# We often wish to conduct a CFA of our measurement model prior to CBSEM # Alternatively, we could estimate our model using CBSEM, which uses the Lavaan package # SEMinR uses multi-core parallel processing to speed up bootstrappingīoot_estimates <- bootstrap_model(pls_model, nboot = 1000, cores = 2) # note: PLS requires seperate bootstrapping for PLS path estimates # Estimate using PLS-PM from model parts defined earlier
Put the above elements together to estimate the model using PLS-PM, CBSEM, or a CFA:. Paths(from = "Value", to = "Satisfaction") Paths(from = c("Image", "Expectation", "Image*Expectation"), to = "Value"), # Quickly create multiple paths "from" and "to" sets of constructs Describe the structural model of causal relationships between constructs (and interaction terms):. Interaction_term(iv = "Image", moderator = "Expectation") # - Later we will convert the omposites into reflectives for CFA/CBSEM (step 3)Ĭomposite("Image", multi_items("IMAG", 1:5)),Ĭomposite("Expectation", multi_items("CUEX", 1:3)),Ĭomposite("Value", multi_items("PERV", 1:2)),Ĭomposite("Satisfaction", multi_items("CUSA", 1:3)), # - We will first use composites in PLS-PM analysis # or reflective (common-factor) measurement (used in CBSEM, CFA, and PLSc) # Distinguish and mix composite measurement (used in PLS-PM) Describe measurement model for each construct and its items, specifying interaction terms and other measurement features:. The following example is generic to either PLS-PM or CBSEM/CFA. Defaults to robust maximum-likelihood (MLR) estimation to account for potential non-normality (Yuan et al. 2000)īriefly, there are three steps to specifying and estimating a structural equation model using SEMinR. Creates product-indicator interactions, or two-stage interactions using ten Berge scores from a CFA (Lodder et al, 2019). Extracts ten Berge factor scores that have the same correlation pattern as the latent constructs (ten Berge et al. 1999 Logan et al. 2019). Implements covariance-based structural equation modeling (Joreskog, 1973). Incorporates high performance, multi-core bootstrapping function (Hair et al., 2017)ĬBSEM/CFA advances and best-practices in SEMinR:. Ringle wende becker smartpls 3 software#
Continuously tested against leading popular PLS-PM software to ensure parity of outcomes: SmartPLS (Ringle et al., 2015) and ADANCO (Henseler and Dijkstra, 2015), semPLS (Monecke and Leisch, 2012) and matrixpls (Rönkkö, 2016). Adjusts for known biases in interaction terms in PLS models (Henseler & Fassot, 2006). Automatically adjusts PLS estimates to ensure consistency (PLSc) wherever common-factors are involved (Dijkstra & Henseler, 2015). Implements PLS path modeling algorithm (Wold, 1985). PLS-PM advances and best-practices in SEMinR: It also brings a few methodological advancements not found in other packages or software, and encourages best practices wherever possible. 
SEMinR uses its own PLS-PM estimation engine and integrates with the Lavaan package for CBSEM/CFA estimation.
High-level functions to quickly specify interactions, higher order constructs, and structural paths. Can use variance-based PLS estimation and covariance-based SEM estimation to model composite and common-factor constructs. A natural feeling, domain-specific language to build and estimate structural equation models in R. Both CBSEM and CFA estimation use the Lavaan package. Confirmatory Factor Analysis (CFA) of reflective measurements models is also supported. 
SEM models can be estimated either using Partial Least Squares Path Modeling (PLS-PM) as popularized by SmartPLS, or using Covariance Based Structural Equation Modeling (CBSEM) as popularized by LISREL and AMOS. The syntax allows applied practitioners of SEM to use terminology that is very close to their familiar modeling terms (e.g., reflective, composite, interactions) instead of specifying underlying matrices and covariances. SEMinR brings a friendly syntax to creating and estimating structural equation models (SEM).
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