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Bayesian statistics - the frog example - an extended version of a student app: An introduction to Bayesian statistics
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Bayesian Correlation Analysis: The "Bayesian correlation analysis" app shows how the results of Bayesian procedures vary in the light of changes regarding data and prior beliefs.
| Bayesian t-Test Teaching App: The goal of the Bayesian t-test app is to provide teachers with a handy tool to show students what a Bayes factor, and more generally, what the results from a Bayesian t-test look like when data points are added in real-time. |
P-values & Bayes factors: This app illustrates the relation between p values and Bayes factors for various widely-used statistical tests of hypotheses.
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James-Stein estimator: The goal of this shiny app is to visualize the effect of shrinkage estimators and compare their performance to other estimators. |
Sequential testing with p-values and Bayes factors This app illustrates the relation between p-values and Bayes factors for various statistical tests under sequential and block sampling procedures.
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Comparison
of Frequentist and Bayesian analysis in ANOVA post-hoc settings: The aim of the app is to give students and researchers a
tool to balance experimental groups before starting an experiment.
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The Skill mApp aims to provide a first contact with both the Knowledge Space Theory and the skill map theory. |
Local independence: This app exemplifies the local independence assumption of the basic local independence model. |
Parameter estimation: This app uses three procedures to estimate the parameters for a data set and a knowledge structure specified by the user. |
Classical and Bayesian parameter estimation: This app demonstrates classical and Bayesian parameter estimation methods for probabilistic knowledge structures.
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Identifiability: This app visualizes the trade-off between the parameters of a non-identifiable basic local independence model (BLIM).
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Surmise Relations: This app demonstrates surmise relations and the corresponding knowledge spaces.
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Probabilistic Knowledge Assessment: This app lets you build your own knowledge structure on a set
of five items on elementary probability theory, and lets you perform a probabilistic knowledge assessment on that
structure. Especially the UI is based on a students' app.
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Deterministic Knowledge Assessment: This app lets you build your own knowledge structure on a set of five items on elementary probability theory, and lets you perform a deterministic knowledge assessment on that structure. It is derived from the
app on probabilistic knowledge assessment.
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BLIM simulation: This app lets you simulate response patterns based on a given knowledge space using the BLIM.
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Properties of Knowledge Spaces: This app lets you enter a knowledge structure and shows various properties of the
corresponding knowledge space.
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Precedence Relation and Corresponding Quasi-Ordinal Knowledge Space: This app illustrates the one-to-one correspondence
between a precedence relation among a number of problems and a quasi-ordinal knowledge space.
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Fringe & Neighbourhood: This app illustrates the fringe
and neighbourhood of knowledge states.
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Animated Learning Paths: This app illustrates learning paths in a knowledge structure.
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Validating Knowledge Structures: This app applies several validation coefficients to a selection of knowledge structures and respective data. It is based on a student app developed at the 2018 TquanT seminar in Glasgow.
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Generating Knowledge Structures: This app illustrates a simple approach to generating knowledge structures from response patterns.
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Inductive Item Tree Analysis (IITA): This app illustrates the use of IITA to generate surmise relations from data.
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The Knowledge Assessment
App aims to give a demonstration of how the adaptive assessment of knowledge developed within the Knowledge
Space Theory (KST) framework works in practice.
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Simulating BLIM: This app illustrates the application of the BLIM model to simulating response patterns from a
knowledge structure.
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Race Model: An app introducing and illustrating the race model for multisensory signals.
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TWIN: Time Window of Multisensory INtegration.
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TWIN 2017: A Shiny App for visualizing, simulating and estimating the Time-Window of INtegration (TWIN) model (version 2017).
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Urn
Problems: This app illustrates discrete distributions via the urn model. It is based on a
learnr tutorial developed by students at the 2018 TquanT seminar in Glasgow.
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Probability Distributions: This app allows a user to explore
many different probability distributions in an interactive manner. It is based
on a student app from the
TquanT Seminar 2018 in Glasgow.
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With this app you can explore basic statistics concepts, compare different models and see how they all boil
down to the same recipe: Data = model + error |
Model
Comparison Approach, Model 1: Model 1 uses one metric independent variable and a metric dependent variable and offers a
basic introduction of the model comparison approach and an introduction to model comparison using linear regressions
and correlations.
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Model
Comparison Approach, Model 2: Model 2 uses one categorical independent variable and a metric dependent variable and
offers a more advanced discussion using linear regressions with dummies, t-test, and factorial ANOVA.
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Comparing
Intraclass Correlations for Schwarz Values across European Countries.
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Multiverse
analysis: This app shows how different choices in constructing the data leads to different analysis results.
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Simulating
Statistical Power: This app illustrates the concept of statistical power by simulation. It is based on a
student app developed at the 2018 TquanT seminar in Glasgow.
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Excess
of Success: This app illustrates how to test for too many successful replications in a series of replication studies.
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Selection Decisions: This app illustrates the effects of several factors on the validity of selection decisions,
and how these factors influence each other.
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Tutorial on Item Response Theory (IRT) This learnr tutorial is based on a tutorial
developed by students at the TquanT 2018 Seminar in Glasgow.
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Distributions:
This app illustrates the effect of violations of assumptions for the F distribution.
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Generalizability of Fit:
An illustration of the concept of generalizability of a fitted statistical model.
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Fpower:
An app on power calculation for the ANOVA F test.
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