Instructions
____________________________________________________________________________________
1. Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
2. The results of Save will be displayed in the datasheet.
3. The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
4. The rationale of snowIRT module is described in the documentation.
5. Feature requests and bug reports can be made on my GitHub.
____________________________________________________________________________________
| Model Fit | |||
|---|---|---|---|
| Person Reliability | |||
| scale | . | ||
| [3] | |||
| Item Statistics | |||
|---|---|---|---|
| Proportion | |||
[3]
Instructions
____________________________________________________________________________________
1. Each variable must be coded as 0 or 1 with the type of numeric-continuous in jamovi.
2. The results of Save will be displayed in the datasheet.
3. The result tables are estimated by Marginal Maximum Likelihood estimation(MMLE).
4. The rationale of snowIRT module is described in the documentation.
5. Feature requests and bug reports can be made on my GitHub.
____________________________________________________________________________________
| Model Fit | |||||||
|---|---|---|---|---|---|---|---|
| Person Reliability | MADaQ3 | p | |||||
| scale | 0.846 | 0.0748 | 1.000 | ||||
| Note. MADaQ3= Mean of absolute values of centered Q_3 statistic with p value obtained by Holm adjustment; Ho= the data fit the Rasch model. | |||||||
| [3] | |||||||
| Q3 Correlation Matrix | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B | A | C | E | D | F | G | H | I | J | ||||||||||||
| B | — | ||||||||||||||||||||
| A | -0.094 | — | |||||||||||||||||||
| C | -0.163 | 0.016 | — | ||||||||||||||||||
| E | -0.133 | -0.132 | -0.133 | — | |||||||||||||||||
| D | -0.011 | -0.011 | -0.011 | -0.023 | — | ||||||||||||||||
| F | -0.006 | -0.001 | -0.002 | -0.004 | -0.009 | — | |||||||||||||||
| G | -0.284 | -0.203 | -0.104 | -0.171 | -0.157 | -0.007 | — | ||||||||||||||
| H | -0.040 | -0.021 | -0.022 | -0.047 | -0.117 | -0.052 | -0.001 | — | |||||||||||||
| I | 0.144 | 0.028 | -0.221 | -0.178 | -0.034 | -0.005 | -0.255 | -0.065 | — | ||||||||||||
| J | -0.266 | -0.024 | -0.012 | -0.001 | -0.186 | -0.033 | -0.037 | -0.001 | -0.046 | — | |||||||||||
| [3] | |||||||||||||||||||||
| Item Statistics | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Proportion | Measure | S.E.Measure | Infit | Outfit | |||||||
| B | 0.6133 | -1.012 | 0.355 | 0.902 | 0.712 | ||||||
| A | 0.8133 | -3.204 | 0.423 | 0.784 | 0.663 | ||||||
| C | 0.7867 | -2.859 | 0.408 | 0.887 | 0.761 | ||||||
| E | 0.6933 | -1.801 | 0.372 | 0.909 | 0.620 | ||||||
| D | 0.1733 | 3.398 | 0.426 | 0.938 | 0.518 | ||||||
| F | 0.0133 | 7.326 | 1.059 | 0.982 | 0.166 | ||||||
| G | 0.5467 | -0.397 | 0.347 | 1.125 | 1.241 | ||||||
| H | 0.2000 | 3.048 | 0.410 | 0.710 | 0.321 | ||||||
| I | 0.6933 | -1.801 | 0.372 | 0.749 | 0.450 | ||||||
| J | 0.3733 | 1.183 | 0.356 | 0.726 | 0.429 | ||||||
| Note. Infit= Information-weighted mean square statistic; Outfit= Outlier-sensitive means square statistic. | |||||||||||
| [3] | |||||||||||
[5]
| Scale Reliability Statistics | |||
|---|---|---|---|
| Cronbach's α | |||
| scale | 0.879 | ||
| [6] | |||
Instructions
_____________________________________________________________________________________________
1. The standard Rasch model is performed by Jonint Maximum Liklihood(JML).
2. Specify the number of 'Step' and model 'Type' in the 'Analysis option'.
3. Step is defined as number of category-1.
4. The results of Save will be displayed in the datasheet.
5. Feature requests and bug reports can be made on my GitHub
_____________________________________________________________________________________________
| Model Information | |
|---|---|
| Class | |
| . | |
| [7] | |
| Item Statistics | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Item mean | Measure | Infit | Outfit | ||||||
| [7] | |||||||||
[1] The jamovi project (2022). jamovi. (Version 2.3) [Computer Software]. Retrieved from https://www.jamovi.org.
[2] R Core Team (2021). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2022-01-01).
[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. (2022). snowIRT: Item Response Theory for jamovi. [jamovi module]. Retrieved from https://github.com/hyunsooseol/snowIRT.
[6] Revelle, W. (2019). psych: Procedures for Psychological, Psychometric, and Personality Research. [R package]. Retrieved from https://cran.r-project.org/package=psych.
[7] Willse, J. (2014). mixRasch: Mixture Rasch Models with JMLE. [R package]. Retrieved from https://CRAN.R-project.org/package=mixRasch.
[8] Seol, H. (2022). snowRMM: Rasch Mixture Model for jamovi. [jamovi module]. Retrieved from https://github.com/hyunsooseol/snowRMM.