Topic Hub

Analysis

Quantitative and qualitative analysis for M&E programs. Cleaning, coding, descriptive and inferential statistics, thematic analysis, mixed methods, and how to turn collected data into defensible findings.

Reference entries

Decision guides

11 entries3 guides

How Do I Choose?· 3

Side-by-side comparisons, decision trees, and practical guidance for common M&E decisions.

How Do I Choose?

Side-by-side comparisons, decision trees, and practical guidance for common M&E decisions.

How to Clean Your Dataset Before Analysis

A step-by-step checklist for cleaning M&E data after collection. Duplicate detection, outlier identification, skip logic validation, consistency checks, and cleaning log documentation.

How to Choose

Qualitative vs Quantitative vs Mixed Methods

Qualitative, quantitative, and mixed methods are not a quality ranking. They answer different questions. Here's when to use each, how to combine them, and what integration actually looks like.

Comparison

Surveys vs Interviews vs Focus Groups

The three most common M&E data collection methods, compared. Surveys tell you how many, interviews tell you why, focus groups tell you what people agree on.

Comparison

Reference Library· 11

Overviews (1)

Data Management

The systematic processes for collecting, storing, securing, and maintaining data quality throughout the data lifecycle to ensure information is accurate, accessible, and usable for decision-making.

Quick Reference (10)

Bias Causal Inference Confounding Variables Content Analysis Counterfactual Qualitative Data Quantitative Data Reliability Triangulation Validity (Internal & External)

Explore Other Topics

Evaluation

Design, commission, and manage evaluations

Design

Theories of change, logframes, MEL plans, proposals, and design artifacts

Data Collection & Quality

Methods, tools, DQAs, cleaning, and validation for field data

Indicators

Select, design, track, and report on indicators

Sampling

Sample size, sampling methods, design effect, and common mistakes