Data Analysis
We help turn raw data into meaningful insights that support further assessment, evaluation and research. The quality of the insights gathered relies on strong execution of defining the purpose of the study, designing an appropriate data collection strategy and methodology planning.
How We Analyze Data
1. Assess the Goals
Define the context, type of data, and goals of the analysis.
2. Clean the Data
Apply standardization, profiling, deduplication, record matching and schema matching techniques.
3. Choose the Best Approach
Decide whether a qualitative or quantitative analysis is best fit based on the data type.
Types of Analyses
Qualitative Analysis
For use with non-numeric data.
Steps: Identify key quotes, group into themes, then build narratives.
Examples: Thematic analysis, case studies and ethnography.
Quantitative Analysis
For use with numeric data.
Data Types: Categorical (labels), Ordinal (ranked), Discrete (counts) or Continuous (measurements).
Common Methods: Descriptive Statistics, Correlation Analysis, Regression Analysis, Hypothesis Testing or Time Series Analysis.
Examples: Program participation and engagement metrics, predictive modeling for student success and enrollment and service utilization trend analysis.