Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.
| Model Fit | |||
|---|---|---|---|
| Person Reliability | |||
| scale | . | ||
| [3] | |||
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
[3]
Welcome to DIF Rasch Model.
Each variable should be coded as 0 or 1 with the 'Grouping variable'in jamovi.
The focal group should be coded as 1.
The Raju's Z statistics are estimated by Marginal Maximum Likelihood Estimation(MMLE),and area method is obtained by using the unsigned areas between the ICCs.
Feature requests and bug reports can be made on my GitHub.
| Raju’s area method | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Statistic | Adj.p | deltaRaju | Effect size | ||||||
[6] [5]
Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.
| Model Fit | |
|---|---|
| scale | |
| [3] | |
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
| ER1 | . | ||
| ER2 | . | ||
| ER3 | . | ||
| ER4 | . | ||
| ER5 | . | ||
| ER6 | . | ||
| ER7 | . | ||
| ER8 | . | ||
| [3] | |||
Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.
| Model Fit | |||
|---|---|---|---|
| Person Reliability | |||
| scale | -0.556 | ||
| [3] | |||
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
| ER5 | 0.233 | ||
| ER6 | 0.534 | ||
| [3] | |||
Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.
| Model Fit | |||
|---|---|---|---|
| Person Reliability | |||
| scale | 0.477 | ||
| [3] | |||
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
| ER1 | 0.671 | ||
| ER2 | 0.507 | ||
| ER3 | 0.726 | ||
| ER4 | 0.315 | ||
| ER5 | 0.233 | ||
| ER6 | 0.534 | ||
| ER7 | 0.342 | ||
| [3] | |||
Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.
| Model Fit | |||
|---|---|---|---|
| Person Reliability | |||
| scale | 0.477 | ||
| [3] | |||
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
| ER6 | 0.671 | ||
| ER7 | 0.507 | ||
| ER5 | 0.726 | ||
| ER4 | 0.315 | ||
| ER3 | 0.233 | ||
| ER2 | 0.534 | ||
| ER1 | 0.342 | ||
| [3] | |||
Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.
| Model Fit | |||
|---|---|---|---|
| Person Reliability | |||
| scale | 0.477 | ||
| [3] | |||
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
| ER1 | 0.671 | ||
| ER2 | 0.507 | ||
| ER3 | 0.726 | ||
| ER4 | 0.315 | ||
| ER5 | 0.233 | ||
| ER6 | 0.534 | ||
| ER7 | 0.342 | ||
| [3] | |||
Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
The rationale of snowIRT module is described in the documentation.
Feature requests and bug reports can be made on my GitHub.
| Model Fit | |||
|---|---|---|---|
| Person Reliability | |||
| scale | 0.477 | ||
| [3] | |||
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
| ER2 | 0.671 | ||
| ER4 | 0.507 | ||
| ER3 | 0.726 | ||
| ER1 | 0.315 | ||
| ER5 | 0.233 | ||
| ER6 | 0.534 | ||
| ER7 | 0.342 | ||
| [3] | |||
[5]
[1] The jamovi project (2020). jamovi. (Version 1.2) [Computer Software]. Retrieved from https://www.jamovi.org.
[2] R Core Team (2019). R: A Language and environment for statistical computing. (Version 3.6) [Computer software]. Retrieved from https://cran.r-project.org/.
[3] Robitzsch,A., Kiefer, T., & Wu, M. (2020). TAM: Test Analysis Modules. [R package]. Retrieved from https://CRAN.R-project.org/package=TAM.
[4] Martinkova, P., & Drabinova, A. (2018). ShinyItemAnalysis: for teaching psychometrics and to enforce routine analysis of educational tests. [R package]. Retrieved from https://CRAN.R-project.org/package=ShinyItemAnalysis.
[5] Seol, H. (2020). snowIRT: Item Response Theory for jamovi. [jamovi module]. Retrieved from https://github.com/hyunsooseol/snowIRT.
[6] Magis, D., Beland, S., Tuerlincks, F., & De Boeck, P. (2010). difR: A general framework and an R package for the detection of dichotomous differential item functioning. [R package]. Retrieved from https://CRAN.R-project.org/package=difR.