USER’S MANUAL

A user’s manual is available here that demonstrates how to use WEB GESCA.

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.
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) 

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.

 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.