Latent variables are unobserved variables that are inferred from observed data, often used to explain patterns or structures that are not directly measurable. They are crucial in statistical models such as factor analysis, structural equation modeling, and latent class analysis, providing a way to model complex phenomena by capturing hidden influences or traits.

Latent Variables

Latent variables

unobserved variables

Observed data refers to information collected through direct measurement or observation of a phenomenon, often used as the foundation for statistical analysis and empirical research. It is crucial for validating hypotheses and building models that reflect real-world conditions and behaviors.

observed data

Statistical models are mathematical representations of observed data that help in understanding and predicting the underlying processes generating the data. They are essential tools in data analysis, allowing researchers to quantify relationships, test hypotheses, and make informed decisions based on empirical evidence.

statistical models

Factor Analysis is a statistical method used to identify underlying relationships between variables by reducing the number of observed variables into a smaller number of latent factors. It helps in understanding the structure of data, simplifying datasets, and is widely used in fields like psychology, finance, and social sciences to uncover hidden patterns and dimensions.

factor analysis

Structural Equation Modeling (SEM) is a comprehensive statistical approach used to test hypotheses about relationships among observed and latent variables. It combines aspects of factor analysis and multiple regression, allowing for the analysis of complex causal models with multiple dependent and independent variables simultaneously.

structural equation modeling

Latent Class Analysis (LCA) is a statistical method used to identify unobserved subgroups within a population based on individuals' responses to multiple observed variables. It assumes that the population is a mixture of distinct classes, each with its own probability distribution over the observed variables, allowing researchers to uncover hidden patterns and infer class membership probabilities for each individual.

latent class analysis

Complex phenomena refer to systems or events characterized by intricate interdependencies and unpredictable behaviors, often arising from the interactions of multiple components. Understanding such phenomena typically requires interdisciplinary approaches and computational models to capture their emergent properties and dynamic nature.

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