The basic power analysis as described in the Vignette Getting started can be extended to using bounded estimation, estimation with constraints over time, and the inclusion of measurement error variances in the generated data and estimation model. These extensions are described below.
Bounded estimation with
To prevent non-convergence for small sample sizes (say, less than
100), bounds can be imposed on the parameter space during estimation of
the model using
bounds = TRUE (De Jonckere &
Rosseel, 2022). This can aid the optimization algorithm to find
unique solutions and prevents it from searching in the completely wrong
direction for one, or multiple parameters. Sensible lower bounds involve
those on the (residual) variances of latent variables (e.g., the random
intercept variances), as negative variances are theoretically not
possible. Upper bound for variances are determined based on the observed
variances for variable. In the context of the RI-CLPM, the factor
loadings are (usually) fixed, and hence these parameters are not
estimated. The lagged effects are theoretically infinite, and hence
there are no sensible bounds we can place à priori on these
Constraints over time
powRICLPM() offers users the option to impose various
constraints over time on the estimation model through the
constraints argument. This has statistical advantages as
constraints over time reduce model complexity, thereby potentially
reducing convergence issues and increasing power. Moreover, some
researchers are interested in so called ‘stationarity’ constraints for
theoretical reasons. A disadvantage of such constraints is that they
assume certain parameters to be time-invariant. This might not be an
assumption researchers are willing to make, especially in developmental
contexts where you expect lagged effects might change over time (e.g.,
wA gets more important in driving
wB as one gets older). Therefore, by default
constraints = "none", implying that all lagged effects, and
within-components (residual) variances and covariances are freely
estimated over time.
Constraint options include:
constraints = "lagged": Autoregressive and cross-lagged effects are constrained to be equal over time.
constraints = "residuals: Within-unit residual variances and covariance (from wave 2 onward) are constrained to be equal over time.
constraints = "within": Both lagged effects and residual variances and covariances are constrained to be equal over time.
constraints = "stationary": Constraints are imposed on the variances of the within-components at the first wave, and residual variances at wave 2 and further, such that the variances of the within-components themselves are all 1. This implies that the variances at the first wave are fixed to 1, and that the residual variances are a function of the lagged effects, and correlation between within-components at the same wave. These constraints are deduced in the supplementary materials of Mulder and Hamaker (2021, see the FAQ “How can I constrain the standardized parameters to be invariant over time?”).
constraints = "ME": Constraints are imposed on the measurement error variances across time (separately for each variable). This constraint is only possible when
estimate_ME = TRUE.
While it is generally advisable to include measurement error when
analyzing psychological data, the RI-CLPM does not include it. Adding
measurement error to the model would result in the bivariate stable
trait autoregressive trait state (STARTS) model by Kenny and Zautra (2001),
and requires at least 4 waves of data to be identified. Users can add
measurement error variances to the estimation model by specifying
estimate_ME = TRUE. Measurement error can be added to the
simulated data using the
Note, however, that the STARTS model has been shown to be prone to empirical under-identification, often requiring upwards of 8 waves of data and sample sizes larger than 500.