*! version 0.4.0 26Apr2023

Title

plssem_mi -- Multiple imputation analysis of partial least
squares structural equation modelling (PLS-SEM)

Syntax

Multiple imputation analysis of partial least squares structural equation modeling

plssem_mi (LV1 > indblock1) (LV2 > indblock2) (...) [if] [in]
[, structural(LV2 LV1, ...) plssem_options
mioptions(mi_options) midisplay]

plssemc_mi (LV1 > indblock1) (LV2 > indblock2) (...) [if] [in]
[, structural(LV2 LV1, ...) plssem_options
mioptions(mi_options) midisplay]

plssem_options options to pass to plssem and
plssemc for performing the primary analysis

mioptions(mi_estimate##options:mi_options) options to pass to mi estimate

midisplay display the results using the mi estimate command layout

The plssem_mi and plssemc_mi commands provide the completed-data analysis
(estimation) and pooling steps of multiple imputation for a PLS-SEM or PLSc-SEM analysis.

Description

First, the data must be mi set. Then, a set of imputed data sets must be generated
using any of the mi impute commands. Finally, once missing data have been imputed,
plssem_mi or plssemc_mi can be used to perform the primary analysis (i.e., PLS-SEM or
PLSc-SEM) on each imputed data set and combine them into a single multiple-imputation result.

The syntax for the PLS-SEM or PLSc-SEM analysis mirrors exactly that of the corresponding
plssem and plssemc commands.

Options

mioptions(mi_estimate##options:mi_options)
accepts all the options available for mi estimate, with the exception of cmdok
which has been already included internally.

midisplay
shows the results using the layout implemented in mi estimate instead of that used
by plssem and plssemc.

Examples

Setup

. sysuse ecsimobi, clear

Generate missing values (MCAR)

. local prop_miss 0.92

. foreach vn of varlist CUEX1-CUEX3 {

2. generate R_mcar = 1 - rbinomial(1, `prop_miss')

3. replace `vn' = . if R_mcar == 1

4. drop R_mcar

5. }

. foreach vn of varlist IMAG1-IMAG5 {

2. generate R_mcar = 1 - rbinomial(1, `prop_miss')

3. replace `vn' = . if R_mcar == 1

4. drop R_mcar

5. }

. foreach vn of varlist PERV1-PERV2 {

2. generate R_mcar = 1 - rbinomial(1, `prop_miss')

3. replace `vn' = . if R_mcar == 1

4. drop R_mcar

5. }

Set up mi

. mi set wide

. mi register imputed CUEX1-CUEX3 IMAG1-IMAG5 PERV1-PERV2

. mi register passive CUSA* CUSCO CUSL* PERQ*

Impute missing values

. mi impute mvn CUEX* IMAG* PERV* = CUSA* CUSCO CUSL* PERQ*, add(20) dots burnin(1000) replace

Perform multiple imputation

. plssem_mi (Expectation > CUEX1 CUEX2 CUEX3) (Satisfaction > CUSA1 CUSA2 CUSA3) (Complaints > CUSCO) (Loyalty < CUSL1 CUSL2 CUSL3) (Image < IMAG1 IMAG2 IMAG3 IMAG4 IMAG5) (Quality > PERQ1 PERQ2 PERQ3 PERQ4 PERQ5 PERQ6 PERQ7) (Value < PERV1 PERV2), structural(Expectation Image, Quality Expectation, Value Expectation Quality, Satisfaction Value Quality Image Expectation, Complaints Satisfaction, Loyalty Complaints Satisfaction Image) wscheme("centroid") boot(200) mioptions(dots)

Authors

Sergio Venturini

Department of Economics and Social Sciences

Università Cattolica del Sacro Cuore, Italy

sergio.venturini@unicatt.it

Mehmet Mehmetoglu

Department of Psychology

Norwegian University of Science and Technology

mehmetm@svt.ntnu.no

Stored results

plssem_mi stores in e() the same results provided by mi estimate.

References

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. 2022. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Third edition. Sage.

Lohmöller, J. B. 1989. Latent Variable Path Modeling with Partial Least Squares. Heidelberg: Physica.

Mehmetoglu, M., and Venturini, S. 2021. Structural Equation Modelling with Partial Least Squares Using Stata and R. CRC Press

Wold, H. O. A. 1975. Path Models with Latent Variables: The NIPALS Approach.
In Blalock, H. M., Aganbegian, A., Borodkin, F. M., Boudon, R., and Cappecchi, V. (ed.), Quantitative Sociology (pp. 307-359). New York: Academic Press.