The factor analysis technique is useful for minimizing a massive volume of variables into fewer numbers of factors. This particular technique can determine the maximum common variance from all variables to put them in a common score. This score can be used for deeper analysis because it is an index of all the variables.
It is a component of the general linear model. It can also lead to several assumptions like linear relationships and no multicollinearity. Dive into this article to explore more details about factor analysis.
Objectives
The key objectives of factor analysis can be broken down into the following pointers:
- Determining the number of factors required to explain common themes within a particular variable set.
- Determining the extent to which every variable in the dataset is connected to a common factor or theme.
- Interpreting the common factors in a dataset.
- Understanding the degree to which each observed data point is representative of a theme or factor.
Types of Factor Analysis
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Confirmatory Factor Analysis
This type of analysis can determine whether any relationship exists between factors or a set of variables. The analysis method confirms the connection between two components of variables in a particular dataset. The purpose of CFA is to evaluate whether certain data is suitable for the requirements of a particular hypothesis.
At the beginning of the process, the researcher formulates a hypothesis related to the lines of a certain theory. The model gets rejected when the constraints imposed on a model aren’t suitable for the data. In such a case, it gets confirmed that no relationship is present between a factor and its underlying components.
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Exploratory Factor Analysis
The objective of exploratory analysis is to understand the underlying latent structure of a large variable set. Exploratory factor analysis can identify the relationship between measured variables inside an entity.
Confirmatory factor analysis focuses on recognizing a connection between an observed variable set and its underlying structure. But exploratory factor analysis focuses on revealing a connection between the different variables of a given dataset.
Methods
The different methods used to extract the factor from a specific data set are as follows:
- Principal Component Analysis: It involves extracting the highest variance and placing them into the first factor. Once the variance determined by the first factor is removed, it starts extracting the highest variance for the second factor. The process continues till the last factor in a dataset.
- Image Factoring: This method of extracting the factor from a given dataset is dependent on the correlation matrix. In image factoring, the OLS Regression method can help predict the factor.
- Common Factor Analysis: This method involves removing the common variance and putting it into factors. This method is particularly useful for SEM, and it does not contain the unique variance of different variables.
- Maximum Likelihood Factor: This is another method of extracting the factor according to the correlation metric. But this method focuses on the maximum likelihood of determining the factor.
Applications of Factor Analysis
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Marketing
Marketing strategies can significantly benefit from the statistical method of analysis. Companies can use these techniques to determine a correlation between different factors or variables of a marketing campaign.
Furthermore, it can build connections with consequent feedback and customer satisfaction. It ensures that you verify the efficacy of a marketing campaign and its impact on the target market.
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Nutrition
It can build a connection between the nutritional health of an individual and their diet. To establish that, this type analysis focuses on the dietary practices of a certain population. Moreover, the nutritional intake of an individual and their consequent health status has enabled nutritionists to determine the right quantity of nutrients one individual should consume within a specific time period.
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Data Mining
In data mining, this analysis is as crucial as artificial intelligence. Factor analysis can classify a complex and vast dataset into filtered-out variables that have some connection with each other. It helps simplify the process of data mining.
Data scientists have always struggled with finding connections between different variables. But data mining has become much more advanced due to factor analysis.
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Machine Learning
Machine Learning and data mining techniques complement one another. Maybe this explains why there are tools and methodologies for machine learning to perform factor analysis.
Factor Analysis in machine learning is used to reduce the number of variables in a given dataset to obtain a more accurate and enhanced collection of observable factors. Multiple algorithms based on machine learning are used to work in this manner.
They are properly trained with massive amounts of data in order to lead the way to new applications. Factor analysis is an unsupervised machine learning approach that is commonly used in machine learning for dimensionality reduction. As a result, machine learning and factor analysis could be used together to create data mining approaches and make data analysis much more efficient.
Advantages
Now that you know the different types of factor analysis and their applications, learn about some of its benefits:
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Cost-Effective
Data research and data mining algorithms are extremely expensive. But the statistical model of factor analysis is available at a surprisingly affordable cost. Moreover, you don’t need too many resources to perform factor analysis. Additionally, it can be performed by experienced professionals as well as beginners.
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Measurable
One of the major benefits of factor analysis is its measurable nature. This statistical model can be worked upon various attributes. Whether it’s subjective or objective, it works well with everything.
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Flexible
Several machine learning algorithms are limited to a single approach. But factor analysis is an exception and offers a lot of flexibility. The flexible approach of the statistical model helps determine the connections between different variables and their underlying components.
How Can You Optimize Your Survey for Factor Analysis
If you plan to perform analyse the data you collect, you will have to focus on the following:
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Target an Adequate Number of Respondents
Large datasets are more useful for factor analysis. If you want your analysis to deliver optimum results, you should have a large group of respondents. Variables like your topic of interest and population size will influence the exact number of respondents you need. But it’s better to follow the approach of “the more the respondents, the better.”
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Ask Multiple Questions
While creating a questionnaire for your survey, you must add as many questions as possible. This analysis won’t be successful if your survey consists of a few broad questions. The end goal of analysis is to simplify an extensive concept by looking at the minute and more contextual information. Therefore, asking multiple questions will help serve the purpose and provide you with the results you need.
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Avoid Open-Ended Questions
Your goal should be collecting quantitative data. Answers to open-ended questions won’t be suitable for analysis. You should provide answer options in the form of different scales.
It will help you avoid trouble while analysing data. But try to use the same scaled answer options for as many questions as possible for data consistency.
Wrapping up
Research and innovations in the field of factor analysis continue to help people make better judgments in a variety of industries. Continuous refinement in methodologies makes it one of the most significant decision-making tools for all sectors in the future.
FAQs:
- What type of data is useful for factor analysis?
If you are planning to perform factor analysis, you should try to collect quantitative data. Therefore, you should avoid asking open-ended questions. Besides, factor analysis will be much better when you have a large dataset.
- What is the necessity of using factor analysis?
This type of analysis helps simplify complex variable sets using statistical procedures. It helps understand the underlying dimensions that establish the relationships between different variables or items.
- What is the real-life use of factor analysis?
Factor analysis is useful in different domains like biology, finance, operational research, marketing, psychology, and more. For instance, it is useful while measuring customer satisfaction with a specific product.
- Can two variables be used for factor analysis?
Two variables can have one common defining factor and their correlation values. If the correlation value seems large enough to you, declare that a factor is present behind both of them.
- Is exploratory factor analysis better than confirmatory factor analysis?
The answer will be influenced by why you need to perform factor analysis. You should go for exploratory factor analysis to establish a hypothesis about the relationship between variables. But confirmatory factor analysis is useful when you need to test a hypothesis about the relationship between different variables.
