By Mary B. Breckenridge
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Additional resources for Age, Time, and Fertility. Applications of Exploratory Data Analysis
1. It provides an overall view of fertility experience recorded for Sweden across two centuries—information that will help guide the modeling of age-specific fertility change using exploratory data analysis (EDA) methods. 2. It provides an overall view of the flexible, data-guided E D A approach to modeling, which has two valuable outcomes for this fertility analysis: an interpretable description of the patterns underlying each long data time sequence and the identification of the nature and timing of departures from the developed model.
A related procedure, trimmed mean polish, w a s used by Page (1977) in her investigation of the effects o f marriage duration o n marital fertility patterns. Instead of the median, the mean of the central two-thirds of a distribution w a s used in the s u c c e s s i v e approximations (see N o t e 6). 6. The E H R procedures also differ from these "polishing" procedures in another important regard—the weighting of the residuals in the iterative fitting procedure. In median polish no weighting o c c u r s ; in trimmed mean polish a fixed and predetermined percentage of residuals from each tail of the distribution is given zero weight at every iteration and all other residuals are given full weight.
In later steps of analysis w e examine the relation of change in pattern to change in fertility level. To calculate cumulated normalized rates, w e start with schedules of age-specific r a t e s / ( « ) for age g r o u p s 7 = 1 , 2 , . . , n, such as the cohort and cross-sectional overall fertility schedules by 5-year age groups shown in Figs. 2a. Each schedule is first cumulated to give the fertility rates for w o m e n a given age and younger. Age cuts are at 19/20, 24/25, . . , 49/50 for schedules by 5-year age groups beginning with ages 15-19 (Figs.