Data Science

Data Science · December 24, 2018
A basic requirement for test and survey items is that they are able to detect variance with respect to a latent variable. To do this, an item scale must discriminate between test subjects and must have a systematic, clear and sufficiently strong relationship with the underlying construct. One possibility to examine the variability of an item is the computation of the relative information content. The relative information content (also called relative entropy) is a dispersion measure for at...

Data Science · October 07, 2018
You have written your own R-script to solve a statistical problem and want to share it with other people who are not familiar with R? Develop your own Shiny-App! Shiny is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. And you can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions.

Data Science · October 05, 2018
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. The basic idea behind meta-analyses is that there is a common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. A key benefit of this approach is the aggregation...

Data Science · August 26, 2018
+++ UPDATE 2018-10-07: Please also try the new ShinyApp! It has never been so easy to calculate statistical power in meta analyses... +++ You want to calculate statistical power of a meta-analysis in order to better interpret the results? Or maybe you are wondering if there is still a need for further research to underpin the results of an existing quantitative review? In this article I explain how you can calculate statistical power of fixed- and random effects model meta-analyses and what...

Data Science · July 28, 2018
In statistics, variance is the expectation of the squared deviation of a random variable from its mean. It measures how far a set of (random) numbers are spread out from their average value. Thus the variance has a central role in statistics, e.g. in descriptive statistics, statistical inference, hypothesis testing, goodness of fit or Monte Carlo sampling. In Conclusion, variance is an important tool in all sciences, where statistical analysis of data is common. When we talk about empirical...