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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 l.jpg

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


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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


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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


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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


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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


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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


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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


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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)


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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)


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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



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Local Questions

  • Questions for further exploration

    • The scatter plot visually reveals no relationship between gap score and Total Annual Expenditures


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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?


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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)


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