Sampling
techniques & sample
Dr. Moataza Mahmoud Abdel
Wahab
Lecturer of Biostatistics
High Institute of Public
Health
University of Alexandria
Important statistical terms
Population:
a set which includes all
measurements of interest to the researcher (The collection of all responses, measurements, or counts
that are of interest)
Sample:
A subset of the population
Why sampling?
Get information about large populations
ê Less costs
ê Less field
time
é More
accuracy i.e. Can Do A Better Job of Data
Collection
î When it’s
impossible to study the whole population
Target Population: The population to be studied/ to which the investigator wants to
generalize his results
Sampling Unit: Smallest unit from which sample can be selected
Sampling frame: List of all the sampling units from which sample is
drawn
Sampling scheme: Method of selecting sampling units from sampling frame
Types of sampling
n Non-probability samples
n Probability samples
Non probability samples
Probability
of being chosen is unknown Cheaper- but unable to generalise potential for bias
Ø Convenience
samples (ease of access)
Sample is selected from elements of a population that are easily
accessible
Ø Snowball
sampling (friend of friend….etc.)
Ø Purposive
sampling (judgemental)
Ø You chose
who you think should be in the study
Ø Quota
sample
Probability samples
n Random
sampling
n Each
subject has a known probability of being selected
n Allows
application of statistical sampling theory to results to:
n Generalise
n Test
hypotheses
Conclusions
n Probability samples are the best
n Ensure
n Representativeness
n Precision
Methods used in probability samples
Ø Simple random sampling
Ø Systematic sampling
Ø Stratified sampling
Ø Multi-stage sampling
Ø Cluster sampling
Simple random sampling
Table of random numbers
6 8 4 2 5 7 9 5 4 1 2 5 6 3 2
1 4 0
5 8 2 0 3 2 1 5 4 7 8 5 9 6 2
0 2 4
3 6 2 3 3 3 2 5 4 7 8 9 1 2 0
3 2 5
9 8 5 2 6 3 0 1 7 4 2 4 5 0 3
6 8 6
Systematic sampling
Sampling
fraction Ratio between sample size and population size
Cluster sampling Cluster: a group of sampling units close to
each other i.e. crowding together in the same area or neighborhood
n Stratified
sampling
n Multi-stage
sampling
Errors in sample
Ø Systematic error (or bias) Inaccurate response (information bias) Selection bias
Ø Sampling error (random error)
Type 1 error
n The probability of finding a
difference with our sample compared to population, and there really isn’t one….
n Known as the α
(or “type 1 error”)
n Usually set at 5% (or 0.05)
Type 2 error
n The probability of not
finding a difference that actually exists between our sample compared to the
population…
n Known as the β (or “type 2
error”)
n Power is (1- β) and is
usually 80%
Problem 1
A study is to be performed to determine a certain
parameter in a community. From a previous study a sd of 46 was obtained.If a sample error of up to 4 is to
be accepted. How many subjects should be included in this study at 99% level of
confidence?
Answer
Problem 2
A
study is to be done to determine effect of 2 drugs (A and B) on blood glucose
level. From previous studies using those drugs, Sd of BGL of 8 and 12 g/dl were obtained respectively.A
significant level of 95% and a power of 90% is required to detect a mean
difference between the two groups of 3 g/dl. How many subjects should be
include in each group?
Answer
n Problem 3
It
was desired to estimate proportion of anaemic children in a certain preparatory
school. In a similar study at another school a proportion of 30 % was detected.Compute
the minimal sample size required at a confidence limit of 95% and accepting a
difference of up to 4% of the true population.
Answer
n Problem 4
In previous studies,
percentage of hypertensives among Diabetics was 70% and among non diabetics was
40% in a certain community.A
researcher wants to perform a comparative study for hypertension among
diabetics and non-diabetics at a confidence limit 95% and power 80%, What is
the minimal sample to be taken from each group with 4% accepted difference of
true value?
Answer
