Status : Draft
Output IRT menghasilkan dua cluster informasi, theta siswa dan item parameter.
Θ, theta, adalah tingkat kepandaian siswa.
IRT modelling dapat menggunakan satu sampai dengan 3 parameter, parameter A, B, dan G.
- Parameter B : Item difficulty, difficulty parameter. Parameter ini menunjukkan, seberapa mudah atau sulit sebuah item. Paramater B digunakan dalam one-parameter (1P) IRT model.
- Parameter A = Discrimination parameter. Nilai parameter ini, menginformasikan seberapa efektif sebuah item dapat membedakan siswa yang pandai dan kurang pandai. Two-parameter (2P) IRT model menggunakan parameter A dan B.
- Parameter C = Dikenal juga dengan nama G parameter atau guessing parameter. Nilai ini memberi informasi kepada kita, seberapa mungkin siswa menjawab suatu item dengan cara menebak. Model yang menggunakan parameter A, B dan G, disebut three-parameter (3P) IRT model.
Untuk menentukan item difficulty dan item discrimination kita bisa menggunakan verbal terms untuk mempermudah membedakannya;
Item difficulty dapat dibagi menjadi beberapa level :
| No |
Verbal label |
Typical value |
| 1 |
very easy |
-2.625 |
| 2 |
easy |
-1.5 |
| 3 |
medium |
0 |
| 4 |
hard |
1.5 |
| 5 |
very hard |
2.625 |
Sedangkan item discrimination dapat dibagi menjadi beberapa level sebagai berikut :
| No |
Verbal label |
Typical value |
| 1 |
none |
0 |
| 2 |
low |
0.4 |
| 3 |
moderate |
1 |
| 4 |
high |
2.1 |
| 5 |
perfect |
999 |
Untuk item discrimination, verbal label-nya, dapat kita bagi sebagai berikut :
| No |
Verbal label |
Range of values |
Typical value |
| 1 |
None |
0 |
0.00 |
| 2 |
Very Low |
0.01 - 0.34 |
0.18 |
| 3 |
Low |
0.35 - 0.64 |
0.50 |
| 4 |
Moderate |
0.65 s.d 1.34 |
1.00 |
| 5 |
High |
1.35 s.d 1.69 |
1.50 |
| 6 |
Very High |
> 1.70 |
2.00 |
| 7 |
Perfect |
+∞ |
+∞ |
ICC, Item Charasteristic Curve
IIF, Item Information Function
TIF, Test Information Function
Odd ratio,
Logit,
Item-Person Map (IPM)
Misfit, outliers
Misfit indices,
Model fit,
Infit Standardized Residuals,
Outfit Standardized Residuals,
Regression Analysis
Systematic Bias
Chi-square,
Item Fit,
Infit Mean Squared,
Outfit Mean Squared,
Degress of freedom (df),
Parallel coordinate,
Person fit,
IRF, Item Response Function,
TRF, Test Response Function,
Referensi
- A Simple Guide to the Item Response Theory (IRT) and Rasch Modeling Chong, Ho Yu, Ph.Ds, https://www.creative-wisdom.com/computer/sas/IRT.pdf
- Introduction to Item Response Theory, https://www.slideshare.net/NathanThompson54/introduction-to-item-response-theory-70872990
- The Basics of Item Response Theory Using R, by Frank B. Baker, Seock-Ho Kim, https://amzn.to/2UM849e
- psych: Procedures for Psychological, Psychometric, and Personality Research, https://cran.r-project.org/web/packages/psych/index.html
- ltm: Latent Trait Models under IRT, https://cran.r-project.org/web/packages/ltm/index.html
- IRT workshop Spring 2014, https://github.com/cddesja/IRTS2014
- Introduction to IRT Using R (2PL), https://wnarifin.github.io/simpler/irt_2PL.html
- How should we handle missing responses?, https://www.researchgate.net/post/How_should_we_handle_missing_responses
- 7 Ways To Handle Missing Data, https://measuringu.com/handle-missing-data/
- Evaluating Performance of Missing Data Imputation Methods in IRT Analyses, http://ijate.net/index.php/ijate/article/view/549
- link: IRT Separate Calibration Linking Methods, https://cran.r-project.org/web/packages/plink/index.html
- A method for designing IRT-based item banks, https://research.utwente.nl/en/publications/a-method-for-designing-irt-based-item-banks
- WrightMap Tutorial - Part 1, http://wrightmap.org/post/80523814110/wrightmap-tutorial-part-1
- Data Analysis Using Item Response Theory Methodology: An Introduction to Selected Programs and Applications., https://digitalcommons.library.umaine.edu/cgi/viewcontent.cgi?article=1019&context=psy_facpub
- PSYCHOMETRICS, Item Response Theory (Part 1), https://rpubs.com/castro/156912
- PSYCHOMETRICS, Item Response Theory (Part 2), https://rstudio-pubs-static.s3.amazonaws.com/156125_d88cf281bd2546c294c519afb2577768.html#
- PerFit: Person Fit, https://rdrr.io/cran/PerFit/
- Person fit assessment using the PerFit package in R, https://www.tqmp.org/RegularArticles/vol12-3/p232/p232.pdf
- HOW DOES DIRECTOR FINANCIAL LITERACY INFLUENCE FINANCIAL MONITORING?, https://eprints.qut.edu.au/213554/1/Jacqueline_Bettington_Thesis.pdf
