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of the latent class variable on the distal is controlled for by those covariates. This is a variation on the modeling just discussed where the covariate X in uences not only Y but also the latent class variable. Following is an illustration of the manual BCH estimation for such a model.
1.if i want to check the Impact of control variable (c) on the main Mediation effect (x-m1-m2-y), can I just Regress m1 on c, and skip the Regression of m2 and y on c? 2. is it up to me to decide whether an indirect effect between control variable and y shall be tested? or actually I shall only do Regression test involving DV and control? 3.
I want to run a CFA with a control variable. The model equations are y1=a0+a1*U+a2*z+epsilon1, y2=b0+b1*U+b2*z+epsilon2, where y1 and y2 are dependent manifest variables, z is the manifest control variable and U is the latent variable, epsilon1 and epsilon2 are errors, and a0,a1,a2,b0,b1 and b2 are regression intercepts and coefficients.
Regarding independent variables, SE groups (dummy variables) and skills (controlled variable) were predictors at the individual level. SE group ratios and mean class scores of the skills were determined as predictor at the classroom level (cluster_mean).
describes the logistic regression of the binary latent inflation variable y1#1 on the covariates x1 and x3. This regression predicts the probability of being unable to assume any value except the censoring point. The inflation variable is referred to by adding to the name of the censored variable the number sign (#) followed by the number 1.
C1 = control variable 1 C2 = control variable 2 W on X C1; P on W C2; Mplus assumes C1 and C2 to be correlated, even if I do not include C2 as a control in the relationship on W. Is this true? So the regression on W is not controlled for C2, but somehow, the correlation between C2 and C1 should affect the relationship between X and W?
I don't have a succinct answer to give you. I think basing reliability estimates on latent correlations such as tetrachorics gives some information, but this focuses on the continuous latent response variables underlying the categorical observed variables, so you don't get a reliability of the observed sum of items.
I am working with single-indicator latent variable, but my indicator is ordered categorical (with 4 categories). I know I can directly work with observed categorical variable in normal case, but my variable is exogenous variable in my SEM model. I am wondering if I can do this: categorical are: x1-x9; Analysis: esitmator=WLSMV;
calculation of region-level partial correlation between x and y, controlled for z1 (control variables on the individual level such as age or race) and z2 (control variable on the region-level such as regional unemployment rate). x is the region mean calculated from the individual level dataset, y is aggregate data from external sources ...
By contrast, randomized placebo-controlled trials (RCT) of diclofenac, nimesulide, and naproxen failed to show an effect (Scharf et al., 1999; Aisen et al., 2002; Aisen et al., 2003). One trial showed a positive effect of indomethacin, but a subsequent study failed to replicate that finding (Rogers et al., 1993; deJong et al., 2008).