Introduction
SPSS means Statistical Package for Social Sciences was made to provide SPSS help to students, scholars, researchers, experts, etc. to reduce their manual efforts and increase their efficiency. Users take help to resolve their issues with data analysis techniques, writing problems, dissertations, passing & clearing tests, etc. Factor analysis is one of those issues with which students face issues and take help with SPSS. This blog post is to get insights about factor analysis, its objectives, when to use it, etc. Let’s read further.
What is Factor Analysis?
Factor analysis is a data reduction approach which condenses big data structures, variables, and factors into smaller actionable and substantial variables. It narrows down the availability of data which allows better insights and deeper visibility of patterns for data research. Hence, the data which cannot be measured directly can be converted into easy-to-understand and actionable data. Applying factor analysis to any research will let you follow the trends faster and you will be able to observe the common data points in your datasets. Unlike other statistical analysis methods, factor analysis does not need defined variables and that’s why it is the most commonly used technique to find the relationships between the variables of a given dataset.
What are the objectives of Factor Analysis?
Factor analysis is a shrink wrap which compresses a large amount of data into smaller sets to make it more manageable and understandable. The complete objectives of factor analysis can be categorised into four smaller categories, which are as follows:
- To understand and count the number of factors required to explain the main purpose of a given set of variables.
- To know the extent to which a single variable is associated with a common factor.
- To provide clarification of the common factors and themes available in the dataset.
- To establish the degree or extent to which each observed variable is representing each theme or factor.
When to use Factor Analysis?
Choosing a particular statistical method to get the most out of the data is quite difficult. There are three forms of factor analysis which can be chosen on the basis of your research goals. The three main forms of factor analysis are:
1. Exploratory Factor Analysis
This kind of factor analysis is used when you are required to develop a hypothesis about a relationship between the variables of a dataset. Its purpose is to determine the latent structure of a large set of variables. It uncovers the relationship present between the variables of any entity.
2. Confirmatory Factor Analysis
This is used when you have to test a hypothesis about the relationship of variables. As its name suggests, it confirms a relationship between factors or variables. The main purpose of this analysis technique is to test whether a particular data fits with the requirements of a required hypothesis.
3. Construct Validity Factor Analysis
It is used when you are conducting a survey to collect data and you have to determine the degree that to what extent your survey actually measures what you need to find.
Applications of Factor Analysis
Factor analysis has a wide range of applications in day-to-day life. We are presenting some of them here:
- Marketing
Marketing means the promotion of goods and services to accelerate the business. Market strategists and experts choose factor analysis techniques to find a correlation between various variables of a promotional marketing campaign. After a marketing campaign or survey development, factor analysis is used to find a connection between customer satisfaction and subsequent feedback. Companies increase their sales by opting for this analysis strategy.
- Data mining
Since factor analysis transforms complex data into a group of filtered variables, hence it works as important as artificial intelligence in the case of data mining. Data analysts and scientists of companies find it difficult to establish a correlation between variables, and hence they take SPSS services into account to perform factor analysis for them.
- Machine Learning
Factor analysis is largely used in machine learning for reducing large datasets into smaller chunks to get more enhanced and accurate results. Since machine learning collaborates with factor analysis very well, hence it makes data mining techniques and data research techniques massively efficient.
- Dietary practices & Science
Factor analysis helps find a relationship between the nutrients and dietary practices of a person and the nutrient health of a person. This helps nutritionists to understand the appropriate quantity of nutrients one should intake.
- Business plans
Factor analysis reduces the manual work of business strategists by eliminating the assumptions made by them and formulating accurate and straightforward decisions for business budgeting, marketing, production, and transportation.
Conclusion
Summing up, factor analysis is a considerable statistical method which is used to reduce large datasets by compressing them into smaller groups. With SPSS data analysis techniques, factor analysis plays an important role for researchers and other users to make their efforts more efficient and achieve desired results. We tried to explain the positives and workings of factor analysis here so that you can analyse your data effectively. Good luck with your data research!
