Multiple mediation: extracting output

Lavaan’s output is copious. Especially when fitting a complex model, lavaan’s output is literally a mine of information. Finding what is relevant is not always easy, therefore I will try to describe a way to summarize lavaan’s summary output. Continue reading “Multiple mediation: extracting output”

Multiple mediation: extracting output

Multiple-mediation example with lavaan

This post extends this previous one on multiple-mediation with lavaan. Here I modeled a ‘real’ dataset instead of a randomly generated one. This dataset we used previously for a paper published some time ago. There we investigated whether fear of an imperfect fat self was a stronger mediator than hope of a perfect thin self on dietary restraint in college women. At the time of the paper’s publication we performed the analysis using the SPSS macro INDIRECT . However,
Continue reading “Multiple-mediation example with lavaan”

Multiple-mediation example with lavaan

Multiple-mediator analysis with lavaan

I wrote this brief introductory post for my friend Simon. I want to show how easy the transition from SPSS to R can be. In the specific case of mediation analysis the transition to R can be very smooth because, thanks to lavaan, the R knowledge required to use the package is minimal. Analysis of mediator effects in lavaan requires only the specification of the model, all the other processes are automated by the package. So, after reading in the data, running the test is trivial.
Continue reading “Multiple-mediator analysis with lavaan”

Multiple-mediator analysis with lavaan

Four dimensions in two dimensions

scatterplot of reaction time differences

This scatterplot is one of the best data visualisation I made. I like it because it concentrates a lot of information into a single visualisation. The scatterplot displays four dimensional data (i.e., four variables) using a two dimensional scatterplot. I made the first implementation in R, but because I wanted to add interactivity I switched to d3.js. Below I describe the choices I made to display the information and how I coded them in d3.js. Continue reading “Four dimensions in two dimensions”

Four dimensions in two dimensions

Streamgraphs in base::R [e.II]

Until recently I did not have a practical application in which to use streamgraphs. In fact, I still find the visualisation complex to understand, abstract and a bit too artistic. While I recognise that the strength of streamgraphs is the display of all the time series’ values into one (possibly interactive) plot, the amount of data displayed is massive, with many streams and even more data points. Because of the amount of data displayed Continue reading “Streamgraphs in base::R [e.II]”

Streamgraphs in base::R [e.II]

Making up for univariate [DAI IVb]

This post is an extension of this one, which was (supposed to be) the final post of the coursera course ‘data analysis and interpretation’. This current post extends or complements the previous one because in that assignment I forgot to include univariate graphs in my plot. Since I only had a bivariate graph, the other reviewers failed my assignment. I was quite disappointed by their reaction, but I understood their motives. If univariate graphs get points and the absence thereof does not, I was righteously failed. Therefore, in this post I try to fix my previous mistake including three univariate graphs. The conclusion one can gather from these graphs remains unchanged and one should Continue reading “Making up for univariate [DAI IVb]”

Making up for univariate [DAI IVb]