Psychometric properties of the Chinese version of Five Facet Mindfulness Questionnaire—short form in cancer patients: a Bayesian structural equation modeling approach

Table 4 Fit indices of the 5-factor BSEM models with different priors for the FFMQ-SF

Model	Prior specification	#	pD	pppp	2.5% PPL	97.5% PPL	PPP	DIC	RMSEA	CFI
1	Uninformative	70	72	–	100.4	204.3	.000	6403	0.091	0.84
Informative priors on cross-loadings
2	var = 0.01	150	100	.12	12.5	132.7	.008	6352	0.072	0.92
3	var = 0.02	150	108	.61	− 3.0	117.5	.030	6343	0.066	0.93
4	var = 0.04	150	115	.94	− 8.0	115.7	.044	6347	0.066	0.94
5	Revised Model 3	150	107	.70	− 4.8	111.7	.037	6339	0.065	0.94
Informative priors on residual covariance
6	d = 10	340	211	.44	− 75.5	50.9	.662	6377	0.010	1.00
7	d = 20	340	209	.20	− 78.9	42.2	.726	6369	0.000	1.00
8	d = 30	340	214	.06	− 73.6	50.0	.644	6381	0.020	0.99
Informative priors on cross-loadings with one residual covariance
9	Revised Model 5	151	108	.75	− 14.9	97.6	.073	6329	0.060	0.95

N = 127; # = number of free parameters; pD = estimated number of parameters; pppp = prior posterior predictive p value; PPL = posterior predictive limit; PPP = posterior predictive p value; DIC = Deviance information criterion; RMSEA = Root mean square error of approximation; CFI = comparative fit index; d = degree of freedom

ISSN: 1477-7525