Calculus-based probability and statistics course homework problems

Source: R/ordered-pswc-data.R

Calculus-based probability and statistics course homework problems

items_ordered_pswc_hw

Format

An object of class matrix (inherits from array) with 288 rows and 29 columns.

Details

Data set contains the subject's responses to calculus-based probability and statistics course homework problems.

  • Item01: Subject's Response to Item 1.

  • Item02: Subject's Response to Item 2.

  • Item03: Subject's Response to Item 3.

  • Item05: Subject's Response to Item 5.

  • Item06: Subject's Response to Item 6.

  • Item07: Subject's Response to Item 7.

  • Item08: Subject's Response to Item 8.

  • Item09: Subject's Response to Item 9.

  • Item10: Subject's Response to Item 10.

  • Item11: Subject's Response to Item 11.

  • Item12: Subject's Response to Item 12.

  • Item13: Subject's Response to Item 13.

  • Item14: Subject's Response to Item 14.

  • Item15: Subject's Response to Item 15.

  • Item16: Subject's Response to Item 16.

  • Item17: Subject's Response to Item 17.

  • Item18: Subject's Response to Item 18.

  • Item19: Subject's Response to Item 19.

  • Item21: Subject's Response to Item 21.

  • Item22: Subject's Response to Item 22.

  • Item23: Subject's Response to Item 23.

  • Item24: Subject's Response to Item 24.

  • Item25: Subject's Response to Item 25.

  • Item26: Subject's Response to Item 26.

  • Item27: Subject's Response to Item 27.

  • Item29: Subject's Response to Item 29.

  • Item30: Subject's Response to Item 30.

  • Item32: Subject's Response to Item 32.

  • Item34: Subject's Response to Item 34.

References

Data used in:

  • Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023). A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy. British Journal of Mathematical and Statistical Psychology, 76, 513–538. doi:10.1111/bmsp.12307

Data originated from:

  • Culpepper, S. A. (2014). If at first you don’t succeed, try, try again: Applications of sequential IRT models to cognitive assessments. Applied Psychological Measurement, 38(8), 632–644. doi:10.1177/0146621614536464