Abstract
This paper is a narrative of our experience in analyzing and merging data files provided to us by the Education Quality and Accountability Office (EQAO). In the paper, we propose a scheme of merging data files by means of Structured Query Language (SQL, pronounced as “sequel”). Although, the narrative of our experiences using this merging scheme could have been extended to any number of data files, the aim of this work was to merge only three EQAO data files. Via this merge process, we were able to gain meaningful information and facilitate the analysis of EQAO data to answer our research questions. By using SQL queries, our approach was not only to analyze the available data files but also to construct a narrative about viewing and handling data contained in the files.
Highlights
In a traditional model of quantitative data analysis, it is expected that a researcher considers data file sources separately (Voronin, 2006)
This paper presents a rich account of challenges faced by our research team during the process of merging of data from the Education Quality and Accountability Office (EQAO), in Ontario, Canada for secondary analysis to answer some specific research questions related to an assessment of Grade 9 mathematics
There may be a data entry error, typo, or duplication of RecIDs. This was an important issue for us as we were considering RecID as a primary key to merge student data sets and for that unique RecID was required. This was again checked by the following SQL query and we found that all RecIDs were unique: SELECT RecID, COUNT(*)
Summary
In a traditional model of quantitative data analysis, it is expected that a researcher considers data file sources separately (Voronin, 2006). While the traditional model of data analysis has an advantage in its simplicity, it does not address the case when some helpful explanatory information about a variable is present in different data files. This information could only become meaningful if the data files were merged. While most of the research is concentrated on the initial steps of data file merging, there are only a few studies about actual and practical merging of the data file sources using query against multiple data files (Naumann & Häussle, 2002). We preferred Microsoft SQL Server 2008, because it is reliable, versatile, and high performing in terms of its capabilities in development and management of a database (Thakar, Szalav, Fekete, & Gray, 2008)
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