probe2WayRC {semTools}

Probing two-way interaction on the residual-centered latent interaction

Description

Probing interaction for simple intercept and simple slope for the residual-centered latent two-way interaction (Geldhof et al., 2013)

Usage

probe2WayRC(fit, nameX, nameY, modVar, valProbe, group = 1L,
  omit.imps = c("no.conv", "no.se"))

Arguments

fit

A fitted lavaan or lavaan.mi object with a latent 2-way interaction.

nameX

character vector of all 3 factor names used as the predictors. The lower-order factors must be listed first, and the final name must be the latent interaction factor.

nameY

The name of factor that is used as the dependent variable.

modVar

The name of factor that is used as a moderator. The effect of the other independent factor will be probed at each value of the moderator variable listed in valProbe.

valProbe

The values of the moderator that will be used to probe the effect of the focal predictor.

group

In multigroup models, the label of the group for which the results will be returned. Must correspond to one of lavInspect(fit, "group.label"), or an integer corresponding to which of those group labels.

omit.imps

character vector specifying criteria for omitting imputations from pooled results. Ignored unless fit is of class lavaan.mi. Can include any of c("no.conv", "no.se", "no.npd"), the first 2 of which are the default setting, which excludes any imputations that did not converge or for which standard errors could not be computed. The last option ("no.npd") would exclude any imputations which yielded a nonpositive definite covariance matrix for observed or latent variables, which would include any "improper solutions" such as Heywood cases. NPD solutions are not excluded by default because they are likely to occur due to sampling error, especially in small samples. However, gross model misspecification could also cause NPD solutions, users can compare pooled results with and without this setting as a sensitivity analysis to see whether some imputations warrant further investigation.

Details

Before using this function, researchers need to make the products of the indicators between the first-order factors and residualize the products by the original indicators (Lance, 1988; Little, Bovaird, & Widaman, 2006). The process can be automated by the indProd function. Note that the indicator products can be made for all possible combination or matched-pair approach (Marsh et al., 2004). Next, the hypothesized model with the regression with latent interaction will be used to fit all original indicators and the product terms. To use this function the model must be fit with a mean structure. See the example for how to fit the product term below. Once the lavaan result is obtained, this function will be used to probe the interaction.

The probing process on residual-centered latent interaction is based on transforming the residual-centered result into the no-centered result. See Geldhof et al. (2013) for further details. Note that this approach based on a strong assumption that the first-order latent variables are normally distributed. The probing process is applied after the no-centered result (parameter estimates and their covariance matrix among parameter estimates) has been computed. See the probe2WayMC for further details.

Value

A list with two elements:

1. SimpleIntercept: The intercepts given each value of the moderator. This element will be NULL unless the factor intercept is estimated (e.g., not fixed at 0).

2. SimpleSlope: The slopes given each value of the moderator.

In each element, the first column represents the values of the moderators specified in the valProbe argument. The second column is the simple intercept or simple slope. The third column is the standard error of the simple intercept or simple slope. The fourth column is the Wald (z) statistic. The fifth column is the p value testing whether the simple intercepts or slopes are different from 0.

Author(s)

Sunthud Pornprasertmanit ([e-mail])

Terrence D. Jorgensen (University of Amsterdam; [e-mail])

References

Tutorial:

Schoemann, A. M., & Jorgensen, T. D. (2021). Testing and interpreting latent variable interactions using the semTools package. Psych, 3(3), 322–335. doi:10.3390/psych3030024

Background literature:

Lance, C. E. (1988). Residual centering, exploratory and confirmatory moderator analysis, and decomposition of effects in path models containing interactions. Applied Psychological Measurement, 12(2), 163–175. doi:10.1177/014662168801200205

Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions. Structural Equation Modeling, 13(4), 497–519. doi:10.1207/s15328007sem1304_1

Marsh, H. W., Wen, Z., & Hau, K. T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9(3), 275–300. doi:10.1037/1082-989X.9.3.275

Geldhof, G. J., Pornprasertmanit, S., Schoemann, A. M., & Little, T. D. (2013). Orthogonalizing through residual centering: Extended applications and caveats. Educational and Psychological Measurement, 73(1), 27–46. doi:10.1177/0013164412445473

See Also

• indProd For creating the indicator products with no centering, mean centering, double-mean centering, or residual centering.

• probe2WayMC For probing the two-way latent interaction when the results are obtained from mean-centering, or double-mean centering

• probe3WayMC For probing the three-way latent interaction when the results are obtained from mean-centering, or double-mean centering

• probe3WayRC For probing the two-way latent interaction when the results are obtained from residual-centering approach.

• plotProbe Plot the simple intercepts and slopes of the latent interaction.

Examples

dat2wayRC <- orthogonalize(dat2way, 1:3, 4:6)

model1 <- "
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f12 =~ x1.x4 + x2.x5 + x3.x6
f3 =~ x7 + x8 + x9
f3 ~ f1 + f2 + f12
f12 ~~ 0*f1 + 0*f2
x1 + x4 + x1.x4 + x7 ~ 0*1 # identify latent means
f1 + f2 + f12 + f3 ~ NA*1
"

fitRC2way <- sem(model1, data = dat2wayRC, meanstructure = TRUE)
summary(fitRC2way)

probe2WayRC(fitRC2way, nameX = c("f1", "f2", "f12"), nameY = "f3",
            modVar = "f2", valProbe = c(-1, 0, 1))


## can probe multigroup models, one group at a time
dat2wayRC$g <- 1:2

model2 <- "
f1  =~ x1 + x2 + x3
f2  =~ x4 + x5 + x6
f12 =~ x1.x4 + x2.x5 + x3.x6
f3  =~ x7 + x8 + x9
f3 ~ c(b1.g1, b1.g2)*f1 + c(b2.g1, b2.g2)*f2 + c(b12.g1, b12.g2)*f12
f12 ~~ 0*f1 + 0*f2
x1 + x4 + x1.x4 + x7 ~ 0*1 # identify latent means
f1 + f2 + f12 ~ NA*1
f3 ~ NA*1 + c(b0.g1, b0.g2)*1
"
fit2 <- sem(model2, data = dat2wayRC, group = "g")
probe2WayRC(fit2, nameX = c("f1", "f2", "f12"), nameY = "f3",
            modVar = "f2", valProbe = c(-1, 0, 1)) # group = 1 by default
probe2WayRC(fit2, nameX = c("f1", "f2", "f12"), nameY = "f3",
            modVar = "f2", valProbe = c(-1, 0, 1), group = 2)