Latent Variables in Data Analysis

Latent variables are variables that are not directly observed, but are inferred through other data. Once data is accumulated it is typically presented through the usage of oval diagrams.

Latent variables correspond to real current physical aspects. For example, when a store determines where to place products on shelves next to other products that were observed to be bought together by most customers, this can be considered latent variables within survey data. The survey results analysis determined through observable variables that those who often bought bread also bought cupcakes, though this was not physically seen by most working at the store, it was based off of data received. While not every customer bought bread and cupcakes together, overall, the majority of the customers who did purchase one bought the other.

Positive impact of latent variables

  • Makes it easier to understand statistics of a large volume of data by condensing information
  • Can link the hypothetical to reality
  • Can be used in multiple areas of life and business including psychology, economics, and social sciences

Negative impact of latent variables

  • Not 100 percent accurate
  • Based off of other factual observations
  • Cannot be measured directly

Latent variables can impact survey results analysis in both positive or negative ways. Just realize it is an inferred metric. In the world of psychology, for example, latent variables are used quite often to measure ones personality traits. This doesn’t mean it is exactly true, but for the most part it is. In the field of economics, latent variables can be used to help measure morality, happiness, and quality of life.

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