Over-the-Counter Data's Impact on Educators' Data Analysis Accuracy
Northcentral University, 2013 - 486 pages
There is extensive research on the benefits of making data-informed decisions, but research also contains evidence many educators incorrectly interpret student data. Meanwhile, the types of detailed labeling on over-the-counter medication have been shown to improve use of non-medication products, as well. However, data systems most educators use to analyze student data usually display data without supporting guidance concerning the data's proper analysis. In this dissertation, the data-equivalent to over-the-counter medicine is termed over-the-counter data: essentially, enlisting medical label conventions to pair data reports with straightforward verbiage on the proper interpretation of report contents. The researcher in this experimental, quantitative study explored the inclusion of such supports in data systems and their reports. The cross-sectional sampling of 211 educators of varied backgrounds and roles at nine elementary and secondary schools throughout California answered survey questions regarding student data reports with varied forms of analysis guidance. Respondents' data analyses were found to be 307% more accurate when a report footer was present, 205% more accurate when an abstract was present, and 273% more accurate when an interpretation guide was present. These findings and others were significant and fill a void in field literature by containing evidence that can be used to identify how data systems can increase data analysis accuracy by offering analysis support through labeling and supplemental documentation. Recommendations for future research include measuring the impact over-the-counter data has on data analysis accuracy when all supports are offered to educators in concert. The following appendices are included: (1) Standards and Codes; (2) Study Survey Pages; (3) Handouts Used in Study (Color Format Is Pertinent to Study); (4) Code Book for Respondent Data File; (5) Independent Samples T-Test for Support Use; (6) Independent Samples T-Test for Footer Use; (7) Independent Samples T-Test for Abstract Use; (8) Independent Samples T-Test for Interpretation Guide Use; (9) Independent Samples T-Test for Support Presence; (10) Independent Samples T-Test for Footer Presence; (11) Independent Samples T-Test for Abstract Presence; (12) Independent Samples T-Test for Interpretation Guide Presence; (13) Independent Samples T-Test for Footer Format; (14) Independent Samples T-Test for Abstract Format; (15) Independent Samples T-Test for Interpretation Guide Format; (16) Crosstabulated Chi-Square Tests for Variable Relationship with Data Analysis Accuracy; (17) Crosstabulated Chi-Square Tests for Variable Relationship with Support Use; and (18) Supplemental Documentation Templates.
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This study is unique in that it tackles an issue almost no other work is addressing: exactly how the tools educators use to analyze data (i.e., the data system and its reports) can be modified to do a better job facilitating accurate data use. There are other studies that look at educator preference and perceived value (e.g., "I like this report best"), but this one actually measures the specific impact of variables on educators' data analysis accuracy and uncovers how this accuracy can be significantly increased (in fact, how educators' data analysis accuracy can be quadrupled!). In addition, the work's extensive literature review profiles all key work done on similar topics, tracing the research history all the way back to the proliferation of the personal computer. This is a must-read for anyone involved in education data.