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| =Notes from the Monthly meetings= | | =Planning your study= |
| | ==Assessing the existing literature== |
| | ==Experimental design== |
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| ==August 7, 2018: Introduction to ggplot (Knut Helge Jensen)== | | =Preparing your study= |
| | ==Experiments== |
| | e-prime |
| | PsychoPy |
| | web experiments |
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| The [https://wiki.uib.no/brug/images/b/ba/Using.ggplot.pdf lecture notes] give an overview how to create figures using ggplot and how to modify and adapt them to your needs (colors, labels, etc.).
| | ==Questionnaires== |
| | web questionnaires (SurveyXact) |
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| ==June 4, 2018: Introduction to Bayesian statistics with R (Knut Helge Jensen)== | | =Analyzing your data= |
| | ==Organizing and storing your data== |
| | ==Evaluation methods== |
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| The [https://wiki.uib.no/brug/images/4/41/Introduction_to_Bayesian_statistics_with_R.pdf lecture notes] give a short introduction to Bayes theorem, show the principal difference between Bayesian and frequentist statistical inference, and give an example of how to perform Bayesian analysis in R through the brms-package. This package allows the user to benefit from the merits of STAN only by using simple, lme4-like formula syntax.
| | =Summarizing / publishing your study= |
| | | ==APA-standards== |
| We also spoke about the "Monty Hall" problem (where a game show participant can win a car after having received additional information). This can be understood as a Bayesian problem. There is a good [https://www.youtube.com/watch?v=ugbWqWCcxrg video-lecture] about the game show problem both with a Bayesian and a more intuitive solution to the question.
| | ==Software== |
| | | Latex |
| ==May 22, 2018: R procedures for meta analysis (Helge Molde)== | | Reference management (EndNote, Mendeley) |
| | | Tips and tricks for standard programmes |
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| ==March 1, 2018: Topic Modelling and the tm package (Rüdiger Pfister)==
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| ==February 8, 2018: Text analysis - the tidytext package (Torbjørn Torsheim)== | |
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| The main topic this time is text analysis. The tidytext package which nicely builds on the tidyverse principles for data management we explored last time will be presented.
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| ==December 4, 2017: Data management (Gisela Böhm & Rüdiger Pfister)==
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| =Other sources of help with R=
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| This web page provides a brief introduction regarding the [https://www.psychologicalscience.org/uncategorized/r-time-has-come.html use of R within psychology], including some links providing introductions into several topics (R in general, bootstrapping, Bayes, etc.).\\
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| The department of biostatistics at UiB created [https://biostats.w.uib.no web pages] to help you with getting into using R. The web page includes some tutorials, e.g., how to organize your data using Excel in order to use them in R later.
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| An introduction to the [https://tex.stackexchange.com/questions/25575/how-can-i-use-a-table-generated-by-r-in-latex generation of tables for LaTex in R].
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