One can conduct the analysis by drawing a path diagram. To start, click the “Path Diagram” button. The interface below will appear:
A path diagram can be drawn through the buttons in the interface. In the example, we have a mediation model where the text is used as a mediator for the association of “hard” (how difficulty the class is) and “rating” (the numerical rating of the class).
Different from a regular SEM, we need to specify the variable “comments” as a text variable by setting “text = comments” in the “Control” field. The app also supports different methods including dictionary based sentiment analysis, AI based method (setting “textmethod=ai”, and embedding method (setting “textmethod=embedding”).
With that, one can click on the run button (the green arrow) to carry out the analysis. For example, for the current model, we have the output as below. It mainly has two parts - the data description and the model results.
Descriptive statistics (N=5000)
Mean sd Min Max Skewness Kurtosis
id 1.4343e+04 8314.0453 9.0000 28521.000 5.7205e-03 1.7654
profid 4.8633e+02 299.9069 1.0000 1000.000 2.9661e-02 1.7294
rating 3.8618e+00 1.4581 1.0000 5.000 -9.5170e-01 2.4063
hard 2.8908e+00 1.3156 1.0000 5.000 5.7725e-02 1.8941
sentiment 2.0682e-01 0.2668 -1.4732 1.803 -6.3469e-04 4.6312
Missing Rate
id 0
profid 0
rating 0
hard 0
sentiment 0
Model information
Observed variables: hard comments rating .
Text variables: comments .
The weight is: 0 .
The software to be used is: TextSEM
lavaan 0.6-12 ended normally after 20 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 9
Number of observations 5000
Number of missing patterns 1
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Model Test Baseline Model:
Test statistic 4142.684
Degrees of freedom 3
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.000
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -15862.021
Loglikelihood unrestricted model (H1) -15862.021
Akaike (AIC) 31742.042
Bayesian (BIC) 31800.696
Sample-size adjusted Bayesian (BIC) 31772.098
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value RMSEA <= 0.05 NA
Standardized Root Mean Square Residual:
SRMR 0.000
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Regressions:
Estimate Std.Err z-value P(>|z|)
comments.OverallSenti ~
hard -0.075 0.003 -28.208 0.000
rating ~
cmmnts.OvrllSn 2.829 0.059 47.785 0.000
hard -0.355 0.012 -29.605 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.cmmnts.OvrllSn 0.424 0.008 50.120 0.000
.rating 4.304 0.043 99.150 0.000
hard 2.891 0.019 155.389 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.cmmnts.OvrllSn 0.061 0.001 50.000 0.000
.rating 1.076 0.022 50.000 0.000
hard 1.730 0.035 50.000 0.000