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Examination for the Certificate of Proficiency in English (ECPE)

Source: R/data.R
ecpe.Rd

This is data from the grammar section of the ECPE, administered annually by the English Language Institute at the University of Michigan. This data contains responses to 28 questions from 2,922 respondents, which ask respondents to complete a sentence with the correct word. This data set has been used by Templin & Hoffman (2013) and Templin & Bradshaw (2014) for demonstrating the log-linear cognitive diagnosis model (LCDM) and the hierarchical diagnostic classification model (HDCM), respectively.

Usage

ecpe_data

ecpe_qmatrix

Format

ecpe_data is a tibble containing ECPE response data with 2,922 rows and 29 variables.

  • resp_id: Respondent identifier

  • E1-E28: Dichotomous item responses to the 28 ECPE items

ecpe_qmatrix is a tibble that identifies which skills are measured by each ECPE item. This section of the ECPE contains 28 items measuring 3 skills. The ecpe_qmatrix correspondingly is made up of 28 rows and 4 variables.

  • item_id: Item identifier, corresponds to E1-E28 in ecpe_data

  • morphosyntactic, cohesive, and lexical: Dichotomous indicator for whether or not the skill is measured by each item. A value of 1 indicates the skill is measured by the item and a value of 0 indicates the skill is not measured by the item.

Details

The skills correspond to knowledge of:

  1. Morphosyntactic rules

  2. Cohesive rules

  3. Lexical rules

For more details, see Buck & Tatsuoka (1998) and Henson & Templin (2007).

References

Buck, G., & Tatsuoka, K. K. (1998). Application of the rule-space procedure to language testing: Examining attributes of a free response listening test. Language Testing, 15(2), 119-157. doi:10.1177/026553229801500201

Henson, R., & Templin, J. (2007, April). Large-scale language assessment using cognitive diagnosis models. Paper presented at the Annual meeting of the National Council on Measurement in Education, Chicago, IL.

Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification model estimates using Mplus. Educational Measurement: Issues and Practice, 32(2), 37-50. doi:10.1111/emip.12010

Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. Psychometrika, 79(2), 317-339. doi:10.1007/s11336-013-9362-0