index.json

[{"authors":["thomvolker"],"categories":null,"content":"I am a PhD candidate at the Methods and Statistics department of Utrecht University, researching different techniques for creating privacy-preserving synthetic data sets, under the supervision of Dr. Erik-Jan van Kesteren, Dr. Peter-Paul de Wolf and Prof. Dr. Stef van Buuren. I aim to work at the intersection of social-scientific research and cutting-edge statistical techniques, to get the most out of expensively collected research data.\nIn the past, I worked on several projects on evidence synthesis, aiming to aggregate evidence over heterogeneous studies that do not allow for meta-analysis. Together with Irene Klugkist I outlined and evaluated the methodology, while I applied it on a set of heterogeneous, sociological studies with Vincent Buskens and Werner Raub. Additionally, I worked on several projects in a broad range of topics (multiple imputation of missing data, unsupervised text analysis and hypothesis evaluation using information criteria).\nBesides research, I teach graduate and post-graduate level courses in data science techniques and multiple imputation of missing data.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1680442133,"objectID":"1d3dd70c465f18c687a539524a8b1864","permalink":"https://thomvolker.github.io/author/thom-volker/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/thom-volker/","section":"authors","summary":"I am a PhD candidate at the Methods and Statistics department of Utrecht University, researching different techniques for creating privacy-preserving synthetic data sets, under the supervision of Dr. Erik-Jan van Kesteren, Dr.","tags":null,"title":"Thom Volker","type":"authors"},{"authors":null,"categories":null,"content":"Flexibility This feature can be used for publishing content such as:\nOnline courses Project or software documentation Tutorials The courses folder may be renamed. For example, we can rename it to docs for software/project documentation or tutorials for creating an online course.\nDelete tutorials To remove these pages, delete the courses folder and see below to delete the associated menu link.\nUpdate site menu After renaming or deleting the courses folder, you may wish to update any [[main]] menu links to it by editing your menu configuration at config/_default/menus.toml.\nFor example, if you delete this folder, you can remove the following from your menu configuration:\n[[main]]\rname = \u0026quot;Courses\u0026quot;\rurl = \u0026quot;courses/\u0026quot;\rweight = 50\rOr, if you are creating a software documentation site, you can rename the courses folder to docs and update the associated Courses menu configuration to:\n[[main]]\rname = \u0026quot;Docs\u0026quot;\rurl = \u0026quot;docs/\u0026quot;\rweight = 50\rUpdate the docs menu If you use the docs layout, note that the name of the menu in the front matter should be in the form [menu.X] where X is the folder name. Hence, if you rename the courses/example/ folder, you should also rename the menu definitions in the front matter of files within courses/example/ from [menu.example] to [menu.\u0026lt;NewFolderName\u0026gt;].\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1593433517,"objectID":"59c3ce8e202293146a8a934d37a4070b","permalink":"https://thomvolker.github.io/courses/example/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/courses/example/","section":"courses","summary":"Learn how to use Academic's docs layout for publishing online courses, software documentation, and tutorials.","tags":null,"title":"Overview","type":"docs"},{"authors":null,"categories":null,"content":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 2 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593433517,"objectID":"74533bae41439377bd30f645c4677a27","permalink":"https://thomvolker.github.io/courses/example/example1/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example1/","section":"courses","summary":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":null,"title":"Example Page 1","type":"docs"},{"authors":null,"categories":null,"content":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 4 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593433517,"objectID":"1c2b5a11257c768c90d5050637d77d6a","permalink":"https://thomvolker.github.io/courses/example/example2/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example2/","section":"courses","summary":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":null,"title":"Example Page 2","type":"docs"},{"authors":[],"categories":null,"content":"\rClick on the Slides button above to view the built-in slides feature.\rSlides can be added in a few ways:\nCreate slides using Academic\u0026rsquo;s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further talk details can easily be added to this page using Markdown and $\\rm \\LaTeX$ math code.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1610226714,"objectID":"96344c08df50a1b693cc40432115cbe3","permalink":"https://thomvolker.github.io/talk/example/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example/","section":"talk","summary":"An example talk using Academic's Markdown slides feature.","tags":[],"title":"Example Talk","type":"talk"},{"authors":["Thom Volker","Gerko Vink"],"categories":null,"content":"All accompanying files and R-code can be found on the project\u0026rsquo;s GitHub page.\n","date":1637625600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1638790217,"objectID":"7e6753f080de11700d1890ae618c16a6","permalink":"https://thomvolker.github.io/publication/synthemice/","publishdate":"2021-11-23T00:00:00Z","relpermalink":"/publication/synthemice/","section":"publication","summary":"We show how the `R`-package `mice` can be used to create and analyze multiply imputed synthetic data sets.","tags":["mice","multiple imputation","synthetic data","statistical disclosure control","privacy"],"title":"Anonymiced Shareable Data: Using mice to Create and Analyze Multiply Imputed Synthetic Datasets","type":"publication"},{"authors":null,"categories":null,"content":"\rIntroduction\rFake news poses problems to present-day societies, and in some instances, it can even become dangerous, for example when people believe in the relationship between vaccines and autism. Multiple sites exist that fact-check online sources of information with regard to whether the provided information was real or fake. An algorithm that could be trained to distinguish real from fake news would make this process easier and faster. Therefore, it is attempted to construct a machine learning method that is able to distinguish fake from real news, by means of analyzing the textual data at hand.\nMethods\rThe dataset contained 500 texts that had on average nearly 800 words, and additionally a title and a label that indicates whether or not the text was fake. Used features were word unigrams, bigrams and trigrams, and combinations of the three. It was investigated whether frequencies of certain words or combinations of words (up to a maximum length of three) were indicative of the truthfulness of the text. The data was prepared for usage by means of removing excessive white spaces, punctuation, numbers, non-ASCII characters, replacing capitals by lower case letters, removing English stopwords, and applying Porter’s stemming algorithm (to make sure that common words are identified as common words), in this specific order. Additionally, sparse terms were removed, with a maximum allowed sparsity of 0.90 in case of the unigrams, 0.95 in case of bigrams and 0.97 in case of the trigrams and combinations of unigrams, bigrams and trigrams. The training set consists of 80% of the original dataset, and is chosen at random; the remaining 20% serves as the validation data. All methods were ran with the same seed, which ensures that all methods use the same observations (texts) to build the model on.\nFour different methods were considered to build a model. The first one was a classification tree, the second one was a classification tree that was pruned by means of the cross-validation error based on the complexity parameter, the third model under consideration was a logistic regression model, and the fourth model was a support vector machine model. All models can handle the dichotomous outcome variable, and it was attempted to predict whether the document was real or fake in the validation data. The performance of all methods under the specified input (unigrams, bigrams, trigrams or a combination of the three) was measured by means of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the F1-measure.\nFigure 1: Figure 1. Wordcloud containing the most frequently occurring words in the data, after the pre-processing steps.\rHowever, when the logistic model was used, the maximum allowed sparsity has to be further reduced when analyzing the unigrams, because otherwise, overfitting would be severe (to such an extent that a single variable for every model can be chosen). To avoid this problem, when unigrams were analyzed by means of the logistic regression model, the maximum allowed sparsity was reduced to 0.70. Combinations of unigrams, bigrams and trigrams were not considered, simply because bigrams and trigrams occured too infrequently in multiple texts, so that in the end, only unigrams would have been analyzed, if there is accounted for the overfitting problem.\nResults\rThe dimensions of the training and test sets were equal, except for the fact that obviously, the training data contained 400 rows, while the test data only contained 100 rows. After initially performing the analysis, it appeared that all models performed reasonable, with an accuracy of around 0.75 when the unigrams were taken into account, eventually in combination with bigrams and/or trigrams (results are mentioned in Appendix A, since they were not of primary interest). When solely bigrams and trigrams were considered, the accuracy ranged between 0.60 and 0.70. However, one of the words that occurred very often as one of the first nodes was “said”, which was not very informative, in my opinion. Therefore, I decided to exclude this word, and run the analysis again. Because removing the word ‘said’ would not make any difference with respect to bigrams and trigrams, and the models fitted on bigrams and trigrams initially did not perform that well, these two models were not considered any further.\nAs can be seen in Table 1, considering bigrams and trigrams next to unigrams did did not strengthen the accuracy of the considered methods. Overall, the results were very similar, and only considering unigrams yielded slightly better results as compared to additionally using bigrams and trigrams. Additionally, there was nearly no difference between the performance of the considered measures. However, it is noteworthy that the sensitivity of the Pruned Classification Tree is very high, while the specificity is much lower, while in case of the regular Classification Tree, the specificity is very high, but the sensitivity is much lower.\nTable 1: Table 1: Results of the classification methods on the text only, without infrequently occurring words and without the word ‘said’\rMethod\rAccuracy\rSensitivity\rSpecificity\rPPV\rNPV\rF1.measure\rUnigrams\rCT\r0.71\r0.700\r0.725\r0.792\r0.617\r0.743\rPCT\r0.71\r0.700\r0.725\r0.792\r0.617\r0.743\rLR\r0.75\r0.700\r0.825\r0.857\r0.647\r0.771\rSVM\r0.77\r0.767\r0.775\r0.836\r0.689\r0.800\rUnigrams+Bigrams\rCT\r0.65\r0.683\r0.600\r0.719\r0.558\r0.701\rPCT\r0.68\r0.783\r0.525\r0.712\r0.618\r0.746\rSVM\r0.74\r0.733\r0.750\r0.815\r0.652\r0.772\rUnigrams+Bigrams+Trigrams\rCT\r0.65\r0.683\r0.600\r0.719\r0.558\r0.701\rPCT\r0.68\r0.783\r0.525\r0.712\r0.618\r0.746\rSVM\r0.74\r0.733\r0.750\r0.815\r0.652\r0.772\rUnigrams+Trigrams\rCT\r0.65\r0.683\r0.600\r0.719\r0.558\r0.701\rPCT\r0.68\r0.783\r0.525\r0.712\r0.618\r0.746\rSVM\r0.74\r0.733\r0.750\r0.815\r0.652\r0.772\rNote. Considered methods were Classification Trees (CT), Pruned Classification Trees based on the validation error (PCT), Logistic Regression (LR) and Support Vector Machines (SVM).\nWords that were regarded as influencial, according to the Classification Tree method containing only unigrams can be found in Figure 2. It appears that persons who mentioned the word ‘gop’, which is an abbreviation of “Grand Old Party”, referring to the Republican Party, are not expected to provide fake news. Texts that do not contain ‘gop’, but do mention ‘sen’, which is a slightly problematic stem, since it can refer to more than 200 distinct word, among others ‘senator’ and ‘sentence’, are also expected to be real. People who do not mention ‘gop’, do not mention ‘sen’, do not mention ‘thursday’, but do mention ‘octob’ at least once are expected to provide fake news. In depth interpretation of these words must however be done by a specialist, who is well aware of “hot topics” among fake news providers and American actualities.\nFigure 2: Figure 2. Plot of the results of the Classification Tree on the unigrams without infrequently occurring words and without the word ‘said’.\rHowever, some cells in the data appeared to be empty, but did have a title. So in addition to the previous analysis, I also considered a model in which the title was added to the text. Since removing the word ‘said’ proved to be useful, I continued with discarding ‘said’ from the analyses. The best overall model was the Pruned Classification Tree that analyzed unigrams only, since it had both the highest accuracy and the highest F1-measure of all models considered. That is, overall, this model classified the most cases accordingly (Table 2).\nTable 2: Table 2: Results of the classification methods on the text including the title, without infrequently occurring words and without the frequently occurring word ‘said’\rMethod\rAccuracy\rSensitivity\rSpecificity\rPPV\rNPV\rF1.measure\rUnigrams\rCT\r0.78\r0.717\r0.875\r0.896\r0.673\r0.796\rPCT\r0.81\r0.883\r0.700\r0.815\r0.800\r0.848\rLR\r0.74\r0.683\r0.825\r0.854\r0.635\r0.759\rSVM\r0.80\r0.800\r0.800\r0.857\r0.727\r0.828\rUnigrams+Bigrams\rCT\r0.73\r0.767\r0.675\r0.780\r0.659\r0.773\rPCT\r0.73\r0.767\r0.675\r0.780\r0.659\r0.773\rSVM\r0.73\r0.717\r0.750\r0.811\r0.638\r0.761\rUnigrams+Bigrams+Trigrams\rCT\r0.73\r0.767\r0.675\r0.780\r0.659\r0.773\rPCT\r0.73\r0.767\r0.675\r0.780\r0.659\r0.773\rSVM\r0.72\r0.700\r0.750\r0.808\r0.625\r0.750\rUnigrams+Trigrams\rCT\r0.73\r0.767\r0.675\r0.780\r0.659\r0.773\rPCT\r0.73\r0.767\r0.675\r0.780\r0.659\r0.773\rSVM\r0.72\r0.700\r0.750\r0.808\r0.625\r0.750\rNote. Considered methods were Classification Trees (CT), Pruned Classification Trees based on the validation error (PCT), Logistic Regression (LR) and Support Vector Machines (SVM).\rSecond results.\nFigure 3: Figure 3. Plot of the results of the Pruned Classification Tree on the unigrams without infrequently occurring words and without the word ‘said’.\rThe pruned classification tree is in this scenario relatively simple. Once again, ‘gop’ is an important phrase, and the same holds for ‘thursday’ and ‘octob’. People who do mention ‘gop’ are expected to provide real news, as well as people who do not mention ‘gop’ at all, but do mention ‘thursday’. People who do not mention ‘gop’, do not mention ‘thursday’, but do mention ’octob are expected to provide fake news.\nIn practice, I would not use the model to base important decisions on, since the accuracy was not extremely high, and overall there were a substantial number of misclassifications. However, the provided models might be useful to guide people with interest in tracking down fake news, because it can narrow their search for texts that might be interesting. Given the fact that these people will probably not be able to identify all fake news going around, a narrower scope might help them to search for suspicious news.\nTo conclude, I would prefer the models based on Classification Trees, due to their simplicity. In my opinion, working with people who might not have profound knowledge about statistical topics, a simpler model might suit better. Of course, it is better to use a model that fits than a poor fitting model. However, in the current situation, it does not same to make a lot of difference which model one uses, and thus a Classification Tree, eventually with pruning after building the tree, might be best suited.\nAppendix A\rTable 3: Results of initial analyses containing unigrams, bigrams and trigrams separately and combinations of the three.\rMethod\rAccuracy\rSensitivity\rSpecificity\rPPV\rNPV\rF1.measure\rUnigrams\rCT\r0.72\r0.800\r0.600\r0.750\r0.667\r0.774\rPCT\r0.76\r0.850\r0.625\r0.773\r0.735\r0.810\rLR\r0.75\r0.700\r0.825\r0.857\r0.647\r0.771\rSVM\r0.77\r0.767\r0.775\r0.836\r0.689\r0.800\rBigrams\rCT\r0.66\r0.817\r0.425\r0.681\r0.607\r0.742\rPCT\r0.65\r0.850\r0.350\r0.662\r0.609\r0.745\rLR\r0.71\r0.717\r0.700\r0.782\r0.622\r0.748\rSVM\r0.68\r0.683\r0.675\r0.759\r0.587\r0.719\rTrigrams\rCT\r0.62\r0.867\r0.250\r0.634\r0.556\r0.732\rPCT\r0.61\r0.867\r0.225\r0.627\r0.529\r0.727\rLR\r0.63\r0.867\r0.275\r0.642\r0.579\r0.738\rSVM\r0.64\r0.867\r0.300\r0.650\r0.600\r0.743\rUnigrams+Bigrams\rCT\r0.68\r0.683\r0.675\r0.759\r0.587\r0.719\rPCT\r0.75\r0.833\r0.625\r0.769\r0.714\r0.800\rSVM\r0.74\r0.733\r0.750\r0.815\r0.652\r0.772\rUnigrams+Bigrams+Trigrams\rCT\r0.68\r0.683\r0.675\r0.759\r0.587\r0.719\rPCT\r0.75\r0.833\r0.625\r0.769\r0.714\r0.800\rSVM\r0.74\r0.733\r0.750\r0.815\r0.652\r0.772\rUnigrams+Trigrams\rCT\r0.68\r0.683\r0.675\r0.759\r0.587\r0.719\rPCT\r0.75\r0.833\r0.625\r0.769\r0.714\r0.800\rSVM\r0.74\r0.733\r0.750\r0.815\r0.652\r0.772\rNote. Considered methods were Classification Trees (CT), Pruned Classification Trees based on the validation error (PCT), Logistic Regression (LR) and Support Vector Machines (SVM).\rSecond results.\n","date":1593648000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593764969,"objectID":"299e13ac9292c5ee2ab119099f5bedfe","permalink":"https://thomvolker.github.io/post/thomvolkerpsychometrics/","publishdate":"2020-07-02T00:00:00Z","relpermalink":"/post/thomvolkerpsychometrics/","section":"post","summary":"Introduction\rFake news poses problems to present-day societies, and in some instances, it can even become dangerous, for example when people believe in the relationship between vaccines and autism. Multiple sites exist that fact-check online sources of information with regard to whether the provided information was real or fake.","tags":null,"title":"Psychometrics Assignment 2 - Societal Data Analytics","type":"post"},{"authors":null,"categories":null,"content":"Academic is designed to give technical content creators a seamless experience. You can focus on the content and Academic handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.\nOn this page, you\u0026rsquo;ll find some examples of the types of technical content that can be rendered with Academic.\nExamples Code Academic supports a Markdown extension for highlighting code syntax. You can enable this feature by toggling the highlight option in your config/_default/params.toml file.\n```python\rimport pandas as pd\rdata = pd.read_csv(\u0026quot;data.csv\u0026quot;)\rdata.head()\r```\rrenders as\nimport pandas as pd\rdata = pd.read_csv(\u0026quot;data.csv\u0026quot;)\rdata.head()\rMath Academic supports a Markdown extension for $\\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.toml file.\nTo render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.\nExample math block:\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}\r{\\left \\|\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right \\|^2}$$\rrenders as\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}{\\left |\\nabla F(\\mathbf{x}{n}) - \\nabla F(\\mathbf{x}{n-1}) \\right |^2}$$\nExample inline math $\\nabla F(\\mathbf{x}_{n})$ renders as $\\nabla F(\\mathbf{x}_{n})$.\nExample multi-line math using the \\\\\\\\ math linebreak:\n$$f(k;p_0^*) = \\begin{cases} p_0^* \u0026amp; \\text{if }k=1, \\\\\\\\\r1-p_0^* \u0026amp; \\text {if }k=0.\\end{cases}$$\rrenders as\n$$f(k;p_0^) = \\begin{cases} p_0^ \u0026amp; \\text{if }k=1, \\\\ 1-p_0^* \u0026amp; \\text {if }k=0.\\end{cases}$$\nDiagrams Academic supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.\nAn example flowchart:\n```mermaid\rgraph TD\rA[Hard] --\u0026gt;|Text| B(Round)\rB --\u0026gt; C{Decision}\rC --\u0026gt;|One| D[Result 1]\rC --\u0026gt;|Two| E[Result 2]\r```\rrenders as\ngraph TD\rA[Hard] --\u0026gt;|Text| B(Round)\rB --\u0026gt; C{Decision}\rC --\u0026gt;|One| D[Result 1]\rC --\u0026gt;|Two| E[Result 2]\rAn example sequence diagram:\n```mermaid\rsequenceDiagram\rAlice-\u0026gt;\u0026gt;John: Hello John, how are you?\rloop Healthcheck\rJohn-\u0026gt;\u0026gt;John: Fight against hypochondria\rend\rNote right of John: Rational thoughts!\rJohn--\u0026gt;\u0026gt;Alice: Great!\rJohn-\u0026gt;\u0026gt;Bob: How about you?\rBob--\u0026gt;\u0026gt;John: Jolly good!\r```\rrenders as\nsequenceDiagram\rAlice-\u0026gt;\u0026gt;John: Hello John, how are you?\rloop Healthcheck\rJohn-\u0026gt;\u0026gt;John: Fight against hypochondria\rend\rNote right of John: Rational thoughts!\rJohn--\u0026gt;\u0026gt;Alice: Great!\rJohn-\u0026gt;\u0026gt;Bob: How about you?\rBob--\u0026gt;\u0026gt;John: Jolly good!\rAn example Gantt diagram:\n```mermaid\rgantt\rsection Section\rCompleted :done, des1, 2014-01-06,2014-01-08\rActive :active, des2, 2014-01-07, 3d\rParallel 1 : des3, after des1, 1d\rParallel 2 : des4, after des1, 1d\rParallel 3 : des5, after des3, 1d\rParallel 4 : des6, after des4, 1d\r```\rrenders as\ngantt\rsection Section\rCompleted :done, des1, 2014-01-06,2014-01-08\rActive :active, des2, 2014-01-07, 3d\rParallel 1 : des3, after des1, 1d\rParallel 2 : des4, after des1, 1d\rParallel 3 : des5, after des3, 1d\rParallel 4 : des6, after des4, 1d\rAn example class diagram:\n```mermaid\rclassDiagram\rClass01 \u0026lt;|-- AveryLongClass : Cool\r\u0026lt;\u0026lt;interface\u0026gt;\u0026gt; Class01\rClass09 --\u0026gt; C2 : Where am i?\rClass09 --* C3\rClass09 --|\u0026gt; Class07\rClass07 : equals()\rClass07 : Object[] elementData\rClass01 : size()\rClass01 : int chimp\rClass01 : int gorilla\rclass Class10 {\r\u0026lt;\u0026lt;service\u0026gt;\u0026gt;\rint id\rsize()\r}\r```\rrenders as\nclassDiagram\rClass01 \u0026lt;|-- AveryLongClass : Cool\r\u0026lt;\u0026lt;interface\u0026gt;\u0026gt; Class01\rClass09 --\u0026gt; C2 : Where am i?\rClass09 --* C3\rClass09 --|\u0026gt; Class07\rClass07 : equals()\rClass07 : Object[] elementData\rClass01 : size()\rClass01 : int chimp\rClass01 : int gorilla\rclass Class10 {\r\u0026lt;\u0026lt;service\u0026gt;\u0026gt;\rint id\rsize()\r}\rAn example state diagram:\n```mermaid\rstateDiagram\r[*] --\u0026gt; Still\rStill --\u0026gt; [*]\rStill --\u0026gt; Moving\rMoving --\u0026gt; Still\rMoving --\u0026gt; Crash\rCrash --\u0026gt; [*]\r```\rrenders as\nstateDiagram\r[*] --\u0026gt; Still\rStill --\u0026gt; [*]\rStill --\u0026gt; Moving\rMoving --\u0026gt; Still\rMoving --\u0026gt; Crash\rCrash --\u0026gt; [*]\rTodo lists You can even write your todo lists in Academic too:\n- [x] Write math example\r- [x] Write diagram example\r- [ ] Do something else\rrenders as\nWrite math example Write diagram example Do something else Tables Represent your data in tables:\n| First Header | Second Header |\r| ------------- | ------------- |\r| Content Cell | Content Cell |\r| Content Cell | Content Cell |\rrenders as\nFirst Header Second Header Content Cell Content Cell Content Cell Content Cell Asides Academic supports a shortcode for asides, also referred to as notices, hints, or alerts. By wrapping a paragraph in {{% alert note %}} ... {{% /alert %}}, it will render as an aside.\n{{% alert note %}}\rA Markdown aside is useful for displaying notices, hints, or definitions to your readers.\r{{% /alert %}}\rrenders as\nA Markdown aside is useful for displaying notices, hints, or definitions to your readers.\rSpoilers Add a spoiler to a page to reveal text, such as an answer to a question, after a button is clicked.\n{{\u0026lt; spoiler text=\u0026quot;Click to view the spoiler\u0026quot; \u0026gt;}}\rYou found me!\r{{\u0026lt; /spoiler \u0026gt;}}\rrenders as\nClick to view the spoiler\rYou found me!\rIcons Academic enables you to use a wide range of icons from Font Awesome and Academicons in addition to emojis.\nHere are some examples using the icon shortcode to render icons:\n{{\u0026lt; icon name=\u0026quot;terminal\u0026quot; pack=\u0026quot;fas\u0026quot; \u0026gt;}} Terminal {{\u0026lt; icon name=\u0026quot;python\u0026quot; pack=\u0026quot;fab\u0026quot; \u0026gt;}} Python {{\u0026lt; icon name=\u0026quot;r-project\u0026quot; pack=\u0026quot;fab\u0026quot; \u0026gt;}} R\rrenders as\nTerminal\nPython\nR\nDid you find this page helpful? Consider sharing it 🙌 ","date":1562889600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593433517,"objectID":"07e02bccc368a192a0c76c44918396c3","permalink":"https://thomvolker.github.io/post/writing-technical-content/","publishdate":"2019-07-12T00:00:00Z","relpermalink":"/post/writing-technical-content/","section":"post","summary":"Academic is designed to give technical content creators a seamless experience. You can focus on the content and Academic handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.","tags":null,"title":"Writing technical content in Academic","type":"post"},{"authors":["admin"],"categories":[],"content":"from IPython.core.display import Image\rImage('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')\rprint(\u0026quot;Welcome to Academic!\u0026quot;)\rWelcome to Academic!\rInstall Python and JupyterLab Install Anaconda which includes Python 3 and JupyterLab.\nAlternatively, install JupyterLab with pip3 install jupyterlab.\nCreate or upload a Jupyter notebook Run the following commands in your Terminal, substituting \u0026lt;MY-WEBSITE-FOLDER\u0026gt; and \u0026lt;SHORT-POST-TITLE\u0026gt; with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:\nmkdir -p \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/\rcd \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/\rjupyter lab index.ipynb\rThe jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.\nEdit your post metadata The first cell of your Jupter notebook will contain your post metadata (\rfront matter).\nIn Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:\n---\rtitle: My post's title\rdate: 2019-09-01\r# Put any other Academic metadata here...\r---\rEdit the metadata of your post, using the documentation as a guide to the available options.\nTo set a featured image, place an image named featured into your post\u0026rsquo;s folder.\nFor other tips, such as using math, see the guide on writing content with Academic.\nConvert notebook to Markdown jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=.\rExample This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593433517,"objectID":"6e929dc84ed3ef80467b02e64cd2ed64","permalink":"https://thomvolker.github.io/post/jupyter/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/post/jupyter/","section":"post","summary":"Learn how to blog in Academic using Jupyter notebooks","tags":[],"title":"Display Jupyter Notebooks with Academic","type":"post"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Academic Academic | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot;\rif porridge == \u0026quot;blueberry\u0026quot;:\rprint(\u0026quot;Eating...\u0026quot;)\rMath In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}}\r{{% fragment %}} **Two** {{% /fragment %}}\r{{% fragment %}} Three {{% /fragment %}}\rPress Space to play!\nOne **Two** Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}}\r- Only the speaker can read these notes\r- Press `S` key to view\r{{% /speaker_note %}}\rPress the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}}\r{{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}}\r{{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}}\rCustom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1,\r.reveal section h2,\r.reveal section h3 {\rcolor: navy;\r}\rQuestions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593433517,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://thomvolker.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Academic's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"\rIn recent years, the importance of replications has received considerable attention (e.g., Open Science Collaboration, 2015; Baker, 2016; Brandt et al., 2014). However, emphasis has been placed primarily on exact, direct or close replication studies. These studies employ an identical methodology and research design as the initial study, and are thus merely concerned with the statistical reliability of the results. If these results depend on methodological flaws, inferences from all studies will lead to suboptimal or invalid conclusions (Munafò \u0026amp; Smith, 2018). To overcome these limitations, the use of conceptual replications has been advocated (e.g., Munafò \u0026amp; Smith, 2018; Lawlor, Tilling, \u0026amp; Davey Smith, 2017). Specifically, conceptual replications scrutinize the extent to which the initial conclusions hold under different conditions, using varying instruments or operationalizations.\nHowever, established methods such as (Bayesian) meta-analysis and Bayesian updating are not applicable when studies differ conceptually. This is due to the fact that these methods require that the parameter estimates (i) share a common scale, and (ii) result from analyses with identical function forms (Lipsey \u0026amp; Wilson, 2001; Schönbrodt, Wagenmakers, Zehetleitner, \u0026amp; Perugini, 2017; Sutton \u0026amp; Abrams, 2001). Consequently, Kuiper, Buskens, Raub, \u0026amp; Hoijtink (2013) proposed Bayesian Evidence Synthesis (BES), which is built upon the foundation of the Bayes Factor (BF; Kass \u0026amp; Raftery, 1995). This method allows researchers to pool evidence for a specific hypothesis over multiple studies, even if the studies have seemingly incompatible designs.\nIn the current project, Irene Klugkist and I aim to reveal under which circumstances BES performs inadequately. Additionally, we hope to propose adjustments to the method that improve its performance, so that an increasing number of researchers can benefit from BES.\nReferences\rBaker, M. (2016). Reproducibility crisis. Nature, 533(26), 353–366. https://doi.org/10.1038/533452a\nBrandt, M. J., IJzerman, H., Dijksterhuis, A., Farach, F. J., Geller, J., Giner-Sorolla, R., … Van’t Veer, A. (2014). The replication recipe: What makes for a convincing replication? Journal of Experimental Social Psychology, 50, 217–224. https://doi.org/10.1016/j.jesp.2013.10.005\nKass, R. E., \u0026amp; Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.\nKuiper, R. M., Buskens, V., Raub, W., \u0026amp; Hoijtink, H. (2013). Combining statistical evidence from several studies: A method using bayesian updating and an example from research on trust problems in social and economic exchange. Sociological Methods \u0026amp; Research, 42(1), 60–81.\nLawlor, D. A., Tilling, K., \u0026amp; Davey Smith, G. (2017). Triangulation in aetiological epidemiology. International Journal of Epidemiology, dyw314. https://doi.org/10.1093/ije/dyw314\nLipsey, M. W., \u0026amp; Wilson, D. B. (2001). Practical meta-analysis. SAGE publications, Inc.\nMunafò, M. R., \u0026amp; Smith, G. D. (2018). Robust research needs many lines of evidence. Nature, 553(7689), 399–401. https://doi.org/10.1038/d41586-018-01023-3\nOpen Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251). https://doi.org/10.1126/science.aac4716\nSchönbrodt, F. D., Wagenmakers, E.-J., Zehetleitner, M., \u0026amp; Perugini, M. (2017). Sequential hypothesis testing with bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322.\nSutton, A. J., \u0026amp; Abrams, K. R. (2001). Bayesian methods in meta-analysis and evidence synthesis. Statistical Methods in Medical Research, 10(4), 277–303. https://doi.org/10.1177/096228020101000404\n","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611331433,"objectID":"ac752063ef182751393e4f05b8775fd9","permalink":"https://thomvolker.github.io/project/bes/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/bes/","section":"project","summary":"Bayesian Evidence Synthesis is a method to integrate the results of multiple studies with varying, seemingly incompatible, designs using Bayes Factors, to enhance the aggregation of scientific evidence.","tags":["Bayesian Evidence Synthesis"],"title":"Bayesian Evidence Synthesis","type":"project"},{"authors":["admin","吳恩達"],"categories":["Demo","教程"],"content":"Create a free website with Academic using Markdown, Jupyter, or RStudio. Choose a beautiful color theme and build anything with the Page Builder - over 40 widgets, themes, and language packs included!\nCheck out the latest demo of what you\u0026rsquo;ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.\n👉 Get Started 📚 View the documentation 💬 Ask a question on the forum 👥 Chat with the community 🐦 Twitter: @source_themes @GeorgeCushen #MadeWithAcademic 💡 Request a feature or report a bug ⬆️ Updating? View the Update Guide and Release Notes ❤️ Support development of Academic: ☕️ Donate a coffee 💵 Become a backer on Patreon 🖼️ Decorate your laptop or journal with an Academic sticker 👕 Wear the T-shirt 👩‍💻 Contribute Academic is mobile first with a responsive design to ensure that your site looks stunning on every device.\rKey features:\nPage builder - Create anything with widgets and elements Edit any type of content - Blog posts, publications, talks, slides, projects, and more! Create content in Markdown, Jupyter, or RStudio Plugin System - Fully customizable color and font themes Display Code and Math - Code highlighting and LaTeX math supported Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more! Beautiful Site - Simple and refreshing one page design Industry-Leading SEO - Help get your website found on search engines and social media Media Galleries - Display your images and videos with captions in a customizable gallery Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site Multi-language - 15+ language packs including English, 中文, and Português Multi-user - Each author gets their own profile page Privacy Pack - Assists with GDPR Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects One-Click Deployment - No servers. No databases. Only files. Themes Academic comes with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the sun/moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.\nChoose a stunning theme and font for your site. Themes are fully customizable.\nEcosystem Academic Admin: An admin tool to import publications from BibTeX or import assets for an offline site Academic Scripts: Scripts to help migrate content to new versions of Academic Install You can choose from one of the following four methods to install:\none-click install using your web browser (recommended) install on your computer using Git with the Command Prompt/Terminal app install on your computer by downloading the ZIP files install on your computer with RStudio Then personalize and deploy your new site.\nUpdating View the Update Guide.\nFeel free to star the project on Github to help keep track of updates.\nLicense Copyright 2016-present George Cushen.\nReleased under the MIT license.\n","date":1461110400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593433517,"objectID":"279b9966ca9cf3121ce924dca452bb1c","permalink":"https://thomvolker.github.io/post/getting-started/","publishdate":"2016-04-20T00:00:00Z","relpermalink":"/post/getting-started/","section":"post","summary":"Create a beautifully simple website in under 10 minutes.","tags":["Academic","开源"],"title":"Academic: the website builder for Hugo","type":"post"},{"authors":null,"categories":["R"],"content":"\rR Markdown\rThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.\nYou can embed an R code chunk like this:\nsummary(cars)\r## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00\rfit \u0026lt;- lm(dist ~ speed, data = cars)\rfit\r## ## Call:\r## lm(formula = dist ~ speed, data = cars)\r## ## Coefficients:\r## (Intercept) speed ## -17.579 3.932\rIncluding Plots\rYou can also embed plots. See Figure 1 for example:\npar(mar = c(0, 1, 0, 1))\rpie(\rc(280, 60, 20),\rc(\u0026#39;Sky\u0026#39;, \u0026#39;Sunny side of pyramid\u0026#39;, \u0026#39;Shady side of pyramid\u0026#39;),\rcol = c(\u0026#39;#0292D8\u0026#39;, \u0026#39;#F7EA39\u0026#39;, \u0026#39;#C4B632\u0026#39;),\rinit.angle = -50, border = NA\r)\rFigure 1: A fancy pie chart.\r","date":1437703994,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593433517,"objectID":"10065deaa3098b0da91b78b48d0efc71","permalink":"https://thomvolker.github.io/post/2015-07-23-r-rmarkdown/","publishdate":"2015-07-23T21:13:14-05:00","relpermalink":"/post/2015-07-23-r-rmarkdown/","section":"post","summary":"R Markdown\rThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.","tags":["R Markdown","plot","regression"],"title":"Hello R Markdown","type":"post"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593764969,"objectID":"8576ec274c98b3831668a172fa632d80","permalink":"https://thomvolker.github.io/about/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/about/","section":"","summary":"","tags":null,"title":"About me","type":"widget_page"},{"authors":null,"categories":null,"content":"Publicly available research data could dramatically improve the scientific returns of research, given the same data collection effort. However, directly sharing the research data might harm the privacy and confidentiality of the participants. A solution has been proposed by Donald Rubin and Roderick Little, who, in two separate papers, have given rise to the procedure of creating synthetic data. Both scholars proposed to create multiple, synthetic versions of a dataset that is stripped of all possibly identifying information, by replacing this information by artificially created synthetic data that is unrelated to the persons in the data. Both approaches are build upon the foundation of multiple imputation for missing data, but rather than imputating the missing values, observed values are overimputed with multiple draws from the posterior predictive distribution of the observed data.\nIf the imputation procedure is of sufficient quality, the synthetic datasets are almost equally informative as the observed data, without disclosing any identifying information. However, to make this work, creating synthetic data should be a cakewalk, as researchers collecting data can generally not be expected to be experts in this field. To ease the effort involved with creating missing data, together with Gerko Vink and Stef van Buuren, I am extending the R-package MICE, which is currently restricted to the imputation of missing data. Hereby, we aim at relieving the burden associated with generating synthetic data, while maintaining the data quality that is required to make valid inferences.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611331433,"objectID":"fed25ec65d4b1d8dbc34014b8ccafbd1","permalink":"https://thomvolker.github.io/project/synthetic_data/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/project/synthetic_data/","section":"project","summary":"Synthetic data allows for openly sharing of research data, without disclosing identifying information of the participants, that could be as informative as the actually observed data.","tags":["Multiple Imputation"],"title":"Multiple Imputation of Synthetic Data","type":"project"},{"authors":null,"categories":null,"content":"\rNew post\rThis is a new post without any information.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593516781,"objectID":"8e6775e9c93aed813bef3fb446a4a564","permalink":"https://thomvolker.github.io/post/empty_post/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/post/empty_post/","section":"post","summary":"New post\rThis is a new post without any information.","tags":null,"title":"New Post I","type":"post"},{"authors":null,"categories":null,"content":"\rR Markdown\rThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.\nWhen you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:\nsummary(cars)\r## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00\rThis browser does not support PDFs. Please download the PDF to view it: Download PDF.\rIncluding Plots\rYou can also embed plots, for example:\nReferences\rAkaike, H. (1973). Information theory and an etension of the maximum likelihood principle. In B. N. Petrov \u0026amp; F. Csaki (Eds.), Second international symposium on information theory (pp. 267–281). Budapest: Akademiai Kiado.\nBurnham, K. P., \u0026amp; Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Springer-Verlag.\nDen Uijl, I. E. M., Fischer, K., Van der Bom, J. G., Grobbee, D. E., Rosendaal, F. R., \u0026amp; Plug, I. (2011). Analysis of low frequency bleeding data: The association of joint bleeds according to baseline fviii activity levels. Haemophilia, 17, 41–44. https://doi.org/10.1111/j.1365-2516.2010.02383.x\nRaftery, A. E. (1995). Bayesian Model Selection in Social Research. Sociological Methodology, 25, 111. https://doi.org/10.2307/271063\nR Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/\nRoyston, P., Altman, D. G., \u0026amp; Sauerbrei, W. (2006). Dichotomizing continuous predictors in multiple regression: A bad idea. Statistics in Medicine, 25, 127–141. https://doi.org/10.1002/sim.2331\nSchwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–462. https://projecteuclid.org/download/pdf_1/euclid.aos/1176344136\nVenables, W. N., \u0026amp; Ripley, B. D. (2002). Modern applied statistics with s (Fourth). Springer. http://www.stats.ox.ac.uk/pub/MASS4\nWickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 43, 1686. https://doi.org/10.21105/joss.01686\nZeileis, A., Kleiber, C., \u0026amp; Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8). http://www.jstatsoft.org/v27/i08/\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593764969,"objectID":"3752b330e9ce9ec8e64ee2766982cce6","permalink":"https://thomvolker.github.io/post/new_post_ref/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/post/new_post_ref/","section":"post","summary":"R Markdown\rThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.","tags":null,"title":"New post with references","type":"post"}]