National Survey of Family Growth Weights Adjusted for Abortion Under-Reporting

Post-Stratification Dimensions

The post-stratifications performed on the weights for each survey given this treatment were:

1. a repeat of the post-stratification already done in preparing the public-use data files, which were based on number of women in the United States of reproductive age in demographic categories such as age, race and/or ethnicity, sex, marital status, and parity,²
2. for a given analysis period preceding the survey, the number of live births in each year by respondent’s age at end of pregnancy, and
3. for the same analysis period preceding the survey, the number of induced abortions in the entirety of the analysis period by respondent’s age at end of pregnancy.

The births by age and abortions by age dimensions only included post-strata for women under 40 years of age for most of the NSFG surveys. Because the target population of the NSFG before 2015 was women 15 years to 44 years of age, there were insufficient numbers of respondents reporting pregnancies while aged 40 years or more in order to do post-stratification. In 2015, the target population was extended to women aged 15 years to 49 years, so a post-stratum for women aged 40 years or more at the end of pregnancy was included in the weight adjustments for the 2015-2019 NSFG.

Post-Stratification Targets

The targets for post-stratifications based on population counts of demographic categories were replicated from the NSFG data sets themselves, based on documentation of each NSFG survey’s weighting procedures.

Targets for post-stratification of live births by age per year were taken from the National Vital Statistics Service’s natality data. For 1995 and after, these came by way of the CDC WONDER system. (“Natality on CDC WONDER Online Database,” n.d.) For years before 1995, these came from data files prepared by the National Bureau of Economic Research.³ (“Vital Statistics Natality Birth Data” 2023)

Targets for post-stratification of induced abortions by age per analysis period were taken from a data set created by Guttmacher Institute personnel based on total counts from the Guttmacher Institute’s Abortion Provider Census with age distributions inferred from Abortion Surveillance Reports of the Centers for Disease Control and Prevention. (Maddow-Zimet, Kost, and Finn 2022) Post-stratifications to number of induced abortions were done for the entirety of each survey’s analysis period rather than by year like live births due to insufficient numbers of respondents reporting abortions to divide up respondents by year.

Analysis Periods

Adjusted survey weights are provided for Cycle 3 (1982) of the NSFG through the 2015-2019 NSFG. No attempt was made to produce adjusted weights for Cycle 1 or Cycle 2 of the NSFG, as these cycles only interviewed women who had ever been married, and so are not useful for estimating the counts of all pregnancies in the United States. The 2011-2019 versions of the NSFG public-use data files, which were released in two-year chunks, were combined into two four-year data sets in order to make the sample sizes similar to earlier surveys.⁴

The analysis periods for each survey were hand-selected in order to give the most accurate estimates that would give complete coverage for all years from 1974 to 2014, and this is summarized in Table 1.

The analysis period of Cycle 5 of the NSFG is particularly long, and so was divided into two separate analysis periods.

When using the adjusted weights, estimates of the number of births in each year of a survey’s analysis period and estimates of the number of induced abortions in the entirety of each survey’s analysis period should match external counts for women less than 40 years of age.

In order to accomplish this, estimates for calculation of post-stratification adjustments were based on the number of abortions a respondent reported as occurring in the analysis period, since there are many respondents to the NSFG who report multiple abortions. This was a departure from typical post-stratification done.

In addition to applying adjustments to the weight of each respondent reporting an abortion in a survey’s analysis period, weight adjustments were applied to any respondent reporting an abortion after the analysis period, based on the adjustment done for respondents reporting abortions during the analysis period. This was done so that the contraceptive use calendar, which includes many respondent-months that occur after a survey’s analysis period, could be used for analysis. This was another a departure from how post-stratification is typically done.

Replicate Weight Schemes

These departures from typical post-stratification necessitated the use of replicate weights. Any adjustments made to the main survey weights were also performed upon all replicate weights, which ensures that the standard errors of estimates can be calculated by statistical software.

The public use data file for Cycle 4 (1985) of the NSFG is distributed with replicate weights. Replicate weights for other surveys were calculated before raking post-stratification was done. What replicate weight scheme was used depended on the structure of strata and clusters in the public use data files, and this is summarized in Table 2.

Data Files Containing Adjusted Weights

The NSFG web site includes example programs for reading NSFG public use data files in the SAS, SPSS, and Stata programming languages. Because of the diversity of potential environments for their use, the adjusted NSFG weights are distributed below in comma separated values (CSV) files, which should be readable in any programming environment.

• Cycle 3 (1982)
• Cycle 4 (1985)
• Cycle 5 (1995)
• Cycle 6 (2002)
• 2006-2010
• 2011-2015
• 2015-2019

Each file contains a column for the CASEID of each female respondent in the data set,⁵ a column FULL_SAMPLE_WGT that contains the main survey weight for the respondent, and multiple columns beginning with REP_WGT_ and ending with a number that contain the replicate weights for each respondent.

Additionally, for those replicate weights that use the jackknife scheme, statistical software requires the jackknife coefficients for each stratum. Coefficients for survey weights that use the jackknife scheme are included in the CSV files linked below.

• Cycle 5 (1995)
• 2006-2010
• 2011-2015
• 2015-2019

Example SAS Programs

data cycle6_fem_resp;
    set nsfgdata.d2002_2003femresp(rename=(CASEID=CASEID_char));
    CASEID=input(CASEID_char, 8.);
    drop CASEID_char;
run;

proc import
    file="&weights_path./NSFG_2002_2003_cycle_6_weights.csv"
    dbms=csv
    out=cycle6_weights;
run;

proc sort data=cycle6_fem_resp;
    by CASEID;
run;

proc sort data=cycle6_weights;
    by CASEID;
run;

data cycle6_fem_resp_adj;
    merge cycle6_fem_resp(drop=FINALWGT) cycle6_weights;
    by CASEID;
run;

proc surveyfreq data=cycle6_fem_resp_adj varmethod=BRR;
    tables AGER;
    weight FULL_SAMPLE_WGT;
    repweights REP_WGT_:;
run;

data cycle6_pregs;
    set nsfgdata.d2002_2003fempreg(rename=(CASEID=CASEID_char));
    CASEID=input(CASEID_char, 8.);
    drop CASEID_char;
run;

proc import
    file="&weights_path./NSFG_2002_2003_cycle_6_weights.csv"
    dbms=csv
    out=cycle6_weights;
run;

proc sort data=cycle6_pregs;
    by CASEID;
run;

proc sort data=cycle6_weights;
    by CASEID;
run;

data cycle6_pregs_adj;
    merge cycle6_pregs(drop=FINALWGT in=in_pregs) cycle6_weights;
    by CASEID;
    if in_pregs;
run;

proc surveyfreq data=cycle6_pregs_adj varmethod=BRR;
    tables OUTCOME;
    weight FULL_SAMPLE_WGT;
    repweights REP_WGT_:;
    where 1141 <= DATEND <= 1224;
run;

data fem_resp_2006_2010;
    format CASEID 8.;
    length CASEID 8;
    set nsfgdata.d2006_2010femresp;
run;

proc import
    file="&weights_path./NSFG_2006_2010_weights.csv"
    dbms=csv
    out=weights_2006_2010;
run;

proc sort data=fem_resp_2006_2010;
    by CASEID;
run;

proc sort data=weights_2006_2010;
    by CASEID;
run;

data fem_resp_2006_2010_adj;
    merge fem_resp_2006_2010(drop=FINALWGT30) weights_2006_2010;
    by CASEID;
run;

proc import
    file="&weights_path./NSFG_2006_2010_jkcoefs.csv"
    dbms=csv
    out=jkcoefs_2006_2010;
run;

proc surveyfreq data=fem_resp_2006_2010_adj varmethod=jackknife;
    tables AGER;
    weight FULL_SAMPLE_WGT;
    repweights REP_WGT_: / jkcoefs=jkcoefs_2006_2010;
run;

data pregs_2006_2010;
    format CASEID 8.;
    length CASEID 8;
    set nsfgdata.d2006_2010fempreg;
run;

proc import
    file="&weights_path./NSFG_2006_2010_weights.csv"
    dbms=csv
    out=weights_2006_2010;
run;

proc sort data=pregs_2006_2010;
    by CASEID;
run;

proc sort data=weights_2006_2010;
    by CASEID;
run;

data pregs_2006_2010_adj;
    merge pregs_2006_2010(drop=FINALWGT30 in=in_pregs) weights_2006_2010;
    by CASEID;
    if in_pregs;
run;

proc import
    file="&weights_path./NSFG_2006_2010_jkcoefs.csv"
    dbms=csv
    out=jkcoefs_2006_2010;
run;

proc surveyfreq data=pregs_2006_2010_adj varmethod=jackknife;
    tables OUTCOME;
    weight FULL_SAMPLE_WGT;
    repweights REP_WGT_: / jkcoefs=jkcoefs_2006_2010;
    where 1225 <= DATEND <= 1272;
run;

Example R Programs

The example R programs use the srvyr library and two tidyverse libraries to process input and output from the survey library.

library(dplyr)
library(readr)
library(srvyr)

cycle_6_weights <- read_csv(paste0(weights_path, 'NSFG_2002_2003_cycle_6_weights.csv'))

cycle_6_femresp |>
  mutate(
    CASEID = as.numeric(CASEID)
  ) |>
  left_join(
    cycle_6_weights,
    by = join_by(CASEID)
  ) |>
  as_survey_rep(
    weights = FULL_SAMPLE_WGT,
    repweights = starts_with('REP_WGT_'),
    type = 'BRR'
  ) |>
  group_by(
    AGER
  ) |>
  summarize(
    est_pop = survey_total(vartype = 'ci')
  )

library(dplyr)
library(readr)
library(srvyr)

cycle_6_weights <- read_csv(paste0(weights_path, 'NSFG_2002_2003_cycle_6_weights.csv'))

cycle_6_pregs |>
  mutate(
    CASEID = as.numeric(CASEID)
  ) |>
  left_join(
    cycle_6_weights,
    by = join_by(CASEID)
  ) |>
  as_survey_rep(
    weights = FULL_SAMPLE_WGT,
    repweights = starts_with('REP_WGT_'),
    type = 'BRR'
  ) |>
  filter(
    1141 <= DATEND & DATEND <= 1224
  ) |>
  group_by(
    OUTCOME
  ) |>
  summarize(
    est_num = survey_total(vartype = 'ci')
  )

library(dplyr)
library(readr)
library(srvyr)

weights_2006_2010 <- read_csv(paste0(weights_path, 'NSFG_2006_2010_weights.csv'))
jkcoefs_2006_2010 <- read_csv(paste0(weights_path, 'NSFG_2006_2010_jkcoefs.csv'))

femresp_2006_2010 |>
  left_join(
    weights_2006_2010,
    by = join_by(CASEID)
  ) |>
  as_survey_rep(
    weights = FULL_SAMPLE_WGT,
    repweights = starts_with('REP_WGT_'),
    type = 'JKn',
    rscales = jkcoefs_2006_2010$JKCoefficient
  ) |>
  group_by(
    AGER
  ) |>
  summarize(
    est_pop = survey_total(vartype = 'ci')
  )

library(dplyr)
library(readr)
library(srvyr)

weights_2006_2010 <- read_csv(paste0(weights_path, 'NSFG_2006_2010_weights.csv'))
jkcoefs_2006_2010 <- read_csv(paste0(weights_path, 'NSFG_2006_2010_jkcoefs.csv'))

fempreg_2006_2010 |>
  left_join(
    weights_2006_2010,
    by = join_by(CASEID)
  ) |>
  as_survey_rep(
    weights = FULL_SAMPLE_WGT,
    repweights = starts_with('REP_WGT_'),
    type = 'JKn',
    rscales = jkcoefs_2006_2010$JKCoefficient
  ) |>
  filter(
    1225 <= DATEND & DATEND <= 1272
  ) |>
  group_by(
    OUTCOME
  ) |>
  summarize(
    est_num = survey_total(vartype = 'ci')
  )