Porównaj metody

	Ritual Density Coding	Moralizing Gods Database Analysis
Dziedzina	Religious Studies	Religious Studies
Rodzina	Process / pipeline	Regression model
Rok powstania	2004	2015
Twórca	Harvey Whitehouse	Peter Turchin and the Seshat: Global History Databank team
Typ	Coding scheme for ritual transmission dynamics	Cross-cultural quantitative database analysis
Źródło pierwotne	Whitehouse, H. (2004). Modes of Religiosity: A Cognitive Theory of Religious Transmission. Walnut Creek, CA: AltaMira Press. ISBN: 9780759106147	Turchin, P., Brennan, R., Currie, T., et al. (2015). Seshat: The Global History Databank. Cliodynamics: The Journal of Quantitative History and Cultural Evolution, 6(1), 77-107.
Inne nazwy	Modes of Religiosity Coding, Frequency-Arousal Ritual Analysis, Imagistic vs Doctrinal Ritual Coding, Ritual Mode Classification	Big Gods Database Analysis, Seshat Moralizing-Gods Analysis, Cross-Cultural Big Gods Modeling, Moralizing High Gods Coding
Pokrewne	3	3
Podsumowanie	Ritual density coding is a method for analyzing religious rituals by coding them on two key dimensions - how often they are performed (frequency) and how emotionally intense or dysphoric they are (arousal) - in order to locate a tradition along Harvey Whitehouse's imagistic-doctrinal continuum. In his 2004 book Modes of Religiosity, Whitehouse argued that rituals tend to cluster at two attractor poles: high-frequency, low-arousal 'doctrinal' practices that build large, hierarchically organized, semantically rich traditions, and rare but emotionally searing 'imagistic' practices that forge small, intensely cohesive communities through vivid episodic memories. The coding scheme operationalizes this theory, testing the predicted inverse relationship between ritual frequency and arousal and linking the resulting modes to distinctive forms of social organization and memory.	Moralizing gods database analysis is a cross-cultural quantitative method that codes the presence of moralizing or 'big' supernatural enforcers and measures of social complexity across many historical polities over time, then models their relationship. The exemplary infrastructure is the Seshat: Global History Databank, introduced by Peter Turchin and colleagues in 2015, which records hundreds of polities on standardized variables - population, territory, hierarchy, infrastructure, information systems, and religious features - with explicit sources and uncertainty codes. A high-profile 2019 Nature paper using Seshat data argued that complex societies tend to precede moralizing gods; that paper was retracted in 2021 over its treatment of missing data. The method is therefore best understood not as a settled finding but as a databank-driven analytical pipeline whose results depend critically on coding decisions, missing-data handling, and modeling of temporal and phylogenetic dependence.