UPI: Unconstrained Product Indicator approach by Marsh et al....

In powerNLSEM: Simulation-Based Power Estimation (MSPE) for Nonlinear SEM

Unconstrained Product Indicator approach by Marsh et al. (2004), with extensions by Kelava and Brandt (2009)

Description

Unconstrained Product Indicator approach by Marsh et al. (2004), with extensions by Kelava and Brandt (2009)

Usage

UPI(
  lavModel_Analysis,
  data,
  data_transformations = NULL,
  matchPI = TRUE,
  PIcentering = "doubleMC",
  liberalInspection = FALSE
)

Arguments

lavModel_Analysis	the lavModel_Analysis object
data	set to fit
data_transformations	Data transformations
matchPI	Logical passed to semTools::indProd in order to compute the product indicators: Specify TRUE to use match-paired approach (Marsh, Wen, & Hau, 2004). If FALSE, the resulting products are all possible products. Default to TRUE. The observations are matched by order given when specifying the measurement model.
PIcentering	String indicating which method of centering should be used when constructing product indicators. String is converted to the arguments meanC, doubleMC, and residualMC, of the semTools::indProd function. Default to "doubleMC" for double mean centering the resulting products (Lin et. al., 2010). Use "meanC" for mean centering the main effect indicator before making the products or "residualC" for residual centering the products by the main effect indicators (Little, Bovaird, & Widaman, 2006). "none" or any other input than the previously described results in no centering (use with caution!).
liberalInspection	Logical whether the inspection of estimation truthworthiness should be very liberal (i.e., allowing for non-positive definite Hessians in standard error estimation or non-positive residual covariance matrices or latent covariance matrices). Default to FALSE. Being liberal is not adviced and should be checked for a single data set!

Value

Returns a data.frame that includes parameter estimates estimated using UPI.

References

Kelava, A., & Brandt, H. (2009). Estimation of nonlinear latent structural equation models using the extended unconstrained approach. Review of Psychology, 16(2), 123–132.

Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies. Structural Equation Modeling, 17(3), 374–391. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10705511.2010.488999")}

Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Structural Equation Modeling, 13(4), 497–519. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/1082-989X.9.3.275")}

Marsh, H. W., Wen, Z., Hau, K. T., Little, T. D., Bovaird, J. A., & Widaman, K. F. (2007). Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and Mean-Centered Approaches. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 570-580. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10705510701303921")}

powerNLSEM documentation built on Sept. 27, 2024, 5:10 p.m.