An Analysis of the Effects of Poststratification on Errors for Estimates 

Using the 2003 Medical Expenditure 

Panel Survey Household Component 

Agency for Healthcare Research and Quality 

U.S. Department of Health & Human Services March 2007 
( ) 

// / / 

/ / /

ABSTRACT 

This report shows the effects of post-stratification of weights for the Household 

Component of the 2003 Medical Expenditure Panel Survey, using various sets of 

marginal control totals on two types of variance estimates for a variety of 

national, regional, and state estimates. The variances estimation methods are a 

balanced repeated replication BRR method, which includes the effects of the 

raking on the variance, and a Taylor series method, which does not include the 

effects of raking. It is shown that although the estimates decrease using the BRR 

method when a limited set of selected controls are added to the raking process, 

the Taylor series estimates for the same values actually increase. 

Suggested Citation: 

Sommers, J. P. An Analysis of the Effects of Post-stratification on Errors for 

Estimates Using the 2003 Medical Expenditure Panel Survey Household 

Components. Methodology Report No. 21. March 2007. Agency for Healthcare 

Research and Quality, Rockville, Md. http: www.meps.ahrq.gov mepsweb

data_files publications mr21 mr21.pdf 

� � �

The estimates in this report are based on the most recent data available at the time 

the report was written. However, selected elements of MEPS data may be revised 

on the basis of additional analyses, which could result in slightly different 

estimates from those shown here. Please check the MEPS Web site for the most 

current file releases. 

Center for Financing, Access, and Cost Trends 

Agency for Healthcare Research and Quality 

540 Gaither Road 

Rockville, MD 20850 

www.meps.ahrq.gov 

ii

The Medical Expenditure Panel Survey (MEPS) 

Background 

The Medical Expenditure Panel Survey (MEPS) is conducted to provide nationally

representative estimates of health care use, expenditures, sources of payment, and 

insurance coverage for the U.S. civilian noninstitutionalized population. MEPS is 

cosponsored by the Agency for Healthcare Research and Quality (AHRQ), formerly the 

Agency for Health Care Policy and Research, and the National Center for Health 

Statistics (NCHS). 

MEPS comprises three component surveys: the Household Component (HC), the 

Medical Provider Component (MPC), and the Insurance Component (IC). The HC is the

core survey, and it forms the basis for the MPC sample and part of the IC sample. 

Together these surveys yield comprehensive data that provide national estimates of the 

level and distribution of health care use and expenditures, support health services 

research, and can be used to assess health care policy implications. 

MEPS is the third in a series of national probability surveys conducted by AHRQ on the 

financing and use of medical care in the United States. The National Medical Care

Expenditure Survey (NMCES) was conducted in 1977, the National Medical 

Expenditure Survey (NMES) in 1987. Beginning in 1996, MEPS continues this series 

with design enhancements and efficiencies that provide a more current data resource to 

capture the changing dynamics of the health care delivery and insurance system. 

The design efficiencies incorporated into MEPS are in accordance with the Department 

of Health and Human Services (DHHS) Survey Integration Plan of June 1995, which 

focused on consolidating DHHS surveys, achieving cost efficiencies, reducing 

respondent burden, and enhancing analytical capacities. To accommodate these goals, 

new MEPS design features include linkage with the National Health Interview Survey

(NHIS), from which the sample for the MEPS-HC is drawn, and enhanced longitudinal 

data collection for core survey components. The MEPS-HC augments NHIS by selecting 

a sample of NHIS respondents, collecting additional data on their health care 

expenditures, and linking these data with additional information collected from the 

respondents’ medical providers, employers, and insurance providers. 

Household Component 

The MEPS-HC, a nationally representative survey of the U.S. civilian noninstitutionalized population, collects medical expenditure data at both the person and household 

levels. The HC collects detailed data on demographic characteristics, health conditions, 

health status, use of medical care services, charges and payments, access to care, 

satisfaction with care, health insurance coverage, income, and employment. 

The HC uses an overlapping panel design in which data are collected through a 

preliminary contact followed by a series of five rounds of interviews over a two and a 

half year period. Using computer-assisted personal interviewing (CAPI) technology, data 

on medical expenditures and use for two calendar years are collected from each

household. This series of data collection rounds is launched each subsequent year on a 

new sample of households to provide overlapping panels of survey data and, when 

iii
combined with other ongoing panels, will provide continuous and current estimates of

health care expenditures. 

The sampling frame for the MEPS-HC is drawn from respondents to NHIS, conducted

by NCHS. NHIS provides a nationally representative sample of the U.S. civilian 

noninstitutionalized population, with oversampling of Hispanics and blacks. 

Medical Provider Component 

The MEPS-MPC supplements and validates information on medical care events reported 

in the MEPS-HC by contacting medical providers and pharmacies identified by household respondents. The MPC sample includes all hospitals, hospital physicians, home 

health agencies, and pharmacies reported in the HC. Also included in the MPC are all 

office-based physicians:

 Providing care for HC respondents receiving Medicaid. 

 Associated with a 75 percent sample of households receiving care through an HMO 

(health maintenance organization) or managed care plan. 

 Associated with a 25 percent sample of the remaining households. Data are collected 

on medical and financial characteristics of medical and pharmacy events reported by

HC respondents, including: 

 Diagnoses coded according to ICD-9 (9th Revision, International Classification of 

Diseases) and DSMIV (Fourth Edition, Diagnostic and Statistical Manual of Mental

Disorders). 

 Physician procedure codes classified by CPT-4 (Current Procedural Terminology, 

Version 4). 

 Inpatient stay codes classified by DRG (diagnosis related group). 

 Prescriptions coded by national drug code (NDC), medication names, strength, and 

quantity dispensed. 

 Charges, payments, and the reasons for any difference between charges and 

payments. 

The MPC is conducted through telephone interviews and mailed survey materials. 

Insurance Component 

The MEPS-IC collects data on health insurance plans obtained through private and 

public sector employers. Data obtained in the IC include the number and types of private 

insurance plans offered, benefits associated with these plans, premiums, contributions by

employers and employees, and employer characteristics. 

Establishments participating in the MEPS-IC are selected through three sampling 

frames: 

 A list of employers or other insurance providers identified by MEPS-HC respondents 

who report having private health insurance at the Round 1 interview. 

 A Bureau of the Census list frame of private-sector business establishments. 

 The Census of Governments from the Bureau of the Census. 

To provide an integrated picture of health insurance, data collected from the first 

sampling frame (employers and other insurance providers) are linked back to data

provided by the MEPS-HC respondents. Data from the other three sampling frames are 

collected to provide annual national and state estimates of the supply of private health 

iv
insurance available to American workers and to evaluate policy issues pertaining to 

health insurance. Since 2000, the Bureau of Economic Analysis has used national 

estimates of employer contributions to group health insurance from the MEPS-IC in the 

computation of Gross Domestic Product (GDP). 

The MEPS-IC is an annual panel survey. Data are collected from the selected

organizations through a prescreening telephone interview, a mailed questionnaire, and a 

telephone follow-up for nonrespondents. 

Survey Management 

MEPS data are collected under the authority of the Public Health Service Act. They are 

edited and published in accordance with the confidentiality provisions of this act and the

Privacy Act. NCHS provides consultation and technical assistance. 

As soon as data collection and editing are completed, the MEPS survey data are released 

to the public in staged releases of summary reports and microdata files. Summary reports 

are released as printed documents and electronic files. Microdata files are released on 

CD-ROM and/or as electronic files.

Printed documents and CD-ROMs are available through the AHRQ Publications 

Clearinghouse. Write or call: 

AHRQ Publications Clearinghouse 

Attn: (publication number) 

P.O. Box 8547 Silver Spring, MD 20907 

800-358-9295 

703-437-2078 (callers outside the United States only)

888-586-6340 (toll-free TDD service; hearing impaired only) 

To order online, send an e-mail to: ahrqpubs@ahrq.gov. 

Be sure to specify the AHRQ number of the document or CD-ROM you are requesting. 

Selected electronic files are available through the Internet on the MEPS Web site: 

http://www.meps.ahrq.gov/

For more information, visit the MEPS Web site or e-mail mepspd@ahrq.gov. 

v
Table of Contents

Background: Post-stratification ....................................................................................... 1

Household Component of the Medical Expenditure Panel Survey 

 and Post-stratification................................................................................................... 4

Results: Taylor Series Error Estimates ............................................................................ 5

Results: Balanced Repeated Replication Error Estimates ................................................ 7

Conclusions and Recommendations ................................................................................ 9

References....................................................................................................................... 9

Appendix ....................................................................................................................... 11

Tables: 

Table A. Average relative standard errors using regional raking and Taylor series, 

 MEPS-HC, 2003........................................................................................................... 6 

Table B. Average relative standard errors using state raking and Taylor series, 

 MEPS-HC, 2003........................................................................................................... 6 

Table C. Average relative standard errors using regional raking and balanced repeated 

 replication, MEPS-HC, 2003 ........................................................................................ 8 

Table D. Average relative standard errors using state raking and balanced repeated 

 replication, MEPS-HC, 2003 ........................................................................................ 8 

vi
An Analysis of the Effects of Post-stratification on Errors 

for Estimates Using the 2003 Medical Expenditure Panel 

Survey Household Component 

John P. Sommers, PhD, Agency for Healthcare Research and Quality

Background: Post-stratification 

Post-stratification is a process used in survey sampling in which estimates from a survey

are normalized to external control totals by adjusting the weights of the sampling units 

so that they add to external totals. These totals are usually Census or other values that

are considered to have no error or far less error than the estimates from the survey. 

Although weight adjustments can be quite complex, a simple example of the most 

straightforward method, cell adjustment, where adjustment is done by adjusting the 

weights for a set of cell as follows: 

Suppose we had a demographic person-level survey where each person had a weight wij 

where i = 1 or 2 and j = 1, 2... ni. Suppose that the value of i indicated the gender of the 

respondent and for some measurement, yij, collected from each person, the gender of the 

person was highly correlated with the value of yij. An example would be the height of 

the respondent. 

Suppose one desired an estimate of the average height of the entire population. Using 

only information from the survey, then the estimate of average height would be 

h

w y w y

w w

j j

j i

j j

j i

j

j i

j

j i

=

+

+

∈ = ∈ =

∈ = ∈ =

∑ ∑

∑ ∑

1 1

1

2 2

2

1

1

2

2

a simple average. 

However, if ci were the census total for the ith group, then a better estimate might be 

 

h

c R c R

c c a = ++

1 1 2 2

1 2 , where 

R

w y

w

i

ij ij

j

n

ij

j

n

i

= =

=

∑

∑

1

1

1

If one defined 

c w $i i

j

ni

= =

∑

1

j

1
then one could see that 

h

c R c R

c c = ++

$ $

$ $

1 1 2 2

1 2

. 

Thus, one has basically formed the new estimate, ha, by replacing estimates of the 

number in each group with a better estimate or census total. If the expected values of the 

R’s for each group are very different and the estimation errors for the R’s are not large 

this new estimate could be much better than the original estimate for the average.

One can see that if one changed the weights to 

wa

c w

c ij

i ij

i

= $

then if these adjusted weights were used in the original estimate the adjusted estimate

would be obtained. Thus, one can use the old form of the estimator and the adjusted 

weights to obtain the new adjusted estimates.

In general, simple cell adjustment post-stratification could break the population into 

many more cells. These cells could be defined by the crosses of several variables. At this 

point, the estimate for the entire data set would be

h

c R

c

a

i i

i

n

i

j

n

c

= =

=

∑

∑

1

1

1

In the cell adjustment method just specified, because adjustments to the weights are the 

same for each person in the cell, the estimates Ri are the original estimates for the 

average for the cell. This is not always the case with weight-adjustment methods. 

If the cells were defined using the cross of more than one cell-defining variable, then the

adjustment is made for cells defined using the cross of all the variables. For instance, if 

one had two gender groups and five age groups, there would be 10 cells and each cell 

would have its population adjusted to the population total, and the R’s would be the 

estimates using the original weights for the rate for the persons with both characteristics 

used to define the cell, persons of both a specific gender and age group. 

There is improvement in precision if the expected values of the R’s are dispersed and the

sampling errors in the R’s are not as large relative to the differences in expected values. 

If the errors are larger than the differences, the errors essentially mask the difference in 

expected values. For example, if the expected R’s were equal in value and their errors

were large because cell samples were small, post-stratification would not help because 

there is no difference in the cell average, so they should be combined to make a better 

estimate of the single R value. In such cases, there could be higher precision if the 

number of cells were reduced. Kish says this happens when the samples in the sub cells 

2
are subject to variability and the sample sizes become significantly out of proportion to 

the cell totals (Kish, 1965). 

The effects of post-stratification on standard errors depend upon the survey and the cells. 

However, its positive effects may be minimal. According to Kish, it seldom results in 

large gains, and Korn and Graubard (1999) imply it has little effect and that most people 

do not consider post-stratification in their error calculations because the difference in 

results would be minimal. Kish also notes that the counts being estimated from the 

survey must be the same item as the control total being used or there is bias in the values

of the post-stratified estimate. Thus, for instance, if the control counts came from

another survey that had a different expected value in its counts per cell due to question 

differences or different methods of administration of the surveys, bias could occur if

counts with different expected values per cell from the other survey were used for poststratification. One should also note that the adjustment of weights may increase the 

variability of the weights and lead to higher errors for domains that are not one of the

weighting cells. 

Another common method of using control totals is raking. In this method, the sum of the 

weights, the estimate of the population for the cell, is again adjusted to population totals. 

However, if multiple variables are used in raking, the weights are adjusted so that the 

marginal totals are equal to the population totals, not the values for the individual cells 

made by crossing the variables. Thus, in our example above with two gender and five 

age classes, the sample totals would equal the population totals for the two gender 

groups and five age groups but not the cell defined by the cross of each a specific gender 

and age group. This type of adjustment is usually done using some type of iterative 

process.(Kalton and Flores-Cervantes, 2003.) 

When raking is used, the values of the R’s for the raked cells are not the original R 

values because weights are changed within the cells. Thus, if, for example, we raked 

over two dimensions, age and race, then if we considered the sum over age: 

ha

c R

c

i i

i

n

i

j

n

c

=

′ =

=

∑

∑

1

1

1

we would need to consider the effect of changed weights on the variance of because 

the raking can change the weights within the cells when raking is done. One needs to be 

cautious, as this unequal weighting may increase the error more than the controlling of 

the weights to population can decrease it. 

R′

Typically, post-stratification cells are defined using age, race/ethnicity, and sex plus 

perhaps a variable that is uniquely related to the purpose of the survey. For instance, the 

National Survey of Family Growth uses number of children born along with the standard 

demographics. Cell classification is likely to be used if sample sizes within cells support 

this method. However, if not, then raking might be used to cut the numbers of cells. 

(Korn and Graubard, 1999) 

3
Household Component of the Medical Expenditure Panel Survey and 

Post-stratification 

The Household Component of the Medical Expenditure Panel Survey (MEPS-HC) is a 

national household survey of the U.S. civilian noninstitutionalized population. The 

survey collects information on the respondent’s health, health care, and health 

expenditures along with certain personal demographic, insurance, and job information. 

Currently, the sample for the HC is selected as a subsample of respondents from the 

National Health Interview Survey (NHIS) (Cohen, 2000). The sample contains an over 

sample of blacks, Hispanics, Asians, and poor (MEPS Web site). The final weights are 

for the HC are created using adjustments to the NHIS weights. The weights are adjusted

for dwelling unit nonresponse, person-level (survey attrition) nonresponse. After 

nonresponse adjustment, the weights are adjusted using a complex raking poststratification adjustment that uses multiple sets of controls developed using sets of cells 

defined using crosses of age, race/ethnicity, sex, region, poverty status, and metropolitan

statistical area (MSA) status (whether one lives in an MSA) (Alvarez-Rojas, 2005). The 

purpose of this analysis is to gain knowledge of the effects of raking post-stratification 

on MEPS-HC error estimates. 

To begin this analysis, we created a large number of sets of post-stratified weights for 

the MEPS-HC 2003 data. These weights were created by raking to an array of control 

totals defined by the factors region, state, race/ethnicity, age, MSA status, poverty status, 

and gender. Basically, we created a set of weights within region that were raked to each 

of the other factors except state and gender, individually, then using these four marginals 

raked to each combination of two marginals, then combinations of three marginals, and 

four marginals. We then did the case with five marginals by including the gender 

marginal control. The list of different sets of marginals considered can be seen more

clearly in the next sections, which discuss results. We did not do any weights where the 

controls were defined by the cross of two of the controls within region. So, when we say

we raked to the region, by age and MSA status, we mean we raked to the age and MSA

status marginals within each of the four regions. We repeated the process by using a set 

of state-defined geographic cells where each of the 30 largest states was an individual

cell similar to a region, and the remaining states within each region made up four more 

cells. Thus, if we say we raked to state by age and MSA status, it means we raked to the 

state marginal totals for age group and MSA status group within each of the 34 state 

groups. This created a much finer break. We did only the 30 states individually because 

from experience we knew that for the remaining states the samples were very small and 

estimates could not be made for the smallest states. The definitions of cells that define

the marginals can be seen in the Appendix. We also created national, regional, and state 

post-stratifications by using the geographic controls to do a cell adjustment for each of 

the three sets of geographic cells. These were done as base cases. Note: We did not trim 

the weights as is done in the actual raking with the MEPS-HC, since in 2003 only one 

weight was trimmed and our largest weights after raking were not much different than 

those in operations after trimming. Thus, we believed for this project trimming was not 

necessary. 

 

With each set of weights, we created a set of national-, regional-, and state-level 

estimates for six types of expenditure classes: all, dental, prescription drugs, office

based, hospital outpatient, and hospital inpatient. For each class, we created estimates of 

the percentage of persons with an expenditure, the mean expenditure for those with an 

4
expenditure, the percentage paid out of pocket, and the percentage paid by private 

insurance. For each estimate, we created a variance estimate using two methods: 

 Taylor series method, which does not consider the effect of post-stratification of 

errors. 

 Balanced repeated replication (BRR), where the sets of weights were raked for each 

of the replicates to the control totals. This method should show effects of the poststratification on the error estimates. (Wolter, 1985) 

Since the MEPS-HC sample that is post-stratified to the set of point-in-time control 

totals is the set of persons in the sample who are still living at the end of the survey year, 

this sample was the only sample used in this study. This sample is the vast majority of 

the total MEPS-HC sample, and the relative results should still hold when the small 

amount of other sample is included in estimates. This dropped sample includes a small 

number of persons in the total sample who die during the year or who are in the civilian 

noninstitutionalized population for only part of the year. This small sample does not go 

through the post-stratification process we are discussing. (MEPS Web site) 

Results: Taylor Series Error Estimates 

The results of Taylor Series estimation of relative standard errors (RSE) using the 

various sets of raked weights are given in tables A and B. Table A has the results for 

estimates made with weights raked at the regional level, and table B has results where 

the raking cells were created at the state level. For each sets of raked weights, there are 4 

averages given. The first is for 120 estimates of means made at the national and regional 

levels. The second set is for the 816 mean estimates at the state level. The third set is for 

estimates of sums made at the national and regional levels. The fourth set is for 

estimates of sums made at the state level. We divided the results into these categories 

because these groupings show the key differences in results. In reviewing these results, 

one must remember that the Taylor Series estimates of error do not reflect the effects of

post-stratification. 

The following are some key features of these results: 

 There is not a wide range of results for any of the types of estimates. 

 For means for each set of estimates (column), the average errors are higher for poststratification to the same defining variables done at the state than the regional level. 

(For example, the results for means for the weights raked to poverty at state level are 

higher than those for weights raked to poverty at the regional level.) 

 For means for each set of estimates, there is a high correlation between the standard 

errors of the distributions of the sets of weights and the average standard errors

obtained with the set of weights. This is true for each of the two mean columns taken

individually in table A or table B. It is also true for each of the two columns if the 

results from tables A and B are pooled. Since the state post-stratifications in table B 

have many more sets of controls, this would imply the fact reported in the second 

feature above. 

5
Table A. Average relative standard errors using regional raking and Taylor series, MEPS-HC, 2003

 Average relative standard errors 

Mean estimates Sum estimates 

Regional raking cells Std error of 

weights 

Regional/national

estimates

State 

estimates

Regional/national

estimates

State 

estimates

National* 6001 0.05523 0.13637 0.08981 0.32013 

None** 6012 0.05524 0.13637 0.08980 0.32013 

Age 6092 0.05581 0.13672 0.09015 0.32018 

Ethnicity 6006 0.05529 0.13639 0.08990 0.32015 

MSA 5958 0.05462 0.13604 0.08927 0.31809 

Poverty 6069 0.05529 0.13629 0.09007 0.32035 

Age, Ethnicity 6075 0.05584 0.13674 0.09021 0.32019 

Age, MSA 6040 0.05513 0.13638 0.08960 0.31818 

Age, Poverty 6145 0.05587 0.13672 0.09036 0.32037 

Ethnicity, MSA 5922 0.05461 0.13606 0.08924 0.31798 

Poverty, Ethnicity 6049 0.05528 0.13627 0.09003 0.32035 

Poverty, MSA 6037 0.05463 0.13587 0.08953 0.31840 

Age, Ethnicity, MSA 5994 0.05506 0.13643 0.09009 0.32001 

Age, Poverty , Ethnicity 6112 0.05586 0.13672 0.09030 0.32036 

Age, Poverty, MSA 6107 0.05514 0.13632 0.08980 0.31844 

Age, Ethnicity, Sex 6078 0.05589 0.13678 0.09025 0.32024 

Poverty, Ethnicity, MSA 5983 0.05453 0.13582 0.08936 0.31823 

Age, Poverty , Ethnicity, MSA 6051 0.05505 0.13630 0.08961 0.31827 

Age, Poverty , Ethnicity, MSA, Sex 6052 0.05506 0.13631 0.08961 0.31831 

*

Raked to national total only **Raked to regional totals only, no sub-regional raking

Table B. Average relative standard errors using state raking and Taylor series, MEPS-HC, 2003 

 Average relative standard errors 

Mean Estimates Sum Estimates 

State raking cells Std error 

of weights 

Regional/national

estimates

State 

estimates

Regional/national

estimates

State 

estimates

National* 6001 0.05523 0.13637 0.08981 0.32013

None** 6210 0.05624 0.13637 0.08844 0.32013

Age 6334 0.05660 0.13735 0.08831 0.31971

Ethnicity 6317 0.05632 0.13603 0.08862 0.32016

MSA 6368 0.05624 0.13771 0.0887 0.31943

Poverty 6404 0.05681 0.13760 0.08884 0.32065

Age, Ethnicity 6435 0.05660 0.13689 0.08843 0.31971

Age, MSA 6485 0.05658 0.13865 0.08867 0.31924

Age, Poverty 6522 0.05697 0.13867 0.08849 0.32019

Ethnicity, MSA 6474 0.05637 0.13734 0.08906 0.31969

Poverty, Ethnicity 6482 0.05673 0.13706 0.08887 0.32072

Poverty, MSA 6565 0.05661 0.13854 0.08890 0.32002

Age, Ethnicity, MSA 6585 0.05634 0.13809 0.08901 0.32124

Age, Poverty , Ethnicity 6600 0.05677 0.13803 0.08839 0.32025

Age, Poverty, MSA 6673 0.05674 0.13958 0.08860 0.31969

Age, Ethnicity, Sex 6445 0.05660 0.13697 0.08842 0.31968

Poverty, Ethnicity, MSA 6643 0.05663 0.13795 0.08918 0.32029

Age, Poverty, Ethnicity, MSA 6752 0.05665 0.13898 0.08871 0.31995

Age, Poverty, Ethnicity, MSA, Sex 6760 0.05662 0.13911 0.08865 0.31989

*

Raked to national total only **Raked to state totals only, no sub-state raking

6
Results: Balanced Repeated Replication Error Estimates 

The BRR results are shown in tables C and D. Table C contains results of average RSE’s 

for regional post-stratifications and table D state-level post-stratifications. The BRR 

results have estimates that are at overall levels similar to those of the Taylor Series. 

However, the patterns and relationships among the results are different since these 

results reflect the effect of post-stratification on the variances. 

Some of the key results from the BRR results include the following: 

 Post-stratification has a small but significant effect on the errors of the estimates. 

For instance, for regional and national estimates and regional post-stratification, the 

best results for means decline about 4 percent relative to the estimates with simple 

one-cell national-level post-stratification. 

 Adding raking variables generally improves results. Results with two variables are 

generally better than those with one variable, three variables better than two, etc. 

Adding the fifth variable, sex, only seems to help for totals. However, this may be 

because sex is a weak predictor, not because we have reached any limit caused by

using too many cells. 

 Of the four main variables used, age, ethnicity, MSA status, and poverty, there 

seems to be no clear pattern of variables that consistently gives better results in 

terms of precision. For instance, for regional-level means, the variable that singly

gave the best improvement when raking to region crossed with that variable, poverty

seems to be the single most important variable. But for the state-level estimates with 

state raking, age seems to be the single most important variable. It may be that

because improvements in results are so small, although all the variables improve 

results, as seen with the overall downward trend when more variables are added, that 

it is impossible to order the variables in their overall effect. 

 Regional raking works best for regional results and state raking works best for state 

results. State-level raking for regional and national estimates may create too many

cells for those types of estimates. 

 Any type of geographic raking to a geographic level equal or below that of the 

estimates has a marked effect on the errors of totals. State or regional raking helped 

regional estimates for totals significantly. State-level raking improved state 

estimates, but regional raking did not. 

 There was no significant correlation within groups of estimates with the standard 

errors of the estimates. 

7
Table C. Average relative standard errors using regional raking and balanced repeated replication, MEPS 

 Average relative standard errors 

Mean estimates Sum estimates 

Regional raking cells Std error of 

weights 

Regional/national

estimates

State 

estimates

Regional/national

estimates

State 

estimates

National* 6001 0.05505 0.14061 .09431 .31878 

None** 6012 0.05504 0.14061 .06794 .31265 

Age 6092 0.05452 0.14044 .06675 .31175 

Ethnicity 6006 0.05481 0.14065 .06870 .31156 

MSA 5958 0.05431 0.14027 .06748 .31042 

Poverty 6069 0.05428 0.14063 .06712 .31238 

Age, Ethnicity 6075 0.05424 0.14046 .06629 .31067 

Age, MSA 6040 0.05379 0.14011 .06610 .30959 

Age, Poverty 6145 0.05393 0.14030 .06614 .31145 

Ethnicity, MSA 5922 0.05402 0.14027 .06666 .30908 

Poverty, Ethnicity 6049 0.05422 0.14038 .06706 .31141 

Poverty, MSA 6037 0.05349 0.13990 .06628 .31025 

Age, Ethnicity, MSA 5994 0.05342 0.14035 .06598 .31018 

Age, Poverty , Ethnicity 6112 0.05383 0.14075 .06598 .31049 

Age, Poverty, MSA 6107 0.05315 0.13992 .06544 .30939 

Age, Ethnicity, Sex 6078 0.05429 0.14053 .06623 .31078 

Poverty, Ethnicity, MSA 5983 0.05333 0.13991 .06616 .30903 

Age, Poverty , Ethnicity, MSA 6051 0.05298 0.13996 .06522 .30820 

Age, Poverty , Ethnicity, MSA, Sex 6052 0.05302 0.14001 .06513 .30830 

*

Raked to national total only **Raked to regional totals only, no sub-regional raking

Table D. Average relative standard errors using state raking and balanced repeated replication, MEPS-HC, 2003 

 Average relative standard errors 

Mean estimates Sum estimates 

State raking cells Std error of 

weights 

Regional/national

estimates

State 

estimates

Regional/national

estimates

State 

estimates

National* 6001 .05505 .14061 .09431 .31878

None** 6210 .05632 .14061 .06868 .18075

Age 6334 .05596 .13746 .06777 .17516

Ethnicity 6317 .05626 .13981 .06870 .18032

MSA 6368 .05555 .13948 .06731 .17889

Poverty 6407 .05637 .14063 .06877 .18068

Age, Ethnicity 6435 .05585 .13664 .06629 .17473

Age, MSA 6485 .05512 .13621 .06669 .17325

Age, Poverty 6525 .05574 .13763 .06725 .17490

Ethnicity, MSA 6473 .05551 .13861 .06748 .17824

Poverty, Ethnicity 6484 .05611 .13950 .06853 .18021

Poverty, MSA 6568 .05544 .13902 .06736 .17833

Age, Ethnicity, MSA 6585 .05476 .13519 .06671 .17392

Age, Poverty , Ethnicity 6602 .05540 .13643 .06693 .17459

Age, Poverty, MSA 6677 .05477 .13582 .06612 .17250

Age, Ethnicity, Sex 6444 .05583 .13658 .06749 .17459

Poverty, Ethnicity, MSA 6646 .05527 .13787 .06737 .17788

Age, Poverty , Ethnicity, MSA 6585 .05455 .13465 .06604 .17224

Age, Poverty , Ethnicity, MSA, Sex 6762 .05453 .13473 .06590 .17218

*

Raked to national total only **Raked to state totals only, no sub-state raking

8
Conclusions and Recommendations 

From this current research performed until now on post-stratification of MEPS-HC data,

we see that: 

 Post-stratification with the key non-geographic HC variables for the most part has 

improved results. No one variable seems to be the most effective over all types of 

estimates.. 

 Post-stratification by state is useful for state estimates, especially totals, but not 

higher-level geographic estimates. 

 It appears that the use of all the five raking dimensions selected does not cause the 

quality of results to diminish when done at the regional level. This would indicate 

that more post-stratification dimensions could possibly be added at the regional level 

without decreasing the quality of the results. 

 Taylor Series estimates of error are of the same magnitude as the BRR estimates that

take into account the effects of post-stratification. However, they increase slightly as 

the weights are post-stratified, even for post-stratification that actually improves the

actual expected error indicated with BRR error estimates that consider the effects of 

post-stratification. 

It is recommended that: 

 State estimates be made with data that is post-stratified to state control totals plus at 

least age and ethnicity. 

 More work be done with post-stratification at the regional level using somewhat 

more complex marginal control totals. For instance, one might want to add cross 

classifications of some the variables for the regional marginal controls; e.g., one 

might use age crossed with ethnicity or MSA status control totals. 

 Other variables be tried in the raking that are not now used in the HC raking. An 

example would be marital status. 

 The effect of post-stratification at the national level using more complex sets of 

cells, such as cells defined by crosses of three variables, be examined for nationaland regional-level estimates.

 The effect of the current HC post-stratification be evaluated. 

References

Alvarez-Rojas, L. (2005). Panel 7 and Panel 8 combined, Full Year 2003: raking person 

weights including the poverty status to obtain the Expenditure person weights. 

Memorandum to Tom Hankins. Internal Memorandum, WGTS 1230.02, D-P7P8FY03: 

10.02, Rockville, Md.: Westat, Inc. 

9
Cohen, S. B. Sample Design of the 1997 Medical Expenditure Panel Survey Household 

Component. MEPS Methodology Report No. 11 AHRQ Pub No. 01-0001. AHRQ: 

Rockville, Md.: 2000. 

Kalton, G. and Flores-Cervantes, I. (2003). Weighting Methods. Journal of Official

Statistics, 19, 81-97. 

Kish, L. Survey Sampling. New York: Wiley: 1965. 

Korn, E. L. and Graubard, B. I. Analysis of Health Surveys. New York: Wiley: 1999. 

MEPS Web site. HC-082 2004 Full Year Population Characteristics File (replaced by

HC-089 Full Year Consolidated Data File). http://www.meps.ahrq.gov/ 

mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-089

Wolter, K. M. Introduction to Variance Estimation. New York: Springer-Verlag: 1985. 

10
Appendix 

Variable Sex 

Male 

Female 

Variable MSA 

non-MSA 

MSA 

Variable Region 

Northeast 

Midwest 

South 

West 

Variable Poverty 

Poor/near poor (less than 125 percent of poverty line) 

Low (125 percent to 200 percent of poverty line) 

Middle (200 percent to 400 percent of poverty line) 

High (over 400 percent of poverty line) 

Variable Ethnicity 

Hispanic 

Non-Hispanic black 

Others 

Variable Age 

0 

1-19 

20-29 

30-44 

45-64 

65 and over 

11