What is Confirmatory Factor Analysis (CFA)?
Confirmatory Factor Analysis (CFA) is a statistical method for testing whether a set of survey questions measures the theoretical constructs you expect. It checks if responses fit a predefined structure of underlying factors (latent variables).
CFA tests whether the pattern of responses to a group of questionnaire items matches a specific measurement model you propose. In plain terms: you decide in advance which questions belong together to measure the same idea (for example, ‘safety’ and ‘satisfaction’), and CFA tells you how well the data support that arrangement. It uses correlations among items to estimate how strongly each question relates to its intended factor and evaluates overall model fit with standard statistics. CFA differs from exploratory methods because it starts with a defined structure rather than searching for patterns without prior expectations.
Usage example
A school runs a parent engagement survey with 12 questions intended to measure two constructs: ‘communication clarity’ and ‘school support’. Before using the survey results to compare programs, the research lead runs a CFA to confirm the 12 items actually cluster into those two factors. If the CFA shows a good fit, the team can summarize and compare scores for each construct with more confidence.
Practical application
CFA matters because it gives confidence that survey scales measure what you think they measure. For organisations running multilingual or multi-group surveys (different languages, schools, or neighbourhoods), CFA is a key step to: - Verify that items form coherent scales before creating summary scores or reports - Identify poorly performing questions to revise or remove - Check whether the same measurement structure holds across groups (an essential step before comparing scores across languages or communities) In short, CFA helps ensure analysis and decisions based on survey scales are valid and defensible.
FAQ
How is CFA different from Exploratory Factor Analysis (EFA)?
EFA looks for patterns in the data without preconceptions; it helps discover possible factor structures. CFA, by contrast, starts with a hypothesis about which items load onto which factors and tests that hypothesis. Use EFA for scale development and CFA to confirm a proposed structure.
Do I need a large sample to run CFA?
CFA is more stable with larger samples. A common rule of thumb is at least 5–10 respondents per estimated parameter or a minimum of a few hundred respondents, but exact needs depend on model complexity, number of items, and item distributions. Simpler models and high-quality items need fewer responses.
What if I want to compare results across languages or groups?
Before comparing group scores, run CFA and then test measurement invariance — a sequence of checks that show whether the factor structure, item loadings and item intercepts are equivalent across groups (for example, English vs. Arabic). If invariance holds, score comparisons are more trustworthy; if not, you may need to adjust items or use group-specific analyses.
Do I need specialist software or expertise to run CFA?
CFA requires statistical software (e.g., R, Mplus, Amos, or Stata) and some methodological knowledge to specify models and interpret fit indices. Many teams work with a researcher or analyst for the first runs; once set up, the findings can inform straightforward survey revisions and reporting.