Tuesday, 14 June 2022

Factor Analysis

SPSS

Analyze --> Dimension Reduction --> Factor 


Extraction --> Method (Maximum likelihood)

  Referensi

  1. SPSS Factor Analysis – Beginners Tutorial, https://www.spss-tutorials.com/spss-factor-analysis-tutorial/

Wednesday, 7 October 2020

Assessing ranks

Pertanyaan  

Misalkan, jika kita memiliki nilai Fisika dari seluruh sekolah yang berada di sebuah Desa, bagaimana cara merangking sekolah berdasarkan capaian kemampuan Fisikanya, jika populasi siswa setiap sekolah sangat berbeda, misal ada sekolah yang mengambil ujian Fisika hanya 5 orang dan ada sekolah lain yang sampai 100 orang. Karena kalau merangking dari nilai rata-rata saja, tidak begitu valid, terutama jika populasinya kecil. 



Referensi 

  1. An Introduction to statistics : Assessing ranks, http://www.floppybunny.org/robin/web/virtualclassroom/stats/basics/part8.pdf
  2. Score Transformations, http://faculty.cbu.ca/~erudiuk/IntroBook/sbk14m.htm
  3. Percentile rank scores are congruous indicators of relative performance, or aren’t they?, https://arxiv.org/pdf/1108.1860.pdf
  4. Comparing Means in R, http://www.sthda.com/english/wiki/comparing-means-in-r
  5. Better Ranking using Bayesian Average, https://arpitbhayani.me/blogs/bayesian-average
  6. Analisis Kruskall-Wallis dengan SPSS,  https://www.semestapsikometrika.com/2018/11/analisis-kruskall-wallis-dengan-spss.html
  7. One-Way Analysis of Variance:Comparing Several Means, https://www.westga.edu/academics/research/vrc/assets/docs/OneWayANOVA_LectureNotes.pdf
  8. Compare the means of two or more variables or groups in the data, https://radiant-rstats.github.io/docs/basics/compare_means.html 
  9.  Using the Bayesian Average in Custom Ranking, https://www.algolia.com/doc/guides/managing-results/must-do/custom-ranking/how-to/bayesian-average/
  10. Use Bayesian Averages to Improve Rating Sorting in your Elasticsearch Index, https://jolicode.com/blog/use-bayesian-averages-to-improve-rating-sorting-in-your-elasticsearch-index
  11. Bayesian Average Ratings, https://www.evanmiller.org/bayesian-average-ratings.html
  12. POSTGRESQL: WEIGHTED AVERAGE INSTEAD OF AVERAGE?-POSTGRESQL, https://www.appsloveworld.com/postgresql/100/95/postgresql-weighted-average-instead-of-average

Saturday, 26 October 2019

WrightMap: IRT Item-Person Map

Secara default, pelabelan item dalam item person map menggunakan WrightMap adalah berdasarkan urutan. Jika ada soal yang dihapus, maka, akan membingungkan membaca item-person map nya.

David sudah menambah fitur untuk menggunakan custom label ini, seperti dijelaskan disini [2], tapi sepertinya update ini, baru bisa digunakan jika input WrightMap berupa CQModel

Referensi


  1. WrightMap: IRT Item-Person Map with 'ConQuest' Integration, https://cran.r-project.org/web/packages/WrightMap/index.html
  2. Using item names as item.labels #6, https://github.com/david-ti/wrightmap/issues/6

Arti Nilai Parameter b yang Terlampau Besar pada Teori IRT

...

Referensi


  1. Meaning of large values of parameter b (|b| >>) 4 in IRT theory, https://stats.stackexchange.com/questions/169247/meaning-of-large-values-of-parameter-b-b-4-in-irt-theory

Wednesday, 25 September 2019

IRT : Item Characteristic Curve (ICC)

Item Characteristic Curve (ICC) atau dikenal juga dengan Item Response Function (IRF).

Referensi

  1. Item Characteristic Curve, https://www.sciencedirect.com/topics/psychology/item-characteristic-curve
  2. Introduction to IRT Using R (2PL), https://wnarifin.github.io/simpler/irt_2PL.html
  3. A visual guide to item response theory, https://www.metheval.uni-jena.de/irt/VisualIRT.pdf
  4. LMS Assessment: using IRT analysis to detect defective multiple-choice test items, https://www.researchgate.net/publication/273460057_LMS_Assessment_using_IRT_analysis_to_detect_defective_multiple-choice_test_items
  5. Hubungan Parameter Item Discrimination dengan Item Fit Menggunakan Model Dua Parameter Logistik, http://repository.ub.ac.id/id/eprint/163714/1/Danang%20Kamal%20M.pdf