R: Make products of indicators using no centering, mean...

indProd {semTools} | R Documentation

## Make products of indicators using no centering, mean centering, double-mean centering, or residual centering

### Description

The `indProd` function will make products of indicators using no centering, mean centering, double-mean centering, or residual centering. The `orthogonalize` function is the shortcut of the `indProd` function to make the residual-centered indicators products.

### Usage

    indProd(data, var1, var2, var3=NULL, match = TRUE, meanC = TRUE,
    	residualC = FALSE, doubleMC = TRUE, namesProd = NULL)
    orthogonalize(data, var1, var2, var3=NULL, match=TRUE, namesProd=NULL)

### Arguments

`data` | The desired data to be transformed.
`var1` | Names or indices of the variables loaded on the first factor
`var2` | Names or indices of the variables loaded on the second factor
`var3` | Names or indices of the variables loaded on the third factor (for three-way interaction)
`match` | Specify `TRUE` to use match-paired approach (Marsh, Wen, & Hau, 2004). If `FALSE`, the resulting products are all possible products.
`meanC` | Specify `TRUE` for mean centering the main effect indicator before making the products
`residualC` | Specify `TRUE` for residual centering the products by the main effect indicators (Little, Bovaird, & Widaman, 2006).
`doubleMC` | Specify `TRUE` for centering the resulting products (Lin et. al., 2010)
`namesProd` | The names of resulting products

### Value

The original data attached with the products.

### Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com) Alexander Schoemann (East Carolina University; schoemanna@ecu.edu)

### References

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, 275-300.

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, 374-391.

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, 497-519.

### See Also

- `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.

- `probe2WayRC` For probing the two-way latent interaction when the results are obtained from residual-centering approach.

- `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

    # Mean centering / two-way interaction / match-paired
    dat <- indProd(attitude[,-1], var1=1:3, var2=4:6)

    # Residual centering / two-way interaction / match-paired
    dat2 <- indProd(attitude[,-1], var1=1:3, var2=4:6, match=FALSE, meanC=FALSE,
    	residualC=TRUE, doubleMC=FALSE)

    # Double-mean centering / two-way interaction / match-paired
    dat3 <- indProd(attitude[,-1], var1=1:3, var2=4:6, match=FALSE, meanC=TRUE,
    	residualC=FALSE, doubleMC=TRUE)

    # Mean centering / three-way interaction / match-paired
    dat4 <- indProd(attitude[,-1], var1=1:2, var2=3:4, var3=5:6)

    # Residual centering / three-way interaction / match-paired
    dat5 <- indProd(attitude[,-1], var1=1:2, var2=3:4, var3=5:6, match=FALSE, meanC=FALSE,
    	residualC=TRUE, doubleMC=FALSE)

    # Double-mean centering / three-way interaction / match-paired
    dat6 <- indProd(attitude[,-1], var1=1:2, var2=3:4, var3=5:6, match=FALSE, meanC=TRUE,
    	residualC=TRUE, doubleMC=TRUE)

[Package semTools version 0.4-14 Index]