Joe F. Hair, Jr.Joe F. Hair, Jr.Founder & Senior ScholarFounder & Senior Scholar

Joe F. Hair, Jr.Joe F. Hair, Jr.Founder & Senior ScholarFounder & Senior Scholar

Using the SmartPLS SoftwareUsing the SmartPLS Software

Getting Started with the SmartPLS SoftwareGetting Started with the SmartPLS Software

The next slide shows the graphical interface for the SmartPLS software. In the following slides we describe how to draw and execute a PLS-SEM model using the SmartPLS software program. Before you draw your model, you need to have data to use in running your model. The data we will use to run our example PLS model can be downloaded at the following URL: http://www.smartpls.de/cr/. When you get to the website scroll down to the Corporate Reputation Example where it says . . . Click on the following links to download Click on the following links to download filesfiles..

SmartPLS can use both data file formats (i.e., .csv or .txt). Follow the onscreen instructions to save one of these two files on your hard drive. Click on Save Target As . . . to save the data to a folder on your hard drive, and then Close. Now go to the folder where you previously downloaded and saved the SmartPLS software on your computer.  Click on the file that runs SmartPLS ( ) and then on the Run tab to start the software. You are now ready to create a new SmartPLS SEM project.

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To start using the To start using the software, left click software, left click

on the File pull-on the File pull-down menu and down menu and

click on Newclick on New. Next . Next click on the Create click on the Create New Project option.New Project option.

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Type a project Type a project name in this name in this

windowwindow. Then . Then click Next.click Next.

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A project name for our A project name for our example in this workshop example in this workshop

has been insertedhas been inserted. You can . You can use a different name if you use a different name if you

wish. Now click Next.wish. Now click Next.

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Click Finish Click Finish Tab to Tab to

continuecontinue..

Click here to establish Click here to establish a link to your dataa link to your data..

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Notice that your Notice that your indicator indicator

variables are variables are now visible in now visible in this windowthis window..

Double click Double click here here (on your project (on your project name) to get this name) to get this screen with the screen with the above tool bar.above tool bar.

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..

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Click on the insertion Click on the insertion mode icon to draw mode icon to draw

constructs.constructs.

Click on the Click on the selection mode selection mode icon to select, icon to select, resize or move resize or move

constructs.constructs.

Click on the connection Click on the connection mode icon to connect mode icon to connect

constructs.constructs.

SmartPLS Software Options SmartPLS Software Options

Find your new project in the Projects window, expand the list of projects Find your new project in the Projects window, expand the list of projects to get project details (see below), click on the .splsm file for your projectto get project details (see below), click on the .splsm file for your project

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..

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Reputation model Reputation model drawn with insertion drawn with insertion mode and connected mode and connected

with connection modewith connection mode..

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Next step is to rename constructsNext step is to rename constructs. . To do so, make sure you are in To do so, make sure you are in

selection mode, place cursor over selection mode, place cursor over construct, right click, and you will construct, right click, and you will

get window on next slide.get window on next slide.

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Notice that new Notice that new constructs now appear constructs now appear

in this window.in this window.

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Left click on Rename Left click on Rename Object to get Object to get

window on next window on next slide.slide.

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Type new name in Type new name in window and click OK.window and click OK.

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New name has been New name has been typed in window. typed in window.

Now click OK.Now click OK.

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Now you must attach the indicator variables. Now you must attach the indicator variables. To do so, drag them from the window on the To do so, drag them from the window on the left (left click, hold down, and move). Drop left (left click, hold down, and move). Drop

them on their constructs (release left button).them on their constructs (release left button).

The four constructs The four constructs are now renamed.are now renamed.

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The three indicators are now attached to The three indicators are now attached to the COMP construct. You can now right the COMP construct. You can now right click on the construct to get the window click on the construct to get the window below, and then left click to reposition below, and then left click to reposition

the indicators at the top of the construct.the indicators at the top of the construct.

Make sure to save the Make sure to save the

model by going to model by going to

File → Save File → Save or click or click

on the Save icon. on the Save icon.

Save IconSave Icon

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17Note that indicators attached to Note that indicators attached to constructs are now yellow.constructs are now yellow.

Structural Model with Names, Data and PathsStructural Model with Names, Data and Paths

Other SmartPLS Options . . .Other SmartPLS Options . . .

Start calculation

Change reflective to formative

Show measurement model

Rename Construct

Hide used indicators

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Competence (COMP)

comp_1 [company] is a top competitor in its market.

comp_2 As far as I know, [company] is recognized world-wide.

comp_3 I believe that [company] performs at a premium level.

Likeability (LIKE)

like_1 [company] is a company that I can better identify with than other companies.

like_2 [company] is a company that I would regret more not having if it no longer existed than I would other companies.

like_3 I regard [company] as a likeable company.

Customer Loyalty (CUSL)

cusl_1 I would recommend [company] to friends and relatives.

cusl_2 If I had to choose again, I would chose [company] as my mobile phone services provider.

cusl_3 I will remain a customer of [company] in the future.

Satisfaction (CUSA)

cusa If you consider your experiences with [company] how satisfied are you with [company]?

Questions for Indicator Variables – Simple ModelQuestions for Indicator Variables – Simple Model

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Click here, select PLS Algorithm Click here, select PLS Algorithm to calculate model results.to calculate model results.

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This dialog box appears when This dialog box appears when you select the PLS Algorithm.you select the PLS Algorithm.

Missing values have not been Missing values have not been configured so you will have to configured so you will have to

Cancel this option and go to the Cancel this option and go to the datafile to set up.datafile to set up.

Default Settings to run PLS Algorithm – Click Finish to runDefault Settings to run PLS Algorithm – Click Finish to run

Trade-off in missing value Trade-off in missing value treatment:treatment:

Case wise replacement can Case wise replacement can greatly reduce the number of greatly reduce the number of

cases but sample mean cases but sample mean imputation reduces variance in imputation reduces variance in

your data.your data.

Preferred approach to deal Preferred approach to deal with missing data is combination with missing data is combination of sub-group mean and nearest of sub-group mean and nearest neighbor, or use EM imputation neighbor, or use EM imputation

using SPSS.using SPSS.

Always use path weighting schemeAlways use path weighting scheme

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After double clicking on the datafile you get After double clicking on the datafile you get this screen. Check the box on the left to this screen. Check the box on the left to

indicate missing data in your datafile.indicate missing data in your datafile.Then change the Missing Value in the Then change the Missing Value in the

window to -99, as shown below. Finally, window to -99, as shown below. Finally, check the X beside the Full Data tab at top. check the X beside the Full Data tab at top.

That will close and save your changes That will close and save your changes

Double click on the datafile to get this screen. Double click on the datafile to get this screen.

After missing values have been After missing values have been reconfigured to -99.0reconfigured to -99.0

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When you select the PLS When you select the PLS Algorithm option this revised Algorithm option this revised dialog box appears. It now dialog box appears. It now shows the newly configured shows the newly configured

missing value option of -99.0.missing value option of -99.0.

All other options are correct All other options are correct so check the Finish tab to so check the Finish tab to

run the modelrun the model..

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PLS Results for PLS Results for Simple ExampleSimple Example

Outer loadings, path Outer loadings, path coefficients, and Rcoefficients, and R22

shown on modelshown on model

PLS Results for PLS Results for Simple ExampleSimple Example

The structural model results enable us to determine, for example, that CUSA has the strongest effect on CUSL (0.504), followed by LIKE (0.342). COMP (0.009) has little effect on the dependent variable CUSL. The three exogenous constructs together explain 56.2% of the variance of the endogenous construct CUSL (R² = 0.562), as indicated by the value in the construct circle. COMP and LIKE also jointly explain 29.5% of the variance of CUSA.

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Click here to obtain reports Click here to obtain reports that summarize model results.that summarize model results.

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This is an example of the reports that are available from SmartPLS. The type of information provided is shown in the menu on the left. For example, the Stop Criterion Changes is highlighted. Above the report shows the software took 4 iterations to obtain a solution.

Checking the Checking the algorithm stop algorithm stop

criterioncriterion

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The above table of values is the The above table of values is the default report for the Path default report for the Path

Coefficients. To make this table Coefficients. To make this table easier to understand left click on easier to understand left click on the Toggle Zero Values button at the Toggle Zero Values button at

the top left. The results are shown the top left. The results are shown below.below.

To determine the To determine the statistical statistical

significance of the significance of the path coefficients, path coefficients, we must run the we must run the bootstrapping bootstrapping

option. To do so option. To do so click on the .splsm click on the .splsm file tab to return to file tab to return to

the SEM model the SEM model with the tool bar.with the tool bar.

Summary of PLS-SEM FindingsSummary of PLS-SEM Findings

1.1.The direct path from COMP to CUSA is 0.162 and The direct path from COMP to CUSA is 0.162 and

the direct path from COMP to CUSL is 0.009.the direct path from COMP to CUSL is 0.009.

2.2.The direct path from LIKE to CUSA is 0.424 and The direct path from LIKE to CUSA is 0.424 and

the direct path from LIKE to CUSL is 0.342.the direct path from LIKE to CUSL is 0.342.

3.3.The direct path from CUSA to CUSL is 0.504.The direct path from CUSA to CUSL is 0.504.

4.4.Overall, the model predicts 29.5% of the variance Overall, the model predicts 29.5% of the variance

in CUSA, and 56.2% of the variance in CUSL.in CUSA, and 56.2% of the variance in CUSL.

To determine significance levels, you must run Bootstrapping To determine significance levels, you must run Bootstrapping option. Look for under the calculate option.option. Look for under the calculate option.

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Click here, select Click here, select Bootstrapping option.Bootstrapping option.

Significance of PLS-SEM Parameters = BootstrappingSignificance of PLS-SEM Parameters = Bootstrapping

PLS-SEM does not assume the data is normally distributed, which implies PLS-SEM does not assume the data is normally distributed, which implies that parametric significance tests used in regression analyses cannot be that parametric significance tests used in regression analyses cannot be applied to test whether coefficients such as outer weights and loadings are applied to test whether coefficients such as outer weights and loadings are significant. Instead, PLS-SEM relies on a nonparametric bootstrap significant. Instead, PLS-SEM relies on a nonparametric bootstrap procedure to test coefficients for their significance.procedure to test coefficients for their significance.

In bootstrapping, a large number of subsamples (i.e., bootstrap samples) is In bootstrapping, a large number of subsamples (i.e., bootstrap samples) is drawn from the original sample – with replacement. Replacement means drawn from the original sample – with replacement. Replacement means that each time an observation is drawn at random from the sampling that each time an observation is drawn at random from the sampling population, it is returned to the sampling population before the next population, it is returned to the sampling population before the next observation is drawn (i.e., the population from which the observations are observation is drawn (i.e., the population from which the observations are drawn always contains all the same elements). Therefore, an observation drawn always contains all the same elements). Therefore, an observation for a certain subsample can be selected more than once, or may not be for a certain subsample can be selected more than once, or may not be selected at all for another subsample. The number of bootstrap samples selected at all for another subsample. The number of bootstrap samples should be high but must be at least equal to the number of valid should be high but must be at least equal to the number of valid observations in the dataset. The recommended number of bootstrap observations in the dataset. The recommended number of bootstrap samples is 5,000.samples is 5,000.

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When you get this dialog box make When you get this dialog box make sure you have chosen No Sign sure you have chosen No Sign

Changes, the number of cases is Changes, the number of cases is your sample size (344), and the your sample size (344), and the

number of samples is 5,000.number of samples is 5,000.

Then click the Finish tab to obtain Then click the Finish tab to obtain the results.the results.

If you have missing data do not use mean If you have missing data do not use mean replacement because bootstrapping draws replacement because bootstrapping draws samples with replacement. Use Casewise samples with replacement. Use Casewise Replacement.Replacement.

Use individual (sign) changes optionUse individual (sign) changes option

• Make sure the number of cases are Make sure the number of cases are equal to the number of equal to the number of validvalid observations in your dataset.observations in your dataset.

• Set Set casescases = samples size (or higher) = samples size (or higher)

Caution!!! Caution!!! It is a common mistake to set It is a common mistake to set samples equal to the overall number of samples equal to the overall number of observations.observations.

SmartPLS Bootstrapping SmartPLS Bootstrapping

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The t values can be compared with the critical values from the standard normal

distribution to decide whether the coefficients are significantly different from zero. For example, the critical values for

significance levels of 1% (a = 0.01) and 5% (a = 0.05) probability of error are 2.57 and

1.96, respectively (two-tailed test) .One-tailed test for 5% (a = 0.05) level is .98.

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By clicking on Report – Default Report you get a detailed overview of the bootstrapping results. The original estimate of the outer

weights is shown in the second column = Original Sample (0). If this number is divided by the Standard Deviation (STDEV) you get

the t value. For example, divide 0.5361 (0) by 0.0445 (STDEV) and you get 12.047 = the t statistic – shown below.

The t statistics in the table on The t statistics in the table on the right indicate that all the right indicate that all

measurement model measurement model loadings are statistically loadings are statistically

significant (> 0.05).significant (> 0.05).

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The t statistics in the table below The t statistics in the table below indicate that four of the five indicate that four of the five

structural path coefficients are structural path coefficients are statistically significant (> 0.05).statistically significant (> 0.05).The only non-significant path is The only non-significant path is COMP – CUSL (t value = 0.1705).COMP – CUSL (t value = 0.1705).

Brief Instructions: Using SmartPLSBrief Instructions: Using SmartPLS

1.1. Load SmartPLS software – click onLoad SmartPLS software – click on

2.2. Create your new project – assign project name and data.Create your new project – assign project name and data.

3.3. Double-click to get Menu Bar.Double-click to get Menu Bar.

4.4. Draw model – see options below:Draw model – see options below:

• Insertion mode = Insertion mode =

• Selection mode = Selection mode =

• Connection mode = Connection mode =

5.5. Save model.Save model.

6.6. Click on calculate icon and select PLS algorithm on Click on calculate icon and select PLS algorithm on

the Pull-Down menu. Now accept the default options (or the Pull-Down menu. Now accept the default options (or

insert your own) by clicking Finish.insert your own) by clicking Finish.

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