simPM
: SIMulation-based power analysis for Planned Missing designs.simPM
is an R package that automates the Monte Carlo simulation-based
search for optimal planned missing (PM) and ‘post hoc’ planned missing
(PHPM) designs. This R package is developed and maintained by Yi
Feng & Dr. Gregory R.
Hancock from the
University of Maryland.
“Oh %&$#!, they cut my funding!”
We have heard that too often, unfortunately, from researchers who rely on external funding to support their longitudinal studies. There are a variety of reasons that may lead to the adjusted (shrunken) budget announced by the funding agency. Further, what often adds to the challenge is that the researchers are asked to provide a revised study plan to convince that their project can be continued, showing satisfactory inferential validity and statistical power, albeit with the reduced budget.
“What can I do except for firing my consultant and (poor) RAs?”
Planned missing data methods present a very promising solution for such challenging situations, providing many practical and methodological advantages. However, careful planning is critical such that the planned missing design can preserve enough information and statistical power.
simPM
was created to help researchers survive the unexpected funding
cut in the course of a longitudinal study. It can be used to find an
optimal ‘post hoc’ planned missing design that allows the
researchers to complete the study at a reduced cost, while maintaining
satisfactory level of statistical power for testing the focal
parameters.
By automizing the simulation-based power analysis for planned missing
designs in longitudinal context, simPM
can free the researchers from
manually configuring the possible PM designs, determining their
eligibility, setting up the simulations, and summarizing the results
over replications, which can be tedious and time-consuming work
especially when there is a large number of plausible PHPM designs to be
evaluated.
The source code of this R package is made public on the author’s Github
page. To install simPM
on your
local machine, please run the following code
install.packages("devtools")
library(devtools)
install_github("YiFengEDMS/simPM")
Below are some tutorials demonstrating the usage of simPM
:
Feng, Y. & Hancock, G. R. (in press). Oh no! They cut my funding! Using ‘post hoc’ planned missing data designs to salvage longitudinal research . Child Development.
Feng, Y., & Hancock, G. R. (2019, April). Oh %&$#!, they cut my funding: Using planned missing data methods to salvage longitudinal research. Paper presented at the annual meeting of the American Educational Research Association (AERA), Division D: Measurement & Research Methodology, Toronto, ON, Canada*.
Feng, Y. & Hancock, G. R. (2019). simPM: SIMulation-based power analysis for Planned Missing designs. R package version 0.0.0.9000. https://yifengedms.github.io/simPM/
Send an email to yifeng94@umd.edu. We are happy to hear about your thoughts!
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