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A one-of-a-kind compilation of modern statistical methods designed to support and advance research across the social sciences Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. With contributions from leading international experts on the topic, the book features in-depth coverage of modern quantitative social sciences topics, including: Correlation Structures Structural Equation Models and Recent Extensions Order-Constrained Proximity Matrix Representations Multi-objective and Multi-dimensional Scaling Differences in Bayesian and Non-Bayesian Inference Bootstrap Test of Shape Invariance across Distributions Statistical Software for the Social Sciences Statistics in the Social Sciences: Current Methodological Developments is an excellent supplement for graduate courses on social science statistics in both statistics departments and quantitative social sciences programs.
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Table of contents List of Figures. We will also develop more advanced R programming skills. At of the end of the course, the student: is able to implement and use basic computational methods for statistical inference, as well as more advanced ones such as the bootstrap and permutation test; will have developed fundamental and computationally efficient R programming skills; is able to conduct and report on simulation studies, comparing the performance of statistical methods in specific settings; is familiar with some widely used numerical methods; will be able to translate new statistical methods from the literature into a usable R program.
Relatie tussen de toetsen en leerdoelen The course gives a broad introduction to the field of psychometrics, followed by a number of advanced topics which give an impression of current developments.
Notebook – International Journal of Social Research Methodology
The introduction will cover classical test theory, generalizability theory and item response theory IRT. As applications of IRT, the topics of test equating and differential item functioning will be presented and practiced. These applications will be presented in the framework of maximum likelihood estimation and model testing and the students will learn to use standard state of the art user software.
The advanced topics are the combination of Bayesian psychometric methods and complex clustered data. These topics will be addressed in a Bayesian framework, and students will learn to build applications to analyze IRT models using Markov chain Monte Carlo estimation methods.leondumoulin.nl/language/humor/hair-salon-and-day-spa.php
In the lab meetings Jags and R will be used to analyze item response data using Bayesian inference. MLM is appropriate for handling nested data, for instance, patients in hospitals, or occasions in people. MLM can be used to study the within cluster and the between cluster relationships between an outcome variable and predictors. SEM covers both factor analyses and path analyses. It can be used to investigate the underlying factor structure and compare this across groups i. In the lab meetings Mplus is used. In this course the theory and practice of Bayesian data analysis will be introduced.
Attention will be given to the difference between classical and Bayesian inference. The following topics will be subsequently be discussed: density of the data, prior and posterior distribution; classical and Bayesian p-values and their flaws; Bayesian estimation; model selection using the DIC; and, model selection using the Bayes factor. Options are for instance courses in one of the other Master's programmes in our Graduate school e. Also other areas e.
It is also possible to take courses on Survey Methodology, Educational Measurement offered by University of Twente course code , or from the track European Master in Official Statistics. The preparation for the Master's thesis 15 EC will take place in the first semester of the second year. The preparation for the thesis runs along with the research seminar. It has the following aims:. Writing a Master's thesis is a major objective in the second year of the Methodology and Statistics programme.
Computational inference with R Statistical inference based on intensive computation or simulation is an important part of the armamentarium of a statistician.
Psychometrics TESTING Written examination of psychometric theory, including classical test theory, generalizability theory, Bayesian item response theory and latent regression, Bayesian psychometric modeling. Furthermore, two home assignments based on the computer lab are also graded. Practical using R to conduct G- and D- study and interpret and report results.
Practical using R including IRT-software to conduct a linking and dif study and interpret and report results. Practical using R and Jags to conduct multilevel IRT-latent regression, Bayesian psychometric modeling, analysis interpret and report results. Bayesian statistics In this course the theory and practice of Bayesian data analysis will be introduced. Introduction to Biomedical Statistics There is no content available for this course. Preparation for the Master's Thesis The preparation for the Master's thesis 15 EC will take place in the first semester of the second year.
It has the following aims: to master the state of the art in methodology and statistics with respect to the research topic chosen.