Factor analysis is a statistical technique that aims to identify underlying factors or dimensions that explain the correlations among a set of observed variables. The technique is often used in market research to explore the relationships between various attributes or characteristics of a product, service, or brand and to identify the underlying factors that influence consumer behaviour.
In factor analysis, the observed variables are first measured and collected through surveys, questionnaires, or other data collection methods. The variables can be quantitative, such as ratings on a Likert scale, or qualitative, such as responses to open-ended questions. The variables are then analyzed using mathematical algorithms to identify the factors that explain their correlation patterns.
There are two main types of factor analysis: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is used to identify the underlying factors that explain the correlations among the observed variables without any prior hypothesis about the structure of the factors. Conversely, CFA is used to test a specific hypothesis about the underlying factor structure based on prior theory or research.
The process of factor analysis involves several steps. First, the researcher decides on the number of factors to extract based on statistical criteria such as eigenvalues or scree plots. Second, the researcher decides on the rotation method to maximise the factors’ interpretability. Third, the researcher interprets the factors based on the pattern of loadings or correlations among the observed variables.
Factor analysis has several benefits for market research. It can help reduce the complexity of a large dataset by identifying underlying factors that explain the patterns of correlation among the observed variables. It can also help identify the key drivers of consumer behaviour and preferences, informing marketing strategies and product design. Additionally, factor analysis can help identify redundant or irrelevant variables, improving the efficiency and accuracy of data collection.
However, factor analysis also has several limitations and assumptions that must be considered. One assumption is that the observed variables are correlated with the underlying factors and that the correlations are linear. Another assumption is that the factors are independent of each other. Violations of these assumptions can lead to inaccurate or misleading results. Additionally, factor analysis is sensitive to sample size and may not be appropriate for small datasets.