Kita mencoba menggunakan Extended Rasch Model.
Ini data mengenai stress dengan item dan ringkasan datanya seperti ini
## V1 V2 V3 V4 ## Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.00 ## 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00 ## Median :0.00 Median :0.00 Median :0.00 Median :0.00 ## Mean :0.47 Mean :0.42 Mean :0.41 Mean :0.35 ## 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00 ## Max. :1.00 Max. :1.00 Max. :1.00 Max. :1.00 ## V5 V6 ## Min. :0.00 Min. :0.00 ## 1st Qu.:0.00 1st Qu.:0.00 ## Median :0.00 Median :0.00 ## Mean :0.27 Mean :0.48 ## 3rd Qu.:1.00 3rd Qu.:1.00 ## Max. :1.00 Max. :1.00
| Ini mengenai hasilnya perhtungan parameter raschnya |
## ## Results of RM estimation: ## ## Call: RM(X = stress) ## ## Conditional log-likelihood: -202.1232 ## Number of iterations: 13 ## Number of parameters: 5 ## ## Item (Category) Difficulty Parameters (eta): with 0.95 CI: ## Estimate Std. Error lower CI upper CI ## I2 -0.110 0.203 -0.507 0.287 ## I3 -0.061 0.203 -0.459 0.337 ## I4 0.241 0.208 -0.166 0.649 ## I5 0.681 0.220 0.249 1.113 ## I6 -0.400 0.201 -0.794 -0.005 ## ## Item Easiness Parameters (beta) with 0.95 CI: ## Estimate Std. Error lower CI upper CI ## beta I1 0.352 0.201 -0.043 0.747 ## beta I2 0.110 0.203 -0.287 0.507 ## beta I3 0.061 0.203 -0.337 0.459 ## beta I4 -0.241 0.208 -0.649 0.166 ## beta I5 -0.681 0.220 -1.113 -0.249 ## beta I6 0.400 0.201 0.005 0.794 |
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