powRICLPM is an R package that aids researchers with
performing a power analysis for the random intercept cross-lagged panel
model (RI-CLPM) by Hamaker, Kuiper, and Grasman (2015), and the Stable
Trait Autoregressive Trait State Model (STARTS) by Kenny and Zautra
(1995) and Kenny and Zautra (2001). It implements the strategy as
proposed by Mulder (2023). Its main functionalities include:
Basic
power analysis: Use Monte Carlo simulations to compute the
power to reject the null-hypothesis (as well as other performance
measures such as bias, mean square error) for all parameters in the
RI-CLPM and STARTS, for a specific experimental condition. A condition
is defined by its sample size, number of repeated measures, proportion
of between-unit variance, and reliability of the indicators.
powRICLPM can perform power analyses across multiple
experimental conditions simultaneously, and report the results back in a
user-friendly manner.
Extensions:
The basic power analysis setup can be extended to include the use of
bounded estimation, various (stationarity) constraints over time on
parameters of the estimation model, the generation of nonnormal data,
among other things.
Mplus:
When Mplus is installed, powRICLPM can create Mplus syntax,
and run the power analyses in Mplus.
Documentation
There are four sources of documentation for
powRICLPM:
The rationale for the power analysis strategy underlying this
package can be found in Mulder (2023).
Every user-facing function in the package is documented, and the
documentation can be accessed by running ?function_name in
the R console (e.g., ?powRICLPM). Here, you can find
explanations on how to use the functions, as well as technical
details.
More elaborate descriptions of this package’s functionality and
analysis options are described in vignettes. These are accessible via
the ‘Vignettes’ tab in the menu, or via R using
vignette(package = "powRICLPM").
The FAQ
contains answers to frequently asked question that reach me via
email.
Installation
To install the development version of powRICLPM,
including the latest bug fixes and new features, run:
To install the latest release of powRICLPM from CRAN,
run:
install.packages("powRICLPM")
Citing powRICLPM
You can cite the R-package with the following citation:
Mulder, J.D., (2023). Power analysis for the random intercept
cross-lagged panel model using the powRICLPM R-package. Structural
Equation Modeling: A Multidisciplinary Journal, 30(4), 645-658. https://doi.org/10.1080/10705511.2022.2122467
Contact
If you have ideas, comments, or issues you would like to raise,
please get in touch.
Hamaker, Ellen L., Rebecca M. Kuiper, and Raoul P. P. P. Grasman. 2015.
“A critique of the cross-lagged panel
model.” Psychological Methods 20 (1): 102–16. https://doi.org/10.1037/a0038889.
Kenny, David A., and Alex Zautra. 1995. “The
trait-state-error model for multiwave data.” Journal of
Consulting and Clinical Psychology1 63 (1): 52–59.
———. 2001. “Trait–state models for longitudinal
data.” In New Methods for the Analysis of Change,
243–63. Washington: American Psychological Association. https://doi.org/10.1037/10409-008.
Mulder, Jeroen D. 2023. “Power Analysis for the Random Intercept
Cross-Lagged Panel Model Using the powRICLPM r-Package.” Structural
Equation Modeling: A Multidisciplinary Journal 30 (4): 645–58. https://doi.org/10.1080/10705511.2022.2122467.
