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Considering the Integration of Qualitative and Quantitative

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Considering the Integration of Qualitative and Quantitative A Test of AAHSL Stats and LibQUAL+ Data Doug Joubert, Lyn Dennison and Tamera Lee Medical College of Georgia Local Questions Do any patterns exist between AAHSL Annual Stats and LibQUAL+ data?

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### Considering the Integration of Qualitative and Quantitative

A Test of AAHSL Stats and LibQUAL+ Data

Doug Joubert, Lyn Dennison and Tamera Lee

Medical College of Georgia

Local Questions
• Do any patterns exist between AAHSL Annual Stats and LibQUAL+ data?
• Required combining the data from both data sets into a common SPSS file
Local Questions
• AAHSL Data Transformation
• Examined the Expenditures Summary data from AAHSL Annual Statistics
• Transformed and recoded the AAHSL data to accommodate for missing scores
• For example, with data import, SPSS needed to understand that “M” was a “system missing” value
Local Questions
• AAHSL Data Transformation
• Coded AAHSL variables:
• Personnel expendituresperexp
• Total Collection Expenditures tocex
• Total Recurring Expenditures toreex
• Capital Budget capbud
• Total Annual Expenditures toanex
Local Questions
• AAHSL Data Transformation
• Combined the information from both data sets into a single SPSS data file
• Grouped data by a common variable: instID
• Merged the two files via SPSS
Local Questions
• SPSS Data Transformation
• Identified stats for the Affect of Service Dimension
• LibQUAL+ already created variables for person level subscales
• Specifically, the minimum, desired, and perceived means for each of the 2002 Dimensions
• Computed the means of Service Affect Dimension for each participating institution
Local Questions
• SPSS Data Transformation
• To compute the means of Service of Affect Dimension for institutions we used the following variables from LibQUAL
• aavgmin1
• aavgdes1
• aavgper1
Local Questions
• SPSS Data Transformation
• Computing the means of Service of Affect Dimension for each institution allowed us to compute a mean gap for each institution
• This was accomplished in much the same way as computing the LibQUAL+ gap
• Average perceived – average minimum = average gap (by institution)
Local Questions
• SPSS Data Transformation
• Having the average gap (by institution) allowed us to look at the “relationship” between it and the total annual expenditures (AAHSL)
Local Questions
• SPSS Data Transformation
• Having the average gap (by institution) also allowed us to transform the gap score into a T-score (Norm Table)
• As discussed Cook et al., T-scores allow one to examine individuals scores in relation to scores of peer insitutions1
Local Questions
• Questions for further exploration
• The scatter plot visually reveals no relationship between gap score and Total Annual Expenditures
Local Questions
• Questions for further exploration
• Q 1: What valid statistical method may be used to measure correlation with the gap score?
• For example: Spearman rank-order, Pearson correlation, or Linear Regression
• Q 2: How do we develop percentile ranks based on T-scores (norms) in any number of questions and dimensions?
References
• Cook, C., Heath, H., and Thompson, B. Score Norms for Improving Library Service Quality: A LibQUAL+ Study. portal: Libraries and the Academy, vol. 2, no. 1, pp. 13-26. (2002)