EFA vs CFA: Key Differences Between Exploratory & Confirmatory Factor Analysis (R)

A comprehensive side-by-side comparison of Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) with full R code examples. EFA uses the psych package to discover unknown factor structures from data, while CFA uses lavaan to test pre-specified models. Covers when to use each method, assumptions, sample size requirements, model fit indices (CFI, TLI, RMSEA, SRMR) with accepted thresholds, common mistakes to avoid, and how to correctly sequence both methods in dissertation research using independent samples.

Nguồn: https://www.r-bloggers.com/2026/06/efa-vs-cfa-key-differences-between-exploratory-confirmatory-factor-analysis-r. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.

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stick function for the EDA in time series

A new stick function (Seasonal, Trend, Irregular Contribution Kit) has been added to the greybox package for both R and Python. Based on Hans Levenbach's STI classification, it uses ANOVA to quantify the strength of seasonal, trend, and irregular components in a time series. Applied to the classic AirPassengers dataset, it reveals that trend accounts for ~86% of variability. The function supports multiple seasonal components and may be extended to include external regressors in the future.

#data-science#r#exploratory-data-analysis