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https://github.com/kss2k/modsem

An R package for estimating interaction effects in structural equation modeling (SEM), and lavaan.
https://github.com/kss2k/modsem

interaction-effect interaction-effects lavaan-syntax r rlang rlanguage sem structural-equation-modeling structural-equation-models

Last synced: 17 days ago
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An R package for estimating interaction effects in structural equation modeling (SEM), and lavaan.

Host: GitHub
URL: https://github.com/kss2k/modsem
Owner: Kss2k
License: other
Created: 2023-10-25T14:36:58.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-04-13T14:51:30.000Z (9 months ago)
Last Synced: 2024-04-14T02:41:41.458Z (9 months ago)
Topics: interaction-effect, interaction-effects, lavaan-syntax, r, rlang, rlanguage, sem, structural-equation-modeling, structural-equation-models
Language: R
Homepage:
Size: 8.19 MB
Stars: 2
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # `modsem` 

[![R-CMD-check](https://github.com/kss2k/modsem/actions/workflows/checks.yml/badge.svg)](https://github.com/kss2k/modsem/actions/workflows/checks.yml)

[![Tests](https://github.com/kss2k/modsem/actions/workflows/tests.yml/badge.svg)](https://github.com/kss2k/modsem/actions/workflows/tests.yml)

[![CRAN](https://www.r-pkg.org/badges/version/modsem)](https://cran.r-project.org/package=modsem)

[![PKGDOWN-Build](https://github.com/kss2k/modsem/actions/workflows/pkgdown.yml/badge.svg)](https://github.com/kss2k/modsem/actions/workflows/pkgdown.yml)

`modsem` is an `R`-package for estimating interaction (i.e., moderation) effects between latent variables

in structural equation models (SEMs). See https://www.modsem.org for a tutorial.

# To Install 

```

# From CRAN 

install.packages("modsem")

# Latest version from GitHub

install.packages("devtools")

devtools::install_github("kss2k/modsem", build_vignettes = TRUE)

```

# Methods/Approaches

There are a number of approaches for estimating interaction effects in SEM. 

In `modsem()`, the `method = "method"` argument allows you to choose which to use.

Different approaches can be categorized into two groups: 

Product Indicator (PI) and Distribution Analytic (DA) approaches.

## Product Indicator (PI) Approaches:

- `"ca"` = constrained approach (Algina & Moulder, 2001)

    - Note that constraints can become quite complicated for complex models, 

      particularly when there is an interaction including enodgenous variables.

      The method can therefore be quite slow. 

- `"uca"` = unconstrained approach (Marsh, 2004)

- `"rca"` = residual centering approach (Little et al., 2006)

- `"dblcent"` = double centering approach (Marsh., 2013)

  - default 

- `"pind"` = basic product indicator approach (not recommended)

## Distribution Analytic (DA) Approaches

- `"lms"` = The Latent Moderated Structural equations (LMS) approach, see the [vignette](https://modsem.org/articles/lms_qml.html)

- `"qml"` = The Quasi Maximum Likelihood (QML) approach, see the [vignette](https://modsem.org/articles/lms_qml.html)

- `"mplus"` 

  - estimates model through Mplus, if it is installed

# Examples 

## Elementary Interaction Model (Kenny & Judd, 1984; Jaccard & Wan, 1995)

```

library(modsem)

m1 <- '

  # Outer Model

  X =~ x1 + x2 +x3

  Y =~ y1 + y2 + y3

  Z =~ z1 + z2 + z3

  

  # Inner model

  Y ~ X + Z + X:Z 

'

# Double centering approach

est1_dca <- modsem(m1, oneInt)

summary(est1_dca)

# Constrained approach

est1_ca <- modsem(m1, oneInt, method = "ca")

summary(est1_ca)

# QML approach 

est1_qml <- modsem(m1, oneInt, method = "qml")

summary(est1_qml, standardized = TRUE) 

# LMS approach 

est1_lms <- modsem(m1, oneInt, method = "lms") 

summary(est1_lms)

```

## Theory Of Planned Behavior

```

tpb <- "

# Outer Model (Based on Hagger et al., 2007)

  ATT =~ att1 + att2 + att3 + att4 + att5

  SN =~ sn1 + sn2

  PBC =~ pbc1 + pbc2 + pbc3

  INT =~ int1 + int2 + int3

  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)

  INT ~ ATT + SN + PBC

  BEH ~ INT + PBC

  BEH ~ PBC:INT

"

# double centering approach

est_tpb_dca <- modsem(tpb, data = TPB, method = "dblcent")

summary(est_tpb_dca)

# Constrained approach using Wrigths path tracing rules for generating

# the appropriate constraints

est_tpb_ca <- modsem(tpb, data = TPB, method = "ca") 

summary(est_tpb_ca)

# LMS approach 

est_tpb_lms <- modsem(tpb, data = TPB, method = "lms")

summary(est_tpb_lms, standardized = TRUE) 

# QML approach 

est_tpb_qml <- modsem(tpb, data = TPB, method = "qml") 

summary(est_tpb_qml, standardized = TRUE)

```

## Interactions between two observed variables

```

est2 <- modsem('y1 ~ x1 + z1 + x1:z1', data = oneInt, method = "pind")

summary(est2)

```

## Interaction between an obsereved and a latent variable 

```

m3 <- '

  # Outer Model

  X =~ x1 + x2 +x3

  Y =~ y1 + y2 + y3

  

  # Inner model

  Y ~ X + z1 + X:z1 

'

est3 <- modsem(m3, oneInt, method = "pind")

summary(est3)

```