Factor Analysis – The Comprehensive Guide
Factor analysis is a statistical approach that divides a group of variables into fewer components by identifying all of their similarities. It’s also known as data reduction.
Numerous similar patterns may be seen while examining a large number of variables; these are called factors. These may be used for further analysis and act as an index of all the relevant variables.
Many assumptions are made while using factor analysis.
- The linear connections of the variables
- Not having multicollinearity
- Adaptability of the variables
- It’s critical that factors and variables have a real association.
As a result, it turns into a statistical method for determining if a group has a common variance. While it is mostly used in psychological research, it may also be used in fields like business and market studies to comprehend employee or consumer work satisfaction as well as in finance to examine stock price volatility.
Factor analysis characteristics
A researcher would often use a survey to ask several questions about a product while researching consumer satisfaction linked to it. These inquiries will include aspects of the product’s attributes like its usage, cost, aesthetic appeal, and convenience of purchase, among others.
Usually, they are measured using a numeric scale. However, a researcher is interested in the fundamental parameters, or “factors,” that influence consumer happiness. The majority of them are immeasurable psychological or emotional aspects of the product. The variables from the survey are used in factor analysis to identify them subtly.
Certain variables are reduced to one or more factors when a researcher makes these assumptions from the survey. The following techniques might be used to retrieve these factors:
1. Principal Components Analysis
This approach is the most popular one. The highest variance is calculated in order to extract the first element. The factor is then added in lieu of the variance. The next biggest variance then determines the second component, and so on until there are no more variances.
2. Standard Factor Analysis
This technique does not contain the unique variances of each variable; instead, the factors are derived from frequently occurring variances.
3. Image Factoring
This approach employs predicted variables produced by the OLS regression technique based on the correlation matrix.
After the variables are retrieved, it is believed that the questionnaire’s score is linearly connected to the components. Along with every other element in the calculation, the margin of error is also taken into account.
Factor Analysis Types
In essence, there are two categories of:
1. Exploratory Factor Analysis
In this kind, no presumptions are made regarding the historical connections between the components. Any variable may be connected to any factor using this approach. This makes it easier to spot complicated correlations between variables and to classify them according to shared traits.
2. Confirmatory Factor Analysis
On the other hand, this kind employs known theories to support its predictions of the model and predicts that variables are associated with certain factors.
Assumptions for Factor Analysis
Several assumptions are made to get the results.
- The data won’t include any outliers.
- The sample will be larger than the factor’s size.
- There won’t be perfect multicollinearity between any of the variables since the approach depends on each other.
- When every random variable in a series has the same finite variance, this is referred to as homoscedasticity. Factor analysis does not need homoscedasticity across variables since it operates as a linear function.
- There is the linearity assumption. This implies that even non-linear variables may be used; yet, after being transferred, they take on a linear form.
- The assumption of interval data is another.
How to Use Factor Analysis
It can be used for the following purposes:
1. Business Marketing
It is used in business models to utilize the matrix of relationships to describe complicated variables or data. It examines how data is interconnected and makes the assumption that complicated variables may be boiled down to a handful of crucial characteristics.
Some of the connections between variables and their dimensions make this feasible. Sometimes the dimension of one variable will lead to the attribute of another. It employs statistical methods on several components to break down the original rating and uses these partial scores to extract other elements.
2. Vehicle Industry
This research may be used to determine all the factors—size, price, choices, extras, and more—that are relevant to the decision-making process when buying an automobile. The research might then be used to identify a few crucial factors that ultimately influence a buyer’s choice. Then, auto dealers may modify their product lines to suit the market.
Diversification is the secret to an effective investing portfolio. Factor analysis is used by investment professionals to forecast movement in a variety of sectors and provide insights on variables that may be overlooked.
The typical portfolio, for instance, includes equities from sectors like commodities and technology. Investment experts will have a solid understanding of what to sell and what to keep by examining the increase in stock prices of a connected business, such as the oil industry.
4. People Resources
The recruiting process of a corporation is influenced by a variety of variables. With the help of these facts, human resource specialists will be able to establish a relaxing and effective work atmosphere.
The ideal team composition, skill mix, and contractual vs. in-house talent may be determined by comparing and analyzing a variety of criteria. This will improve the efficiency of the company as a whole.
Factor analysis may be used in restaurants to identify demographics and target customers while developing menus. If a fast-food restaurant were to locate next to a university campus instead of a high-end shopping centre, it would need to prepare its menu differently. Success is influenced by things like the competitors in the area, foot traffic, age groupings, and location.
When choosing a school year’s curriculum and selecting instructors, factor analysis is crucial. For the school year to function well, it is used to define classroom sizes, personnel restrictions, compensation distribution, and a broad variety of other criteria.
In other words, factor analysis simplifies after removing variables. Along with segmentation research, factor analysis, particularly principal component analysis, is often used. In this article, we learned about the factor analysis approach and the presuppositions that were established in order to develop the method. A variety of factors were also shown, along with how they may be carried out on various platforms.
1. What is the use of factor analysis in psychology?
To find “factors” that can explain a range of test outcomes. For instance, intelligence studies have shown that people who do well on verbal aptitude exams also perform well on other verbal aptitude tests.
2. What is a factor analysis example?
The goal of factor analysis is to consolidate several independent variables into a small number of dimensions.