Statistics for Librarians: How to Use and Evaluate Statistical Evidence

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Presentation to UCLA Librarians, December 1, 2005

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Statistics for Librarians: How to Use and Evaluate Statistical Evidence

  1. 1. Statistics in Libraries How to use and evaluate statistical information in library research <ul><ul><li>John McDonald Acquisitions Librarian Caltech </li></ul></ul>
  2. 2. What can statistics do? <ul><li>Statistics are just numbers…But they can provide information to: </li></ul><ul><ul><li>Assess Value </li></ul></ul><ul><ul><li>Evaluate Impact </li></ul></ul><ul><ul><li>Inform Decisions </li></ul></ul><ul><ul><li>Justify Actions </li></ul></ul>
  3. 3. Types of Library Statistics <ul><li>Gate Counts </li></ul><ul><li>Computer Use </li></ul><ul><ul><li>Session Length </li></ul></ul><ul><ul><li>Total Use </li></ul></ul><ul><li>Reference Questions </li></ul><ul><ul><li>Asked and Answered </li></ul></ul><ul><li>Circulation </li></ul><ul><ul><li>Borrowed, renewed </li></ul></ul><ul><li>Collection Size </li></ul><ul><ul><li>Volumes Held, Added, Cataloged </li></ul></ul><ul><li>Journal Use </li></ul><ul><ul><li>Reshelving, Copying, Circulation, Downloads </li></ul></ul><ul><li>Citations </li></ul><ul><li>Transaction Logs </li></ul><ul><ul><li>OPACs, Databases, Web logs, etc. </li></ul></ul>
  4. 4. Statistics <ul><li>Part I : Research Design </li></ul><ul><li>Part II : Statistical Concepts </li></ul><ul><li>Part III : Evaluating Library Statistics </li></ul>
  5. 5. Research Design <ul><li>Validity </li></ul><ul><ul><li>How well an indicator accurately measures the concept being studied. Is the technique appropriate to measure the concept being studied? </li></ul></ul><ul><li>Reliability </li></ul><ul><ul><li>How consistent is the measurement. Does it yield the same results over repeated attempts and by different researchers? How certain are the results? </li></ul></ul><ul><li>Generalizability </li></ul><ul><ul><li>How well (or likely) can the findings be applied to other situations? </li></ul></ul>
  6. 6. Research Design Steps <ul><li>Research Question </li></ul><ul><li>Hypotheses </li></ul><ul><li>Data definitions </li></ul><ul><li>Data collection </li></ul><ul><li>Data analysis </li></ul><ul><li>Conclusions </li></ul>
  7. 7. Research Question <ul><ul><li>What is the study designed to answer? </li></ul></ul><ul><ul><li>Why is the study important? </li></ul></ul><ul><ul><li>The more specific, the better! </li></ul></ul><ul><ul><li>Example: Should the library increase hours during finals week? </li></ul></ul>
  8. 8. Hypothesis <ul><ul><li>A statement about the expected results. </li></ul></ul><ul><ul><li>What you will test after collecting data. </li></ul></ul><ul><ul><li>Null Hypothesis , that there is no difference between Group 1 & Group 2 or Before/After. Notated H o = H a </li></ul></ul><ul><ul><li>Alternate Hypothesis , that there is a difference and what that difference will be. Notated H o ≠ H a </li></ul></ul><ul><ul><li>Can also be directional if theory or prior research indicates : H o > H a </li></ul></ul>
  9. 9. Data collection <ul><ul><li>Observation </li></ul></ul><ul><ul><li>Interviews </li></ul></ul><ul><ul><li>Focus Groups </li></ul></ul><ul><ul><li>Surveys </li></ul></ul><ul><ul><li>Transaction Logs </li></ul></ul><ul><ul><li>Others? </li></ul></ul>
  10. 10. Data Definitions <ul><li>Data Scales </li></ul><ul><ul><li>Nominal </li></ul></ul><ul><ul><li>Ordinal </li></ul></ul><ul><ul><li>Interval </li></ul></ul><ul><ul><li>Ratio </li></ul></ul><ul><li>Frequency Distributions </li></ul><ul><ul><li>Flat </li></ul></ul><ul><ul><li>Normal </li></ul></ul><ul><ul><li>Skewed </li></ul></ul><ul><li>Variable Types </li></ul><ul><ul><li>Dependent </li></ul></ul><ul><ul><li>Independent </li></ul></ul><ul><ul><li>Extraneous </li></ul></ul>
  11. 11. Data Scales <ul><li>Nominal : scaled without order, indicating that classifications are different. Example : Public & private institutions. </li></ul><ul><li>Ordinal : scaled with order, but without distance between values. Example : Carnegie classifications </li></ul><ul><li>Interval : scaled with order and establishes numerically equal distances on the scale. Example : Patron classification (freshman, sophomore, etc.) </li></ul><ul><li>Ratio : scaled with equal intervals and a zero starting point. Example : Fulltext downloads. </li></ul><ul><li>Nominal or ordinal variables are discrete , while interval and ratio variables are continuous </li></ul>
  12. 12. Data Distributions <ul><li>Described by their kurtosis (variability) and skew (extremes) </li></ul>Non-normal (skewed): extreme values with steep slopes Normal : bell shaped curve with gradual slopes
  13. 13. Variables <ul><li>Dependent: the variable being measured, studied, and predicted. </li></ul><ul><li>Independent : variables that can be manipulated or theorized to be predictors of the dependent variable. </li></ul><ul><li>Extraneous : variables other than the independent variables that can influence the dependent variable. </li></ul>
  14. 14. Data analysis <ul><ul><li>Descriptive statistics </li></ul></ul><ul><ul><ul><li>Mean, Median, Mode </li></ul></ul></ul><ul><ul><ul><li>Standard Deviation </li></ul></ul></ul><ul><ul><li>Correlational statistics </li></ul></ul><ul><ul><ul><li>Correlation </li></ul></ul></ul><ul><ul><li>Inferential statistics </li></ul></ul><ul><ul><ul><li>Chi-square </li></ul></ul></ul><ul><ul><ul><li>Regression </li></ul></ul></ul><ul><ul><ul><li>ANOVA </li></ul></ul></ul>
  15. 15. Review: Research Design <ul><li>Research Question </li></ul><ul><ul><ul><li>What will the study answer? </li></ul></ul></ul><ul><li>Hypotheses </li></ul><ul><ul><ul><li>What do you think the results will be? </li></ul></ul></ul><ul><li>Data definitions </li></ul><ul><ul><ul><li>What scales are the variables, what is the distribution, and what are the dependent, independent & extraneous variables? </li></ul></ul></ul><ul><li>Data collection </li></ul><ul><ul><ul><li>What is the best method for collecting the variables of interest? </li></ul></ul></ul><ul><li>Data analysis </li></ul><ul><ul><ul><li>What are the proper statistical tests to use on the data? </li></ul></ul></ul><ul><li>Conclusions </li></ul><ul><ul><ul><li>What does the data show us or indicate? </li></ul></ul></ul>
  16. 16. Case Studies <ul><li>Citation Analysis </li></ul><ul><ul><li>Antelman, K (2004) “Do Open-Access Articles Have a Greater Research Impact?” College & Research Libraries News 65(5):pp. 372-382 </li></ul></ul><ul><li>Usage Analysis </li></ul><ul><ul><li>Blecic, DD (1999) “Measurements of journal use: an analysis of the correlations between three methods.” Bull Med Libr Assoc 87(1): 20-25. </li></ul></ul><ul><li>Service Analysis </li></ul><ul><ul><li>Nichols, J; Shaffer, B; Shockey, K. (2003). “Changing the Face of Instruction: Is Online or In-class More Effective?” College & Research Libraries , 64:5: 378-389. </li></ul></ul>
  17. 17. “ Changing the Face of Instruction…” Is an online tutorial as effective in teaching library instruction as a classroom setting? H3. Students will report as much or more satisfaction with online instruction as students taking traditional instruction. Research Question Hypotheses H1. Students will have higher scores in information literacy tests after library instruction. H2. Students will have the same or higher scores in info-lit tests after taking online tutorials as students taking traditional instruction.
  18. 18. “ Changing the Face of Instruction…” Variables: Test scores & survey results Data Collection: Pretest/Posttest & Survey Variables & Data Collection Statistical Tests Conclusions Accept H1: Instruction improves literacy. Desc Stats incl. mean, standard deviation, standard error, T-tests (1 & 2 tailed) Accept H3 alternative hypothesis – Student satisfaction is equal with both methods. Accept H2 alternative hypothesis – Online has no significant difference from traditional.
  19. 19. Discussion <ul><li>Questions about developing Research Questions? About Data Definitions, Data Collection, or Data Analysis? </li></ul><ul><li>What Research Questions need to be answered at the College Library? </li></ul><ul><li>Which of these can be analyzed using statistical methods? </li></ul>
  20. 20. My favorite statistic Baseball is 90% mental – the other half is physical.

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