/* TO DO: 1. help for estat mediate after plssemc must still be added */ /* ISSUES: 1. */ {smcl} {* *! version 0.0.1 27Apr2018}{...} {vieweralsosee "plssemc" "help plssemc"}{...} {vieweralsosee "plssemplot" "help plssemplot"}{...} {viewerjumpto "Postestimation commands" "plssemc postestimation##description"}{...} {viewerjumpto "estat" "plssemc postestimation##syntax_estat"}{...} {viewerjumpto "estat options" "plssemc postestimation##options_estat"}{...} {viewerjumpto "predict" "plssemc postestimation##syntax_predict"}{...} {viewerjumpto "predict options" "plssemc postestimation##options_predict"}{...} {viewerjumpto "predict stored results" "plssemc postestimation##results_predict"}{...} {viewerjumpto "Examples" "plssemc postestimation##examples"}{...} {viewerjumpto "Authors" "plssemc postestimation##authors"}{...} {viewerjumpto "References" "plssemc postestimation##references"}{...} {title:Title} {p 4 18 2} {hi:plssemc postestimation} {hline 2} Postestimation tools for {helpb plssemc} {marker description}{...} {title:Postestimation commands} {pstd} The following postestimation commands are of special interest after {cmd:plssemc}: {synoptset 22 tabbed}{...} {p2coldent:Command}Description{p_end} {synoptline} {synopt:{helpb plssemc postestimation##indirect:estat indirect}}estimation and inference for indirect effects{p_end} {synopt:{helpb plssemc postestimation##total:estat total}}decomposition of total effects{p_end} {p2coldent:* {helpb plssemc postestimation##vif:estat vif}}variance inflation factors for the structural model equations sample{p_end} {synoptline} {p2colreset}{...} {p 4 6 2} * {cmd:estat vif} and {cmd:estat unobshet} are not available for models fitted using bootstrap. {p_end} {pstd} The following standard postestimation commands are also available: {synoptset 20 tabbed}{...} {p2coldent :Command}Description{p_end} {synoptline} {synopt :{helpb plssemc postestimation##predict:predict}}fitted values and residuals{p_end} {synoptline} {p2colreset}{...} {marker syntax_estat}{...} {title:Syntax for estat} {marker indirect}{...} {pstd} Display the estimation results for up to 5 indirect effects {p 8 14 2} {cmd:estat} {cmdab:in:direct}{cmd:,} {cmdab:e:ffects(}{it:efflist}{cmd:)} [{opt b:oot(#)} {opt s:eed(#)} {opt l:evel(#)} {opt dig:its(#)}] {marker total}{...} {pstd} Display the decomposition of the total effects in the corresponding direct and indirect effects {p 8 14 2} {cmd:estat} {cmdab:to:tal} [{cmd:,} {opt dig:its(#)} {opt p:lot}] {marker vif}{...} {pstd} Display the variance inflation factors for the structural model equations {p 8 14 2} {cmd:estat} {cmdab:vi:f} [{cmd:,} {opt dig:its(#)}] {marker desc_estat}{...} {title:Description for estat} {pstd} {cmd:estat indirect} estimates the (standardized) indirect effects and the corresponding tests of significance using the Sobel's {it:z} statistic (default) as well as the bootstrap approach ({help plssem_postestimation##Sobel1982:Sobel 1982}, {help plssem_postestimation##BaronKenny1986:Baron and Kenny 1986}, {help plssem_postestimation##VanderWeele2015:VanderWeele 2015}). The command can estimate up to five different indirect effects at a time. Each of these should specified by sequentially typing the dependent, mediator and independent variable from any PLS-SEM model. By adding the sub-option {cmd:boot(#)}, you can obtain the results based on the bootstrap approach. To facilitate the reproducibility of bootstrap results, the sub-option {cmd:seed(#)} can further be added. Confidence intervals ({cmd:0.95} is the default) for the estimated indirect effects are also provided. To change the level of confidence interval, the sub-option {cmd:level(#)} can be added. To change the number of decimals used to display the model estimates, you can change the default ({cmd:3}) to any other value by adding the sub-option {cmd:digits(#)}. {pstd} {cmd:estat total} produces the decomposition of the total effects into standardized direct and indirect effects. Adding the sub-option {cmd:plot} generates a bar plot of the effects. You can change the number decimals digits reported by setting the sub-option {cmd:digits(#)}. {pstd} {cmd:estat vif} computes the variance inflation factors (VIFs) for the independent variables of the equations in the structural part of a PLS-SEM model. With the {cmd:digit(#)} sub-option you change the number decimals digits displayed. {marker options_estat}{...} {title:Options for estat} {phang} {opt boot(#)}, an option used with {cmd:estat indirect}, allows to estimate the indirect effects using bootstrap; the number of replications is specified via {#}. {phang} {opt seed(#)}, an option used with {cmd:estat indirect}, allows to set the bootstrap seed number. {phang} {opt level(#)}, an option used with {cmd:estat indirect}, allows to set the confidence level to use for indirect effects confidence intervals; default is {cmd:0.95}. {phang} {opt digits(#)}, specifies the number of decimal digits to display in the output; default is {cmd:3}. {phang} {opt plot}, an option used with {cmd:estat total}, provides a graphical representation of the total effects decomposition. {phang} {opt seed(#)}, allows to set the seed for reproducing results. {marker syntax_predict}{...} {marker predict}{...} {title:Syntax for predict} {p 8 16 2} {cmd:predict} [{cmd:,} {it:statistic} {opt noout:er} {opt noin:ner}] {synoptset 20 tabbed}{...} {synopthdr :statistic} {synoptline} {syntab :Main} {synopt :{cmd:xb}}linear predictions{p_end} {synopt :{opt res:iduals}}residuals{p_end} {synoptline} {marker des_predict}{...} {title:Description for predict} {pstd} {cmd:predict} creates new variables containing linear predictions and residuals. These quantities are provided only for reflective blocks of manifest variables in the measurement/outer model and for endogenous latent variables in the structural/inner model. {pstd} The newly computed predictions will replace those already present in the data set. {marker options_predict}{...} {title:Options for predict} {dlgtab:Main} {phang} {opt xb} calculates the linear predictions (fitted values). {phang} {opt residuals} calculates the residuals. {dlgtab:Options} {phang} {opt nooouter} fitted values and residuals for the measurement/outer model are not saved in the data set. {phang} {opt nooinner} fitted values and residuals for the structural/inner model are not saved in the data set. {marker results_predict}{...} {title:Stored results for predict} {pstd} {cmd:predict} stores the following in {cmd:r()}: {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Matrices}{p_end} {synopt:{cmd:r(fitted)}}matrix of fitted values for the outer and inner models{p_end} {synopt:{cmd:r(residuals)}}matrix of residuals for the outer and inner models{p_end} {p2colreset}{...} {marker examples}{...} {title:Examples} {hline} {pstd}Setup{p_end} {phang2}{cmd:. sysuse workout2, clear}{p_end} {pstd}Model estimation{p_end} {phang2}{cmd:. plssemc (Attractive > face sexy) (Appearance > body appear attract) (Muscle > muscle strength endur) (Weight > lweight calories cweight), structural(Appearance Attractive, Muscle Appearance, Weight Appearance)}{p_end} {pstd}Multicollinearity assessment{p_end} {phang2}{cmd:. estat vif}{p_end} {pstd}Indirect effects{p_end} {phang2}{cmd:. estat indirect, effects(Muscle Appearance Attractive, Weight Appearance Attractive)}{p_end} {pstd}Predictions{p_end} {phang2}{cmd:. predict, xb residuals}{p_end} {phang2}{cmd:. describe *_hat *_res}{p_end} {hline} {marker authors}{...} {title:Authors} {pstd} Sergio Venturini{break} Department of Management{break} Università degli Studi di Torino, Italy{break} {browse "mailto:sergio.venturini@unito.it":sergio.venturini@unito.it}{break} {pstd} Mehmet Mehmetoglu{break} Department of Psychology{break} Norwegian University of Science and Technology{break} {browse "mailto:mehmetm@svt.ntnu.no":mehmetm@svt.ntnu.no}{break} {p_end} {marker references}{...} {title:References} {marker BaronKenny1986}{...} {phang} Baron, R. M., and Kenny, D. A. 1986. The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51, 1173-1182. {marker Hahnetal2002}{...} {phang} Hahn, C., Johnson, M. D., Herrmann, A., and Huber, F. 2002. Capturing Customer Heterogeneity Using a Finite Mixture PLS Approach. Schmalenbach Business Review, 54, 243-269. {marker Hairetal2017}{...} {phang} Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. 2017. {it:A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)}. Second edition. Sage. {marker Hairetal2018}{...} {phang} Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. 2018. {it:Advanced Issues in Partial Least Squares Structural Equation Modeling}. Sage. {marker Ringleetal2014}{...} {phang} Ringle, C. M., Sarstedt, M., and Schlittgen, R. 2014. Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36, 251–276. {marker Sobel1982}{...} {phang} Sobel, M. N. 1982. Asymptotic Confidence Intervals for Indirect Effects in Structural Equations Models. In Leinhart, S. (ed.), {it:Sociological Methodology}, pp. 290-312. Jossey-Bass. {marker Trinchera2007}{...} {phang} Trinchera, L. 2007. {it:Unobserved Heterogeneity in Structural Equation Models: a new approach to latent class detection in PLS Path Modeling}. Ph.D. Thesis. {marker VanderWeele2015}{...} {phang} VanderWeele, T. J. 2015. {it:Explanation in Causal Inference}. Oxford University Press. {p_end}