Publications by Jens Roeser
Modelling writing hesitations in text writing as finite mixture process
1 Analysis 2 Data processing 3 C2L1 3.1 Model comparisons 3.2 Posterior parameter estimates of mixture model 4 CATO 4.1 Model comparisons 4.2 Posterior parameter estimates of mixture model 5 SPL2 5.1 Methods 5.2 Model comparisons 5.3 Posterior parameter estimates of mixture model 6 PLanTra 6.1 Methods 6.2 Model comparisons 6.3 Posterior pa...
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Real time updating for writing process data (simulation)
Simulate data: lookbacks Off-line parameter-value estimation On-line parameter-value estimation Real data: Comparison for extreme value classification References Real-time updating for writing-process data – Simulation Jens Roeser Compiled Oct 04 2022 The following will demonstrate that we can use an on-line (incremental) parameter approx...
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Reproducible data analysis using RMarkdown documentation
Aims for today Introducing the data I’ll use in my sessions. Sorting out our RStudio working environment. Getting you ready to produce reproducible reports in RMarkdown. Example data set: Blomkvist et al. (2017) Age-related changes in cognitive performance through adolescence and adulthood in a real-world task. Real-world task: StarCraft 2 ...
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Statistics III -- Module Overview
About us: Jens Roeser Senior lecturer in psycholinguistics @ psychology department (Nottingham Trent University) Theory: psycholinguistics; language production / comprehension / acquisition (e.g. Roeser, Torrance, and Baguley 2019) Methods: Bayesian modelling (talk to me about mixture models, Roeser et al. 2021) in Stan; keystroke logging; eyetr...
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Normal linear models
Regression models Often introduced as fitting lines to points. Limited perspective that makes more complex regression models, like generalised linear models, hard to understand. Backbone of statistical modelling For multiple / simple linear regressions, t-tests, ANOVAs, ANCOVAs, MANCOVAs, time series models Basis for path analysis, structural eq...
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Slides for model comparisons workshop
Update your workshop repository Go to github.com/jensroes/stats-iii Download the repository Extract the model-comparison folder Why do we need model comparion? “All models are wrong, but some are useful” George E.P. Box Models are statistical representations of a hypothesis. We want to compare hypotheses. How useful is my model compared t...
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A cumulative ordinal model of text quality as a function of writing process measures
1 Modelling text quality as a function of writing process measures 2 Predicting text quality on the basis of incrementally obtained writing process estimates References Ordinal model of text quality Jens Roeser Compiled Oct 05 2022 1 Modelling text quality as a function of writing process measures Text quality is assessed by a set of rubric...
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A cumulative ordinal model of text quality as a function of writing process measures
1 Modelling text quality as a function of writing process measures 2 Predicting text quality on the basis of incrementally obtained writing process estimates References Ordinal model of text quality Jens Roeser Compiled Oct 06 2022 1 Modelling text quality as a function of writing process measures The quality rating assigned to a particular...
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Concurrent learning of adjacent and nonadjacent dependencies
1 Methods 1.1 Design All participants were exposed to adjacent and non-adjacent dependencies. In a sequence A-B-C, the location of a dot B was target of an adjacent dependency when always following the same A location while the location of dot C was random. In nonadjacent dependencies, the location of a dot C was always following a location A do...
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Modelling writing hesitations in text writing as finite mixture process
1 Analysis 2 CATO 2.1 Data processing 2.2 Model comparisons 2.3 Posterior parameter estimates of mixture model 3 SPL2 3.1 Methods 3.2 Data processing 3.3 Model comparisons 3.4 Posterior parameter estimates of mixture model 4 PLanTra 4.1 Methods 4.2 Data processing 4.3 Model comparisons 4.4 Posterior parameter estimates of mixture model 5 LI...
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