An interesting package! I am going teach lavaan this semester. I will try it first and may introduce it to my student. I still think writing and running script is the most efficient work flow. However, a shiny-based interface may be good for learning and testing.


I must confess that I rarely care about BIC in SEM. However, the more I read about it, the more I wonder why "the BIC has received little attention in the structural equation modeling (SEM) literature" (Bollen, Harden, Ray, & Zavisca, 2014, p. 1). BIC, and variants Bollen et al. investigated, can be converted to approximate Bayes factor, which "expresses the odds of observing a given set of data under one model versus an alternative model" (Bollen et al., p. 3). To me, this is more meaningful than CFI, TLI, and even RMSEA and SRMR. Yes, I know people will ask for cutoff values for BIC. But if converted to Bayes factor, a cutoff value for BIC seems to be more meaningful than .90 or .95 for TLI and CFI (Raftery, 1993).
I need to learn more about BIC.

Major Reference:

Bollen, K. A., Harden, J. J., Ray, S., & Zavisca, J. (2014). BIC and alternative Bayesian information criteria in the selection of structural equation models. Structural Equation Modeling: An Multidisciplinary Journal, 21, 1-19. doi:10.1080/10705511.2014.85669

Source windows in RStudio

Link: Using Source Windows (@ RStudio Support)

A good feature! Some users like one single window with panes. Some users like multiple windows. Some users like being able to choose.

I like the interface of GIMP, in which you can switch between single and multiple window modes. I also like the interface of Blender, in which you can work in one window with panes, as in RStudio, or you can work in multiple windows, which can also have mroe than one pane each, just by creating new windows.

I hope RStudio can be more flexible. I don't like the the single window mode when working on a small screen notebook.

"Replication in Psychological Science": An editorial at Psychological Science

Lindsay, D. S. (2015). Replication in psychological science. Psychological Science. doi:10.1177/0956797615616374

A good move. We need to pay attention to power, p-hacking, effect size, confidence interval, distribution (mean and SD are not enough), scatter plot (correlation is not enough), and replication. For many of the issues, we have recommended to pay attention to them for a long time. But we still have a long way to go to have a culture in which these are common sense and common practices in psychology.

One thing I don't agree. I do think it is appropriate and even necessary to have exploratory studies. What we need is an appropriate way to do them.