficijent opterećenja studenata | UDIO OCJENE (%) |
| Pohađanje nastave | 1.5 | 0 |
| Kolokvij-međuispit | 1.8 | 40 |
| Seminarski rad | 0.9 | 20 |
| Seminarsko izlaganje | 0.45 | 10 |
| Ukupno tijekom nastave | 4.65 | 70 |
| Završni ispit | 1.35 | 30 |
| UKUPNO BODOVA (nastava+zav.ispit) | 6 | 100 |
Course Objectives:
This course delves into the practical aspects of applied statistics,
encompassing the formulation of research questions, hypothesis
generation, research design, and data analysis. Students will
acquire hands-on experience utilizing statistical software and the
correct application of various statistical tests. Furthermore, the
course emphasizes the significance of effectively communicating
research findings to diverse audiences, equipping students with
the necessary skills to present their results coherently.
By the conclusion of the course, students are expected to
independently design and execute experiments, analyze the
gathered data using suitable statistical
methodologies, and proficiently convey their findings to a
scientific audience. This fosters an active engagement with the
course material and encourages participation in discussions and
group tasks. In addition to attending lectures and seminars,
students are required to complete a data analysis project,
culminating in an oral seminar presentation. This project offers
students the opportunity to apply their acquired data analysis
skills to a real-world problem in the context of social science
research.
To successfully complete the course, students must obtain at least
70% of their grade through various class activities, including
midterm exams, oral presentations, and the seminar project. This
ensures consistent engagement with the course content and
promotes the ongoing development of mastery of the skills and
concepts explored throughout the course.
Course Content:
Foundations of Applied Statistics: Introduction to the key concepts
and principles of applied statistics in contemporary research
contexts.
Statistical Programming Essentials: Familiarization with
programming language for statistical analysis, including basic
syntax and functionality.
Statistical Concepts Refresher: A review of essential statistical
concepts, including probability theory, distributions, hypothesis
testing, and parametric and nonparametric methods.
Exploratory Data Analysis Techniques: In-depth exploration of
data visualization, descriptive statistics, and methods for detecting
patterns and relationships in data sets.
Confirmatory Data Analysis Approaches: A comprehensive study
of inferential statistical techniques, such as regression analysis,
ANOVA, and hypothesis testing, for validating research
hypotheses.
Empirical Research Project: Application of acquired statistical
knowledge and skills in designing, conducting, and analyzing a
real-world research problem, culminating in an oral seminar
presentation.
1. Develop research questions and hypotheses appropriate for
empirical research in social science. 2. Design research studies that
incorporate appropriate research methods and sampling techniques. 3.
Conduct statistical analyses using advanced statistical techniques to
answer research questions. 4. Use statistical software to effectively
manage and analyze data. 5. Evaluate statistical models for their
appropriateness in answering research questions. 6. Interpret
statistical results and conclude them in the context of the research
questions. 7. Communicate research findings effectively to different
audiences using appropriate visual aids. 8. Develop critical thinking
skills to evaluate the appropriateness of statistical analyses for
different research questions. 9. Design and conduct an independent
data analysis project and present the findings orally to the class.
Navarro, D. J. (2019). Learning Statistics with R: A tutorial for
psychology students and other beginners. Adelaide, Australia:
University of Adelaide Press.
Avaliable online: https://learningstatisticswithr.com/
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R.
London: SAGE Publications Ltd.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An
Introduction to Statistical Learning: with Applications in R. New York:
Springer.
Freedman, D. A. (2009). Statistical Models: Theory and Practice.
Cambridge: Cambridge University Press.
Everitt, B. S., & Hothorn, T. (2011). An Introduction to Applied
Multivariate Analysis with R. New York: Springer.
Tabachnick, B. G., & Fidell, L. S. (2018). Using Multivariate Statistics.
Boston: Pearson.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018).
Multivariate Data Analysis. London: Cengage Learning.
Attendance is crucial for success in this course, and students
are expected to attend at least 70% of lectures and seminar
sessions.
Class activities: Midterm exam (written), seminar
presentation (written and oral) and final exam
Final course grade is based on 100 points earned through
student’s continuous involvement in class activities:
Fair (2) – 50 to 64 points
Good (3) – 65 to 79 points
Very good (4) – 80 to 89 points
Excellent (5) – 90 to 100 points
Earning credits:
Class activities contribute to 70% of the grade:
Midterm exam – maximum 40 points
Seminar – maximum 20 points
Seminar presentation – maximum 10 points
Final exam contributes to 30% of the grade:
Final exam – maximum of 30 points
| VRSTA AKTIVNOSTI | ECTS bodovi - koeficijent opterećenja studenata | UDIO OCJENE (%) |
| Pohađanje nastave | 1.5 | 0 |
| Kolokvij-međuispit | 1.8 | 40 |
| Seminarski rad | 0.9 | 20 |
| Seminarsko izlaganje | 0.45 | 10 |
| Ukupno tijekom nastave | 4.65 | 70 |
| Završni ispit | 1.35 | 30 |
| UKUPNO BODOVA (nastava+zav.ispit) | 6 | 100 |