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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

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
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 questions3
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 questions4
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 questions5
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 questions6
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 questions7
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 questions8
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 questions9
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 questions11
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 questions13
Local Questions
  • Questions for further exploration
    • The scatter plot visually reveals no relationship between gap score and Total Annual Expenditures
local questions14
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
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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