The course covers models used to work with the underlying variables in psychological measurements will be introduced. In the first course (Models for underlying variables I) we will work with confirmatory factor models and structural equation models (also known as path models). We will cover the assumptions of the models, how to work with them, and interpretation of results. Methods to work with different types of data will be discussed. In the second course (Models for underlying variables II) we will start by introducing methods for categorical variables and then move to the closely related item response models. Primary focus will be on models for binary data but the most common models for categorical data will be introduced. In the second part of the course, we will move on to models for longitudinal data that use underlying variables: latent growth models, cross-lagged product models, and models for intensive longitudinal methods (also known as Daily-diary data, Ambulatory assessment, or Ecological-momentary data). Emphasis will be on practical training in analyzing data with models through projects, as well as the theoretical basis of models.

This course is designed to introduce students to the practice of psychological scale development and testing. Classical test theory is introduced with an emphasis on understanding statistical concepts related to scale construction. The main focus of the course is on practical training in scale development and the controversial issues related to developing a psychological scale from scratch.

In process

This course builds upon the foundation in measurement models (CFA and IRT) from the previous semester. The course addresses relatively simple as well as more complex structural equations models as well as other methods for estimating relationships between latent factors.  The hands-on approach provides students with practical experience in estimating cross-sectional, longitudinal and hierarchical models. The interpretations of different models as well as oral and written expositions of results will be emphasized through-out the course.

Course description in process

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." 

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

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.

Kemur síðar

The main subject of the course is regression, interpretation of results, evaluation and comparison of statistical models. The emphasis is on practical analysis and evaluation of model quality. Topics include transformations, categorical variables and interaction.

Research Project

The programming language Java will be used in the course. Various data structures, algorithms and abstract data types will be covered. Among the data types and structures covered are lists, stacks, queues, priority queues, trees, binary trees, binary search trees and heaps along with related algorithms. Various search and sort algorithms will be covered. Algorithms will be analysed for their space and time complexity. There will be programming assignments in Java using the given data structures and algorithms. There will be many small assignments.

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

Kemur síðar

Kemur síðar

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

MS thesis in Applied Psychology; Quantitative Psychology

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

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.

The course is about the theory and application of random effects, or linear mixed models, and related models for correlated response variables. The course will cover methods for continuous and approximately normally distributed variables. A statistical model for such data has to describe both the expected value and the covariance between observations. The theory extends the theory of general linear models. Special software is needed for such an analysis and the necessary packages are provided in R, STATA, and SAS. The application will be based on R but other programs will be introduced for comparison.
The course will be taught between beginning of September and end of November, meeting once a week. The teaching will be in a flipped class manner. All the material (both notes and lectures) are online (see below) and it is expected that students will have viewed the material before class. In class the theory will be discussed and then students are expected to work on the application on their own computer.

Univariate calculus (basic algebra, functi ons, polynomials, logarithms and exponenti al functi ons, conti nuity and limits, diff erenti ati on, local extrema andintegrati on).
Linear algebra (vectors, matrices, linear projecti ons with matrices, matrix inverses and determinants).
Programming in R (arithmeti c, functi ons and organizing R code).
Multi variate calculus (Jacobian, Hessian and double integrals).
The approach will be to address each mathemati cs topic using a mix of (a) basic theory (in the form of concepts rather than proofs), (b) computerprogramming using R to visualize the theory, and (c) examples exclusively from stati stics. Formal lectures are not planned, but students will be able toseek assistance with their weekly assignments.
The goal of the course is to cover the calculus, linear algebra and computer programming concepts most commonly needed in stati sti cs. A student who has completed this course should have the mathematical basis for statistics courses currently taught at the MSc level by the mathematics department(and thus also for all stati sti cs courses taught by other departments).

Students will author examples and multiple-choice questi ons, and their submissions will be graded by their peers.

The material is openly available at https://open-educati on-hub.github.io/ccas/.

The Java programming language will be used to introduce basic practices in computer programming. Practice in programming is scheduled throughout the semester. An emphasis is placed on logical methods for writing program and good documentation. Main ideas related to computers and programming. Classes, objects and methods. Control statements. Strings and arrays, operations and built-in functons. Input and output. Inheritance. Ideas relatied to system design and good practices for program writing. Iteration and recursion. Searching and Sorting.

Programming in Python (for computations in engineering and science): Main commands and statements (computations, control statements, in- and output), definition and execution of functions, datatypes (numbers, matrices, strings, logical values, records), operations and built-in functions, array and matrix computation, file processing, statistics, graphics. Object-oriented programming: classes, objects, constructors and methods. Concepts associated with design and construction of program systems: Programming environment and practices, design and documentation of function and subroutine libraries, debugging and testing of programmes.

The course covers models used to work with the underlying variables in psychological measurements will be introduced. In the first course (Models for underlying variables I) we will work with confirmatory factor models and structural equation models (also known as path models). We will cover the assumptions of the models, how to work with them, and interpretation of results. Methods to work with different types of data will be discussed. In the second course (Models for underlying variables II) we will start by introducing methods for categorical variables and then move to the closely related item response models. Primary focus will be on models for binary data but the most common models for categorical data will be introduced. In the second part of the course, we will move on to models for longitudinal data that use underlying variables: latent growth models, cross-lagged product models, and models for intensive longitudinal methods (also known as Daily-diary data, Ambulatory assessment, or Ecological-momentary data). Emphasis will be on practical training in analyzing data with models through projects, as well as the theoretical basis of models.

This course is designed to introduce students to the practice of psychological scale development and testing. Classical test theory is introduced with an emphasis on understanding statistical concepts related to scale construction. The main focus of the course is on practical training in scale development and the controversial issues related to developing a psychological scale from scratch.

In process

This course builds upon the foundation in measurement models (CFA and IRT) from the previous semester. The course addresses relatively simple as well as more complex structural equations models as well as other methods for estimating relationships between latent factors.  The hands-on approach provides students with practical experience in estimating cross-sectional, longitudinal and hierarchical models. The interpretations of different models as well as oral and written expositions of results will be emphasized through-out the course.

Course description in process

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." 

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

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.

Kemur síðar

The main subject of the course is regression, interpretation of results, evaluation and comparison of statistical models. The emphasis is on practical analysis and evaluation of model quality. Topics include transformations, categorical variables and interaction.

Research Project

The programming language Java will be used in the course. Various data structures, algorithms and abstract data types will be covered. Among the data types and structures covered are lists, stacks, queues, priority queues, trees, binary trees, binary search trees and heaps along with related algorithms. Various search and sort algorithms will be covered. Algorithms will be analysed for their space and time complexity. There will be programming assignments in Java using the given data structures and algorithms. There will be many small assignments.

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

Kemur síðar

Kemur síðar

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

MS thesis in Applied Psychology; Quantitative Psychology

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

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.

The course is about the theory and application of random effects, or linear mixed models, and related models for correlated response variables. The course will cover methods for continuous and approximately normally distributed variables. A statistical model for such data has to describe both the expected value and the covariance between observations. The theory extends the theory of general linear models. Special software is needed for such an analysis and the necessary packages are provided in R, STATA, and SAS. The application will be based on R but other programs will be introduced for comparison.
The course will be taught between beginning of September and end of November, meeting once a week. The teaching will be in a flipped class manner. All the material (both notes and lectures) are online (see below) and it is expected that students will have viewed the material before class. In class the theory will be discussed and then students are expected to work on the application on their own computer.

Univariate calculus (basic algebra, functi ons, polynomials, logarithms and exponenti al functi ons, conti nuity and limits, diff erenti ati on, local extrema andintegrati on).
Linear algebra (vectors, matrices, linear projecti ons with matrices, matrix inverses and determinants).
Programming in R (arithmeti c, functi ons and organizing R code).
Multi variate calculus (Jacobian, Hessian and double integrals).
The approach will be to address each mathemati cs topic using a mix of (a) basic theory (in the form of concepts rather than proofs), (b) computerprogramming using R to visualize the theory, and (c) examples exclusively from stati stics. Formal lectures are not planned, but students will be able toseek assistance with their weekly assignments.
The goal of the course is to cover the calculus, linear algebra and computer programming concepts most commonly needed in stati sti cs. A student who has completed this course should have the mathematical basis for statistics courses currently taught at the MSc level by the mathematics department(and thus also for all stati sti cs courses taught by other departments).

Students will author examples and multiple-choice questi ons, and their submissions will be graded by their peers.

The material is openly available at https://open-educati on-hub.github.io/ccas/.

The Java programming language will be used to introduce basic practices in computer programming. Practice in programming is scheduled throughout the semester. An emphasis is placed on logical methods for writing program and good documentation. Main ideas related to computers and programming. Classes, objects and methods. Control statements. Strings and arrays, operations and built-in functons. Input and output. Inheritance. Ideas relatied to system design and good practices for program writing. Iteration and recursion. Searching and Sorting.

Programming in Python (for computations in engineering and science): Main commands and statements (computations, control statements, in- and output), definition and execution of functions, datatypes (numbers, matrices, strings, logical values, records), operations and built-in functions, array and matrix computation, file processing, statistics, graphics. Object-oriented programming: classes, objects, constructors and methods. Concepts associated with design and construction of program systems: Programming environment and practices, design and documentation of function and subroutine libraries, debugging and testing of programmes.

The course covers models used to work with the underlying variables in psychological measurements will be introduced. In the first course (Models for underlying variables I) we will work with confirmatory factor models and structural equation models (also known as path models). We will cover the assumptions of the models, how to work with them, and interpretation of results. Methods to work with different types of data will be discussed. In the second course (Models for underlying variables II) we will start by introducing methods for categorical variables and then move to the closely related item response models. Primary focus will be on models for binary data but the most common models for categorical data will be introduced. In the second part of the course, we will move on to models for longitudinal data that use underlying variables: latent growth models, cross-lagged product models, and models for intensive longitudinal methods (also known as Daily-diary data, Ambulatory assessment, or Ecological-momentary data). Emphasis will be on practical training in analyzing data with models through projects, as well as the theoretical basis of models.

This course is designed to introduce students to the practice of psychological scale development and testing. Classical test theory is introduced with an emphasis on understanding statistical concepts related to scale construction. The main focus of the course is on practical training in scale development and the controversial issues related to developing a psychological scale from scratch.

In process

This course builds upon the foundation in measurement models (CFA and IRT) from the previous semester. The course addresses relatively simple as well as more complex structural equations models as well as other methods for estimating relationships between latent factors.  The hands-on approach provides students with practical experience in estimating cross-sectional, longitudinal and hierarchical models. The interpretations of different models as well as oral and written expositions of results will be emphasized through-out the course.

Course description in process

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." 

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

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.

Kemur síðar

The main subject of the course is regression, interpretation of results, evaluation and comparison of statistical models. The emphasis is on practical analysis and evaluation of model quality. Topics include transformations, categorical variables and interaction.

Research Project

The programming language Java will be used in the course. Various data structures, algorithms and abstract data types will be covered. Among the data types and structures covered are lists, stacks, queues, priority queues, trees, binary trees, binary search trees and heaps along with related algorithms. Various search and sort algorithms will be covered. Algorithms will be analysed for their space and time complexity. There will be programming assignments in Java using the given data structures and algorithms. There will be many small assignments.

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

Kemur síðar

Kemur síðar

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

MS thesis in Applied Psychology; Quantitative Psychology

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

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.

The course is about the theory and application of random effects, or linear mixed models, and related models for correlated response variables. The course will cover methods for continuous and approximately normally distributed variables. A statistical model for such data has to describe both the expected value and the covariance between observations. The theory extends the theory of general linear models. Special software is needed for such an analysis and the necessary packages are provided in R, STATA, and SAS. The application will be based on R but other programs will be introduced for comparison.
The course will be taught between beginning of September and end of November, meeting once a week. The teaching will be in a flipped class manner. All the material (both notes and lectures) are online (see below) and it is expected that students will have viewed the material before class. In class the theory will be discussed and then students are expected to work on the application on their own computer.

Univariate calculus (basic algebra, functi ons, polynomials, logarithms and exponenti al functi ons, conti nuity and limits, diff erenti ati on, local extrema andintegrati on).
Linear algebra (vectors, matrices, linear projecti ons with matrices, matrix inverses and determinants).
Programming in R (arithmeti c, functi ons and organizing R code).
Multi variate calculus (Jacobian, Hessian and double integrals).
The approach will be to address each mathemati cs topic using a mix of (a) basic theory (in the form of concepts rather than proofs), (b) computerprogramming using R to visualize the theory, and (c) examples exclusively from stati stics. Formal lectures are not planned, but students will be able toseek assistance with their weekly assignments.
The goal of the course is to cover the calculus, linear algebra and computer programming concepts most commonly needed in stati sti cs. A student who has completed this course should have the mathematical basis for statistics courses currently taught at the MSc level by the mathematics department(and thus also for all stati sti cs courses taught by other departments).

Students will author examples and multiple-choice questi ons, and their submissions will be graded by their peers.

The material is openly available at https://open-educati on-hub.github.io/ccas/.

The Java programming language will be used to introduce basic practices in computer programming. Practice in programming is scheduled throughout the semester. An emphasis is placed on logical methods for writing program and good documentation. Main ideas related to computers and programming. Classes, objects and methods. Control statements. Strings and arrays, operations and built-in functons. Input and output. Inheritance. Ideas relatied to system design and good practices for program writing. Iteration and recursion. Searching and Sorting.

Programming in Python (for computations in engineering and science): Main commands and statements (computations, control statements, in- and output), definition and execution of functions, datatypes (numbers, matrices, strings, logical values, records), operations and built-in functions, array and matrix computation, file processing, statistics, graphics. Object-oriented programming: classes, objects, constructors and methods. Concepts associated with design and construction of program systems: Programming environment and practices, design and documentation of function and subroutine libraries, debugging and testing of programmes.

This course is an introduction to statistics in the life sciences. The course covers the following topics. Types of data: categorical data, count data, data on continuous variables. Descriptive statistics; numerical statistics and statistical graphs. Probability distributions, the binomial distribution, the Poisson distribution and the normal distribution. The definitions of a random sample and of a population. Sampling distributions. Confidence intervals and hypothesis testing. Comparison of means between groups. Statistical tests for frequency tables. Linear and logistic regression with ROC analysis. Survival analysis with the methods of Kaplan-Meier and Cox. The course is based on lectures and practical sessions in computer labs. In the practical sessions exercises are solved with the statistical software package R and the RStudio environment.

The increase in human numbers and the scale of economic activity has put humans in a position to greatly influence environmental and resource change. Explaining the extent and impact of this influence or selecting and designing appropriate management methods is well beyond the theory and analytical tools of individual disciplines, such as economics, ecology, social or physical sciences. Before introducing the perspective and tools of various disciplines students must have at a minimum a basic understanding of the driving forces behind in addition to the physical and ecological principles of environmental and resource change. The aim of this course is to provide such a background. Some of the topics covered are:the ecological footprint, population growth, economic growth, technology and the environment, natural capital and ecosystem services, diversity as a resource, soil degradation, Pollution and health, Air, water and soil pollution. Climate change and ozone depletion. Urban smog and pollution from heavy industry. Municipal and hazardous waste. Freshwater resources, Marine resources. Forests and wetlands. Energy resources and Energy and the environment.

The course provides an overview of the main determinants of health in a westernized society (such as Iceland) and preventive interventions at different levels of such societies. With main emphasis on planning, implementing and documentation of the effectiveness of interventions aiming at general health promotion and primary prevention, the course also covers examples of secondary and tertiary prevention. The students get training in planning their own preventive interventions.

The course covers models used to work with the underlying variables in psychological measurements will be introduced. In the first course (Models for underlying variables I) we will work with confirmatory factor models and structural equation models (also known as path models). We will cover the assumptions of the models, how to work with them, and interpretation of results. Methods to work with different types of data will be discussed. In the second course (Models for underlying variables II) we will start by introducing methods for categorical variables and then move to the closely related item response models. Primary focus will be on models for binary data but the most common models for categorical data will be introduced. In the second part of the course, we will move on to models for longitudinal data that use underlying variables: latent growth models, cross-lagged product models, and models for intensive longitudinal methods (also known as Daily-diary data, Ambulatory assessment, or Ecological-momentary data). Emphasis will be on practical training in analyzing data with models through projects, as well as the theoretical basis of models.

This course is designed to introduce students to the practice of psychological scale development and testing. Classical test theory is introduced with an emphasis on understanding statistical concepts related to scale construction. The main focus of the course is on practical training in scale development and the controversial issues related to developing a psychological scale from scratch.

This course examines how social psychology can be applied to a wide range of social problems. We will, for example, discuss how to apply social psychological theories to the understanding of social matters such as social status, discrimination, prejudice, inequality, multiculturalism, aging, trauma, climate change, and more. Students will gain a theoretical perspective on how to positively influence people’s behaviour and will learn how to base public policy and prevention efforts on sound psychological knowledge.

This course builds upon the foundation in measurement models (CFA and IRT) from the previous semester. The course addresses relatively simple as well as more complex structural equations models as well as other methods for estimating relationships between latent factors.  The hands-on approach provides students with practical experience in estimating cross-sectional, longitudinal and hierarchical models. The interpretations of different models as well as oral and written expositions of results will be emphasized through-out the course.

The course explores the application of social psychological methods, theories, principles or research findings to understanding or finding solutions to real social problems. The course will cover diverse topics such as social marketing, environmental psychology, cross-cultural psychology, group behaviour, prejudice, biases and fallacies in economic contexts, advertising and consumer behaviour, to name a few. Students will need to work independently towards developing their skills in the application of social psychology to problem solving. Such knowledge will be valuable in the job market upon graduation for those interested in careers involving research or policy-related work in applied contexts. Course assessment involves seminar presentations and written assignments.

Kemur síðar

Kemur síðar

MS thesis in Applied Psychology; Social Psychology

The course will present gerontology as a multi-professional discipline with an emphasis on social gerontology. The main concepts and research methods will be introduced. Formal and informal care will be discussed. Personal meaning and well-being in the social contexts of later adulthood and old age will be explored and methods to enhance personal meaning will be introduced. Emphasis is placed on deepening student’s understanding by helping them become more adept at working with other professions on elderly welfare issues.

The course is taught an intensive session.

Attendance in the intensive session is required.

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 course provides an overview of definitions, history, aims, legislation, methods and ethical considerations in public health and public health sciences. The course lays emphasis on global public health as well as on the Icelandic health care system, its administration and funding in comparison with health care systems in other nations. An overview is provided on Icelandic and international databases on health and disease and possibilities for their utilization in research and policy making for health promotion. In addition, current public health issues at each time are emphasized.

This course covers a detailed overview of theories in criminology and sociology of deviance. Students will read empirical research testing these theories in Iceland and elsewhere.

Different types of crimes and topics will be discussed in criminological/sociological light, such as gender and crime, immigration and crime.

Emphasis is placed on linking theoretical discussion with empirical research.

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.

The course addresses the main issues in gender studies through the lens of diversity in modern societies. The gender perspective is applied to provide an overview of the status and condition of men and women. The origins and development of the fight for women’s rights and gender studies as an academic field. The main concepts of gender studies are introduced, including sex, gender, essentialism and constructivism. Finally, the course looks into how gender necessarily intersects with other social factors.

Teaching Arrangement: The course is based on flipped learning, which means that all lectures will be available on Canvas. On-campus and distance students attend weekly discussion sessions at the university or on Teams, and online students participate in weekly discussions on Canvas

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

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.

This introductory course in public administration provides students with a holistic overview over how public administration is organized and how it has developed over the last decades. The main characteristics of the public administration in Iceland is introduces, its foundations and main formative elements. The course covers the basic theories and concepts of the discipline of public administration and its scholarly endeavour. It introduces the main organizational theories, and the main theories on decentralization and delegation of power and decision-making. The focus is on the relationship between public administration and politics and how that relationship shapes organizational practices and public policy making. The emphasis is on analysing and understanding the differences between the private and the public sector.

Various incentives, policies and management initiatives are used to influence human behavior, to limit the ecological footprint (EF), and to promote sustainable development. This course focuses on environmental and resource management and policy - in the context of sustainable development (SD). The course is broken to three sessions. In the first session we assess the concept SD from various perspectives - followed by an attempt to operationalize the concept. We compare the concepts growth and SD and ask if the two are compatible and discuss sustainability indicators. In the second session we critically examine various tools that are frequently used in environmental and resource decision-making, such as formal decision analysis, cost-benefit and cost-effectiveness analysis in addition to valuing ecosystem services. In the third session we examine the ideological foundations behind environmental and resource policy, and assess various policy and management initiatives for diverse situations in a comparative international context. Examples are much based on student interests but possible examples include bottle-deposit systems, ITQ's, voluntary approaches and multi-criteria management.

Climate change is a global issue and one of the more challenging environmental problems of the present and near future. Since 1992 there have been many meetings and agreement under the auspices of the United Nations.

This course will cover the topic of climate change from several angles. Starting with the basic evidence and science behind climate change and modeling of future scenarios, then through impacts and vulnerability to efforts to mitigate and adapt to climate change. Issues such as climate refugees, gender aspects and negotiations are addressed.

Grading is based on a writing assignment, short quiz, course participation and presentations, in addition to group assignments where mitigation, future scenarios and basic processes are examined further. Students taking this course generally have very different backgrounds and you will have a chance to learn about climate change from different viewpoints.

The course will present gerontology as a multi-professional discipline with an emphasis on social gerontology. The main concepts and research methods will be introduced. Formal and informal care will be discussed. Personal meaning and well-being in the social contexts of later adulthood and old age will be explored and methods to enhance personal meaning will be introduced. Emphasis is placed on deepening student’s understanding by helping them become more adept at working with other professions on elderly welfare issues.

The course is taught an intensive session.

Attendance in the intensive session is required.

The course gives an overview of various models for defining mental disorders. Students are introduced to methods of current interventions and the results of outcome research. In addition, health will be defined and how thoughts are believed to influence health and health behaviour. The basic theory of Cognitive Behaviour Therapy (CBT) will be introduced and a theoretical overview of CBT, models, concepts and methods presented.

This course concerns health behaviour in general. The health behaviour of different age groups will be addressed as well as the association between biological factors, health behaviour and social status. How can behaviour, coping and stress affect health? Behaviour in relations to food and consumption habits is of particular interest. How is it possible to shape healthy habits from childhood, for example to influence food choice and overcome pickiness about food? Societal influence and the part of media is also addressed. The course literature is from various books and scientific articles from different fields and is intended to capture the subject in an interdisciplinary manner.

The course focuses on young people’s risk behavior (e.g. drug use, deviance, inconsistent school attendance) and resilience related to various pedagogical, social, educational, and psychological factors. Subjects dealt with in the course will for example be young people’s social development, communication skills, mental disorders, sexual reproductive health, trauma and their view on different challenges in their life. Different preventive measures will be discussed and the role of homes, schools and recreations in different preventions. A special focus is on developmental research that explores the relationship between developmental growth and risk behavior. Projects are designed to seek understanding on how young people perceive risk factors in their lives.

The course is offered as a distant education course. Teaching lessons will be recorded and put on the CANVAS education management system but if there is real time teaching then it will be recorded and put on CANVAS. Discussion lessons are once a week (60 min.) were students can choose between being in-house or online. The same goes for essay presentations which are at the end of the semester.

In this course, students are introduced to concepts and methods for planning, monitoring and evaluating projects/activities. It covers developing a problem statement, mapping stakeholders, development of a project plan, design of project evaluations, introduction to data collection, and reporting on project progress. Emphasis will be placed on the importance of stakeholder participation and gender mainstreaming. Approaches taught in the course are rooted in international development but are useful in the planning, monitoring and evaluation of projects/activities across all sectors. This course is designed to be practical and develop skills that are directly applicable in many workplaces. The teaching is based on a combination of theoretical instruction, discussion of real-life applications, interactive workshops, and guided group work. 

The goal of this course is to deepen students' knowledge of gerontology and geriatrics. The social, biological and psychological aspects of aging will be discussed. Services for older adults  will be discussed in general as well as caring for special needs. Icelandic and international research will be presented. Various theories in gerontology will be examined and their effects on attitudes towards and services for older people. Cross discipline teamwork will be discussed with emphasis placed on working with various professions.

Students learn about selected concepts and research from social psychology, behavioral economics, judgment and decision making that can be used for the design, evaluation and implementation of public policy. Theories on rationality and bounded rationality of human thought will be compared and contrasted. Students will gain understanding of how people make decisions and evaluate risk, the influence of incentives on decisions, and how to influence attitudes and behavior. The course will also cover intergroup relations and negotiations. Finally, will we discuss the relationship between public policy and well-being.

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.

This course is taught every other year.

The main subject of the course is regression, interpretation of results, evaluation and comparison of statistical models. The emphasis is on practical analysis and evaluation of model quality. Topics include transformations, categorical variables and interaction.

Qualitative Methods provides students with an introduction to some of the most commonly used qualitative methods and methodological tools in political science. The main focus in the course is on case studies (including process tracing) and various tools and techniques used within case studies, e.g., qualitative content analysis, interviewing, and focus groups. One part of the course is also dedicated to discourse analysis. The course begins with a very brief introduction to philosophy of science and outlines basic ontological, epistemological and methodological issues in the social sciences. The remainder of the course is dedicated to the methods and tools/techniques listed above. Students will gain a deeper understanding of the philosophical underpinnings, assumptions and ambitions of the different methods, but they will also gain practical experience as to the design and execution of research within the different traditions.

The course is designed in a highly interactive way and emphasizes active student participation. It is expected that students have done at least the required reading assigned for the given day and are ready to participate in group work and discussions in class. There are two types of classes in this course: lecture & discussion classes and workshops. Each lecture & discussion class will be divided into three parts: a short agenda-setting lecture by the lecturer (40 minutes), group work (40 minutes), and a concluding general discussion (40 minutes). This design is highly effective with regard to achieving the course’s learning outcomes, but it also requires that students have familiarized themselves with the assigned reading for the day. In the workshops, the class will be divided into two groups (A and B).

Contrary to what might be expected from contemporary political discourse, fear, conspiracy theories and mistrust have characterized politics since time immemorial. In this course, we explore these phenomena from the interdisciplinary perspective of political psychology with support from other related disciplines. Initially, we will discuss trust in politics, politicians and citizens as an important but complicated concept within politics. We reflect on the byproducts of mistrust, such as the polarization of social groups and political participation. Next, students learn about the psychology of fear and its known effects on people's beliefs. The psychology of conspiracy theories, their causes and consequences for political behavior and attitudes will also be discussed.

In some settings, humans interact with the environment and use natural resources sustainably, but not in others. What explains such differences arising from human-environment interactions is the role of governance. Environmental governance can in its most basic form be understood as a social function centered on efforts to steer or guide the action of humans – being an individual, a small local user groups or the international community – towards desired outcomes and away from outcomes regarded as undesirable (Young, 2013).

This course has a focus on the introduction and understanding of different dimensions of environmental and natural resources governance in the context of sustainability.

It is divided into four interconnected sections:

1. Environmental Governance: The basics. What is governance? The environment as an arena for coordination and conflict. How do we understand actors, their roles and decision making? Power and power relations. Institutions and institutional change. Social-ecological systems. Governance structures. Public goods.
2. International and Domestic Environmental Governance. International environmental governance and institutions, e.g. EU, UN, UN Environment, FAO, World Bank etc. North-South issues. Environmental regimes; ozone, climate change, desertification, etc. Synergies. Introduction to environmental governance in Iceland and how it relates to decision-making with regard to environment and resources. Governance structure, central, local decision-making. Relationship between various levels of governance, parliament, ministries, agencies.
3. Public Responsibility and the Environment. Public participation. How can the public affect decision-making? Domestic and international environmental Non-governmental organizations.
4. Corporate Governance in the sustainability context. This part of the course focuses on corporate governance, such as outlined in the Nasdaq Corporate Governance Guidelines in the context of corporate sustainability. Relevant to the discussion is fiduciary duty, the ESRS Governance Standards (ESRS 1 and 2; ESRS G1), the SDGs 8-10, 12, 13, 17, the UN Global Compact Principle number 10, GRI Universal Standards and 200 Series, the Economic layer canvas, and more.

This is an intensive course with the active participation of students. It is taught over 13 weeks. The course takes as its starting point the idea that although governments and nonprofits are crucial to modern society, businesses are largely responsible for creating the wealth upon which the well-being of society depends, while also being a part of the problems created. As they create that wealth, their actions affect society, which is composed of a wide variety of stakeholders, and the natural environment. In turn, society shapes the rules and expectations by which businesses must navigate their internal and external environments. These include the Sustainable Development Goals, i.e., Goals 1-5, 8, 9, 10, 11, 12, 13, and 16, the Paris Agreement, the UN Global Compact, the European Sustainability Reporting Standards (ESRS), the Global Reporting Initiative, and more. This interaction between corporations, society (in its broadest sense) and the natural environment is the concern of corporate social responsibility (CSR), but the issues need to be addressed from a strategic point of view. 

Regardless of one’s viewpoint about the interaction of business and society, the continued co-existence of for-profit organizations is essential. This course seeks to explore the dimensions of that interaction from a multi-stakeholder perspective. That exploration is intended to be interactive, with the journey of exploration involving an analysis of CSR-related issues, simulation, and case studies.

The course is organized into six broad sections. In the first section, we will explore what corporate social responsibility (CSR) means and the driving forces of CSR. The second section focuses on the stakeholder perspective, and in the third section, we study the legal perspective. In the fourth section, we will explore the behavioral perspective, in the fifth section the strategic perspective and in the sixth section the focus will be on the sustainable perspective and sustainable value creation.   

This course is an introduction to statistics in the life sciences. The course covers the following topics. Types of data: categorical data, count data, data on continuous variables. Descriptive statistics; numerical statistics and statistical graphs. Probability distributions, the binomial distribution, the Poisson distribution and the normal distribution. The definitions of a random sample and of a population. Sampling distributions. Confidence intervals and hypothesis testing. Comparison of means between groups. Statistical tests for frequency tables. Linear and logistic regression with ROC analysis. Survival analysis with the methods of Kaplan-Meier and Cox. The course is based on lectures and practical sessions in computer labs. In the practical sessions exercises are solved with the statistical software package R and the RStudio environment.

The increase in human numbers and the scale of economic activity has put humans in a position to greatly influence environmental and resource change. Explaining the extent and impact of this influence or selecting and designing appropriate management methods is well beyond the theory and analytical tools of individual disciplines, such as economics, ecology, social or physical sciences. Before introducing the perspective and tools of various disciplines students must have at a minimum a basic understanding of the driving forces behind in addition to the physical and ecological principles of environmental and resource change. The aim of this course is to provide such a background. Some of the topics covered are:the ecological footprint, population growth, economic growth, technology and the environment, natural capital and ecosystem services, diversity as a resource, soil degradation, Pollution and health, Air, water and soil pollution. Climate change and ozone depletion. Urban smog and pollution from heavy industry. Municipal and hazardous waste. Freshwater resources, Marine resources. Forests and wetlands. Energy resources and Energy and the environment.

The course provides an overview of the main determinants of health in a westernized society (such as Iceland) and preventive interventions at different levels of such societies. With main emphasis on planning, implementing and documentation of the effectiveness of interventions aiming at general health promotion and primary prevention, the course also covers examples of secondary and tertiary prevention. The students get training in planning their own preventive interventions.

The course covers models used to work with the underlying variables in psychological measurements will be introduced. In the first course (Models for underlying variables I) we will work with confirmatory factor models and structural equation models (also known as path models). We will cover the assumptions of the models, how to work with them, and interpretation of results. Methods to work with different types of data will be discussed. In the second course (Models for underlying variables II) we will start by introducing methods for categorical variables and then move to the closely related item response models. Primary focus will be on models for binary data but the most common models for categorical data will be introduced. In the second part of the course, we will move on to models for longitudinal data that use underlying variables: latent growth models, cross-lagged product models, and models for intensive longitudinal methods (also known as Daily-diary data, Ambulatory assessment, or Ecological-momentary data). Emphasis will be on practical training in analyzing data with models through projects, as well as the theoretical basis of models.

This course is designed to introduce students to the practice of psychological scale development and testing. Classical test theory is introduced with an emphasis on understanding statistical concepts related to scale construction. The main focus of the course is on practical training in scale development and the controversial issues related to developing a psychological scale from scratch.

This course examines how social psychology can be applied to a wide range of social problems. We will, for example, discuss how to apply social psychological theories to the understanding of social matters such as social status, discrimination, prejudice, inequality, multiculturalism, aging, trauma, climate change, and more. Students will gain a theoretical perspective on how to positively influence people’s behaviour and will learn how to base public policy and prevention efforts on sound psychological knowledge.

This course builds upon the foundation in measurement models (CFA and IRT) from the previous semester. The course addresses relatively simple as well as more complex structural equations models as well as other methods for estimating relationships between latent factors.  The hands-on approach provides students with practical experience in estimating cross-sectional, longitudinal and hierarchical models. The interpretations of different models as well as oral and written expositions of results will be emphasized through-out the course.

The course explores the application of social psychological methods, theories, principles or research findings to understanding or finding solutions to real social problems. The course will cover diverse topics such as social marketing, environmental psychology, cross-cultural psychology, group behaviour, prejudice, biases and fallacies in economic contexts, advertising and consumer behaviour, to name a few. Students will need to work independently towards developing their skills in the application of social psychology to problem solving. Such knowledge will be valuable in the job market upon graduation for those interested in careers involving research or policy-related work in applied contexts. Course assessment involves seminar presentations and written assignments.

Kemur síðar

Kemur síðar

MS thesis in Applied Psychology; Social Psychology

The course will present gerontology as a multi-professional discipline with an emphasis on social gerontology. The main concepts and research methods will be introduced. Formal and informal care will be discussed. Personal meaning and well-being in the social contexts of later adulthood and old age will be explored and methods to enhance personal meaning will be introduced. Emphasis is placed on deepening student’s understanding by helping them become more adept at working with other professions on elderly welfare issues.

The course is taught an intensive session.

Attendance in the intensive session is required.

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 course provides an overview of definitions, history, aims, legislation, methods and ethical considerations in public health and public health sciences. The course lays emphasis on global public health as well as on the Icelandic health care system, its administration and funding in comparison with health care systems in other nations. An overview is provided on Icelandic and international databases on health and disease and possibilities for their utilization in research and policy making for health promotion. In addition, current public health issues at each time are emphasized.

This course covers a detailed overview of theories in criminology and sociology of deviance. Students will read empirical research testing these theories in Iceland and elsewhere.

Different types of crimes and topics will be discussed in criminological/sociological light, such as gender and crime, immigration and crime.

Emphasis is placed on linking theoretical discussion with empirical research.

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.

The course addresses the main issues in gender studies through the lens of diversity in modern societies. The gender perspective is applied to provide an overview of the status and condition of men and women. The origins and development of the fight for women’s rights and gender studies as an academic field. The main concepts of gender studies are introduced, including sex, gender, essentialism and constructivism. Finally, the course looks into how gender necessarily intersects with other social factors.

Teaching Arrangement: The course is based on flipped learning, which means that all lectures will be available on Canvas. On-campus and distance students attend weekly discussion sessions at the university or on Teams, and online students participate in weekly discussions on Canvas

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

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.

This introductory course in public administration provides students with a holistic overview over how public administration is organized and how it has developed over the last decades. The main characteristics of the public administration in Iceland is introduces, its foundations and main formative elements. The course covers the basic theories and concepts of the discipline of public administration and its scholarly endeavour. It introduces the main organizational theories, and the main theories on decentralization and delegation of power and decision-making. The focus is on the relationship between public administration and politics and how that relationship shapes organizational practices and public policy making. The emphasis is on analysing and understanding the differences between the private and the public sector.

Various incentives, policies and management initiatives are used to influence human behavior, to limit the ecological footprint (EF), and to promote sustainable development. This course focuses on environmental and resource management and policy - in the context of sustainable development (SD). The course is broken to three sessions. In the first session we assess the concept SD from various perspectives - followed by an attempt to operationalize the concept. We compare the concepts growth and SD and ask if the two are compatible and discuss sustainability indicators. In the second session we critically examine various tools that are frequently used in environmental and resource decision-making, such as formal decision analysis, cost-benefit and cost-effectiveness analysis in addition to valuing ecosystem services. In the third session we examine the ideological foundations behind environmental and resource policy, and assess various policy and management initiatives for diverse situations in a comparative international context. Examples are much based on student interests but possible examples include bottle-deposit systems, ITQ's, voluntary approaches and multi-criteria management.

Climate change is a global issue and one of the more challenging environmental problems of the present and near future. Since 1992 there have been many meetings and agreement under the auspices of the United Nations.

This course will cover the topic of climate change from several angles. Starting with the basic evidence and science behind climate change and modeling of future scenarios, then through impacts and vulnerability to efforts to mitigate and adapt to climate change. Issues such as climate refugees, gender aspects and negotiations are addressed.

Grading is based on a writing assignment, short quiz, course participation and presentations, in addition to group assignments where mitigation, future scenarios and basic processes are examined further. Students taking this course generally have very different backgrounds and you will have a chance to learn about climate change from different viewpoints.

The course will present gerontology as a multi-professional discipline with an emphasis on social gerontology. The main concepts and research methods will be introduced. Formal and informal care will be discussed. Personal meaning and well-being in the social contexts of later adulthood and old age will be explored and methods to enhance personal meaning will be introduced. Emphasis is placed on deepening student’s understanding by helping them become more adept at working with other professions on elderly welfare issues.

The course is taught an intensive session.

Attendance in the intensive session is required.

The course gives an overview of various models for defining mental disorders. Students are introduced to methods of current interventions and the results of outcome research. In addition, health will be defined and how thoughts are believed to influence health and health behaviour. The basic theory of Cognitive Behaviour Therapy (CBT) will be introduced and a theoretical overview of CBT, models, concepts and methods presented.

This course concerns health behaviour in general. The health behaviour of different age groups will be addressed as well as the association between biological factors, health behaviour and social status. How can behaviour, coping and stress affect health? Behaviour in relations to food and consumption habits is of particular interest. How is it possible to shape healthy habits from childhood, for example to influence food choice and overcome pickiness about food? Societal influence and the part of media is also addressed. The course literature is from various books and scientific articles from different fields and is intended to capture the subject in an interdisciplinary manner.

The course focuses on young people’s risk behavior (e.g. drug use, deviance, inconsistent school attendance) and resilience related to various pedagogical, social, educational, and psychological factors. Subjects dealt with in the course will for example be young people’s social development, communication skills, mental disorders, sexual reproductive health, trauma and their view on different challenges in their life. Different preventive measures will be discussed and the role of homes, schools and recreations in different preventions. A special focus is on developmental research that explores the relationship between developmental growth and risk behavior. Projects are designed to seek understanding on how young people perceive risk factors in their lives.

The course is offered as a distant education course. Teaching lessons will be recorded and put on the CANVAS education management system but if there is real time teaching then it will be recorded and put on CANVAS. Discussion lessons are once a week (60 min.) were students can choose between being in-house or online. The same goes for essay presentations which are at the end of the semester.

In this course, students are introduced to concepts and methods for planning, monitoring and evaluating projects/activities. It covers developing a problem statement, mapping stakeholders, development of a project plan, design of project evaluations, introduction to data collection, and reporting on project progress. Emphasis will be placed on the importance of stakeholder participation and gender mainstreaming. Approaches taught in the course are rooted in international development but are useful in the planning, monitoring and evaluation of projects/activities across all sectors. This course is designed to be practical and develop skills that are directly applicable in many workplaces. The teaching is based on a combination of theoretical instruction, discussion of real-life applications, interactive workshops, and guided group work. 

The goal of this course is to deepen students' knowledge of gerontology and geriatrics. The social, biological and psychological aspects of aging will be discussed. Services for older adults  will be discussed in general as well as caring for special needs. Icelandic and international research will be presented. Various theories in gerontology will be examined and their effects on attitudes towards and services for older people. Cross discipline teamwork will be discussed with emphasis placed on working with various professions.

Students learn about selected concepts and research from social psychology, behavioral economics, judgment and decision making that can be used for the design, evaluation and implementation of public policy. Theories on rationality and bounded rationality of human thought will be compared and contrasted. Students will gain understanding of how people make decisions and evaluate risk, the influence of incentives on decisions, and how to influence attitudes and behavior. The course will also cover intergroup relations and negotiations. Finally, will we discuss the relationship between public policy and well-being.

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.

This course is taught every other year.

The main subject of the course is regression, interpretation of results, evaluation and comparison of statistical models. The emphasis is on practical analysis and evaluation of model quality. Topics include transformations, categorical variables and interaction.

Qualitative Methods provides students with an introduction to some of the most commonly used qualitative methods and methodological tools in political science. The main focus in the course is on case studies (including process tracing) and various tools and techniques used within case studies, e.g., qualitative content analysis, interviewing, and focus groups. One part of the course is also dedicated to discourse analysis. The course begins with a very brief introduction to philosophy of science and outlines basic ontological, epistemological and methodological issues in the social sciences. The remainder of the course is dedicated to the methods and tools/techniques listed above. Students will gain a deeper understanding of the philosophical underpinnings, assumptions and ambitions of the different methods, but they will also gain practical experience as to the design and execution of research within the different traditions.

The course is designed in a highly interactive way and emphasizes active student participation. It is expected that students have done at least the required reading assigned for the given day and are ready to participate in group work and discussions in class. There are two types of classes in this course: lecture & discussion classes and workshops. Each lecture & discussion class will be divided into three parts: a short agenda-setting lecture by the lecturer (40 minutes), group work (40 minutes), and a concluding general discussion (40 minutes). This design is highly effective with regard to achieving the course’s learning outcomes, but it also requires that students have familiarized themselves with the assigned reading for the day. In the workshops, the class will be divided into two groups (A and B).

Contrary to what might be expected from contemporary political discourse, fear, conspiracy theories and mistrust have characterized politics since time immemorial. In this course, we explore these phenomena from the interdisciplinary perspective of political psychology with support from other related disciplines. Initially, we will discuss trust in politics, politicians and citizens as an important but complicated concept within politics. We reflect on the byproducts of mistrust, such as the polarization of social groups and political participation. Next, students learn about the psychology of fear and its known effects on people's beliefs. The psychology of conspiracy theories, their causes and consequences for political behavior and attitudes will also be discussed.

In some settings, humans interact with the environment and use natural resources sustainably, but not in others. What explains such differences arising from human-environment interactions is the role of governance. Environmental governance can in its most basic form be understood as a social function centered on efforts to steer or guide the action of humans – being an individual, a small local user groups or the international community – towards desired outcomes and away from outcomes regarded as undesirable (Young, 2013).

This course has a focus on the introduction and understanding of different dimensions of environmental and natural resources governance in the context of sustainability.

It is divided into four interconnected sections:

1. Environmental Governance: The basics. What is governance? The environment as an arena for coordination and conflict. How do we understand actors, their roles and decision making? Power and power relations. Institutions and institutional change. Social-ecological systems. Governance structures. Public goods.
2. International and Domestic Environmental Governance. International environmental governance and institutions, e.g. EU, UN, UN Environment, FAO, World Bank etc. North-South issues. Environmental regimes; ozone, climate change, desertification, etc. Synergies. Introduction to environmental governance in Iceland and how it relates to decision-making with regard to environment and resources. Governance structure, central, local decision-making. Relationship between various levels of governance, parliament, ministries, agencies.
3. Public Responsibility and the Environment. Public participation. How can the public affect decision-making? Domestic and international environmental Non-governmental organizations.
4. Corporate Governance in the sustainability context. This part of the course focuses on corporate governance, such as outlined in the Nasdaq Corporate Governance Guidelines in the context of corporate sustainability. Relevant to the discussion is fiduciary duty, the ESRS Governance Standards (ESRS 1 and 2; ESRS G1), the SDGs 8-10, 12, 13, 17, the UN Global Compact Principle number 10, GRI Universal Standards and 200 Series, the Economic layer canvas, and more.

This is an intensive course with the active participation of students. It is taught over 13 weeks. The course takes as its starting point the idea that although governments and nonprofits are crucial to modern society, businesses are largely responsible for creating the wealth upon which the well-being of society depends, while also being a part of the problems created. As they create that wealth, their actions affect society, which is composed of a wide variety of stakeholders, and the natural environment. In turn, society shapes the rules and expectations by which businesses must navigate their internal and external environments. These include the Sustainable Development Goals, i.e., Goals 1-5, 8, 9, 10, 11, 12, 13, and 16, the Paris Agreement, the UN Global Compact, the European Sustainability Reporting Standards (ESRS), the Global Reporting Initiative, and more. This interaction between corporations, society (in its broadest sense) and the natural environment is the concern of corporate social responsibility (CSR), but the issues need to be addressed from a strategic point of view. 

Regardless of one’s viewpoint about the interaction of business and society, the continued co-existence of for-profit organizations is essential. This course seeks to explore the dimensions of that interaction from a multi-stakeholder perspective. That exploration is intended to be interactive, with the journey of exploration involving an analysis of CSR-related issues, simulation, and case studies.

The course is organized into six broad sections. In the first section, we will explore what corporate social responsibility (CSR) means and the driving forces of CSR. The second section focuses on the stakeholder perspective, and in the third section, we study the legal perspective. In the fourth section, we will explore the behavioral perspective, in the fifth section the strategic perspective and in the sixth section the focus will be on the sustainable perspective and sustainable value creation.   

This course is an introduction to statistics in the life sciences. The course covers the following topics. Types of data: categorical data, count data, data on continuous variables. Descriptive statistics; numerical statistics and statistical graphs. Probability distributions, the binomial distribution, the Poisson distribution and the normal distribution. The definitions of a random sample and of a population. Sampling distributions. Confidence intervals and hypothesis testing. Comparison of means between groups. Statistical tests for frequency tables. Linear and logistic regression with ROC analysis. Survival analysis with the methods of Kaplan-Meier and Cox. The course is based on lectures and practical sessions in computer labs. In the practical sessions exercises are solved with the statistical software package R and the RStudio environment.

The increase in human numbers and the scale of economic activity has put humans in a position to greatly influence environmental and resource change. Explaining the extent and impact of this influence or selecting and designing appropriate management methods is well beyond the theory and analytical tools of individual disciplines, such as economics, ecology, social or physical sciences. Before introducing the perspective and tools of various disciplines students must have at a minimum a basic understanding of the driving forces behind in addition to the physical and ecological principles of environmental and resource change. The aim of this course is to provide such a background. Some of the topics covered are:the ecological footprint, population growth, economic growth, technology and the environment, natural capital and ecosystem services, diversity as a resource, soil degradation, Pollution and health, Air, water and soil pollution. Climate change and ozone depletion. Urban smog and pollution from heavy industry. Municipal and hazardous waste. Freshwater resources, Marine resources. Forests and wetlands. Energy resources and Energy and the environment.

The course provides an overview of the main determinants of health in a westernized society (such as Iceland) and preventive interventions at different levels of such societies. With main emphasis on planning, implementing and documentation of the effectiveness of interventions aiming at general health promotion and primary prevention, the course also covers examples of secondary and tertiary prevention. The students get training in planning their own preventive interventions.

The course covers models used to work with the underlying variables in psychological measurements will be introduced. In the first course (Models for underlying variables I) we will work with confirmatory factor models and structural equation models (also known as path models). We will cover the assumptions of the models, how to work with them, and interpretation of results. Methods to work with different types of data will be discussed. In the second course (Models for underlying variables II) we will start by introducing methods for categorical variables and then move to the closely related item response models. Primary focus will be on models for binary data but the most common models for categorical data will be introduced. In the second part of the course, we will move on to models for longitudinal data that use underlying variables: latent growth models, cross-lagged product models, and models for intensive longitudinal methods (also known as Daily-diary data, Ambulatory assessment, or Ecological-momentary data). Emphasis will be on practical training in analyzing data with models through projects, as well as the theoretical basis of models.

This course is designed to introduce students to the practice of psychological scale development and testing. Classical test theory is introduced with an emphasis on understanding statistical concepts related to scale construction. The main focus of the course is on practical training in scale development and the controversial issues related to developing a psychological scale from scratch.

This course examines how social psychology can be applied to a wide range of social problems. We will, for example, discuss how to apply social psychological theories to the understanding of social matters such as social status, discrimination, prejudice, inequality, multiculturalism, aging, trauma, climate change, and more. Students will gain a theoretical perspective on how to positively influence people’s behaviour and will learn how to base public policy and prevention efforts on sound psychological knowledge.

This course builds upon the foundation in measurement models (CFA and IRT) from the previous semester. The course addresses relatively simple as well as more complex structural equations models as well as other methods for estimating relationships between latent factors.  The hands-on approach provides students with practical experience in estimating cross-sectional, longitudinal and hierarchical models. The interpretations of different models as well as oral and written expositions of results will be emphasized through-out the course.

The course explores the application of social psychological methods, theories, principles or research findings to understanding or finding solutions to real social problems. The course will cover diverse topics such as social marketing, environmental psychology, cross-cultural psychology, group behaviour, prejudice, biases and fallacies in economic contexts, advertising and consumer behaviour, to name a few. Students will need to work independently towards developing their skills in the application of social psychology to problem solving. Such knowledge will be valuable in the job market upon graduation for those interested in careers involving research or policy-related work in applied contexts. Course assessment involves seminar presentations and written assignments.

Kemur síðar

Kemur síðar

MS thesis in Applied Psychology; Social Psychology

The course will present gerontology as a multi-professional discipline with an emphasis on social gerontology. The main concepts and research methods will be introduced. Formal and informal care will be discussed. Personal meaning and well-being in the social contexts of later adulthood and old age will be explored and methods to enhance personal meaning will be introduced. Emphasis is placed on deepening student’s understanding by helping them become more adept at working with other professions on elderly welfare issues.

The course is taught an intensive session.

Attendance in the intensive session is required.

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 course provides an overview of definitions, history, aims, legislation, methods and ethical considerations in public health and public health sciences. The course lays emphasis on global public health as well as on the Icelandic health care system, its administration and funding in comparison with health care systems in other nations. An overview is provided on Icelandic and international databases on health and disease and possibilities for their utilization in research and policy making for health promotion. In addition, current public health issues at each time are emphasized.

This course covers a detailed overview of theories in criminology and sociology of deviance. Students will read empirical research testing these theories in Iceland and elsewhere.

Different types of crimes and topics will be discussed in criminological/sociological light, such as gender and crime, immigration and crime.

Emphasis is placed on linking theoretical discussion with empirical research.

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.

The course addresses the main issues in gender studies through the lens of diversity in modern societies. The gender perspective is applied to provide an overview of the status and condition of men and women. The origins and development of the fight for women’s rights and gender studies as an academic field. The main concepts of gender studies are introduced, including sex, gender, essentialism and constructivism. Finally, the course looks into how gender necessarily intersects with other social factors.

Teaching Arrangement: The course is based on flipped learning, which means that all lectures will be available on Canvas. On-campus and distance students attend weekly discussion sessions at the university or on Teams, and online students participate in weekly discussions on Canvas

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

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.

This introductory course in public administration provides students with a holistic overview over how public administration is organized and how it has developed over the last decades. The main characteristics of the public administration in Iceland is introduces, its foundations and main formative elements. The course covers the basic theories and concepts of the discipline of public administration and its scholarly endeavour. It introduces the main organizational theories, and the main theories on decentralization and delegation of power and decision-making. The focus is on the relationship between public administration and politics and how that relationship shapes organizational practices and public policy making. The emphasis is on analysing and understanding the differences between the private and the public sector.

Various incentives, policies and management initiatives are used to influence human behavior, to limit the ecological footprint (EF), and to promote sustainable development. This course focuses on environmental and resource management and policy - in the context of sustainable development (SD). The course is broken to three sessions. In the first session we assess the concept SD from various perspectives - followed by an attempt to operationalize the concept. We compare the concepts growth and SD and ask if the two are compatible and discuss sustainability indicators. In the second session we critically examine various tools that are frequently used in environmental and resource decision-making, such as formal decision analysis, cost-benefit and cost-effectiveness analysis in addition to valuing ecosystem services. In the third session we examine the ideological foundations behind environmental and resource policy, and assess various policy and management initiatives for diverse situations in a comparative international context. Examples are much based on student interests but possible examples include bottle-deposit systems, ITQ's, voluntary approaches and multi-criteria management.

Climate change is a global issue and one of the more challenging environmental problems of the present and near future. Since 1992 there have been many meetings and agreement under the auspices of the United Nations.

This course will cover the topic of climate change from several angles. Starting with the basic evidence and science behind climate change and modeling of future scenarios, then through impacts and vulnerability to efforts to mitigate and adapt to climate change. Issues such as climate refugees, gender aspects and negotiations are addressed.

Grading is based on a writing assignment, short quiz, course participation and presentations, in addition to group assignments where mitigation, future scenarios and basic processes are examined further. Students taking this course generally have very different backgrounds and you will have a chance to learn about climate change from different viewpoints.

The course will present gerontology as a multi-professional discipline with an emphasis on social gerontology. The main concepts and research methods will be introduced. Formal and informal care will be discussed. Personal meaning and well-being in the social contexts of later adulthood and old age will be explored and methods to enhance personal meaning will be introduced. Emphasis is placed on deepening student’s understanding by helping them become more adept at working with other professions on elderly welfare issues.

The course is taught an intensive session.

Attendance in the intensive session is required.

The course gives an overview of various models for defining mental disorders. Students are introduced to methods of current interventions and the results of outcome research. In addition, health will be defined and how thoughts are believed to influence health and health behaviour. The basic theory of Cognitive Behaviour Therapy (CBT) will be introduced and a theoretical overview of CBT, models, concepts and methods presented.

This course concerns health behaviour in general. The health behaviour of different age groups will be addressed as well as the association between biological factors, health behaviour and social status. How can behaviour, coping and stress affect health? Behaviour in relations to food and consumption habits is of particular interest. How is it possible to shape healthy habits from childhood, for example to influence food choice and overcome pickiness about food? Societal influence and the part of media is also addressed. The course literature is from various books and scientific articles from different fields and is intended to capture the subject in an interdisciplinary manner.

The course focuses on young people’s risk behavior (e.g. drug use, deviance, inconsistent school attendance) and resilience related to various pedagogical, social, educational, and psychological factors. Subjects dealt with in the course will for example be young people’s social development, communication skills, mental disorders, sexual reproductive health, trauma and their view on different challenges in their life. Different preventive measures will be discussed and the role of homes, schools and recreations in different preventions. A special focus is on developmental research that explores the relationship between developmental growth and risk behavior. Projects are designed to seek understanding on how young people perceive risk factors in their lives.

The course is offered as a distant education course. Teaching lessons will be recorded and put on the CANVAS education management system but if there is real time teaching then it will be recorded and put on CANVAS. Discussion lessons are once a week (60 min.) were students can choose between being in-house or online. The same goes for essay presentations which are at the end of the semester.

In this course, students are introduced to concepts and methods for planning, monitoring and evaluating projects/activities. It covers developing a problem statement, mapping stakeholders, development of a project plan, design of project evaluations, introduction to data collection, and reporting on project progress. Emphasis will be placed on the importance of stakeholder participation and gender mainstreaming. Approaches taught in the course are rooted in international development but are useful in the planning, monitoring and evaluation of projects/activities across all sectors. This course is designed to be practical and develop skills that are directly applicable in many workplaces. The teaching is based on a combination of theoretical instruction, discussion of real-life applications, interactive workshops, and guided group work. 

The goal of this course is to deepen students' knowledge of gerontology and geriatrics. The social, biological and psychological aspects of aging will be discussed. Services for older adults  will be discussed in general as well as caring for special needs. Icelandic and international research will be presented. Various theories in gerontology will be examined and their effects on attitudes towards and services for older people. Cross discipline teamwork will be discussed with emphasis placed on working with various professions.

Students learn about selected concepts and research from social psychology, behavioral economics, judgment and decision making that can be used for the design, evaluation and implementation of public policy. Theories on rationality and bounded rationality of human thought will be compared and contrasted. Students will gain understanding of how people make decisions and evaluate risk, the influence of incentives on decisions, and how to influence attitudes and behavior. The course will also cover intergroup relations and negotiations. Finally, will we discuss the relationship between public policy and well-being.

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.

This course is taught every other year.

The main subject of the course is regression, interpretation of results, evaluation and comparison of statistical models. The emphasis is on practical analysis and evaluation of model quality. Topics include transformations, categorical variables and interaction.

Qualitative Methods provides students with an introduction to some of the most commonly used qualitative methods and methodological tools in political science. The main focus in the course is on case studies (including process tracing) and various tools and techniques used within case studies, e.g., qualitative content analysis, interviewing, and focus groups. One part of the course is also dedicated to discourse analysis. The course begins with a very brief introduction to philosophy of science and outlines basic ontological, epistemological and methodological issues in the social sciences. The remainder of the course is dedicated to the methods and tools/techniques listed above. Students will gain a deeper understanding of the philosophical underpinnings, assumptions and ambitions of the different methods, but they will also gain practical experience as to the design and execution of research within the different traditions.

The course is designed in a highly interactive way and emphasizes active student participation. It is expected that students have done at least the required reading assigned for the given day and are ready to participate in group work and discussions in class. There are two types of classes in this course: lecture & discussion classes and workshops. Each lecture & discussion class will be divided into three parts: a short agenda-setting lecture by the lecturer (40 minutes), group work (40 minutes), and a concluding general discussion (40 minutes). This design is highly effective with regard to achieving the course’s learning outcomes, but it also requires that students have familiarized themselves with the assigned reading for the day. In the workshops, the class will be divided into two groups (A and B).

Contrary to what might be expected from contemporary political discourse, fear, conspiracy theories and mistrust have characterized politics since time immemorial. In this course, we explore these phenomena from the interdisciplinary perspective of political psychology with support from other related disciplines. Initially, we will discuss trust in politics, politicians and citizens as an important but complicated concept within politics. We reflect on the byproducts of mistrust, such as the polarization of social groups and political participation. Next, students learn about the psychology of fear and its known effects on people's beliefs. The psychology of conspiracy theories, their causes and consequences for political behavior and attitudes will also be discussed.

In some settings, humans interact with the environment and use natural resources sustainably, but not in others. What explains such differences arising from human-environment interactions is the role of governance. Environmental governance can in its most basic form be understood as a social function centered on efforts to steer or guide the action of humans – being an individual, a small local user groups or the international community – towards desired outcomes and away from outcomes regarded as undesirable (Young, 2013).

This course has a focus on the introduction and understanding of different dimensions of environmental and natural resources governance in the context of sustainability.

It is divided into four interconnected sections:

1. Environmental Governance: The basics. What is governance? The environment as an arena for coordination and conflict. How do we understand actors, their roles and decision making? Power and power relations. Institutions and institutional change. Social-ecological systems. Governance structures. Public goods.
2. International and Domestic Environmental Governance. International environmental governance and institutions, e.g. EU, UN, UN Environment, FAO, World Bank etc. North-South issues. Environmental regimes; ozone, climate change, desertification, etc. Synergies. Introduction to environmental governance in Iceland and how it relates to decision-making with regard to environment and resources. Governance structure, central, local decision-making. Relationship between various levels of governance, parliament, ministries, agencies.
3. Public Responsibility and the Environment. Public participation. How can the public affect decision-making? Domestic and international environmental Non-governmental organizations.
4. Corporate Governance in the sustainability context. This part of the course focuses on corporate governance, such as outlined in the Nasdaq Corporate Governance Guidelines in the context of corporate sustainability. Relevant to the discussion is fiduciary duty, the ESRS Governance Standards (ESRS 1 and 2; ESRS G1), the SDGs 8-10, 12, 13, 17, the UN Global Compact Principle number 10, GRI Universal Standards and 200 Series, the Economic layer canvas, and more.

This is an intensive course with the active participation of students. It is taught over 13 weeks. The course takes as its starting point the idea that although governments and nonprofits are crucial to modern society, businesses are largely responsible for creating the wealth upon which the well-being of society depends, while also being a part of the problems created. As they create that wealth, their actions affect society, which is composed of a wide variety of stakeholders, and the natural environment. In turn, society shapes the rules and expectations by which businesses must navigate their internal and external environments. These include the Sustainable Development Goals, i.e., Goals 1-5, 8, 9, 10, 11, 12, 13, and 16, the Paris Agreement, the UN Global Compact, the European Sustainability Reporting Standards (ESRS), the Global Reporting Initiative, and more. This interaction between corporations, society (in its broadest sense) and the natural environment is the concern of corporate social responsibility (CSR), but the issues need to be addressed from a strategic point of view. 

Regardless of one’s viewpoint about the interaction of business and society, the continued co-existence of for-profit organizations is essential. This course seeks to explore the dimensions of that interaction from a multi-stakeholder perspective. That exploration is intended to be interactive, with the journey of exploration involving an analysis of CSR-related issues, simulation, and case studies.

The course is organized into six broad sections. In the first section, we will explore what corporate social responsibility (CSR) means and the driving forces of CSR. The second section focuses on the stakeholder perspective, and in the third section, we study the legal perspective. In the fourth section, we will explore the behavioral perspective, in the fifth section the strategic perspective and in the sixth section the focus will be on the sustainable perspective and sustainable value creation.