Structural equation models | Factor analysis

Confirmatory factor analysis

In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social research. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor). As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. This hypothesized model is based on theory and/or previous analytic research. CFA was first developed by JΓΆreskog (1969) and has built upon and replaced older methods of analyzing construct validity such as the MTMM Matrix as described in Campbell & Fiske (1959). In confirmatory factor analysis, the researcher first develops a hypothesis about what factors they believe are underlying the measures used (e.g., "Depression" being the factor underlying the Beck Depression Inventory and the Hamilton Rating Scale for Depression) and may impose constraints on the model based on these a priori hypotheses. By imposing these constraints, the researcher is forcing the model to be consistent with their theory. For example, if it is posited that there are two factors accounting for the covariance in the measures, and that these factors are unrelated to each other, the researcher can create a model where the correlation between factor A and factor B is constrained to zero. Model fit measures could then be obtained to assess how well the proposed model captured the covariance between all the items or measures in the model. If the constraints the researcher has imposed on the model are inconsistent with the sample data, then the results of statistical tests of model fit will indicate a poor fit, and the model will be rejected. If the fit is poor, it may be due to some items measuring multiple factors. It might also be that some items within a factor are more related to each other than others. For some applications, the requirement of "zero loadings" (for indicators not supposed to load on a certain factor) has been regarded as too strict. A newly developed analysis method, "exploratory structural equation modeling", specifies hypotheses about the relation between observed indicators and their supposed primary latent factors while allowing for estimation of loadings with other latent factors as well. (Wikipedia).

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πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

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πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

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JASP 0.14 Tutorial: Confirmatory Factor Analysis (CFA) (Episode 30)

EDIT/CORRECTION: There's an error in my description of the chi-square model fit outcome. I state that it is good that the p-value is very small and reflects a good model fit. As mentioned by a keen viewer, this chi-square application is the opposite for other NHST outcomes. Here, a signifi

From playlist JASP Tutorials

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Confirmatory factor analysis in AMOS | Part 1

In this video, I demonstrate how to use AMOS for confirmatory factor analysis (CFA). For a discussion on normality analysis, please see the following videos: #1: https://www.youtube.com/watch?v=1gSyZ_DPQRQ #2: https://www.youtube.com/watch?v=uCjOoEKQJvo AMOS (trial version) can be downlo

From playlist Structural Equation Modeling

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R - SEM - Confirmatory Factor Analysis Class Assignment

Recorded: Summer 2015 Lecturer: Dr. Erin M. Buchanan Packages needed: lavaan, semPlot Class assignment for structural equation modeling. Topic covers how program a confirmatory factor analysis (CFA), heywood cases, fit indices, loadings, residuals, modification indices, and model comparis

From playlist Structural Equation Modeling

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Evaluating the composition of Functions

πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

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Evaluating the composition of Functions

πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

Video thumbnail

Evaluating the composition of Functions

πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

Video thumbnail

Evaluating the composition of Functions

πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

Video thumbnail

Evaluating the composition of Functions

πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

Video thumbnail

Evaluating the composition of Functions

πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

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R - Confirmatory Factor Analysis Lecture

Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers the basics of confirmatory factor analysis or measurement models. You will learn about how to build, analyze, summarize, and diagram a measurement model in lavan. You can learn more at:

From playlist Structural Equation Modeling 2020

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R - Confirmatory Factor Analysis Lecture

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From playlist Structural Equation Modeling

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In this video, I demonstrate how to conduct a structural equation modeling (SEM) analysis in AMOS. As SEM is based on confirmatory factor analysis (CFA), I would suggest you watch the following videos: Video 1: https://www.youtube.com/watch?v=HKs9vIkpIXE&list=PLTjlULGD9bNLPjpFqDlVMFu0GyN

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Evaluating the composition of inverse functions

πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

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R - Multigroup CFA with lavaan Example

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From playlist Structural Equation Modeling

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From playlist Growth Curve Models

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πŸ‘‰ Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We

From playlist Evaluate a Composition of Inverse Trigonometric Functions

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R - Exploratory Factor Analysis Lecture

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Related pages

Structural equation modeling | Construct validity | Latent variable model | Exploratory factor analysis | Covariance matrix | Factor analysis | A priori probability | Measurement invariance | Statistics | Covariance