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Exploratory Factor Analysis (EFA) Vs Confirmatory Factor Analysis (CFA) – The Difference

Exploratory Factor Analysis (EFA) Vs Confirmatory Factor Analysis (CFA) – The Difference

Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) are both statistical techniques used in the field of psychometrics and social sciences to analyze the underlying structure of a set of observed variables. They are commonly employed in fields like psychology, sociology, education, and marketing research.

Exploratory Factor Analysis (EFA):

Objective:

  1. Identify underlying factors: EFA is used when researchers want to explore and identify the latent (unobservable) factors that may be influencing a set of observed variables. It helps in understanding the underlying structure or patterns in the data.
  2. Data reduction: EFA can be used to reduce a large number of variables to a smaller set of factors, making it easier to interpret and work with the data. This is particularly useful when dealing with complex datasets.
  3. Hypothesis generation: EFA is often used in the initial stages of research to generate hypotheses about the relationships between variables and to inform the development of theoretical frameworks.

 

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Confirmatory Factor Analysis (CFA):

Objective:

  1. Model testing and validation: CFA is used to test and confirm a hypothesized factor structure that is derived from theory or previous research. It is used to validate whether the observed variables align with the proposed latent factors.
  2. Measurement validation: CFA is employed to assess the validity and reliability of measurement instruments. Researchers use CFA to confirm that the observed variables are indeed measuring the latent constructs they are intended to measure.
  3. Comparative analysis: CFA allows for the comparison of different models to determine which one fits the data best. This can involve comparing alternative factor structures or assessing whether a model fits different subgroups within a sample.

Application:

Exploratory Factor Analysis:

  • Steps: EFA involves analyzing the correlation matrix of observed variables to identify common factors. Rotation methods (varimax, oblimin, etc.) are often applied to aid interpretation.
  • Software: Commonly used statistical software for EFA includes SPSS, R, and SAS.

Confirmatory Factor Analysis:

  • Model Specification: CFA requires a predefined model specifying the relationships between observed variables and latent factors. This is usually based on theoretical or empirical grounds.
  • Goodness of Fit Indices: CFA utilizes various fit indices (e.g., chi-square, CFI, RMSEA) to assess how well the specified model fits the observed data.
  • Software: Popular software for CFA includes AMOS, Mplus, and lavaan in R.

Summary:

In summary, EFA is used for exploring and uncovering the underlying structure of data, while CFA is employed to confirm or validate a pre-specified factor structure. Both techniques play crucial roles in psychometric research, helping researchers understand the relationships among variables and ensuring the validity of measurement instruments.

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