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Factor Analysis

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Table of Contents

01

What is Factor Analysis?

Factor analysis is a tool used to condense a large amount of data including many variables into a much smaller number of variables, or factors, in order to help study them. It is used to investigate variable relationships for complex concepts, as it allows researchers to collapse many variables into a few interpretable factors.Principal Component Analysis, or PCA, is the most common method of factor analysis.

Factor Analysis Factor Analysis

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02

How to use Factor Analysis to Simplify Research Findings

When you ask respondents questions that are all different, but regarding the same topic, you may want to represent all these questions under one variable. This can be done by finding an average between the responses of all related questions, or by creating a factor dependent variable.

To create a factor dependent variable, principal component analysis can be used by keeping the first principal component. As PCA automatically weights each variable in the calculation, this method can give more reliable results than the averages could. These weights are known as “factor loadings” and they correspond to the relationship of each variable to the underlying factor. 

Factor analysis can help researchers in many useful ways:

  1. Clubbing and studying clusters of responses
  2. Condensing factors (variables)
  3. Evaluating psychometric qualities
  4. Assessing the dimensionality of a set of variables 
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03

Factor Loadings

Some variables have a stronger association to the underlying latent variable than others, and this is reflected by “weights” known as factor loadings. 

For instance, if we were conducting a simple factor analysis to understand the indicators of wealth, we may use the following three variables with two resulting factors:

Variables

Factor 1

Factor 2

Income

0.70

0.15

Education 

0.62

0.23

House Value

0.40

0.20

The table depicts that the strongest association between factor 1 and an underlying variable is the association with income, which has a factor loading of 0.7.

Factor loadings may also be interpreted as standardized regression coefficients. Hence, it could also be said that variable income has a correlation of 0.7 with factor 1.

Factor Analysis Factor Analysis
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04

When is Factor Analysis Used

Factor analysis works well on surveys that employ the use of likert scales in obtaining responses and sum to 100 question types. Deciding which variables to collapse takes experimentation and creativity. 

These are a few question genres that can benefit from the use of factor analysis:

  • Behavioral (Agree/Disagree)

Questions inquiring about human behavior and how humans make decisions in the real-world. For example, “I often prioritise price over quality while purchasing a product”.

  • Attitudinal (Agree/Disagree)

Questions inquiring about consumers’ attitudes toward a product/brand based on their knowledge and impressions of it. For example, “I am pleased with the product’s performance”.

  • Psychographic (Agree/Disagree)

Questions inquiring about people’s values, desires, goals, and interests. For example, “I believe supporting a brand inevitably means supporting certain values”.

  • Activity-Based (Agree/Disagree)

Questions inquiring about activities consumers participate in. For example, “I love shopping on the weekends”.

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05

FAQs on Factor Analysis

Answer: There are three main types of factor analysis used in different kinds of market research, and they are:

  1. Exploratory Factor Analysis: Used to measure underlying factors that have an association with variables within the data structure without setting a predefined structure to the outcome.
  2. Structural Equation Modelling: Involves hypothesising the relationship between a set of variables and factors. It then tests these relationships using a linear equation model.
  3. Confirmatory Factor Analysis: Used as a tool to reconfirm the relationship and effects of an existing set of predetermined factors and the variables that affect it.

Answer: Factor analysis involves reducing the number of variables into a smaller set of factors. Cluster analysis, on the other hand, has to do with reducing the number of observations by grouping them into smaller sets, or clusters. Factor analysis and cluster analysis, both, make no distinction between independent and dependent variables.

Answer: Factor analysis is most commonly used in psychometrics, marketing, biology, operations research, and product management. It is a useful method of analysis when a research involves data sets with a large number of observed variables that reflect a smaller number of underlying/latent variables.

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