Practical Evaluation II: Advanced Research Tools and Techniques
(EVALA, Live Instructor-Led Training, 3 days)


Description

Building on the Practical Evaluation I, this course delves further into evaluation research designs for real-world program evaluation. Using case studies to push your research prowess, this course covers a range of evaluation study design, research methods, and qualitative and quantitative tools and analysis.

Dates and Pricing


Jan 24 to Jan 26, 2018$1,199/person Feb 21 to Feb 23, 2018$1,199/person Mar 21 to Mar 23, 2018$1,199/person

Outline

Introduction and Overview
Review of evaluation fundamentals
Importance of context
Values and ethics
Elements of an Evaluation Framework (Review)
Evaluation frameworks - various fields, contexts
Developing S.M.A.R.T outcomes, indicators, measures and metrics
Criteria for choosing an evaluation design
Criteria for choosing evaluation methods
Exploratory Designs
What are exploratory designs
Examples of exploratory designs
Strengths and limitations
Descriptive Designs
What are descriptive designs
Examples of descriptive designs
Strengths and limitations
Alternative designs
What are alternative design evaluations
Examples of descriptive designs
Strengths and limitations
Experimental and Quasi-Experimental Designs?
Experimental and Quasi-Experimental Designs?
The regression framework for impact analysis
The regression-discontinuity design
The comparative change design
The criterion population design
Time-Series designs
Research Methods in Evaluation
Numerical data (demographic, financial, assessments)
Observation and immersion
Interviews
Surveys
Focus groups
Content analysis of visual and textual materials
Oral history
Data Analysis
Mapping data to evaluation questions and study design
Quantitative, qualitative and mixed methods
Data Management Tools
Correlation versus causation
Data checking - bias, error, relevance, reliability, validity
Data visualization and presentation
Data Analysis - Quantitative
Types of quantitative analysis - univariate, bivariate, multivariate, inferential statistical analysis, descriptive statistical analysis, etc.
Strengths and limitations of quantitative data
How to do quantitative analysis - preparation, variable types, process, tools
Interpreting findings
Data Analysis - Qualitative
Types of qualitative analysis - narrative, content, netnography, grounded theory, ethnomethodology, etc.
Strengths and limitations of qualitative data
How to do qualitative analysis - process, tools, templates
Quantifying qualitative data
Interpreting findings