Factor Analysis: Exploring Underlying Constructs in Test Data

Purpose and Applications of Factor Analysis

The primary goal of factor analysis is to identify how different observed variables relate and group into broader, unobserved constructs. For instance, in psychology, a test assessing multiple cognitive skills might have individual items clustered into broader abilities like memory or spatial reasoning.

This technique is widely used in psychometric testing to identify traits like intelligence, in survey analysis to reduce questions to key dimensions, and in behavioral research to reveal patterns in job performance, learning, or preferences.

Types of Factor Analysis

There are two primary types of factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA helps discover potential relationships between variables, while CFA tests hypotheses about predefined relationships between variables and factors.

EFA involves factor extraction and rotation, while CFA uses fit indices like chi-square and CFI to evaluate how well a model fits the data. Researchers use these methods depending on whether they are exploring or confirming theories.

Key Concepts in Factor Analysis

Several core concepts are central to factor analysis, including:

• Factor Loadings: The correlation between each variable and the underlying factor, indicating how well a variable represents a factor.
• Eigenvalues: Represent the variance explained by each factor, with factors having eigenvalues over 1 usually retained for analysis.
• Scree Plot: A graph used to determine the number of factors to retain based on where the plot levels off, known as the “elbow.”
• Communalities: The proportion of variance in each variable explained by the factors.

Assumptions of Factor Analysis

Factor analysis relies on several assumptions for valid results:

• Linearity: The relationships between variables should be linear, as the method relies on correlations.
• Adequate Sample Size: A large sample size is typically required, at least five to ten times the number of variables.
• Multicollinearity: Variables should show moderate correlations to avoid distorting the results.
• Normality: While factor analysis can handle some deviation from normality, extreme violations can affect accuracy.

Steps in Conducting Factor Analysis

Conducting factor analysis follows these steps:

1. Data Collection and Preparation: Gather and clean the dataset, ensuring it is ready for analysis.
2. Correlation Matrix: Construct a correlation matrix to understand the relationships between variables.
3. Factor Extraction: Use methods like Principal Axis Factoring to extract factors.
4. Factor Rotation: Apply a rotation method for better interpretability of the factors.
5. Interpretation of Results: Evaluate factor loadings and communalities to name and understand each factor.
6. Validity and Reliability Testing: Assess reliability through measures like Cronbach’s alpha and fit indices.

Limitations of Factor Analysis

While powerful, factor analysis has limitations, such as:

• Subjectivity in Interpretation: Naming and interpreting factors can introduce bias, relying on the researcher’s judgment.
• Sample Size Dependency: Small sample sizes can lead to unstable factor solutions and reduced generalizability.
• Model Fit in CFA: Confirmatory factor analysis models can sometimes overfit, reducing their applicability to other datasets.