Sammenlign metoder

	Brass P/F Ratio Method	Relational Gompertz Fertility Model
Fagfelt	Demografi	Demografi
Familie	Process / pipeline	Regression model
Opprinnelsesår	1964	1984
Opphavsperson	William Brass	William Brass; standard refined by Heather Booth
Type	Indirect fertility estimation adjusting period rates using reported parities	Relational parametric model of the cumulative fertility schedule
Opprinnelig kilde	Brass, W. (1975). Methods for Estimating Fertility and Mortality from Limited and Defective Data. Carolina Population Center, University of North Carolina at Chapel Hill.	Booth, H. (1984). Transforming Gompertz's function for fertility analysis: The development of a standard for the relational Gompertz function. Population Studies, 38(3), 495–506.
Alias	P/F Ratio Method, Brass P/F Ratio Technique, Parity/Fertility Ratio Method	Brass Relational Gompertz Model, Gompertz Relational Fertility Model, Relational Gompertz Function
Relaterte	4	4
Sammendrag	The Brass P/F ratio method is the foundational technique of indirect fertility estimation, designed to correct fertility levels in populations whose vital registration is incomplete but where a census or survey reports both recent births and lifetime children ever born. It compares F — the period fertility a synthetic cohort would have accumulated by each age — with P, the average parity (children ever born) actually reported by women of that age. The ratio of the two diagnoses and corrects errors in the reported level of current fertility, yielding an adjusted total fertility rate from data too defective for direct calculation.	The relational Gompertz model expresses any population's cumulative fertility schedule as a simple linear transformation of a fixed standard schedule, after both are mapped through a double-logarithm (gompit) transform. Developed by William Brass and given its widely used standard by Heather Booth, it characterizes the entire age pattern of fertility with just two parameters — α, which shifts the schedule earlier or later, and β, which controls how concentrated or spread out childbearing is. This makes it a robust tool for smoothing, fitting, and especially for correcting and estimating fertility from the limited and error-prone data common in developing countries.