Factor Analysis vs Path Analysis – The Difference

Factor analysis and path analysis are both statistical techniques used in the field of multivariate analysis, particularly in the context of structural equation modeling (SEM). While they share some similarities, they serve different purposes and have distinct methodologies.

  1. Factor Analysis:
    • Purpose: Factor analysis is primarily used to identify underlying factors or latent variables that explain patterns of correlations among observed variables. It aims to reduce the dimensionality of the data by extracting a smaller number of factors that capture the common variance among the observed variables.
    • Methodology: Factor analysis begins by analyzing the correlation matrix of the observed variables. It then employs techniques like principal component analysis (PCA) or maximum likelihood estimation to extract factors and estimate factor loadings, which represent the relationships between the observed variables and the underlying factors. Factor analysis does not specify a causal relationship between variables.


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  1. Path Analysis:
    • Purpose: Path analysis, on the other hand, is used to explore the relationships between variables in a causal model. It allows researchers to test hypothesized causal relationships among variables by specifying a directed graph of relationships, known as a path diagram.
    • Methodology: In path analysis, variables are represented as nodes in a graph, and directional paths represent hypothesized causal relationships between variables. Path coefficients are estimated to quantify the strength and direction of these relationships. Path analysis can also incorporate latent variables (factors) into the model, allowing for the examination of both direct and indirect effects.

Key Differences:

  • Purpose: Factor analysis aims to identify underlying factors that explain patterns of correlations, while path analysis focuses on testing causal relationships between variables.
  • Methodology: Factor analysis extracts latent variables from observed variables based on patterns of correlations, while path analysis estimates causal relationships specified in a directed graph.
  • Interpretation: Factor analysis results in factor loadings, which represent the relationships between observed variables and latent factors. Path analysis results in path coefficients, which represent the direct effects of variables on one another within a causal model.
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