This document discusses population sampling methods for healthcare research. It defines target, source, sample, and study populations and provides examples of each. Several types of probability sampling techniques are mentioned. Vulnerable populations require extra consideration in research due to increased risks. Sample size is important, as larger samples better reflect the true population and provide more statistical power. Sample size calculators can estimate appropriate sample sizes based on study design and expected population characteristics. Statistical power must be considered to ensure sample sizes are large enough to detect real effects if present. The study approach may need revising if power is insufficient.

1. PHC215
By Dr. Khaled Ouanes Ph.D.
E-mail: k.ouanes@seu.edu.sa
Twitter: @khaled_ouanes
INTRODUCTION TO
HEALTHCARE RESEARCH
METHODS

2. Primary Studies:
Selecting a Sample Population

3. Types of Research Populations
At least 4 different types of populations must be
considered when preparing to collect data:
1. The results of the study should be applicable to the target
population
2. The source population is a well-defined subset of individuals
from the target population
3. The sample population is the individuals from the source
population who are asked to participate
4. The study population is the members of the sample population
who actually participate in the study

4. Types of Research Populations

5. Target Populations
 A well-defined study question identifies a target
population to which the results of the study should
apply.
 A target population might be quite narrow (like one
wing of a long-term acute care hospital) or relatively
large (like a whole country).
 Unless the target population is very small, measuring
the entire target population or even randomly
sampling from it may be impossible.

6. Source Populations
A source population (AKA sampling frame) consists of an
enumerated list of population members.
Example:
 All students of the same faculty/College
 All women with a breast cancer diagnosis in the past 5 years
who are indexed in a particular cancer registry
 All members of a professional sports league
 All households within 2 miles of a particular nuclear power
plant

7. Sample Populations
A source population is often much larger than
the sample size required for a study. In this
situation, only a portion of the source population
is selected to serve as a sample population.
A variety of probability-based sampling
techniques can be used to select a sample
population.

8. Examples of Types of
Probability Sampling

9. Sometimes a non-probability-based convenience
population can be selected based on the ease of access
to those individuals, schools, or communities.
However, convenience sampling must always be used
with caution. Convenient sample populations are often
systematically different than the communities they are
intended to represent.
Sample Populations

10. Study Populations
 The study population will consist of the members of
the sample population who can be located, who
consent to participation, and who meet all eligibility
criteria.
 A 100% participation rate is extremely rare.
 A low response rate may result in nonresponse bias
if the members of the sample population who agree
to be in the study are systematically different from
nonparticipants.

11. Study Populations
A less than 100% participation rate is usually not a
problem as long as the researcher:
 Uses suitable and carefully explained sampling
methods
 Takes appropriate steps to maximize the participation
rate
 Recruits an adequately large sample size

12. Cross-Sectional Surveys
 The goal of most cross-sectional surveys is to
describe a specific target population accurately.
 Convenience samples rarely result in a study
population that is representative of the target
population.
 Ideally, the researcher needs some way to confirm
that the source population is similar to the target
population and that the sample population is similar
to the source population.

13. Population
Example for
a Cross-
Sectional
Survey

14. Case-Control Studies
All cases must have the same disease,
disability, or other health-related condition.
The controls must be similar to the cases in
every way except for their disease status, so
cases and controls should be drawn from
populations with similar demographics.

15. Population
Example for
a Case-
Control
Study

16. Cohort Studies
 Longitudinal cohort studies: the participants should be
representative of the source and target populations  The
requirements for longitudinal studies are similar to those for
cross-sectional studies, since both study designs recruit
population-based samples.
 Prospective / retrospective cohort studies: the exposed and
unexposed should be drawn from similar populations  The
recruitment of exposed and unexposed for cohort studies is like
the recruitment of the cases and controls for case-control
studies.

17. Population
Example for a
Cohort Study

18. Experimental Studies
 Experimental studies require a source population
that is reasonably representative of the target
population.
 Safety is always the top priority in designing an
experimental study. The risk of harm to participants
can be reduced by selecting an appropriate source
population and defining strict inclusion and
exclusion criteria.

19. Population
Example
for an
Experiment
al Study

20. Vulnerable Populations
 Vulnerable populations in health research include some
people with poor health, some people with limited decision-
making capacity, and members of some socially
marginalized groups, among others.
 Despite the potential risks of including members of these
populations in research studies, including them is the only
way to study health issues in these groups.
Example: The health of prisoners can only be studied by conducting research in
prisons.

21.  Research conducted with members of vulnerable
populations requires extra consideration of the
potential risks of research to participants.
 The ability of every participant to provide informed
consent free from coercion must be assured.
 Concerns about the increased risks of adverse
effects from study participation must be addressed.
Vulnerable Populations

22. Community Involvement
Some studies benefit from or require the
participation and/or support of whole
geographic, cultural, or social communities
and their leaders.
Community-based studies often work best
when they use methods such as those
developed for Community-Based Participatory
Research.

23. Primary Studies:
Estimating Sample Size

24. Importance of Sample Size
An adequate number of study
participants is required to achieve valid
and significant results

25. Importance of Sample Size

26. Importance of Sample Size
What you saw in the previous slide are 2 distributions of possible sample means
for 20 people (n=20) and 40 people (n=40), both drawn from the same
population. On each we have superimposed a sample mean weight change of
3kg. The curves are both centered on zero to indicate a null hypothesis of "no
difference" (ie. that the diet has no effect). It is more likely to be significant when
n=40 because the distribution curve is narrower and 3kg is more extreme in
relation to it than it is in the n=20 scenario, which points to how you can increase
the power of your experiment. The reason the n=40 curve is spikier is because of
something called the standard error of the mean.
Essentially, the larger the sample sizes, the more accurately the
sample will reflect the population it was drawn from, so it is
distributed more closely around the population mean. (Except
for some genetics studies)

27. Sample Size and Means

28. Bigger Samples Are Better
Large samples from a population are usually
better than small ones at yielding a sample
mean close to the true population value.

29.  When the sample size is small, the sample mean may
be quite far from the mean in the total population
from which the sample was drawn. This is represented
by a wide confidence interval that reaches far from
the sample mean.
 When the sample size is large, the sample mean is
expected to be close to the population mean, and
the confidence interval will be narrower.
Bigger Samples Are Better

30. Larger Samples from a Population Have a
Narrower 95% Confidence Interval Than Smaller
Samples

31. So, the goal is to recruit just the right number of
participants based on statistical estimations of how
many people are required to answer the study
question with a specified level of certainty.
 If more participants are recruited than are
statistically required, resources are wasted.
 If too few participants are recruited, the whole study
will be almost worthless because there will not be
enough statistical power to answer the study
question.
Importance of Sample Size

32. Sample Size Estimation
A sample size calculator – more accurately called a
sample size estimator – should be used to identify an
appropriate sample size goal.
Sample size estimators suggest an appropriate
minimum sample size based on a series of “best
guesses” the researcher makes about the expected
characteristics of the sample population.
When in doubt, err on the size of a larger sample!

33. Examples of
Sample Size
Calculation

34. Power Estimation
Another way to check for sample size requirements is
to work backward from the number of participants
likely to be recruited to see whether that sample size
provides adequate statistical power for the study
design.
Statistical power is the ability of a statistical test to
detect significant differences in a population when
differences really do exist.

35. Sometimes a sample population does not capture the
true experience of the population:
 Type 1 errors (α) occur when a study population
yields a significant statistical test result when one
does not exist in the source population.
 Type 2 errors (β) occur when a statistical test of data
from the study population finds no significant result
when one actually exists in the source population.
 Power = 1 – β
Power Estimation

36. Power
and
Errors

37. Examples of Power Calculation

38. Refining the Study Approach
Be prepared to rethink the study
question, study approach, and/or target
and source populations if the power for
the estimated number of participants is
not sufficient.

39. PHC215
By Dr. Khaled Ouanes Ph.D.
E-mail: k.ouanes@seu.edu.sa
Twitter: @khaled_ouanes
HEALTHCARE RESEARCH METHODS
Based on the textbook of introduction to health research methods – K.H. Jacobsen