## What is a Bifactor model in psychology?

The bifactor model hypothesizes a general factor, onto which all items load, and a series of orthogonal (uncorrelated) skill-specific grouping factors. The model is particularly valuable for evaluating the empirical plausibility of subscales and the practical impact of dimensionality assumptions on test scores.

**What is higher order factor analysis?**

Higher-order factor analysis is a statistical method consisting of repeating steps factor analysis – oblique rotation – factor analysis of rotated factors. Its merit is to enable the researcher to see the hierarchical structure of studied phenomena.

**What is CFA model?**

CFA allows for the assessment of fit between observed data and an a prioriconceptualized, theoretically grounded model that specifies the hypothesized causal relations between latent factors and their observed indicator variables.

### What is factor analysis psychology?

Factor analysis is a multivariate statistical technique for data reduction. It has many applications in psychology. In this technique, several variables are reduced to few latent variables for explaining group characteristics. Factor analysis technique is used for both explorative and confirmative studies.

**What are the two types of factor analysis?**

There are two types of factor analyses, exploratory and confirmatory.

**What is difference between factor analysis and PCA?**

The mathematics of factor analysis and principal component analysis (PCA) are different. Factor analysis explicitly assumes the existence of latent factors underlying the observed data. PCA instead seeks to identify variables that are composites of the observed variables.

## What is CFI and TLI?

CFI is a normed fit index in the sense that it ranges between 0 and 1, with higher values indicating a better fit. The most commonly used criterion for a good fit is CFI ≥ . 95 (Hu & Bentler, 1999; West et al., 2012). The TLI (Tucker & Lewis, 1973) measures a relative reduction in misfit per degree of freedom.

**What is the difference between CFA and SEM?**

CFA is used to confirm and trim these constructs and items (measurement model). SEM is used to find if relationships exist between these items and constructs (structural model). Collectively they are known as CFA-SEM, where SEM is an umbrella term, and CFA is a subset.

**Why factor analysis is used?**

Factor analysis is a powerful data reduction technique that enables researchers to investigate concepts that cannot easily be measured directly. By boiling down a large number of variables into a handful of comprehensible underlying factors, factor analysis results in easy-to-understand, actionable data.

### What is factor analysis in simple terms?

Factor analysis is a way to take a mass of data and shrinking it to a smaller data set that is more manageable and more understandable. It’s a way to find hidden patterns, show how those patterns overlap and show what characteristics are seen in multiple patterns.

**Is Cronbach’s alpha A factor analysis?**

Exploratory factor analysis is one method of checking dimensionality. Technically speaking, Cronbach’s alpha is not a statistical test – it is a coefficient of reliability (or consistency).

**What are the 3 purposes of factor analysis?**

To determine the extent to which each variable in the dataset is associated with a common theme or factor. To provide an interpretation of the common factors in the dataset. To determine the degree to which each observed data point represents each theme or factor.