Publications by Jens Roeser
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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Model-evaluation quiz [answers]
Week 15: Model-evaluation quiz [answers] Week 15: Model-evaluation quiz [answers] Part 1 (1): assumptions and statistical modelling Part 1 (2): residuals and normality Part 2: independence of residuals Part 3: quiz evaluation Jens Roeser Compiled 2022-02-21 A total of 12 students completed this quiz (status: 2022-02-21 09:17:32). The correc...
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Model assumptions workshop
Workshop 17 [Week 7]: Model assumptions Workshop 17 [Week 7]: Model assumptions Lecture review Learning outcomes Setup The normal distribution The area under the curve Central limit theorem Summary References Jens Roeser Compiled 2021-02-18 Lecture review Parametric models (t-test, ANOVA, linear regression) make assumptions about their inp...
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Model evaluation
Model evaluation and violations Dr Jens Roeser Learning outcomes After completing this lecture, the workshop and your own reading you should be able to … explain what residuals are. run regression models with categorical predictors. evaluate statistical models on the basis of their unexplained (residual) variance. Model evaluation “Model...
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Model-assumptions quiz [answers]
Week 14: Model-assumptions quiz [answers] Week 14: Model-assumptions quiz [answers] Part 1: Models and parametric tests Part 2: Properties of the normal distribution Part 3: CLT Part 4: iid Part 5: quiz evaluation Jens Roeser Compiled 2022-02-21 A total of 31 students completed this quiz (status: 2022-02-21 09:18:26). The correct answers ar...
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Model assumptions workshop (with answers)
Workshop 17 [Week 7]: Model assumptions Workshop 17 [Week 7]: Model assumptions Lecture review Learning outcomes Setup Normal distribution The area under the curve Central limit theorem Summary References Jens Roeser Compiled 2021-02-18 Lecture review Parametric models (t-test, ANOVA, linear regression) make assumptions about their input (...
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Writing modality comparison
1 Results Data were analysed in Bayesian linear mixed effects models (Gelman et al. 2014; McElreath 2016). The R (R Core Team 2020) package brms (Bürkner 2017b, 2018) was used to model the data using the probabilistic programming language Stan (Carpenter et al. 2016; Hoffman and Gelman 2014). Models were fitted with weakly informative priors (se...
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Year 1 stats spin-off lecture
Year 1 stats spin-off Dr Jens Roeser Learning outcomes After completing this lecture and the workshop you should have had the opportunity to … revisiting inferential statistics covered during year 1 statistics. practice your understanding of inferential statistics in a quiz (people may call this a mock exam). use an app to explore the relati...
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Walkthrough: fitting a mixture model on copy-task data
Walkthrough: fitting a mixture model on copy-task data Walkthrough: fitting a mixture model on copy-task data Fitting the model Preparing the data Load model Initiate sampling Reusing model output Model diagnostics Convergence Posterior predictive check Posterior probability distribution Parameter-value estimates Difference between copy-ta...
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Example for categorical models
Categorical model of strategy 1 Data 2 Models 3 Model comparison 4 Posterior Jens Roeser Compiled Aug 03 2021 Categorical model of strategy 1 Data This analysis will test whether the relative distribution of response strategies varies across participants’ writing levels. data <- read_delim("long_data_format.tsv", delim="\t") The outcome v...
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