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EvalC3

…tools for developing, exploring and evaluating predictive models of expected outcomes

  • About EvalC3
    • About EvalC3 – the basics
    • Example uses
    • Types of causes
    • Prediction vs explanation
    • Compared to what…?
    • Does EvalC3 use Machine Learning?
    • Contra Regression Analysis
    • Pro and Contra QCA
    • Realist Evaluation and Process Tracing
    • Background reading
    • Origins
    • Short introductory videos
  • 1. Input data
    • 1. Input data – how to
    • 1.1 Usable data
      • 1.1.1 Multiple observations of one case
    • 1.2 Data sets
    • 1.3 Data preparation
      • 1.3.1 Using a Data Analysis Matrix
    • 1.4 Participatory predictive modeling
  • 2. Select data
    • 2. Select data – how to
    • 2.1 Selecting cases
    • 2.2 Selecting attributes and outcomes
  • 3. Design model
    • 3. Design model – how to
    • 3.1 Search options
      • 3.1.1 Search parameters
    • 3.2 Analysis sequence
    • 3.3 Decision Trees
    • 3.4 Solver – a genetic algorithm
  • 4. Evaluate model
    • 4. Evaluate model – how to
    • 4.1 Sensitivity and INUS Analysis
    • 4.2 The adjacent possible
    • 4.3 Context effects (aka Scope condtiions)
    • 4.4 “Boring” versus “interesting” models
    • 4.5 Finding Positive Deviants
    • 4.6 Testing models with new data
  • 5. Compare models
    • 5. Compare models – how to
    • 5.1 Reviewing models
    • 5.2 Mapping a fitness landscape?
  • 6. Select cases
    • 6. Select cases – how to
    • 6.1 Within-case analysis
    • 6.2 Network analysis of cases
  • Obtain EvalC3
    • Obtain EvalC3 – how to
    • Latest news re bugs and new features
    • Feature request
    • Subscribe to the EvalC3 email list
EvalC3

Contact

rick.davies@gmail.com

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Contact Rick Davies

rick.davies@gmail.com

Links

  • 1. Rick Davies – Monitoring and Evaluation Consultant
  • 2. Monitoring and Evaluation NEWS
  • 3. Aptivate
  • 4. Rick Davies on Twitter
  • About EvalC3
    • About EvalC3 – the basics
    • Example uses
    • Types of causes
    • Prediction vs explanation
    • Compared to what…?
    • Does EvalC3 use Machine Learning?
    • Contra Regression Analysis
    • Pro and Contra QCA
    • Realist Evaluation and Process Tracing
    • Background reading
    • Origins
    • Short introductory videos
  • 1. Input data
    • 1. Input data – how to
    • 1.1 Usable data
      • 1.1.1 Multiple observations of one case
    • 1.2 Data sets
    • 1.3 Data preparation
      • 1.3.1 Using a Data Analysis Matrix
    • 1.4 Participatory predictive modeling
  • 2. Select data
    • 2. Select data – how to
    • 2.1 Selecting cases
    • 2.2 Selecting attributes and outcomes
  • 3. Design model
    • 3. Design model – how to
    • 3.1 Search options
      • 3.1.1 Search parameters
    • 3.2 Analysis sequence
    • 3.3 Decision Trees
    • 3.4 Solver – a genetic algorithm
  • 4. Evaluate model
    • 4. Evaluate model – how to
    • 4.1 Sensitivity and INUS Analysis
    • 4.2 The adjacent possible
    • 4.3 Context effects (aka Scope condtiions)
    • 4.4 “Boring” versus “interesting” models
    • 4.5 Finding Positive Deviants
    • 4.6 Testing models with new data
  • 5. Compare models
    • 5. Compare models – how to
    • 5.1 Reviewing models
    • 5.2 Mapping a fitness landscape?
  • 6. Select cases
    • 6. Select cases – how to
    • 6.1 Within-case analysis
    • 6.2 Network analysis of cases
  • Obtain EvalC3
    • Obtain EvalC3 – how to
    • Latest news re bugs and new features
    • Feature request
    • Subscribe to the EvalC3 email list
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