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User Manual

WEB GESCA Version 1.5

http://sem-gesca.com/webgesca/

Heungsun Hwang1, Kwanghee Jung2, and Seungman Kim2

1McGill University

2Texas Tech University

Please cite this software as follows:

Hwang, H., Jung, K., & Kim, S. (2019). WEB GESCA (Version 1.5) [Software]. Available

from http://sem-gesca.com/webgesca/.

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1. How to use WEB GESCA

a. Sample data and model

Bergami and Bagozzi’s (2000) organizational identification data are used for

illustrative purposes. The number of cases is equal to 305. The model specified for the

data is displayed in Figure 1 (No residual terms are displayed in the figure). As shown

in the figure, this model consists of four latent variables and 21 reflective indicators.

Specifically, Organizational Prestige (Org_Pres) is measured by eight indicators (cei1

– cei8), Organizational Identification (Org_Iden) by six indicators (ma1 – ma6),

Affective Commitment-Joy (AC_Joy) by four indicators (orgcmt1, 2, 3 and 7), and

Affective Commitment – Love (AC_Love) by three indicators (orgcmt5, 6, and 8).

Figure 1. The specified structural equation model for the example data

b. Prepare a raw data file

WEB GESCA is run on individual-level raw data.

• The raw data file is to be prepared as comma separated values file format (.csv), or

ASCII file format (.txt or .dat). You can use comma, tab, semicolon, or pipe as

separator in any of these cases.

• It is recommended that the first row contains the names of indicators. If your data

file doesn’t contain the names of indicators, you must indicate it (See 2.e.ii).

The following figures show the dataset opened in Excel and Notepad for the present

example:

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Figure 2. A comma separated values file format opened in Excel

Figure 3. A tab separated txt file opened in Notepad

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c. Connect to WEB GESCA

i. Open your web browser, e.g., Google Chrome, Microsoft Edge, Firefox, etc.

Figure 4. Web browser

ii. Enter ‘http://sem-gesca.com/webgesca/’ or ‘http://sem-gesca.org/webgesca/’ in

the address area.

Figure 5. Address for WEB GESCA

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iii. Now you can see the front page of WEB GESCA.

Figure 6. Front page of WEB GESCA

d. Data

The page ‘Data’ is the first page when you visit WEB GESCA. The first step for

running WEB GESCA is upload your data, which are already prepared as described in

Section 2.b. You can also download sample data by clicking the button.

i. Upload data file

- Click the ‘Browse’ button and select a data file of your choosing.

Figure 7. Upload data file (step 1)

- Select your data file and click the open button in the window. If the file is

successfully uploaded, you will see the first six cases of the data. The Model

menu will also become available in the left-hand sidebar.

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Figure 8. Upload data file (step 2)

ii. Input specifications

Once the data is uploaded, you can specify your input data on the right-hand side

of the Data page. The default set up is ‘data has header’ and ‘it is separated by

comma.’ If your data is not formatted as required, the data will not appear

appropriately in the ‘First 6 cases of the data’ area. Note that if you uncheck the

checkbox for header because your data doesn’t contain the names of indicators, the

program will assign arbitrary names to them.

Figure 9. Input specification.

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iii. Missing Data

WEB GESCA currently provides users with three options for dealing with missing

observations: (1) listwise deletion, (2) mean substitution, and (3) least-squares

imputation (refer to Hwang & Takane, 2014, Chapter 3). Users can select one of

the options in the box of “Missing Data” on the right side of the page. Once users

select an option for missing data, another box “Missing value identifier” will

appear, where users input a numeric value that indicates missing observations in

their data. The default value indicating missing observations is -9999. However,

users can put any value of their choosing.

Figure 10. Handling missing data

iv. Analysis Type

If you want to conduct a multi-group analysis, choose ‘Multi-group analysis’.

Once you check the check box, all variables’ names of your data will appear.

Please select a variable that you want to use as a grouping variable from those

listed.

Figure 11. Options for multi-group analysis

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e. Model

After uploading and specifying data, you need to specify your model. Click Model in

the left main menu. There are two ways to build your model: using gesca R syntax or

Table. If you are familiar with gesca R syntax and want to use the syntax, select the

tab ‘Build a model by syntax’ on the top. Otherwise, select the tab ‘Build a model by

table.’

Figure 12. Two methods for modeling

i. Build a model by syntax

This is the simplest way to run WEB GESCA. Simply enter your measurement

model and structural model into the textbox of ‘Input your model.’ After you input

the syntax, click the button. Then, WEB GESCA will run to analyze

your model. Please refer to gesca R package manual for the use of gesca R syntax

(https://cran.r-project.org/web/packages/gesca/gesca.pdf).

Figure 13. Build a model by syntax

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ii. Build a model by table

1) Choose the number of latent variables

Drag the slider to choose the number of latent variables in your model. If your

model has second-order latent variables, choose the number of them as well.

After choosing the number of latent variables, click ‘Confirm the number of

latent variables’.

Figure 14. Select number of latent variables

2) Input names of latent variables

If you confirmed the number of latent variables, the box for inputting the name

of each latent variable will appear. Input the names of your latent variables. If

you don’t label your latent variables, they will be named like LV_1, LV_2, …

LV_n by default.

Figure 15. Naming for latent variables

If all the names of latent variables are entered correctly, then please click

‘confirm the name(s)’.

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3) Specifying structural model

Once you confirmed latent variables’ names, a table for specifying your

structural model will appear in the middle of the main contents area. You can

specify a structural model by checking checkboxes. The figure below shows

that LV_1 affects LV_2.

Figure 7. Specifying structural model

If you want to fix a path coefficient to a constant, enter the constant value

ranging from 0.0 to 1.0 to the blank textbox.

Figure 8. Fixing path coefficient

If you selected ‘multi-group analysis’ (2.e.iii), you can constrain all path

coefficients to be equal across groups simultaneously by checking ‘Constraining

path coefficients across groups.’ This checkbox only appears when you selected

multi-group analysis.

Figure 98. Constraining all path coefficients across groups

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After you completed structural model specification, click ‘confirm the model.’

4) Assigning indicators

You can assign indicators to latent variables by using the checkboxes.

Figure 10. Assigning indicators

Figure 19 shows that the latent variable LV_1 is assigned the indicators cei1,

cei2, and cei3, LV_2 is assigned cei4, cei5, and cei6, and LV_3 is assigned cei7,

cei8 and ma1.

If you want to fix a loading to a constant in advance, put the constant value

ranging from 0.0 to 1.0 in the blank textbox.

Figure 20. Fixing value of loading for an indicator

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If you selected multi-group analysis (2.e.iii), you have two more options for

constrained analyses. First, you can constrain the loadings of selected indicators to be

identical by inserting a label (e.g., an alphabet letter or number) across groups. In the

below example, three indicators were chosen, and labeled by different alphabet

letters (“a”, “b”, and “c”). This indicates that each loading of the three indicators is

to be held equal across groups. Note that any loadings with the same label will be

constrained to be equal to each other.

Figure 21. Constraining specific loadings across group

Also, if you choose ‘Constraining (fix) loadings across groups,’ all loadings will

be constrained to be equal across groups.

Figure 22. Constraining all loadings across group

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If formative indicators are assumed for a latent variable, you check the

‘Formative relationship’ box per latent variable located at the top of the table. If

they are reflective indicators, just leave the checkboxes unchecked.

Figure 23. Specifying formative relationship between the indicators and the latent variable

The above figure shows that cei1, cei2, and cei3 are formative indicators for

LV_1, whereas the remaining indicators for LV_2 and LV_3 are reflective.

5) Second-order latent variable modeling

If you have a second-order latent variable in your model (2.f.ii), you need to

specify the relationship between second-order and first-order latent variables.

Another table will appear below the table for assigning indicators if your model

has a second-order latent variable. The below figure shows that a second-order

latent variable, LV2_1, is related to its lower-order latent variables LV_2 and

LV_3, which serve as formative indicators for LV2_1.

Figure 24. Specifying relation between second-order latent variable and lower-order latent variables

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iii. Analyze Model

Once you have finished all your model specification (either using syntax or table),

please click . WEB GESCA will run your model. When the analysis is

complete without any errors, you will see Summary, Parameter Estimates, and

Plots appear on the main menu.

f. Summary

On the summary page, you can see Estimation Summary and Model Fit.

Figure 25. Summary of the analysis

g. Parameter estimates

This page shows each parameter estimate.

Figure 26. Parameter estimates for loading

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h. Plots

This page displays the same parameter estimates graphically.

Figure 27. Plots for parameter estimation (weight)

i. Qualitative Measures

This page shows a variety of reliability and validity measures.

Figure 28. A variety of reliability and validity measures

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j. Effects

This page shows following total and indirect effects of variables

- Total effects of latent variables (Std. Error)

- Indirect effects of latent variables (Std. Error)

- Total effects of latent variables on indicators (Std. Error)

- Indirect effects of latent variables on indicators (Std. Error)

Figure 29. Total effects of latent variables (Std. Error) and Indirect effects of latent variables (Std. Error)

Figure 30. Total effects of latent variables on indicators (Std. Error)

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Figure 31. Indirect effects of latent variables on indicators (Std. Error)

k. Latent Measures

This page shows means, variances, and correlations of latent variables

Figure 32. means, variances, and correlation of latent variables

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2. How to interpret the results

Table 1. Model fit of the result

Model Fit

FIT .535

AFIT .532

GFI .993

SRMR .078

FIT_M .606

FIT_S .168

Table 1. provides six measures of model fit.

• FIT indicates the total variance of all variables explained by a model specification.

The values of FIT range from 0 to 1. The larger this value, the more variance in the

variables is accounted for by the specified model.

• AFIT (Adjusted FIT) is similar to FIT, but takes model complexity into account. The

AFIT may be used for model comparison. The model with the largest AFIT value

may be chosen among competing models.

• (Unweighted least-squares) GFI and SRMR (standardized root mean square

residual). Both are proportional to the difference between the sample covariances and

the covariances reproduced by the parameter estimates of generalized structured

component analysis. The GFI values close to 1 and the SRMR values close to 0 may

be taken as indicative of good fit.

• FIT_M signifies how much the variance of indicators is explained by a measurement

model

• FIT_S indicates how much the variance of latent variable is accounted for by a

structural model. Both FIT_M and FIS_S can be interpreted in a manner similar to

the FIT.

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Table 2. Parameter Estimates of Loadings

Loadings

Estimate SE 95% CI_LB 95% CI_UB

cei1 .781 .025 .716 .831

cei2 .825 .021 .786 .854

cei3 .770 .029 .703 .820

cei4 .804 .030 .727 .868

cei5 .801 .028 .741 .846

cei6 .843 .028 .796 .883

cei7 .776 .024 .728 .824

cei8 .801 .033 .705 .856

ma1 .787 .026 .731 .825

ma2 .758 .025 .700 .805

ma3 .637 .039 .547 .704

ma4 .823 .028 .761 .803

ma5 .811 .024 .757 .849

ma6 .743 .040 .675 .793

orgcmt1 .748 .036 .675 .795

orgcmt2 .790 .024 .747 .834

orgcmt3 .820 .020 .773 .857

orgcmt7 .707 .033 .620 .778

orgcmt5 .796 .029 .721 .842

orgcmt6 .709 .050 .597 .796

orgcmt8 .782 .030 .726 .843

Table 2 displays the estimates of loadings and their bootstrap standard errors (SEs), and

95% confidence intervals. (Note: when indicators are formative, their loading estimates

will not be reported)

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Table 3. Parameter Estimates of weights

Weights

Estimate SE 95% CI_LB 95% CI_UB

cei1 .150 .010 .132 .170

cei2 .160 .010 .141 .179

cei3 .157 .010 .140 .177

cei4 .147 .009 .126 .163

cei5 .162 .011 .143 .184

cei6 .168 .009 .153 .191

cei7 .150 .009 .130 .167

cei8 .154 .009 .140 .168

ma1 .219 .021 .182 .263

ma2 .211 .020 .175 .252

ma3 .194 .017 .163 .233

ma4 .261 .019 .228 .293

ma5 .237 .019 .199 .267

ma6 .184 .021 .150 .238

orgcmt1 .302 .018 .263 .348

orgcmt2 .330 .016 .296 .371

orgcmt3 .364 .019 .337 .400

orgcmt7 .303 .018 .257 .344

orgcmt5 .453 .025 .403 .500

orgcmt6 .387 .026 .328 .441

orgcmt8 .467 .031 .420 .529

Table 3 displays the estimates of weights and their bootstrap standard errors (SEs), and

95% confidence intervals

Table 4. Parameter estimates of path coefficients

Path Coefficients

Estimate SE 95% CI_LB 95% CI_UB

LV_1 ~ LV_2 .362 .067 .260 .465

LV_2 ~ LV_3 .614 .042 .549 .678

LV_2 ~ LV_4 -.404 .049 -.515 -.288

Table 4. shows the estimates of path coefficients and their bootstrap standard errors (SE)

and 95% confidence intervals.

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Table 5. R squares of latent variables

R squares of Latent Variables

LV_1 0

LV_2 .131

LV_3 .377

LV_4 .163

Table 5 provides the R square values of each “endogenous” latent variable, indicating

how much variance of an endogenous latent variable is explained by its exogenous latent

variables. In the present example, the first latent variable (LV_1) is exogenous, so that its

R square is zero.

Table 6. Reliability and validity of measures

Cronbach’s

alpha

Dillon-

Goldstein’s rho AVE

Number of eigenvalues

greater than one per block

of indicators

LV_1 .8242 .8956 .7414 1

LV_2 .7173 .8419 .6404 1

LV_3 .7492 .8567 .6659 1

LV_4 .6418 .8071 .5828 1

In Table 6 Cronbach’s alpha and Dillon-Goldstein’s rho (or the composite reliability) can

be used for checking internal consistency of indicators for each latent variable. The

average variance extracted (AVE) can be used to examine the convergent validity of a

latent variable. The number of eigenvalues greater than one per block of indicators can be

used to check uni-dimensionality of the indicators.

Table 7. Correlations of Latent Variables (SE)

Correlations of Latent Variables

LV_1 LV_2 LV_3 LV_4

LV_1 1

LV_2 .362 1

LV_3 .388 .614 1

LV_4 -.209 -.404 -.461 1

Table 7 shows the correlations among latent variables.