A Modified AIM Quality Questionnaire
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McKenna, L. Debruyne, C. & O'Sullivan, D., A Modified AIM Quality Questionnaire, 2019
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This is a modified version of the AIM Quality (AIMQ) questionnaire (Lee, Strong, Kahn & Wang, 2002). The AIMQ questionnaire consists of 65 statements regarding DQ about which the user rates their level of agreement on a scale of 0 (disagree) to 10 (agree). The AIMQ measures DQ according to 14 quality dimensions: Appropriate Amount, Believability, Completeness, Concise Representation, Consistent Representation, Ease of Operation, Free of Error, Interpretability, Objectivity, Relevancy, Reputation, Security, Timeliness, and Understandability. In terms of scoring, higher ratings indicate a more positive perception of the statements (note that scores for negative statements are reversed). This modified version includes a subset of 25 statements and was used in the PhD thesis - NAISC-L: A Linked Data Interlinking Framework for Libraries, Archives and Museums. References: Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & Management, 40(2), 133-146. McKenna Lucy Mary, NAISC-L: A Linked Data Interlinking Framework for Libraries, Archives and Museums, Trinity College Dublin.School of Computer Science & Statistics, 2020 (http://hdl.handle.net/2262/94131).
This is a modified version of the AIM Quality (AIMQ) questionnaire (Lee, Strong, Kahn & Wang, 2002). The AIMQ questionnaire consists of 65 statements regarding DQ about which the user rates their level of agreement on a scale of 0 (disagree) to 10 (agree). The AIMQ measures DQ according to 14 quality dimensions: Appropriate Amount, Believability, Completeness, Concise Representation, Consistent Representation, Ease of Operation, Free of Error, Interpretability, Objectivity, Relevancy, Reputation, Security, Timeliness, and Understandability. In terms of scoring, higher ratings indicate a more positive perception of the statements (note that scores for negative statements are reversed). This modified version includes a subset of 25 statements and was used in the PhD thesis - NAISC-L: A Linked Data Interlinking Framework for Libraries, Archives and Museums. References: Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & Management, 40(2), 133-146. McKenna Lucy Mary, NAISC-L: A Linked Data Interlinking Framework for Libraries, Archives and Museums, Trinity College Dublin.School of Computer Science & Statistics, 2020 (http://hdl.handle.net/2262/94131).
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Author's Homepage: http://people.tcd.ie/mckennl3
Type of material: Test or assessment