To get started:
jamovi treats all variables as qualitative/categorical/nominal.
Variables must contain only integer values, and must be coded with consecutive values from 1 to the maximum number.
The results of Class membership will be displayed in the datasheet.
The output columm can NOT be used as an input to the same analysis.
To analyze 'Profile' analysis, click the LCA analysis again.
Feature requests and bug reports can be made on my GitHub.
| Model fit | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class | AIC | BIC | Entropy | Gsq | Chisq | ||||||
| . | . | . | . | . | . | ||||||
| Note. Gsq=the likelihood-ratio statistic; Chisq=Pearson Chi-square goodness of fit statistic; Entropy=non-normalized entropy which ranges between 0 and infinity. | |||||||||||
| [3] | |||||||||||
[4]
To get started:
jamovi treats all variables as qualitative/categorical/nominal.
Variables must contain only integer values, and must be coded with consecutive values from 1 to the maximum number.
The results of Class membership will be displayed in the datasheet.
The output columm can NOT be used as an input to the same analysis.
To analyze 'Profile' analysis, click the LCA analysis again.
Feature requests and bug reports can be made on my GitHub.
| Model fit | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class | AIC | BIC | Entropy | Gsq | Chisq | ||||||
| . | . | . | . | . | . | ||||||
| Note. Gsq=the likelihood-ratio statistic; Chisq=Pearson Chi-square goodness of fit statistic; Entropy=non-normalized entropy which ranges between 0 and infinity. | |||||||||||
| [3] | |||||||||||
[4]
To get started:
jamovi treats all variables as qualitative/categorical/nominal.
Variables must contain only integer values, and must be coded with consecutive values from 1 to the maximum number.
The results of Class membership will be displayed in the datasheet.
The output columm can NOT be used as an input to the same analysis.
To analyze 'Profile' analysis, click the LCA analysis again.
Feature requests and bug reports can be made on my GitHub.
| Model fit | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class | AIC | BIC | Entropy | Gsq | Chisq | ||||||
| . | . | . | . | . | . | ||||||
| Note. Gsq=the likelihood-ratio statistic; Chisq=Pearson Chi-square goodness of fit statistic; Entropy=non-normalized entropy which ranges between 0 and infinity. | |||||||||||
| [3] | |||||||||||
[4]
To get started:
jamovi treats all variables as qualitative/categorical/nominal.
Variables must contain only integer values, and must be coded with consecutive values from 1 to the maximum number.
The results of Class membership will be displayed in the datasheet.
The output columm can NOT be used as an input to the same analysis.
To analyze 'Profile' analysis, click the LCA analysis again.
Feature requests and bug reports can be made on my GitHub.
| Model fit | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class | AIC | BIC | Entropy | Gsq | Chisq | ||||||
| . | . | . | . | . | . | ||||||
| Note. Gsq=the likelihood-ratio statistic; Chisq=Pearson Chi-square goodness of fit statistic; Entropy=non-normalized entropy which ranges between 0 and infinity. | |||||||||||
| [3] | |||||||||||
| Estimates and effect sizes for categorical predictors | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| 95% Confidence Interval | |||||||||
| Variable | Level | Estimate | Lower | Upper | |||||
| Difference between factor levels for categorical predictors | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 95% Confidence Interval | |||||||||||
| Variable | Comparison | Difference | Lower | Upper | Cohen's d | ||||||
| R² and semi-partial R² estimates | |||
|---|---|---|---|
| Source | Estimate | ||
| Models Info | |||||
|---|---|---|---|---|---|
| Setup | . | we need at least 1 endogenous variable | |||
| [5] | |||||
| Model Tests | |||||||
|---|---|---|---|---|---|---|---|
| Label | X² | df | p | ||||
| Fit Indices | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AIC | BIC | adj. BIC | SRMR | RMSEA | Lower | Upper | RMSEA p | ||||||||
| Fit Indices | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| CFI | TLI | RNI | GFI | adj. GFI | pars. GFI | ||||||
| R-squared | |||||||
|---|---|---|---|---|---|---|---|
| Variable | R² | Lower | Upper | ||||
| Parameter Estimates | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dep | Pred | Estimate | SE | Lower | Upper | β | z | p | |||||||||
[6]
| Variances and Covariances | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable 1 | Variable 2 | Estimate | SE | Lower | Upper | β | z | p | Method | Type | |||||||||||
| Intercepts | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Intercept | SE | Lower | Upper | z | p | |||||||
To get started:
jamovi treats all variables as qualitative/categorical/nominal.
Variables must contain only integer values, and must be coded with consecutive values from 1 to the maximum number.
The results of Class membership will be displayed in the datasheet.
The output columm can NOT be used as an input to the same analysis.
To analyze 'Profile' analysis, click the LCA analysis again.
Feature requests and bug reports can be made on my GitHub.
| Model fit | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class | AIC | BIC | Entropy | Gsq | Chisq | ||||||
| . | . | . | . | . | . | ||||||
| Note. Gsq=the likelihood-ratio statistic; Chisq=Pearson Chi-square goodness of fit statistic; Entropy=non-normalized entropy which ranges between 0 and infinity. | |||||||||||
| [3] | |||||||||||
[4]
To get started:
jamovi treats all variables as qualitative/categorical/nominal.
Variables must contain only integer values, and must be coded with consecutive values from 1 to the maximum number.
The results of Class membership will be displayed in the datasheet.
The output columm can NOT be used as an input to the same analysis.
To analyze 'Profile' analysis, click the LCA analysis again.
Feature requests and bug reports can be made on my GitHub.
| Model fit | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class | AIC | BIC | Entropy | Gsq | Chisq | ||||||
| . | . | . | . | . | . | ||||||
| Note. Gsq=the likelihood-ratio statistic; Chisq=Pearson Chi-square goodness of fit statistic; Entropy=non-normalized entropy which ranges between 0 and infinity. | |||||||||||
| [3] | |||||||||||
[4]
[1] The jamovi project (2021). jamovi. (Version 2.0) [Computer Software]. Retrieved from https://www.jamovi.org.
[2] R Core Team (2021). R: A Language and environment for statistical computing. (Version 4.0) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2021-04-01).
[3] Linzer,D., & Lewis, J. (2021). poLCA: An R Package for Polytomous Variable Latent Class Analysis. [R package]. Retrieved from https://CRAN.R-project.org/package=poLCA.
[4] Seol, H. (2020). snowRMM:Rasch Mixture Model for jamovi. [jamovi module]. Retrieved from https://github.com/hyunsooseol/snowRMM.
[5] Gallucci, M. (2021). PATHj: jamovi Path Analysis. [jamovi module]. For help please visit https://pathj.github.io/.
[6] Rosseel, Y. (2019). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. link.