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{"references.qmd":[],"4_rt.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological"],"2_predictors.qmd":[],"5_individual.qmd":[],"3_scales.qmd":[],"4_1_Normal.qmd":["wagenmakers2008diffusion","theriault2024check","lo2015transform","schramm2019reaction"],"1_introduction.qmd":[],"index.qmd":[]} | ||
{"5_individual.qmd":[],"1_introduction.qmd":[],"index.qmd":[],"3_scales.qmd":[],"references.qmd":[],"4_rt.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological"],"4_1_Normal.qmd":["wagenmakers2008diffusion","theriault2024check","lo2015transform","schramm2019reaction"],"2_predictors.qmd":[]} |
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{"title":"Predictors","markdown":{"headingText":"Predictors","containsRefs":false,"markdown":"\n![](https://img.shields.io/badge/status-not_started-red)\n\n## Categorical predictors (Condition, Group, ...)\n\nNested interactions, contrasts, ...\n\n## Ordered predictors (Likert Scales)\n\nLikert scales, i.e., ordered multiple *discrete* choices are often used in surveys and questionnaires. While such data is often treated as a *continuous* variable, such assumption is not necessarily valid. Indeed, distance between the choices is not necessarily equal. For example, the difference between \"strongly agree\" and \"agree\" might not be the same as between \"agree\" and \"neutral\". Even when using integers like 1, 2, 3, 4; people might implicitly process \"4\" as more extreme relative to \"3\" as \"3\" to \"2\".\n\n![](media/probability_perception.png)\n\n> The probabilities assigned to discrete probability descriptors are not necessarily equidistant (https://github.com/zonination/perceptions)\n\nWhat can we do to better reflect the cognitive process underlying a Likert scale responses? [Monotonic effects](https://cran.r-project.org/web/packages/brms/vignettes/brms_monotonic.html).\n\n## Interactions\n\nTodo. \n\n## Non-linear relationships (polynomial, GAMs)\n\nTodo. ","srcMarkdownNoYaml":""},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":true,"freeze":"auto","echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"execute":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":true,"code-overflow":"scroll","code-link":false,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","output-file":"2_predictors.html"},"language":{"toc-title-document":"Table 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{"title":"Predictors","markdown":{"headingText":"Predictors","containsRefs":false,"markdown":"\n![](https://img.shields.io/badge/status-not_started-red)\n\n## Categorical predictors (Condition, Group, ...)\n\nIn the previous chapter, we have mainly focused on the relationship between a response variable and a single **continuous** predictor.\n\n- Nested interactions, contrasts, ...\n\n## Ordered predictors (Likert Scales)\n\nLikert scales, i.e., ordered multiple *discrete* choices are often used in surveys and questionnaires. While such data is often treated as a *continuous* variable, such assumption is not necessarily valid. Indeed, distance between the choices is not necessarily equal. For example, the difference between \"strongly agree\" and \"agree\" might not be the same as between \"agree\" and \"neutral\". Even when using integers like 1, 2, 3, 4; people might implicitly process \"4\" as more extreme relative to \"3\" as \"3\" to \"2\".\n\n![](media/probability_perception.png)\n\n> The probabilities assigned to discrete probability descriptors are not necessarily equidistant (https://github.com/zonination/perceptions)\n\nWhat can we do to better reflect the cognitive process underlying a Likert scale responses? [Monotonic effects](https://cran.r-project.org/web/packages/brms/vignettes/brms_monotonic.html).\n\n## Interactions\n\nTodo. \n\n## Non-linear relationships (polynomial, GAMs)\n\nTodo. ","srcMarkdownNoYaml":""},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":true,"freeze":"auto","echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"execute":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":true,"code-overflow":"scroll","code-link":false,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","output-file":"2_predictors.html"},"language":{"toc-title-document":"Table 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{"headings":[],"entries":[],"options":{"chapters":true}} | ||
{"headings":[],"options":{"chapters":true},"entries":[]} |
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{"entries":[],"headings":["very-quick-intro-to-julia-and-turing","generate-data-from-normal-distribution","recover-distribution-parameters-with-turing","linear-models","boostrapping","hierarchical-models","bayesian-estimation","bayesian-mixed-linear-regression"],"options":{"chapters":true}} | ||
{"headings":["very-quick-intro-to-julia-and-turing","generate-data-from-normal-distribution","recover-distribution-parameters-with-turing","linear-models","boostrapping","hierarchical-models","bayesian-estimation","bayesian-mixed-linear-regression"],"entries":[],"options":{"chapters":true}} |
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{"headings":[],"entries":[],"options":{"chapters":true}} | ||
{"headings":[],"options":{"chapters":true},"entries":[]} |
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{"options":{"chapters":true},"headings":["categorical-predictors-condition-group","ordered-predictors-likert-scales","interactions","non-linear-relationships-polynomial-gams"],"entries":[]} | ||
{"entries":[],"headings":["categorical-predictors-condition-group","ordered-predictors-likert-scales","interactions","non-linear-relationships-polynomial-gams"],"options":{"chapters":true}} |
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{"options":{"chapters":true},"entries":[],"headings":["preface","why-julia","why-bayesian","the-plan","looking-for-coauthors"]} | ||
{"headings":["preface","why-julia","why-bayesian","the-plan","looking-for-coauthors"],"entries":[],"options":{"chapters":true}} |
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{"headings":[],"options":{"chapters":true},"entries":[]} |
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{"entries":[],"options":{"chapters":true},"headings":["the-data","descriptive-models","gaussian-aka-linear-model","model-specification","posterior-predictive-check","scaled-gaussian-model","solution-1-directional-effect-of-condition","solution-2-avoid-exploring-negative-variance-values","the-problem-with-linear-models","shifted-lognormal-model","prior-on-minimum-rt","model-specification-1","interpretation","exgaussian-model","model-specification-2","wald-model","generative-models-ddm","other-models-lba-lnr","including-random-effects","additional-resources"]} | ||
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