External Quality Review of Medicaid MCOs and Supporting Regulations in 42 CFR 438.360, 438.362, and 438.364

2010 EXTERNAL QUALITY REVIEW (EQR) PROTOCOLS
APPENDIX II: SAMPLING APPROACHES

TABLE OF CONTENTS
PURPOSE OF THE APPENDIX ................................................................................................ 1
PROBABILITY SAMPLING ........................................................................................................ 1
NON-PROBABILITY SAMPLING ............................................................................................... 3

PURPOSE OF THE APPENDIX
This Appendix provides an overview of potential sampling methods that may be used in
Protocols 3, 5, 7, and 8. A statistician or staff with expertise in the design and
implementation of sampling should advise the State and/ or EQRO of the most
appropriate sampling strategy.

PROBABILITY SAMPLING
Probability (or random) sampling methods leave selection of population units totally to
chance and not to preference on the part of the individuals conducting or otherwise
participating in the study. Biases are removed in these methods. There are several
types of probability (or random) sampling:
Simple Random Sampling
Simple random sampling is used when members of the study population have an equal
chance of being selected for the sample. Population members are numbered and
random numbers generated by a computer select units from the population. This
sampling approach ensures that all members of the target population have an equal
chance of selection and assure the sample is fully representative of the population.
Systematic Random Sampling
Systematic random sampling is used when the nth unit in a list is selected. This can be
used when a sampling frame is organized in a way that does not bias the sample.

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Sampling Approaches
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Steps to organize and select a systematic sample are:
1. Construct a comprehensive sampling frame (e.g., list of all beneficiaries);
2. Divide the size of the sampling frame by the required sample size to produce
a sampling interval or skip interval (e.g., if there are 250 beneficiaries and a
sample of 25 is needed, then divide 250/25 = 10);
3. From a random number table select a random number between 1 and 10;
4. Count down the list to get the Nth name (i.e., the # identified in step 3);
5. Skip down 10 names on the list and select a second name. Repeat the
process as many times as needed until the required sample size has been
reached.
Stratified Random Sampling
Stratified random sampling is used when the target population consists of independent
sub-groups or strata. This technique divides the population into specific, strata or
subgroups that are homogeneous (same) within a strata and heterogeneous (different)
between strata with respect to certain characteristics such as ethnicity (e.g., Hispanic,
non-Hispanic), age (e.g., under 30, over 30, or diagnosis (e.g., diabetic, non-diabetic).
Stratification is done both to improve the accuracy of estimating the total population’s
characteristics and to provide information about the characteristics of interest within
subgroups. Stratified random sampling requires more information about the population
and requires a larger overall sample size than simple random sampling. Once strata
are identified and selected, sampling must be conducted within each strata using
probability (or random) sampling. As a result, it is typically more expensive than simple
random sampling. Stratified sampling may also involve “weighting” the sample. In this
process, a survey selects a disproportionately larger number of units of analysis from
one or more of the strata to allow the survey to produce information on that particular
stratum (e.g., individuals dually receiving both Medicare and Medicaid).
Cluster Sampling
Cluster sampling is used when a comprehensive sampling frame is NOT available.
Units in the population are gathered or classified into groups, similar to stratified
sampling. Unlike the stratified sampling method, the groups must be heterogeneous
with respect to the measured characteristic. This method requires prior knowledge
about the population. Once clusters are identified, a random sample of clusters is
selected.

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Sampling Approaches
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NON-PROBABILITY SAMPLING
Non-probability sampling methods are used when subjects are scarce and the study
relies on volunteers, or for comparisons of a subset of the population with a large
population or comparisons of non-stratified groups. They are based on the choice of
those administering the survey rather than chance; therefore, some bias can be
expected. Non-random sampling methods do not lend themselves to statistical
analysis. Considering the risk of biased results and the obstacles to statistical analysis,
non-probability sampling is discouraged. However, at times it can be an appropriate
and efficient way of collecting needed information. The following are types of nonprobability sampling:
a. Judgment sampling- units are selected based on whether they are judged to be
representative of the population. By doing so, the sample is constructed to be a
sub-population.
b. Convenience sampling- uses readily available or convenient units. For example,
if the objective was beneficiary opinions regarding a group practice, patients in
the office on any given day or during a specific month could be interviewed.
c. Quota sampling- ensures that units in the sample appear in the same proportion
as in the population. For instance, if a certain target population is 55 percent
female and 45 percent male, the quota sample requires a similar female/male
distribution.

EQR Protocol: Appendix II
Sampling Approaches
December 2011

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