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Replicate Weights

The short answer to "what and why" is that replicate weights are a series of variables that contain the information necessary for correctly computing (via the replicate weight method) the standard errors of point estimates when analyzing survey data. Before we get into the specifics of what replicate weights are and how they are created, we need to know why they are needed in the first place. To understand that, we need to step back and look at how the analysis of survey data is different from the analysis of data collected in other ways, e.g., experiments, quasi-experiments.

When we speak of survey data, we mean data that have been collected from
subjects who were chosen based on a sampling plan. The sampling plan is
extremely important, because by using it, we have violated one of the
assumptions of the statistical formulas used to calculate the statistics of
interest to us. The statistics described in most statistics texts
assume that the data are collected based on a simple random sample of the
elements of the population. In survey research, this is almost never the
case. Why? Because in most situations, it is too impractical and/or
too expensive to collect data in this way. Because the SRS assumption has
been violated, corrections to the calculation of the statistics are needed.
Where the standard errors are concerned, there are two possible ways of making
this correction. One way is called a Taylor series linearization method, and the
other is called the replicate weight method. * *Before we can explain what the
replicate weights are, we need to first understand some common elements found in
many survey data sets (especially older data sets). These elements are
used in the Taylor series linearization method.

There are several elements that are unique to survey data that are necessary for correctly computing statistics based on the data. Each of these elements are variables that you will likely find in the data set. They are the probability weight (AKA sampling weight, pweight) variable, the PSU (primary sampling unit) variable, the stratification (AKA strata) variable, and the FPC (finite population correction) variable.

Most people do not conduct their own surveys with sampling designs. Rather, they use survey data that some agency or company collected and made available to the public. The documentation must be read carefully to find out what kind of sampling design was used to collect the data. This is very important because many of the estimates and standard errors are calculated differently for the different sampling designs. Hence, if you mis-specify the sampling design, the point estimates and standard errors will likely be wrong.

Below are some common features of many sampling designs.

__Weights__: There are many types of weights that can be associated with
a survey. Perhaps the most common is the sampling weight, sometimes called a
probability weight, which is used to denote the inverse of the probability of being
included in the sample due to the sampling design (except for a certainty PSU,
see below). The probability weight is calculated as N/n, where N = the number of elements
in the population and n = the number of elements in the sample. For example, if
a population has 10 elements and 3 are sampled at random with replacement, then
the probability weight would be 10/3 = 3.33. In a two-stage design, the probability weight is
calculated as f_{1}f_{2}, which means that the inverse of the
sampling fraction for the first stage is multiplied by the inverse of the
sampling fraction for the second stage. Under many sampling plans, the sum of
the probability weights will equal the population total. For more information on weights,
please see our FAQ:
What types of weights do SAS, Stata and SPSS support?

__PSU__: This is the **p**rimary **s**ampling **u**nit. This is
the first unit that is sampled in the design. For example, school districts
from California may be sampled and then schools within districts may be
sampled. The school district would be the PSU. If states from the US were
sampled, and then school districts from within each state, and then schools from
within each district, then states would be the PSU. One does not need to use
the same sampling method at all levels of sampling. For example,
probability-proportional-to-size sampling may be used at level 1 (to select
states), while cluster sampling is used at level 2 (to select school
districts). In the case of a simple random sample, the PSUs and the elementary
units are the same.

__Strata__: Stratification is a method of breaking up the population into
different groups, often by demographic variables such as gender, race or SES.
Once these groups have been defined, one samples from each group as if it were
independent of all of the other groups. For example, if a sample is to be
stratified on gender, men and women would be sampled independent of one
another. This means that the probability weights for men will likely be different from the
probability weights for the women. In most cases, you need to have two or more PSUs in
each stratum. The purpose of stratification is to improve the precision of the
estimates, and stratification works most effectively when the variance of the
dependent variable is smaller within the strata than in the sample as a whole.

__FPC__: This is the **f**inite **p**opulation **c**orrection.
This is used when the sampling fraction, the number of elements or respondents
sampled relative to the population, becomes large. The FPC is used in the
calculation of the standard error of the estimate. If the value of the FPC is
close to 1, it will have little impact and can be safely ignored. In some
survey data analysis programs, such as SUDAAN, this information will be needed
if you specify that the data were collected without replacement (see below for
a definition of "without replacement"). The formula for calculating the FPC is
((N-n)/(N-1))^{1/2}, where N is the number of elements in the population
and n is the number of elements in the sample. To see the impact of the FPC for
samples of various proportions, suppose that you had a population of 10,000
elements.

Sample size (n) FPC 1 1.0000 10 .9995 100 .9950 500 .9747 1000 .9487 5000 .7071 9000 .3162

Most samples collected in the real world are collected "without replacement". This means that once a respondent has been selected to be in the sample and has participated in the survey, that particular respondent cannot be selected again to be in the sample. Many of the calculations change depending on if a sample is collected with or without replacement. Hence, programs like SUDAAN request that you specify if a survey sampling design was implemented with our without replacement, and an FPC is used if sampling without replacement is used, even if the value of the FPC is very close to one.

Until recently, one needed to use special software (such as SUDAAN or WesVar) to correctly analyze survey data. Today, commonly used programs such as SAS, Stata and SPSS have procedures specially designed to handle the features of survey data. No matter which package is used, one still needs to specify the probability weight, PSU, strata and FPC, if one is needed. The Taylor series linearization method of correcting the standard errors was preferred to the use of replicate methods mostly for computational purposes: It took less computing power to use the Taylor series. Back when computing power was a real concern, this method became popular. However, a problem with this method arose (here is where we get to the replicate weight part!). In some cases, the number of respondents in a particular PSU was small, and people could start to figure out who the respondent was, even though no identifying information was contained in the data set. For a little example of how this works, suppose that we have a survey that is stratified on gender and race, and the PSUs are cities in Southern California. In some of these cities, there may be very few individuals in a particular strata, such as female Alaska Natives. Once the user of the survey figures out which PSU number corresponds to a particular city, the user may discover that there are only two female Alaska Natives in this city. Perhaps other information in the survey, say age, can be used to determine exactly who the respondent is. Now the answers to the survey that were supposed to be confidential are no longer confidential. One way to avoid this problem is to not release data on strata that have fewer than say 100 respondents in it. However, this can lead to misleading results because not all of the strata are being included in the analysis. Another solution is use replicate weights. Because replicate weights are a series of many variables (often between 50 and 100) and their values are based on information not provided to the user of the survey data set, it is nearly impossible for the user to figure out the identity of a given respondent. Note that when the replicate weight method is used, the PSU and strata variables are not included in the data set. However, the probability weight will be included, and both the probability weight and the replicate weights must be used for the correct calculation of the point estimate and its standard error.

There are several ways to create replicate weights. However, they are all based on a similar underlying logic. The sample is broken up into subsamples, called replicates. Next, the estimate of interest is calculated from both the full sample and from each replicate. Finally, the differences between the estimate from the full sample and each of the replicates is used to determine the variance, i.e., the standard error, around the estimate. Different methods of creating the subsamples yield the different types of replicate weights. The different types of replicate weights include balanced repeated replication (BRR), jackknife (JK-1, JK-2 and JK-n) and successive differences. The choice of what kind of replicate weight to create is determined by the type of sampling design that was used to collect the data, in particular, whether or not stratification was use and how many PSUs were in each strata. If stratification was not used, then the appropriate replicate weight method would be jackknife delete-1. If stratification was used and there were exactly two PSUs per strata, then either BRR (or BRR with Fay's correction) or jackknife delete-2 could be used. If there were more than two PSUs per strata, jackknife delete-n would be used. For a complete and extremely readable treatment of BBR and the various types of jackknife replicate weights, please see the WesVar manual . For more information on successive difference replicate weights, please see Fay and Train (1995) .

Besides protecting the privacy of survey respondents, the replicate weight method has other advantages. One is that the replicate weights can include information other than just about the strata and PSUs. Many surveys have corrections to the probability weight to account for nonresponse, poststratification and/or raking to known totals, such as current Census figures. The effects of these adjustments can be incorporated into the replicate weights. Of course, there are some disadvantages to the replicate weight method. One is seen in extremely large data sets that have a huge number of replicate weights. In such instances, limitations of the software or computer could make the computation time extremely long or not possible. Another disadvantage has to do with the calculation of nonlinear statistics, such as ratios and quantiles. If the number of strata is small, there is a possibility of bias.

One last note about replicate weights. When specifying them in a program, you have to know by which method the replicate weights were created. Your estimates will be inaccurate if you "tell" the program that you have JK-1 replicate weights when in fact the replicates were formed using BRR. If the replicate weights are provided as part of the data set, the documentation will tell you how the replicates were formed. This information can often be found in the section on the calculation of standard errors.

On rare occasions, one may need to create replicate weights for a survey data
set. Several programs can be used for this. WesVar will create
replicate weights, and there is a Stata .ado program by Nicholas Winters called
**svr** (from the Stata command line, type **findit svr** to find and download
this .ado). Within this program is a command called **survwgt** that
will create brr, jk1, jk2 and jkn replicate weights. A general
introduction to the replicate weight method (and Taylor series) can be found in
chapter 4 of Analyzing Complex Survey Data by Eun Sul Lee, Ronald N. Forthofer,
and Ronald J. Lorimor. The mathematical
formulas on which the replicate weights are based can be found in many texts,
including the WesVar 4 manual, which is online at
http://www.westat.com/Westat/pdf/wesvar/WV_4-3_Manual.pdf
Documentation and a bibliography can be found at
http://www.westat.com/Westat/expertise/information_systems/WesVar/wesvar_documentation.cfm . Of course,
Introduction to Variance Estimation by Kirk M. Wolter is the classic in this
area.

Now that we have a general idea about what replicate weights are and why
they need to be used, it is time to use them. For our examples, we will
use the adult CHIS data set (please see
http://www.chis.ucla.edu/ ). The California Health Interview Survey (CHIS)
is divided into several data sets. We will be using the "adult" data set.
In the adult CHIS data set, there are 80 replicate weights created using the
jackknife method (technically, the jackknife delete-2 method). We will use
these and the final pweight, called **rakedw0**, in our **svyset**
command. In addition to specifying the probability weight and replicate weights, we
also need to provide the jackweight adjustment multiplier, which for this data
set, is 1. This information is found in the same part of the survey
documentation that indicates how the replicate weights were created. If
the type of replicate weights was BRR instead of jackknife, we would look to see
if there was a Fay's adjustment.

NOTE: The use of replicate weights is a new feature in Stata 9. The commands below will not work in earlier versions of Stata.

use "D:\temp\adult_chis.dta", clear svyset [pw = rakedw0], jkrw(rakedw1 - rakedw80, multiplier(1))

Now that we have told Stata about the features of our data set, let's make
sure that we have done this correctly. We can use the **svydes** command to do
this. You will notice that, at the bottom of the output, there appears to
be only one stratum and only one observation per unit (PSU). This is
because the information for the stratification and PSUs is contained in the
replicate weights and therefore is not shown in that table.

svydesSurvey: Describing stage 1 sampling units pweight: rakedw0 VCE: linearized jkrweight: rakedw1 rakedw2 rakedw3 rakedw4 rakedw5 rakedw6 rakedw7 rakedw8 rakedw9 rakedw10 rakedw11 rakedw12 rakedw13 rakedw14 rakedw15 rakedw16 rakedw17 rakedw18 rakedw19 rakedw20 rakedw21 rakedw22 rakedw23 rakedw24 rakedw25 rakedw26 rakedw27 rakedw28 rakedw29 rakedw30 rakedw31 rakedw32 rakedw33 rakedw34 rakedw35 rakedw36 rakedw37 rakedw38 rakedw39 rakedw40 rakedw41 rakedw42 rakedw43 rakedw44 rakedw45 rakedw46 rakedw47 rakedw48 rakedw49 rakedw50 rakedw51 rakedw52 rakedw53 rakedw54 rakedw55 rakedw56 rakedw57 rakedw58 rakedw59 rakedw60 rakedw61 rakedw62 rakedw63 rakedw64 rakedw65 rakedw66 rakedw67 rakedw68 rakedw69 rakedw70 rakedw71 rakedw72 rakedw73 rakedw74 rakedw75 rakedw76 rakedw77 rakedw78 rakedw79 rakedw80 Strata 1: <one> SU 1: <observations> FPC 1: <zero> #Obs per Unit ---------------------------- Stratum #Units #Obs min mean max -------- -------- -------- -------- -------- -------- 1 55428 55428 1 1.0 1 -------- -------- -------- -------- -------- -------- 1 55428 55428 1 1.0 1

Next, we will run a simple regression example using **ae13** as the
response (dependent) variable and **ae14** as the predictor (independent)
variable. These variables were chosen at random. Although the command will run (and run faster) without the
jackknife option after the **svy**, you will get linearized standard errors instead
of the jackknife standard error. This jackknife standard error matches the
standard errors produced by both SUDAAN and WesVar.

svy jackknife: regress ae13 ae14Jackknife replications (80) ----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 ................................................. 50 ............................. Survey: Linear regression Number of strata = 1 Number of obs = 55428 Population size = 23847415 Replications = 80 Design df = 79 F( 1, 79) = 648.74 Prob > F = 0.0000 R-squared = 0.2419 ------------------------------------------------------------------------------ | Jackknife ae13 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ae14 | .3356038 .0131763 25.47 0.000 .3093772 .3618305 _cons | 1.884704 .0123061 153.15 0.000 1.86021 1.909199 ------------------------------------------------------------------------------

Because SUDAAN has been able to handle replicate weights much longer than
Stata has, the official documentation for a survey
may include a sample setup for SUDAAN but not for Stata, although you might find
some Stata examples on the web. Don't brush by the SUDAAN example thinking that it is
useless to you as a Stata user; rather, it is often the easiest way to get all
of the information that you need for your **svyset** command. All
survey data analysis programs need to have the same information: the
probability weight, type of replicate weight that is to be used, the names of the replicate
weight variables, and the adjustment factor. These elements are needed
regardless of the type of sampling plan that was used. In SUDAAN, the
probability weight will be listed on the **weight** statement. The type of weight
will be listed in the **design** option on the **proc** statement.
The names of the replicate weights can be found on the **jackwgts** statement
for jackknife replicate weights or on the **repwgt** statement for BRR
replicate weights. The adjustment can be found on the same statement - **
adjjack** for jackknife replicate weights and **adjfay** for BRR replicate
weights.

Some modern data sets are being released with pseudo-strata and pseudo-PSUs. These can be used in a Taylor series linearization just as their non-pseudo counterparts would be. These elements are "pseudo" in the sense that they have been modified so that, while the point estimates and standard errors are estimated correctly, users of the data set are unable to use the strata and PSU information to figure out who individual respondents are. The results obtained using these pseudo elements may differ more from the results obtained using the replicate weights than the results from using the non-pseudo elements. You may find that wider confidence intervals are obtained when using the pseudo-strata and pseudo-PSUs than when using the replicate weights, if both are available in the data set.

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