The phrase "a latent factor for" is correct and usable in written English.
It can be used when discussing underlying influences or causes that affect a particular outcome or phenomenon.
Example: "In psychological research, a latent factor for anxiety may be early childhood experiences that are not immediately apparent."
Alternatives: "an underlying cause of" or "a hidden influence on".
Addressing this research objective involved creating a latent factor for cultural stressors, establishing invariance for this factor over time, estimating a growth curve for this factor over time, and examining the effects of initial levels (intercepts) and trajectories (slopes) of cultural stressors on adolescent outcomes.
For the first step, this requires finding a suitable measure of sentiment as a latent factor for the valuation of asset prices.
An r-dimensional vector u s is a latent factor for node s that consists of r latent components.
When controlling for each other, spanking, grounding, and psychotherapy all showed significantly adverse effects, except when controlling for a latent factor for externalizing behavior problems.
The differences in adverse effects by corrective action did not vary as much by covariate comprehensiveness, except when minimizing measurement error by controlling for a latent factor for externalizing behavior problems.
In addition, a latent factor for negative symptoms was not identified for the QIDS-SR16, despite that several items overlap with negative symptoms, such as of concentration difficulties (question #10), lack of interest (#13) and lack of energy (#14).
Fourth, structural equation modeling was used to control for a latent factor of externalizing problems, thereby minimizing measurement error in the covariate.
Therefore a latent factor score for SWB was estimated for each participant in the dataset.
Parsimonious Gaussian mixture models [ 61 ] for step 1 and k-means with k = 2 for step 2. PGMM utilizes a latent factor structure for the data and is available in the R package pgmm [ 62 ].
GPCM for step 1 followed by GPCM with G = 2 for step 2. PGMM for step 1 followed by GPCM with G = 2 for step 2. Parsimonious Gaussian mixture models [ 61] for step 1 and k-means with k = 2 for step 2. PGMM utilizes a latent factor structure for the data and is available in the R package pgmm [ 62].
In this new class, the spatial and temporal components are conditionally independently modeled via a latent factor analysis structure for the (canonical) transformation of the measurements mean function.
