EFA vs. ENA

Attribute	EFA	ENA
Definition	Exploratory Factor Analysis	Exploratory Network Analysis
Focus	Identifying underlying factors that explain patterns of correlations among observed variables	Examining relationships and interactions among variables in a network structure
Methodology	Factor analysis	Network analysis
Goal	To reduce data complexity and identify latent variables	To understand the structure and dynamics of relationships among variables
Assumptions	Assumes that observed variables are influenced by underlying latent factors	Does not assume a causal relationship between variables

Introduction

Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are two widely used statistical techniques in the field of psychology and other social sciences. Both methods are used to analyze the relationships between observed variables and underlying latent constructs. While EFA is used to explore the underlying structure of a set of variables, CFA is used to test a specific hypothesis about the structure of the variables. In this article, we will compare the attributes of EFA and CFA to understand their similarities and differences.

Similarities

Both EFA and CFA are factor analysis techniques that aim to identify the underlying structure of a set of variables. They both involve the use of statistical models to analyze the relationships between observed variables and latent constructs. Additionally, both techniques are used to reduce the dimensionality of a dataset by identifying the most important factors that explain the variance in the data. Furthermore, both EFA and CFA can be used to assess the reliability and validity of a measurement instrument.

Differences in Purpose

One of the key differences between EFA and CFA is their purpose. EFA is used when the researcher does not have a specific hypothesis about the underlying structure of the variables and wants to explore the data to identify the most important factors. In contrast, CFA is used when the researcher has a specific hypothesis about the relationships between the variables and wants to test this hypothesis using a confirmatory approach. Therefore, EFA is more exploratory in nature, while CFA is more confirmatory.

Data Requirements

Another difference between EFA and CFA lies in their data requirements. EFA can be used with any type of data, including continuous, ordinal, and categorical variables. It is also suitable for small sample sizes and non-normal data. On the other hand, CFA requires larger sample sizes and assumes that the data are normally distributed. Additionally, CFA is typically used with continuous variables and may not be suitable for categorical data.

Model Specification

When it comes to model specification, EFA is more flexible compared to CFA. In EFA, the researcher does not need to specify the relationships between the observed variables and latent constructs in advance. Instead, the model is allowed to emerge from the data. In contrast, CFA requires the researcher to specify a priori the relationships between the variables based on theory or previous research. This makes CFA more rigid in terms of model specification compared to EFA.

Interpretation of Results

The interpretation of results also differs between EFA and CFA. In EFA, the researcher examines the factor loadings of the observed variables on the latent factors to understand the underlying structure of the data. The researcher may also look at the communalities and eigenvalues to assess the goodness of fit of the model. In CFA, the focus is on evaluating the fit of the hypothesized model to the data using fit indices such as chi-square, RMSEA, CFI, and TLI. The researcher may also examine modification indices to improve the model fit.

Conclusion

In conclusion, EFA and CFA are both valuable tools in the field of factor analysis, each with its own strengths and limitations. While EFA is more exploratory and flexible, CFA is more confirmatory and rigid. The choice between EFA and CFA depends on the research question, data characteristics, and the level of prior knowledge about the underlying structure of the variables. Researchers should carefully consider these factors when deciding which technique to use in their analysis.