GCMMGroupe Coordonnateur de la Mobilité de la Main d'Oeuvre (French: Group Coordinator Mobility of Labor; Canada)
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From 2009 to May 2016, the organizational (operational) structure of the GCMM can be summarized as consisting of the commander (at the top of the hierarchy), followed by supervisors who command the institution's eight platoons, followed by civil guards.
The GCMM workforce is divided into eight platoons, which are shown in Figure 6 in the discussion and analysis section.
This method was used primarily to collect data relevant to the GCMM that were not available through documentary research.
To analyze the data relating to the objective of analyzing the bases of power exercised by the GCMM leadership as perceived by the workers, the factor means were scaled, one for each basis of power measured.
Pearson's correlation coefficient (r) was used to achieve the primary aim of this study, which was to determine whether there are correlations between the constructs "bases of power" and "affective organizational commitment" among the civil guards who comprise the GCMM. This measurement assesses the strength and direction of linear correlation between the two variables (Hair, William, Babin, & Anderson, 2009).
Growth curve mixture models (GCMMs [6]) are a type of latent variable model that extend the latent class model to the longitudinal setting where subjects are grouped based on the observed longitudinal trend over time.
Jung and Wickrama [13] provide a good review of GCMMs and their implementation.
Software for fitting GCMMs is fairly specialized and generally unavailable in standard statistical packages.
GCMMs extend commonly used mixed effects methods to allow for multiple classes, each with its own mixed effects model.
A special case of GCMMs is latent class growth analysis (LCGA) [15,16] which does not allow for departure from the average trajectory within each latent class (by setting [[alpha].sub.0i] and [[alpha].sub.1i] equal to zero in equation 1.3).
In this section, we show the simulation of GCMM, and verify whether the GCMM is consistent with the statistical characteristic that we observed from the GPS trace of human.
GCMM. By extracting the hot region set that the human frequently visited position in an observation area, we modeled human's movement pattern, meanwhile, in order to mimic human's dynamic decision-making process, we present a destination selection scheme and attraction attenuation mechanism.