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665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
665 Sessions13-14-stats data vis-s13
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665 Sessions13-14-stats data vis-s13

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Spring 2013 …

Spring 2013
Dr. Diane Nahl
LIS 665 Teaching Information Technology Literacy University of Hawaii
LIS Program

Published in: Education, Technology
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  • 1. Data Analysis & Data VisualizationSpring 2013| Nahl | LIS 665 Teaching Information Technology Literacy University of Hawaii | LIS Program
  • 2. Assessment Cycle Gather Data Implement Analyze & the Plan Interpret Share Data Plan for & Plan ImprovementRadcliff et al. p. 171 Nahl 2013 LIS 665 Teaching Information Technology Literacy 2
  • 3. Tables & Figures: Naming, Explaining, and Interpreting ResultsNahl 2013 LIS 665 Teaching Information Technology Literacy 3
  • 4. Figure 1. Demographics: Types of LibrariesNahl 2013 LIS 665 Teaching Information Technology Literacy 4
  • 5. Figure 2. Demographics: Library DepartmentsNahl 2013 LIS 665 Teaching Information Technology Literacy 5
  • 6. Data Presentation Exercise: Write a Complete Title for the Figure and Labels for the each pre-post bar setNahl 2013 LIS 665 Teaching Information Technology Literacy 6
  • 7. Data Presentation Exercise: Write a Complete Title for the Table and a Sentence about what these results meanNahl 2013 LIS 665 Teaching Information Technology Literacy 7
  • 8. IV & DV Defined  Independent Variable(s)  Treatment or Intervention or Condition  Random Selection & Random Assignment  Dependent Variable(s) or Dependent Measure(s)  Measuring instrument (worksheet, clicker questions, pre-post test, single test, open-ended instrument, e.g., Muddiest point, Minute paper, session evaluation comments, etc.)  Outcome, results, data (their ratings or scores or frequencies or other result types)Nahl 2013 LIS 665 Teaching Information Technology Literacy 8
  • 9. Exercise: Identifying Independent and Dependent Variables in Research Article Titles Evaluation of the Effectiveness of Two Library Instructional Videotapes IV: Library videotape 1, Library videotape 2DV: Effectiveness [evaluated or measured in some way]Nahl 2013 LIS 665 Teaching Information Technology Literacy 9
  • 10. What is being measured? Library Jargon: Student Comprehension of Technical Language Used by Librarians IV: DV:Nahl 2013 LIS 665 Teaching Information Technology Literacy 10
  • 11. What is being done? What is being measured? Information Literacy Skills: An Exploratory Focus Group Study of Student Perceptions IV: DV:Nahl 2013 LIS 665 Teaching Information Technology Literacy 11
  • 12. What is being changed? What is being measured? A Comparison of Presentation Formats for Instruction: Teaching First-Year Students IV: DV:Nahl 2013 LIS 665 Teaching Information Technology Literacy 12
  • 13. What is being altered? What is being measured? A Longitudinal Study of the Effects of OPAC Screen Changes on Searching Behavior and Searcher Success IV: DV:Nahl 2013 LIS 665 Teaching Information Technology Literacy 13
  • 14. What is being varied? What is being measured? Programmatic Assessment: Turning Process Into Practice by Teaching for Learning IV: DV:Nahl 2013 LIS 665 Teaching Information Technology Literacy 14
  • 15. Statistical Significance Defined  Only relevant in Experimental Designs (IV DV)  Statistical Significance: p value <.05  In 100 different samples, 95% will have the same mean or average, and only 5% of the samples will have different means  Systematic or consistent results, confidence levels  Significant results vs. Important results (size of the difference, or size of the correlation)  Size of the sample (n = 30 minimum, more depending on type of study and population size)  Allows generalizing results to a population (as defined in the study)Nahl 2013 LIS 665 Teaching Information Technology Literacy 15
  • 16. Correlation Defined Relationship between two variables  Not causal (X does not cause Y) but related, multivariate, multifactorial, co-occurrence, identify trends  Intervening or confounding variables  Direction [+ positive (up) or - negative (down)]  Square the correlation (r2) to get its size in Percent (%)  Magnitude (size of the correlation)  r = .4 to .6 moderate [16% - 36% due to the X/Y variables, 84% - 64% due to other unidentified variables]  r = .6 to .8 strong  r = .9 near perfect & 1.0 perfect  Significance (p value <.05) permits applying results from one sample to the entire population (as defined in the study)Nahl 2013 LIS 665 Teaching Information Technology Literacy 16
  • 17. Positive Correlation Figure 1. Correlation for Computer Literacy and Search SuccessNahl 2013 LIS 665 Teaching Information Technology Literacy 17
  • 18. Negative Correlation Figure 2. Correlation for Computer Anxiety and Search SuccessNahl 2013 LIS 665 Teaching Information Technology Literacy 18
  • 19. Correlation MatrixTable 1. Correlations for Computer Literacy, Computer Anxiety, Visual Acuity, and Search Success Computer Search Computer Visual Literacy Success Anxiety Acuity Computer 1.0 Literacy Search Success .87 1.0 Computer -.95 -.83 1.0 Anxiety Visual Acuity .08 .20 -.09 1.0 Nahl 2013 LIS 665 Teaching Information Technology Literacy 19
  • 20. Correlation Results Table 2. Correlation for Computer Literacy and Search Success Literacy and correlation count Z-Test P-Value Success .807 75 9.491 <.0001Significant correlations (p = <.05) suggest the results are systematicand apply to the whole population (as defined in the study)P < .0001 = 1 sample in 10,000 samples will have a different correlationP < .001 = 1 sample in 1,000 samples will have a different correlationP < .01 = 1 sample in 100 samples will have a different correlationNahl 2013 LIS 665 Teaching Information Technology Literacy 20
  • 21. Next WeekNahl 2013 LIS 665 Teaching Information Technology Literacy 21

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