The course’s objective is to introduce students to the diverse, academic criteria of qualitative research in social sciences and secondly that student’s gain experience in using qualitative methods. Furthermore, the course is practical in nature where each student works on an independent research assignment, which consists of designing and preparing a research project, collecting and analyzing data, and writing the main findings with guidance from the teacher. Research preparation, the creation of a research plan, data collection and analysis along with academic writing will be extensively covered during the course.

An introduction to the master's program in sociology, methodology and criminology; structure and work methods.

The primary objective of the seminar is to provide a general foundation for MA studies in sociology, methodology, and criminology. The department, its faculty, and the wider academic community will be introduced. Students will present their research interests and possible topics for their MA thesis. The assignments in the course will focus on the diversity and hierarchy of academic journals, effective uses of Web of Science and artificial, and critical engagement with research articles. The course will conclude with student submission and oral presentation of a written final assignment.

This course has three main objectives. i) that students gain a better understanding of the research process and common methods, ii) that students get training in reading and criticizing existing research, and iii) that students get training in developing research questions with respect to theoretical issues and existing research. Lectures: We discuss concepts and methodologies emphasizing i) the strengths and limitations of various methods, ii) the connections among methodologies, methods, and theoretical issues. Discussion sessions: Students read research articles and discuss research methods in relation to specific sociological topics.

This is a comprehensive course in multiple-regression analysis. The goal of the course is that students develop enough conceptual understanding and practical knowledge to use this method on their own. The lectures cover various regression analysis techniques commonly used in quantitative social research, including control variables, the use of nominal variables, linear and nonlinear models, techniques that test for mediation and statistical interaction effects, and so on. We discuss the assumptions of regression analysis and learn techniques to detect and deal with violations of assumptions. In addition, logistic regression will be introduced, which is a method for a dichotomous dependent variable. We also review many of the basic concepts involved in statistical inference and significance testing. Students get plenty of hands-on experience with data analysis. The instructor hands out survey data that students use to practice the techniques covered in class. The statistical package SPSS will be used.

The purpose of this course is to provide students with understanding on how to plan and conduct survey research. The course will address most common sampling design and different type of survey research (phone, face-to-face, internet, mail etc.). The basic measurement theories will be used to explore fundamental concepts of survey research, such as validity, reliability, question wording and contextual effect. The use of factor analysis and item analysis will be used to evaluate the quality of measurement instruments.  The course emphasizes students’ active learning by planning survey research and analyzing survey data.

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

Presentation of final thesis in sociology, methodology and criminology. 

The primary objective of the seminar is to provide a dynamic, supportive space for MA students in sociology and criminology to deepen their engagement with their thesis research and encourage reciprocal support among students. Early in the semester, students participate in lightning-round introductions of their research, followed by more detailed presentations as their work progresses. Faculty members, PhD students and other scholars may also be invited to participate in the seminar. This seminar should encourage constructive feedback and collaborative discussions among students and faculty, refine students’ presentation skills, and enhance their professional development and scholarly identity.

The course is intended for students who have started working on their master's thesis. 

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

Course description In this course students will use regression models to complete a supervised project with binomial, ordinal or multinomial outcome variables.

The supervising teacher will assist students in finding advisors for their project.

Students are expected to have completed the course Regression Analysis

The goal of the course is to introduce students to basic methods in mathematics and statistics used in economic analysis. The mathematics part covers the following topics: functions, differentiation, integration, maximization with and without constraints, series and sequences, and simple difference and differential equations. The statistics part covers the following topics: random variables, probability, distributions, mean, variance, covariance, correlation, law of large numbers, samples, hypothesis testing, point estimation, and interval estimation.

The course is taught in collaboration with Statistics Iceland, Bifröst University and the University of Akureyri, with support from the Icelandic university cooperation fund.

The goal of this course is to enhance students' ability to understand and analyze domestic and international statistics. Students will gain knowledge about the purpose of statistics, their production, and the methodology behind their production. They will also receive training in analyzing published statistics, presenting, and interpreting them in a domestic and international context, depending on their subject matter.

Students will gain practical experience by working on realistic projects where statistics are used to analyze economic and social developments and to evaluate government actions based on statistics. Students will submit an analysis report aimed at providing information that can be used as a basis for government policy and evaluation of their actions in specific areas.

Teaching will take place from September 1 to October 17. Teaching will be online with meetings in real-time. First online-meeting is Tuesday September 2. Further information is available in the syllabus.  

The course Applied Econometrics aims to enhance students' ability to read, analyze, and evaluate scientific research in the field of economics. Students will read academic papers, deliver presentations on them, and participate in discussions on research topics. Additionally, students will receive training in systematically summarizing the state of knowledge in a specific area of economics. The course lays the foundation for a better understanding of research methods in economics and prepares students for writing their final thesis as well as for further studies or careers where the analysis and interpretation of research are key components.

This course is a continuation of Biostatistics I and constitutes a practical guide to statistical analyses of student's own research projects. The course covers the following topics. Estimation of relative risk/odds ratios and adjusted estimation of relative risk/odds ratios, correlation and simple linear regression, multiple linear regression and logistic regression. This course is centered around two core principles that define its thematic focus: Prediction from Statistical Models
Students will learn to build models—such as logistic regression and Cox proportional hazards models—that estimate the probability of future medical events based on recorded data. Evaluation of Discrimination Capacity A key theme is assessing how well models distinguish between individuals with different outcomes, using metrics such as the Area Under the ROC Curve (AUC), concordance index, and related performance measures.

The course is based on lectures and practical sessions using R for statistical analyses.

ARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.

This course covers key topics in statistics and pricing in finance. Emphasis is placed on introducing students to the use of statistical and mathematical methods for analysing, pricing, and obtaining information about financial instruments. Real‑world examples are highlighted, giving students practical experience in solving problems similar to those they may encounter in their professional work in financial markets.
The statistics portion of the course includes an overview of continuous and discrete probability distributions, expected values, variance and standard deviation, confidence intervals, hypothesis testing, and linear regression analysis, both simple and multiple. The course also covers the fundamental concepts of the Capital Asset Pricing Model (CAPM).
The pricing portion of the course focuses on the valuation of forward contracts on equities, bonds, and foreign exchange, as well as interest rate swaps, the construction of yield curves, and the types and characteristics of options.

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and will learn how to apply statistical methods they know in R. Main topics are loading data, graphical representation, descriptive statistics and how to perform the most common hypothesis tests (t- test, chi-square test, etc.) in R. In addition, students will learn how to make reports using the knitr package.

The course is taught during a five week period. A teacher gives lectures and students work on a project in class.

Students will perform traditional statistical analysis on real data sets. Special focus will be on regression methods, including multiple regression analysis. Students will apply sophisticated methods of graphical representation and automatic reporting. Students will hand in a projects where they apply the above mentioned methods on real datasets in order to answer research questions

Course description In this course students will use regression models to complete a supervised project with binomial, ordinal or multinomial outcome variables.

The supervising teacher will assist students in finding advisors for their project.

Students are expected to have completed the course Regression Analysis

In the course we examine the field methods and train students in their application. The focus is on ethical issues, research design, the fieldwork setting, participant observation, different kinds of interviews, use of visual material and the analysis of data and presentation of research results.

The course is intended for postgraduate students only. It is adapted to the needs of students from different fields of study. The course is taught over a six-week period.

The course is taught over the first six weeks of spring semester on Fridays from 1:20 pm - 3:40 pm.

Description: 
The topics of the course include: Professionalism and the scientist’s responsibilities. Demands for scientific objectivity and the ethics of research. Issues of equality and standards of good practice. Power and science. Conflicts of interest and misconduct in research. Science, academia and industry. Research ethics and ethical decision making.

Objectives: 
In this course, the student gains knowledge about ethical issues in science and research and is trained in reasoning about ethical controversies relating to science and research in contemporary society.

The instruction takes the form of lectures and discussion. The course is viewed as an academic community where students are actively engaged in a focused dialogue about  the topics. Each student (working as a member of a two-person team) gives a presentation according to a plan designed at the beginning of the course, and other students acquaint themselves with the topic as well for the purpose of participating in a teacher-led discussion.

The course covers methods for analysis of cohort studies using methods for time to event or survival analysis. It is based on the course Biostat III – Survival analysis for epidemiologists in R at the Karolinska Institutet: See (https://biostat3.net/index.html): "Topics covered include methods for estimating patient survival (life table and Kaplan-Meier methods), comparing survival between patient subgroups (log-rank test), and modelling survival (primarily Poisson regression, Cox proportional hazards model and flexible parametric models). The course addresses the concept of 'time' as a potential confounder or effect modifier and approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification." 

This course focuses on the variety of approaches and methods found within  research. Five qualitative approaches to inquiry are mainly in focus, namely; case study, narrative research, ethnography, phenomenology and grounded theory. Students gain a deeper experiences in data collection and in use of different methods for analyzing their qualitative data. They also gain experience in presenting their findings in written form. Additionally, students have the opportunity to reflect on their own research practices and on themselves as qualitative researchers.

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and the statistical software R. Students will learn to apply a broad range of statistical methods in R (such as classification methods, resampling methods, linear model selection and tree-based methods). The course on 12 weeks and will be on "flipped" form. This means that no lectures will be given but students will read some material and watch videos before attending classes. Students will then work on assignments during the classes.

The course’s objective is to introduce students to the diverse, academic criteria of qualitative research in social sciences and secondly that student’s gain experience in using qualitative methods. Furthermore, the course is practical in nature where each student works on an independent research assignment, which consists of designing and preparing a research project, collecting and analyzing data, and writing the main findings with guidance from the teacher. Research preparation, the creation of a research plan, data collection and analysis along with academic writing will be extensively covered during the course.

An introduction to the master's program in sociology, methodology and criminology; structure and work methods.

The primary objective of the seminar is to provide a general foundation for MA studies in sociology, methodology, and criminology. The department, its faculty, and the wider academic community will be introduced. Students will present their research interests and possible topics for their MA thesis. The assignments in the course will focus on the diversity and hierarchy of academic journals, effective uses of Web of Science and artificial, and critical engagement with research articles. The course will conclude with student submission and oral presentation of a written final assignment.

This course has three main objectives. i) that students gain a better understanding of the research process and common methods, ii) that students get training in reading and criticizing existing research, and iii) that students get training in developing research questions with respect to theoretical issues and existing research. Lectures: We discuss concepts and methodologies emphasizing i) the strengths and limitations of various methods, ii) the connections among methodologies, methods, and theoretical issues. Discussion sessions: Students read research articles and discuss research methods in relation to specific sociological topics.

This is a comprehensive course in multiple-regression analysis. The goal of the course is that students develop enough conceptual understanding and practical knowledge to use this method on their own. The lectures cover various regression analysis techniques commonly used in quantitative social research, including control variables, the use of nominal variables, linear and nonlinear models, techniques that test for mediation and statistical interaction effects, and so on. We discuss the assumptions of regression analysis and learn techniques to detect and deal with violations of assumptions. In addition, logistic regression will be introduced, which is a method for a dichotomous dependent variable. We also review many of the basic concepts involved in statistical inference and significance testing. Students get plenty of hands-on experience with data analysis. The instructor hands out survey data that students use to practice the techniques covered in class. The statistical package SPSS will be used.

The purpose of this course is to provide students with understanding on how to plan and conduct survey research. The course will address most common sampling design and different type of survey research (phone, face-to-face, internet, mail etc.). The basic measurement theories will be used to explore fundamental concepts of survey research, such as validity, reliability, question wording and contextual effect. The use of factor analysis and item analysis will be used to evaluate the quality of measurement instruments.  The course emphasizes students’ active learning by planning survey research and analyzing survey data.

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

Presentation of final thesis in sociology, methodology and criminology. 

The primary objective of the seminar is to provide a dynamic, supportive space for MA students in sociology and criminology to deepen their engagement with their thesis research and encourage reciprocal support among students. Early in the semester, students participate in lightning-round introductions of their research, followed by more detailed presentations as their work progresses. Faculty members, PhD students and other scholars may also be invited to participate in the seminar. This seminar should encourage constructive feedback and collaborative discussions among students and faculty, refine students’ presentation skills, and enhance their professional development and scholarly identity.

The course is intended for students who have started working on their master's thesis. 

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

Course description In this course students will use regression models to complete a supervised project with binomial, ordinal or multinomial outcome variables.

The supervising teacher will assist students in finding advisors for their project.

Students are expected to have completed the course Regression Analysis

The goal of the course is to introduce students to basic methods in mathematics and statistics used in economic analysis. The mathematics part covers the following topics: functions, differentiation, integration, maximization with and without constraints, series and sequences, and simple difference and differential equations. The statistics part covers the following topics: random variables, probability, distributions, mean, variance, covariance, correlation, law of large numbers, samples, hypothesis testing, point estimation, and interval estimation.

The course is taught in collaboration with Statistics Iceland, Bifröst University and the University of Akureyri, with support from the Icelandic university cooperation fund.

The goal of this course is to enhance students' ability to understand and analyze domestic and international statistics. Students will gain knowledge about the purpose of statistics, their production, and the methodology behind their production. They will also receive training in analyzing published statistics, presenting, and interpreting them in a domestic and international context, depending on their subject matter.

Students will gain practical experience by working on realistic projects where statistics are used to analyze economic and social developments and to evaluate government actions based on statistics. Students will submit an analysis report aimed at providing information that can be used as a basis for government policy and evaluation of their actions in specific areas.

Teaching will take place from September 1 to October 17. Teaching will be online with meetings in real-time. First online-meeting is Tuesday September 2. Further information is available in the syllabus.  

The course Applied Econometrics aims to enhance students' ability to read, analyze, and evaluate scientific research in the field of economics. Students will read academic papers, deliver presentations on them, and participate in discussions on research topics. Additionally, students will receive training in systematically summarizing the state of knowledge in a specific area of economics. The course lays the foundation for a better understanding of research methods in economics and prepares students for writing their final thesis as well as for further studies or careers where the analysis and interpretation of research are key components.

This course is a continuation of Biostatistics I and constitutes a practical guide to statistical analyses of student's own research projects. The course covers the following topics. Estimation of relative risk/odds ratios and adjusted estimation of relative risk/odds ratios, correlation and simple linear regression, multiple linear regression and logistic regression. This course is centered around two core principles that define its thematic focus: Prediction from Statistical Models
Students will learn to build models—such as logistic regression and Cox proportional hazards models—that estimate the probability of future medical events based on recorded data. Evaluation of Discrimination Capacity A key theme is assessing how well models distinguish between individuals with different outcomes, using metrics such as the Area Under the ROC Curve (AUC), concordance index, and related performance measures.

The course is based on lectures and practical sessions using R for statistical analyses.

ARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.

This course covers key topics in statistics and pricing in finance. Emphasis is placed on introducing students to the use of statistical and mathematical methods for analysing, pricing, and obtaining information about financial instruments. Real‑world examples are highlighted, giving students practical experience in solving problems similar to those they may encounter in their professional work in financial markets.
The statistics portion of the course includes an overview of continuous and discrete probability distributions, expected values, variance and standard deviation, confidence intervals, hypothesis testing, and linear regression analysis, both simple and multiple. The course also covers the fundamental concepts of the Capital Asset Pricing Model (CAPM).
The pricing portion of the course focuses on the valuation of forward contracts on equities, bonds, and foreign exchange, as well as interest rate swaps, the construction of yield curves, and the types and characteristics of options.

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and will learn how to apply statistical methods they know in R. Main topics are loading data, graphical representation, descriptive statistics and how to perform the most common hypothesis tests (t- test, chi-square test, etc.) in R. In addition, students will learn how to make reports using the knitr package.

The course is taught during a five week period. A teacher gives lectures and students work on a project in class.

Students will perform traditional statistical analysis on real data sets. Special focus will be on regression methods, including multiple regression analysis. Students will apply sophisticated methods of graphical representation and automatic reporting. Students will hand in a projects where they apply the above mentioned methods on real datasets in order to answer research questions

Course description In this course students will use regression models to complete a supervised project with binomial, ordinal or multinomial outcome variables.

The supervising teacher will assist students in finding advisors for their project.

Students are expected to have completed the course Regression Analysis

In the course we examine the field methods and train students in their application. The focus is on ethical issues, research design, the fieldwork setting, participant observation, different kinds of interviews, use of visual material and the analysis of data and presentation of research results.

The course is intended for postgraduate students only. It is adapted to the needs of students from different fields of study. The course is taught over a six-week period.

The course is taught over the first six weeks of spring semester on Fridays from 1:20 pm - 3:40 pm.

Description: 
The topics of the course include: Professionalism and the scientist’s responsibilities. Demands for scientific objectivity and the ethics of research. Issues of equality and standards of good practice. Power and science. Conflicts of interest and misconduct in research. Science, academia and industry. Research ethics and ethical decision making.

Objectives: 
In this course, the student gains knowledge about ethical issues in science and research and is trained in reasoning about ethical controversies relating to science and research in contemporary society.

The instruction takes the form of lectures and discussion. The course is viewed as an academic community where students are actively engaged in a focused dialogue about  the topics. Each student (working as a member of a two-person team) gives a presentation according to a plan designed at the beginning of the course, and other students acquaint themselves with the topic as well for the purpose of participating in a teacher-led discussion.

The course covers methods for analysis of cohort studies using methods for time to event or survival analysis. It is based on the course Biostat III – Survival analysis for epidemiologists in R at the Karolinska Institutet: See (https://biostat3.net/index.html): "Topics covered include methods for estimating patient survival (life table and Kaplan-Meier methods), comparing survival between patient subgroups (log-rank test), and modelling survival (primarily Poisson regression, Cox proportional hazards model and flexible parametric models). The course addresses the concept of 'time' as a potential confounder or effect modifier and approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification." 

This course focuses on the variety of approaches and methods found within  research. Five qualitative approaches to inquiry are mainly in focus, namely; case study, narrative research, ethnography, phenomenology and grounded theory. Students gain a deeper experiences in data collection and in use of different methods for analyzing their qualitative data. They also gain experience in presenting their findings in written form. Additionally, students have the opportunity to reflect on their own research practices and on themselves as qualitative researchers.

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and the statistical software R. Students will learn to apply a broad range of statistical methods in R (such as classification methods, resampling methods, linear model selection and tree-based methods). The course on 12 weeks and will be on "flipped" form. This means that no lectures will be given but students will read some material and watch videos before attending classes. Students will then work on assignments during the classes.

The course’s objective is to introduce students to the diverse, academic criteria of qualitative research in social sciences and secondly that student’s gain experience in using qualitative methods. Furthermore, the course is practical in nature where each student works on an independent research assignment, which consists of designing and preparing a research project, collecting and analyzing data, and writing the main findings with guidance from the teacher. Research preparation, the creation of a research plan, data collection and analysis along with academic writing will be extensively covered during the course.

An introduction to the master's program in sociology, methodology and criminology; structure and work methods.

The primary objective of the seminar is to provide a general foundation for MA studies in sociology, methodology, and criminology. The department, its faculty, and the wider academic community will be introduced. Students will present their research interests and possible topics for their MA thesis. The assignments in the course will focus on the diversity and hierarchy of academic journals, effective uses of Web of Science and artificial, and critical engagement with research articles. The course will conclude with student submission and oral presentation of a written final assignment.

This course has three main objectives. i) that students gain a better understanding of the research process and common methods, ii) that students get training in reading and criticizing existing research, and iii) that students get training in developing research questions with respect to theoretical issues and existing research. Lectures: We discuss concepts and methodologies emphasizing i) the strengths and limitations of various methods, ii) the connections among methodologies, methods, and theoretical issues. Discussion sessions: Students read research articles and discuss research methods in relation to specific sociological topics.

This is a comprehensive course in multiple-regression analysis. The goal of the course is that students develop enough conceptual understanding and practical knowledge to use this method on their own. The lectures cover various regression analysis techniques commonly used in quantitative social research, including control variables, the use of nominal variables, linear and nonlinear models, techniques that test for mediation and statistical interaction effects, and so on. We discuss the assumptions of regression analysis and learn techniques to detect and deal with violations of assumptions. In addition, logistic regression will be introduced, which is a method for a dichotomous dependent variable. We also review many of the basic concepts involved in statistical inference and significance testing. Students get plenty of hands-on experience with data analysis. The instructor hands out survey data that students use to practice the techniques covered in class. The statistical package SPSS will be used.

The purpose of this course is to provide students with understanding on how to plan and conduct survey research. The course will address most common sampling design and different type of survey research (phone, face-to-face, internet, mail etc.). The basic measurement theories will be used to explore fundamental concepts of survey research, such as validity, reliability, question wording and contextual effect. The use of factor analysis and item analysis will be used to evaluate the quality of measurement instruments.  The course emphasizes students’ active learning by planning survey research and analyzing survey data.

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

Presentation of final thesis in sociology, methodology and criminology. 

The primary objective of the seminar is to provide a dynamic, supportive space for MA students in sociology and criminology to deepen their engagement with their thesis research and encourage reciprocal support among students. Early in the semester, students participate in lightning-round introductions of their research, followed by more detailed presentations as their work progresses. Faculty members, PhD students and other scholars may also be invited to participate in the seminar. This seminar should encourage constructive feedback and collaborative discussions among students and faculty, refine students’ presentation skills, and enhance their professional development and scholarly identity.

The course is intended for students who have started working on their master's thesis. 

The master's thesis is the final project in a master's program and is based on independent research or a work-related research and development project. The aim of the final project is to train students in independent academic work. The final project must be an individual project.

Master's students have a supervisor chosen from among lecturers, associate professors, or professors. The supervisor guides the work on the final project. Usually, the supervisor and the academic advisor are the same person. It is permissible to appoint a co-supervisor, but such an appointment requires the approval of the Faculty.

The length of the master's thesis depends on the nature of the project, the number of credits, and the subject matter.

An external examiner must always evaluate the master's final project together with the supervisor. 

Course description In this course students will use regression models to complete a supervised project with binomial, ordinal or multinomial outcome variables.

The supervising teacher will assist students in finding advisors for their project.

Students are expected to have completed the course Regression Analysis

The goal of the course is to introduce students to basic methods in mathematics and statistics used in economic analysis. The mathematics part covers the following topics: functions, differentiation, integration, maximization with and without constraints, series and sequences, and simple difference and differential equations. The statistics part covers the following topics: random variables, probability, distributions, mean, variance, covariance, correlation, law of large numbers, samples, hypothesis testing, point estimation, and interval estimation.

The course is taught in collaboration with Statistics Iceland, Bifröst University and the University of Akureyri, with support from the Icelandic university cooperation fund.

The goal of this course is to enhance students' ability to understand and analyze domestic and international statistics. Students will gain knowledge about the purpose of statistics, their production, and the methodology behind their production. They will also receive training in analyzing published statistics, presenting, and interpreting them in a domestic and international context, depending on their subject matter.

Students will gain practical experience by working on realistic projects where statistics are used to analyze economic and social developments and to evaluate government actions based on statistics. Students will submit an analysis report aimed at providing information that can be used as a basis for government policy and evaluation of their actions in specific areas.

Teaching will take place from September 1 to October 17. Teaching will be online with meetings in real-time. First online-meeting is Tuesday September 2. Further information is available in the syllabus.  

The course Applied Econometrics aims to enhance students' ability to read, analyze, and evaluate scientific research in the field of economics. Students will read academic papers, deliver presentations on them, and participate in discussions on research topics. Additionally, students will receive training in systematically summarizing the state of knowledge in a specific area of economics. The course lays the foundation for a better understanding of research methods in economics and prepares students for writing their final thesis as well as for further studies or careers where the analysis and interpretation of research are key components.

This course is a continuation of Biostatistics I and constitutes a practical guide to statistical analyses of student's own research projects. The course covers the following topics. Estimation of relative risk/odds ratios and adjusted estimation of relative risk/odds ratios, correlation and simple linear regression, multiple linear regression and logistic regression. This course is centered around two core principles that define its thematic focus: Prediction from Statistical Models
Students will learn to build models—such as logistic regression and Cox proportional hazards models—that estimate the probability of future medical events based on recorded data. Evaluation of Discrimination Capacity A key theme is assessing how well models distinguish between individuals with different outcomes, using metrics such as the Area Under the ROC Curve (AUC), concordance index, and related performance measures.

The course is based on lectures and practical sessions using R for statistical analyses.

ARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.

This course covers key topics in statistics and pricing in finance. Emphasis is placed on introducing students to the use of statistical and mathematical methods for analysing, pricing, and obtaining information about financial instruments. Real‑world examples are highlighted, giving students practical experience in solving problems similar to those they may encounter in their professional work in financial markets.
The statistics portion of the course includes an overview of continuous and discrete probability distributions, expected values, variance and standard deviation, confidence intervals, hypothesis testing, and linear regression analysis, both simple and multiple. The course also covers the fundamental concepts of the Capital Asset Pricing Model (CAPM).
The pricing portion of the course focuses on the valuation of forward contracts on equities, bonds, and foreign exchange, as well as interest rate swaps, the construction of yield curves, and the types and characteristics of options.

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and will learn how to apply statistical methods they know in R. Main topics are loading data, graphical representation, descriptive statistics and how to perform the most common hypothesis tests (t- test, chi-square test, etc.) in R. In addition, students will learn how to make reports using the knitr package.

The course is taught during a five week period. A teacher gives lectures and students work on a project in class.

Students will perform traditional statistical analysis on real data sets. Special focus will be on regression methods, including multiple regression analysis. Students will apply sophisticated methods of graphical representation and automatic reporting. Students will hand in a projects where they apply the above mentioned methods on real datasets in order to answer research questions

Course description In this course students will use regression models to complete a supervised project with binomial, ordinal or multinomial outcome variables.

The supervising teacher will assist students in finding advisors for their project.

Students are expected to have completed the course Regression Analysis

In the course we examine the field methods and train students in their application. The focus is on ethical issues, research design, the fieldwork setting, participant observation, different kinds of interviews, use of visual material and the analysis of data and presentation of research results.

The course is intended for postgraduate students only. It is adapted to the needs of students from different fields of study. The course is taught over a six-week period.

The course is taught over the first six weeks of spring semester on Fridays from 1:20 pm - 3:40 pm.

Description: 
The topics of the course include: Professionalism and the scientist’s responsibilities. Demands for scientific objectivity and the ethics of research. Issues of equality and standards of good practice. Power and science. Conflicts of interest and misconduct in research. Science, academia and industry. Research ethics and ethical decision making.

Objectives: 
In this course, the student gains knowledge about ethical issues in science and research and is trained in reasoning about ethical controversies relating to science and research in contemporary society.

The instruction takes the form of lectures and discussion. The course is viewed as an academic community where students are actively engaged in a focused dialogue about  the topics. Each student (working as a member of a two-person team) gives a presentation according to a plan designed at the beginning of the course, and other students acquaint themselves with the topic as well for the purpose of participating in a teacher-led discussion.

The course covers methods for analysis of cohort studies using methods for time to event or survival analysis. It is based on the course Biostat III – Survival analysis for epidemiologists in R at the Karolinska Institutet: See (https://biostat3.net/index.html): "Topics covered include methods for estimating patient survival (life table and Kaplan-Meier methods), comparing survival between patient subgroups (log-rank test), and modelling survival (primarily Poisson regression, Cox proportional hazards model and flexible parametric models). The course addresses the concept of 'time' as a potential confounder or effect modifier and approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification." 

This course focuses on the variety of approaches and methods found within  research. Five qualitative approaches to inquiry are mainly in focus, namely; case study, narrative research, ethnography, phenomenology and grounded theory. Students gain a deeper experiences in data collection and in use of different methods for analyzing their qualitative data. They also gain experience in presenting their findings in written form. Additionally, students have the opportunity to reflect on their own research practices and on themselves as qualitative researchers.

The course focuses on statistical analysis using the R environment. It is assumed that students have basic knowledge of statistics and the statistical software R. Students will learn to apply a broad range of statistical methods in R (such as classification methods, resampling methods, linear model selection and tree-based methods). The course on 12 weeks and will be on "flipped" form. This means that no lectures will be given but students will read some material and watch videos before attending classes. Students will then work on assignments during the classes.