Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Analisis Moderasi (Interaksi)× | Regresi Logistik× | |
|---|---|---|
| Bidang≠ | Inferensi Kausal | Statistika Penelitian |
| Keluarga≠ | Regression model | Process / pipeline |
| Tahun asal≠ | 2018 | 1958 |
| Pencetus≠ | Aiken & West (1991); Hayes (PROCESS, 2018) | David Roxbee Cox |
| Tipe≠ | Linear regression with interaction term | Method |
| Sumber perintis≠ | Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis (2nd ed.). Guilford Press. ISBN: 978-1462534654 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | interaction analysis, moderated regression, simple moderation, Düzenleyici Değişken Analizi (Moderation / İnteraksiyon) | logit model, binomial logistic regression, LR |
| Terkait≠ | 5 | 3 |
| Ringkasan≠ | Moderation analysis tests whether the effect of a predictor X on an outcome Y changes with the level of a third variable W, the moderator. It is estimated within a regression framework through an interaction term X×W, popularised by Aiken & West (1991) and Hayes's PROCESS macro (2018). | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
| ScholarGateSet data ↗ |
|
|