An Explanation of Statistical Tools from DocumentingExcellence.com A consulting practice focusing on working with colleges', organizations', and individuals' utilization of quantitative and qualitative assessment tools to analyze and document their quality outcomes through providing staff development, research design and analysis, and psychometric evaluations.
Statistical Tools TOC Conceptual Issues Measurment Issues Conceptual focus Measurement Focus Effectivenes Evaluating - Single variable Descriptive statistics Validity, Reliabiltiy, Cause & Effect Sound Reasoning Cause and Effect Reporting Modeling Correlation Regression & Correlation Statistical Control Unemployment & Crime Model Reduction Outcomes Assessment FAQ Consulting Services

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Introduction to Statistical Tools

At some point most quantitative assessments use statistical tools.  When the word "statistics" is mentioned many people have one of two reactions.  "That was the best class I ever had!" or "That is the course that almost ended my college studies!"

Understanding and evaluation of many assessment efforts require some use of statistics.  So, I've written a brief primer describing the central ideas we use in examining data.  Since the application of statistics can become complex, and computers now make the calculations accessible, I've tried to explain each concept in terms of what is measured and how to use the statistical findings.

The following pages are organized first into a section that focuses on considering a single variable (evaluation), a second section that focuses on relationships between two or more variables (modeling), and thirdly an overview of outcomes assessment processes.

1. Analytical procedures
   1. Examination of conceptual issues:
      1. Competency and testing
      2. Learning domains
      3. Item construction
      4. Item assessment
      5. Scale construction
         1. Item Coherence with a scale
   2. Measurement issues
      1. Focus on measurement scales' characteristics
         1. Dichotomies
         2. Nominal categories
         3. Ordinal, ranks
         4. Interval scales
         5. Ratio scales
      2. Characteristics of measurement processes
      3. Distribution of data
         1. Normal distribution
         2. Skewed distributions
         3. Uniform distributions
         4. Bi-modal distributions
   3. Evaluating single variable data
      1. Descriptive statistics
         1. Averages
         2. Distribution
         3. Variance
         4. Standard deviation
         5. Standard error
         6. Z-score
      2. Criteria for evaluating measurement scale data
         1. Reliability
         2. Validity
         3. Clarifying reliability and validity
   4. Reporting assessment findings
      1. Outline of a report to an instrument developer
      2. Outline of a report for public release
2. Finding differences between classifications of cases
3. Modeling relationships
   1. Correlation
   2. Regression and Correlation
   3. Control for external variables
   4. Statistical controls
   5. Cause and effect
   6. Example of why control of extranious variables is important: Unemployment and Crime
   7. Model reduction
      1. Reduced Math model
      2. Reduced Scientific thinkng model
      3. Reduced Critical thinking model
      4. Reduced Writing model
      5. Reduced College Reading model
4. Outcomes assessment procedures
   1. General education assessments
      1. Using Standarized Tests for Assessment of General Educaiton outcomes
      2. Using a Rubric for asssessmen of General Education outcomes
   2. Discipline assessments
   3. Classroom assessments
5. FAQ
6. Glossary

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Copyright © 2012 by Peter T. Klassen, Ph.D. Principal, www.DocumentingExcellence.com
2 March, 2012