Latent variable

A latent variable is a variable that is not directly observed but is inferred or estimated from observed variables in a model, often used in fields such as psychology, economics, and social sciences.
Written by
Reviewed by
Updated on Jun 21, 2024
Reading time 4 minutes

3 key takeaways

Copy link to section
  • Latent variables are not directly observable but are inferred from other observed variables.
  • They are used to model underlying structures or processes that influence observed data.
  • Common applications include factor analysis, structural equation modeling, and psychometrics.

What is a latent variable?

Copy link to section

A latent variable is a hidden or unobservable variable that influences observable variables in a model. Unlike observed variables, which can be measured directly, latent variables are inferred through their relationships with observed variables. These relationships are typically modeled using statistical techniques, allowing researchers to understand and quantify underlying factors that drive observed data.

Latent variables are crucial in many fields where direct measurement of certain constructs is challenging or impossible. For example, in psychology, latent variables might represent abstract concepts like intelligence or anxiety, which cannot be measured directly but can be inferred from related behaviors or test scores.

Applications of latent variables

Copy link to section

Factor analysis

Copy link to section

Factor analysis is a statistical method used to identify underlying relationships between observed variables. It assumes that multiple observed variables are influenced by a smaller number of latent factors. For instance, in a psychological test measuring different skills, factor analysis might reveal a latent variable representing overall cognitive ability.

Structural equation modeling (SEM)

Copy link to section

Structural equation modeling is a comprehensive statistical technique that incorporates multiple regression equations to model complex relationships between observed and latent variables. SEM is widely used in social sciences to test theoretical models that include latent constructs, such as customer satisfaction or social attitudes.

Psychometrics

Copy link to section

In psychometrics, latent variables are used to measure unobservable traits like intelligence, personality, or depression. Tests and questionnaires often serve as observed variables that reflect these latent traits. By analyzing the patterns of responses, researchers can estimate the underlying latent variables and assess individual differences.

Examples of latent variables

Copy link to section

Intelligence

Copy link to section

Intelligence is a classic example of a latent variable in psychology. It cannot be measured directly but is inferred from performance on various cognitive tasks and tests, such as reasoning, memory, and problem-solving abilities.

Economic sentiment

Copy link to section

In economics, consumer confidence or economic sentiment is a latent variable representing the overall optimism or pessimism of consumers regarding economic conditions. It is inferred from survey responses on topics like personal financial situations, business conditions, and spending intentions.

Quality of life

Copy link to section

Quality of life is another latent variable often studied in health and social sciences. It encompasses various aspects such as physical health, mental well-being, and social relationships, which are measured through surveys and assessments to infer the overall quality of life.

Advantages and limitations

Copy link to section

Advantages

Copy link to section
  • Capturing complex constructs: Latent variables allow researchers to model and quantify abstract concepts that cannot be directly measured.
  • Data reduction: They help in reducing the dimensionality of data by summarizing multiple observed variables into fewer latent factors.
  • Theory testing: Latent variables facilitate the testing of theoretical models and hypotheses in various fields.

Limitations

Copy link to section
  • Model dependence: The estimation of latent variables depends heavily on the chosen model and assumptions. Different models can lead to different interpretations.
  • Complexity: Statistical techniques for latent variables can be complex and require careful consideration of model fit and validity.
  • Measurement error: The accuracy of latent variable estimation can be affected by measurement errors in the observed variables.
Copy link to section
  • Factor analysis: Understand the statistical method used to identify underlying relationships between observed variables.
  • Structural equation modeling (SEM): Explore SEM, which combines multiple regression equations to model complex relationships involving latent variables.
  • Psychometrics: Learn about the field that studies the measurement of latent traits like intelligence and personality through tests and assessments.

Latent variables are powerful tools for understanding and modeling complex, unobservable constructs that influence observed data. By employing statistical techniques to infer these hidden variables, researchers can gain deeper insights into underlying processes and relationships in various fields.


Sources & references

Arti

Arti

AI Financial Assistant

  • Finance
  • Investing
  • Trading
  • Stock Market
  • Cryptocurrency
Arti is a specialized AI Financial Assistant at Invezz, created to support the editorial team. He leverages both AI and the Invezz.com knowledge base, understands over 100,000 Invezz related data points, has read every piece of research, news and guidance we\'ve ever produced, and is trained to never make up new...