Sökning: "Dif"
Visar resultat 1 - 5 av 69 avhandlingar innehållade ordet Dif.
1. Elektriska flickor och mekaniska pojkar : Om gruppskillnader på prov - en metodutveckling och en studie av skillnader mellan flickor och pojkar på centrala prov i fysik
Sammanfattning : This dissertation served two purposes. The first was to develop a method of detecting differential item functioning (DIF) within tests containing both dichotomously and polytomously scored items. LÄS MER
2. Theory and validity evidence for a large-scale test for selection to higher education
Sammanfattning : Validity is a crucial part of all forms of measurement, and especially in instruments that are high-stakes to the test takers. The aim of this thesis was to examine theory and validity evidence for a recently revised large-scale instrument used for selection to higher education in Sweden, the Swedish Scholastic Assessment Test (SweSAT), as well as identify threats to its validity. LÄS MER
3. Functional study of Nucleoporins Nup88 and Nup214 during Drosophila Development
Sammanfattning : More than a million macromolecules per minute pass through the nuclear envelope of a eukaryotic cell. An important challenge is to understand how the NPC coordinate efficiently the bi-directional trafficking necessary for the basal cellular activities and the rapid translocation of regulatory proteins in response to signaling. LÄS MER
4. Does language matter? : sources of inequivalence and demand of reading ability of mathematics tasks in different languages
Sammanfattning : Practicing mathematics is not possible without the use of language. To communicate mathematical content, not only words in natural language are used but also non-verbal forms of communication such as mathematical symbols, graphs, and diagrams. All these forms of communication can be seen as part of the language used when doing mathematics. LÄS MER
5. Simulation-based Inference : From Approximate Bayesian Computation and Particle Methods to Neural Density Estimation
Sammanfattning : This doctoral thesis in computational statistics utilizes both Monte Carlo methods(approximate Bayesian computation and sequential Monte Carlo) and machine-learning methods (deep learning and normalizing flows) to develop novel algorithms for inference in implicit Bayesian models. Implicit models are those for which calculating the likelihood function is very challenging (and often impossible), but model simulation is feasible. LÄS MER