The method you use to select this sample is known as your Sampling Technique.

1. Egyptian Ministry of Health and Population
Training and Research Sector
Administration of Research and Health Development

2. Vocabulary key points
• Definitions
• Significance
• Population vs. sample
• Sampling frame
• Probability sampling methods
• Non-probability sampling methods

3. https://hotcubator.com.au/research/a-complete-guide-to-sampling-techniques
/

4. Definitions

5. • Population; An entire set of persons, animals, objects or events which the researcher intends
to
study.
• Sample; A subset selected for the study from the larger population.
• Randomization; Randomization is the process of making something random; in various
contexts this involves.Randomization is the arrangement of experimental units to simulate
a chance distribution, reduce the interference by irrelevant variables, and yield unbiased
statistical data.
• Probability sampling; refers to the selection of a sample from a population, when this
selection is based on the principle of randomization, that is, random selection or chance.
Probability sampling is more complex, more time-consuming and usually more costly than
non-probability sampling.
• Non-probability sampling; is defined as a sampling technique in which the researcher selects
samples based on the subjective judgment of the researcher rather than random selection. It is a
less stringent method. This sampling method depends heavily on the expertise of the

6. researchers. It is carried out by observation, and researchers use it widely for qualitative
research.
• Assignment; The process in an experiment where the researcher allocates subjects to two or
more groups, trying to achieve having groups as identical as possible to allow a valid
comparison of the results. Matching and random assignment are the two most common
methods.
• Blinding; A randomized controlled trial may be blinded if participants in the trial are likely to
change their behavior in a systematic way that may influence the outcome of the study when
they are aware of which intervention they receive. The term “masking” is often used instead of
“blinding”.
• Matching; A sampling method to ensure that the two groups to be compared have similar
characteristics. In an intervention study, pairs of similar “matched” subjects are formed and then
one member of the pair is randomly assigned to one group and the other member to the
othergroup.
• Selection bias; A systematic difference between people who are selected for a study and those
who are not selected.

7. Significance

8. • Gathering data is the most crucial part whether in research or evaluation projects.
• Once the investigator knows what the research question is, the next important factor is to
identify the determination of the research approach and then select the population and sample of
the study.
• Choosing the correct sampling technique is absolutely vital and determines the success or
failure of a research or evaluation project.
• Because, sampling the wrong subject will not provide the correct information as it is intended
by the research or evaluation question.
• In general terms, sampling is the process of identifying the representative group of subjects
from the population.

9. • The word population in the realm of research denotes the total group of subjects identified in
a study.
• The selected group of samples are usually known as ‘Participants’ or ‘Respondents’.
• The method you use to select this sample is known as your Sampling Technique.
• When you conduct research about a group of people, it’s rarely possible to collect data from
every person in that group.
• Instead, you select a sample. The sample is the group of individuals who will actually
participate in the research.
• To draw valid conclusions from your results, you have to carefully decide how you will select
a sample that is representative of the group as a whole.

10. Story
• Sometimes, it is thought that it is possible to improve on a random allocation by altering the
allocation to make it “fairer.”
• An example of the disaster that this can cause is provided by the famous Lanarkshire milk
experiment. This took place over 4 months in 1930 in Lanarkshire, Scotland, and was intended
to compare the growth of schoolchildren given raw milk, pasteurized milk, or no milk.
Unfortunately, within a school the selection of children as “feeders” (receiving milk) or
“controls” (not receiving milk) was left to the principal of the school. Initially, a more or less
random allocation was made, but unfortunately in the description of the experiment it is stated
that “in any particular school where there was any group to which these methods had given an
undue proportion of well-fed or ill-nourished children, others were substituted in order to obtain
a more level selection.” Given this flexibility, it seems that the teachers tended to allocate milk
to poorly nourished children whom they thought needed it. This, together with other problems,
turned a perfectly reasonable experiment into one in which the validity of the final results is
very questionable.

11. Types of sampling
methods

12. • Two types of sampling methods:
• Probability sampling involves random selection, allowing you
to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based
on convenience or other criteria, allowing you to easily collect
data.
• You should clearly explain how you selected the sample in the
methodology section of your paper or thesis.

13. Distinguishing
Between a Sample and
a Population

14. https://www.scribbr.com/methodology/sampling-methods/

15. • First, you need to understand the difference between a population and a sample, and identify the
target population of your research.
• The population is the entire group that you want to draw conclusions about.The term
population means all members that meet a set of specifications or a specified criterion.
For example, the population of the United States is defined as all people residing in the
United States..
• An element; A single member of any given population.
• The sample is the specific group of individuals that you will collect data from.When
only some elements are selected from a population, we refer to that as a sample.When
all elements are included, we call it a census.
• The population can be defined in terms of geographical location, age, income, and many other
characteristics.
• It can be very broad or quite narrow: maybe you want to make inferences about the whole
adult population of your country; maybe your research focuses on customers of a certain
company,

16. patients with a specific health condition, or students in a single school.
• It is important to carefully define your target population according to the purpose and
practicalities of your project.
• If the population is very large, demographically mixed, and geographically dispersed, it might
be difficult to gain access to a representative sample.

17. Sampling frame

18. • The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally,
it should include the entire target population (and nobody who is not part of that population).
• Example of sampling frame;
You are doing research on working conditions at Company X. Your population is all 1000
employees of the company. Your sampling frame is the company’s HR database which lists the
names and contact details of every employee.

19. Probability sampling
vs.
Non probability
sampling

20. • Researchers use two major sampling techniques:
• Probability sampling
• Non probability sampling.
• With probability sampling, a researcher can specify the probability of an element’s
(participant’s) being included in the sample.
• With non probability sampling, there is no way of estimating the probability of an
element’s being included in a sample.
• If the researcher’s interest is in generalizing the findings derived from the sample to the
general population, then probability sampling is far more useful and precise.
• Unfortunately, it is also much more difficult and expensive than non probability
sampling.

21. Probability sampling
methods

22. • Probability sampling means that every member of the population has a chance of being
selected.
• A researcher can specify the probability of an element’s (participant’s) being included in the
sample.
• It is mainly used in quantitative research.
• If you want to produce results that are representative of the whole population, probability
sampling techniques are the most valid choice.
• There are four main types of probability sample.
1. Simple random (SRS;representative) sampling
2. Systematic sampling
3. Stratified sampling
4. Cluster sampling

23. Simple random
sampling

24. https://www.scribbr.com/methodology/sampling-methods
/

25. • Random sampling is where sources of data are chosen in a completely haphazard way.
• SRS is the most commonly used method.
• In a simple random sample, every member of the population has an equal chance of being
selected. Your sampling frame should include the whole population.
• The word random describes the procedure used to select elements (participants, cars, test
items) from a population.
• To conduct this type of sampling, you can use tools like random number generators or other
techniques that are based entirely on chance.
• Random assignment describes the process of placing participants into different experimental
groups.
• One way of obtaining a random sample is to give each individual in a population a
number, and then use a table of random numbers to decide which individuals to include.

26. • The four steps of simple random sampling are (1) defining the population, (2) constructing
a list of all members, (3) drawing the sample, and (4) contacting the members of the sample.
• Example;
•
‫بالتسلسل‬ ‫إدخال‬ ‫كل‬ ‫ترقيم‬ ‫يجب‬ ، ‫الهاتف‬ ‫دليل‬ ‫من‬ ‫بسيطة‬ ‫عشوائية‬ ‫عينة‬ ‫لسحب‬
.
‫هناك‬ ‫كان‬ ‫إذا‬
10000
‫العينة‬ ‫حجم‬ ‫كان‬ ‫وإذا‬ ‫الهاتف‬ ‫دليل‬ ‫في‬ ‫إدخال‬
2000
‫إنشاء‬ ‫يتم‬ ‫أن‬ ‫فيجب‬ ،
2000
‫بين‬ ‫رقم‬
1
‫و‬
10000
‫الكمبيوتر‬ ‫بواسطة‬ ‫عشوائي‬ ‫بشكل‬
.
‫بواسطة‬ ‫إنشاؤها‬ ‫يتم‬ ‫أن‬ ‫في‬ ‫الفرصة‬ ‫نفس‬ ‫لديها‬ ‫األرقام‬ ‫جميع‬
‫الكمبيوتر‬
.
‫تشكل‬
2000
‫تقابل‬ ‫هاتفي‬ ‫إدخال‬
2000
‫العينة‬ ‫الكمبيوتر‬ ‫بواسطة‬ ‫إنشاؤها‬ ‫تم‬ ‫عشوائي‬ ‫رقم‬
.
•
‫المقبل‬ ‫الشهر‬ ‫الرعب‬ ‫أفالم‬ ‫مهرجان‬ ‫ستقدم‬ ‫وأنك‬ ‫سينما‬ ‫دار‬ ‫تمتلك‬ ‫أنك‬ ‫تخيل‬
.
‫لسؤال‬ ‫السينما‬ ‫رواد‬ ‫مسح‬ ‫يمكنك‬ ، ‫عرضها‬ ‫سيتم‬ ‫التي‬ ‫الرعب‬ ‫أفالم‬ ‫لتحديد‬
‫هم‬
‫لديهم‬ ‫المفضلة‬ ‫المدرجة‬ ‫األفالم‬ ‫عن‬
.
‫من‬ ‫عينة‬ ‫تأخذ‬ ‫أن‬ ‫قررت‬ ، ‫الستطالعك‬ ‫المطلوبة‬ ‫األفالم‬ ‫قائمة‬ ‫إلنشاء‬
10
‫أفضل‬ ‫من‬
100
‫اإلطالق‬ ‫على‬ ‫رعب‬ ‫فيلم‬
.
‫فارغ‬ ‫صندوق‬ ‫في‬ ‫ووضعها‬ ‫الورق‬ ‫من‬ ‫قصاصات‬ ‫على‬ ‫األفالم‬ ‫عناوين‬ ‫جميع‬ ‫كتابة‬ ‫في‬ ‫العينة‬ ‫اختيار‬ ‫طرق‬ ‫إحدى‬ ‫تتمثل‬
.
‫اسحب‬ ، ‫ذلك‬ ‫بعد‬
10
‫عناوين‬
‫عينتك‬ ‫على‬ ‫وستحصل‬
.
‫االختيار‬ ‫في‬ ‫متساوية‬ ‫احتمالية‬ ‫له‬ ‫فيلم‬ ‫كل‬ ‫أن‬ ‫من‬ ‫تأكدت‬ ‫قد‬ ‫ستكون‬ ، ‫النهج‬ ‫هذا‬ ‫باستخدام‬
.
‫هذا‬ ‫االختيار‬ ‫احتمال‬ ‫حساب‬ ‫ا‬ً‫ض‬‫أي‬ ‫يمكنك‬
‫العينة‬ ‫حجم‬ ‫بقسمة‬
(
‫ن‬
=
10
)
‫ألفضل‬ ‫المجتمع‬ ‫حجم‬ ‫على‬
100
‫اإلطالق‬ ‫على‬ ‫رعب‬ ‫فيلم‬
(
‫العدد‬
=
100
.
‫االحتمال‬ ‫هذا‬ ‫سيكون‬
0.10
(
10/100
)
‫أو‬
1
‫من‬
10
.

27. • The advantage of this technique; it does not require any information on the survey frame
other than the complete list of units of the survey population along with contact information. Also,
since SRS is a simple method and the theory behind it is well established, standard formulas exist
to determine the sample size, the estimates and so on, and these formulas are easy to use.

28. Systematic sampling

29. https://www.scribbr.com/methodology/sampling-methods
/

30. • Systematic sampling means that there is a gap, or interval, between each selected unit in
the sample.
• This technique is also random but done through maintaining a system or formula.
• The subjects could be selected at regular intervals from the entire population. The intervals are
chosen to ensure an adequate sample size.
• If you need a sample size n from a population of size x, you should select every x/n th individual
for the sample.
• Systematic sampling is similar to simple random sampling, but it is usually slightly easier to
conduct.
• Every member of the population is listed with a number, but instead of randomly generating
numbers, individuals are chosen at regular intervals.
• Example;
All employees of the company are listed in alphabetical order. From the first 10 numbers, you
randomly select a starting point: number 6. From number 6 onwards, every 10th person

31. on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.
• If you use this technique, it is important to make sure that there is no hidden pattern in the list
that might skew the sample.
• For example, if the HR (Human Resources) database group's employees by team, and team
members are listed in order of seniority, there is a risk that your interval might skip over people in
junior roles, resulting in a sample that is skewed towards senior employees.
• You could follow these steps:
Number the units on your frame from 1 to N (where N is the total population size).
Determine the sampling interval (K) by dividing the number of units in the population by the
desired sample size. For example, to select a sample of 100 from a population of 400, you
would need a sampling interval of 400/100 = 4. Therefore, K = 4. You will need to select one
unit out of every four units to end up with a total of 100 units in your sample.
Select a number between one and K at random. This number is called the

32. random start and it would be the first number included in your sample. If you choose 3, the third
unit on your frame would be the first unit included in your sample; if you choose 2, your
sample would start with the second unit on your frame.
Select every Kth (in this case, every fourth) unit after that first number. For example, the sample
might consist of the following units to make up a sample of 100: 3 (the random start), 7, 11, 15,
19 …395, 399 (up to N, which is 400 in this case).
• In the example above, you can see that there are only four possible samples that can be
selected, corresponding to the four possible random starts:
1, 5, 9, 13 … 393, 397
2, 6, 10, 14 … 394, 398
3, 7, 11, 15 … 395, 399
4, 8, 12, 16 … 396, 400
• Each member of the population belongs to only one of the four samples and each sample has
the same chance of being selected. This is

33. the same probability as if a simple random sample of 100 units was selected.
• The main difference is that with SRS, any combination of 100 units would have a chance of
making up the sample, while with systematic sampling; there are only four possible samples. The
units’ order on the frame will determine the possible samples for systematic sampling.
• If the population is randomly distributed on the frame, then systematic sampling should yield
results that are similar to SRS.
• This method is often used in industry, for a standard quality.
• The disadvantages of systematic sampling are that the sample selection cannot be easier.
• The biggest drawback of the systematic sampling method is that if there is some periodical
feature, the possible samples may not be representative of the population.

34. • Sampling with probability proportional to size; Probability sampling requires that each
member of the survey population has a known probability of being included in the sample, but it
does not require that this probability be the same for everyone. If there is information available on
the frame about the size of each unit (e.g. number of employees for each business) and if those
units vary in size, this information can be used in the sampling selection in order to increase the
efficiency. This is known as sampling with probability proportional to size (PPS). With this
method, the bigger the size of the unit, the higher the chance of being included in the sample.
For this method to bring increased efficiency, the measure of size needs to be accurate. This is a
more complex sampling method that will not be discussed in further detail here.

35. Stratified sampling

36. https://www.scribbr.com/methodology/sampling-methods
/

37. • Stratified sampling involves dividing the population into subpopulations that may differ in
important ways.
• It allows you draw more precise conclusions by ensuring that every subgroup is properly
represented in the sample.
• To use this sampling method, you divide the population into subgroups (called strata) based
on the relevant characteristic (e.g. gender, age range, income bracket, job role).
• Each element in the population has a known probability of being selected.
• Based on the overall proportions of the population, you calculate how many people should be
sampled from each subgroup. Then you use random or systematic sampling to select a sample
from each subgroup.
• Example;

38. •
‫بالشركة‬ ‫يعمل‬
800
‫و‬ ‫موظفة‬
200
‫موظف‬
.
‫لذلك‬ ، ‫الشركة‬ ‫في‬ ‫الجنسين‬ ‫بين‬ ‫التوازن‬ ‫تعكس‬ ‫العينة‬ ‫أن‬ ‫من‬ ‫التأكد‬ ‫تريد‬
‫تقوم‬
‫الجنس‬ ‫أساس‬ ‫على‬ ‫طبقتين‬ ‫إلى‬ ‫السكان‬ ‫بفرز‬
.
‫وتختار‬ ، ‫مجموعة‬ ‫كل‬ ‫من‬ ‫عشوائية‬ ‫عينات‬ ‫تستخدم‬ ‫ثم‬
80
‫و‬ ‫امرأة‬
20
‫مما‬ ، ً‫ال‬‫رج‬
‫من‬ ‫تمثيلية‬ ‫عينة‬ ‫يمنحك‬
100
‫شخص‬
.
• Stratification is most useful when the stratifying variables are
• Simple to work with,
• Easy to observe
• Closely related to the topic of the survey.

39. Cluster sampling
(
Multistage sampling
)

40. https://www.scribbr.com/methodology/sampling-methods
/

41. • Cluster sampling also involves dividing the population into subgroups, but each subgroup
should have similar characteristics to the whole sample.
• Instead of sampling individuals from each subgroup, you randomly select entire subgroups.
• If it is practically possible, you might include every individual from each sampled cluster.
• If the clusters themselves are large, you can also sample individuals from within each
cluster using one of the techniques above. This is called multistage sampling.
• No units from non-selected clusters are included in the sample.
• They are represented by those from selected clusters.
• This method is good for dealing with large and dispersed populations.
• But there is more risk of error in the sample, as there could be substantial differences between
clusters.

42. • It’s difficult to guarantee that the sampled clusters are really representative of the whole
population.
• Sometimes it is too expensive to have a sample too spread out geographically.
• Travel costs can become expensive if interviewers have to survey people from one end of the
country to the other. To reduce costs, statisticians may choose a cluster sampling technique.
• This differs from stratified sampling, where some units are selected from each stratum.
• Examples of clusters are factories, schools and geographic areas such as electoral
subdivisions.
• Multi-stage sampling is like cluster sampling, except that it involves selecting a sample within
each selected cluster, rather than including all units from the selected clusters. This type of
sampling requires at least two stages. In the first stage, large clusters are identified and selected. In
the second stage, units are selected from within the selected

43. clusters using any of the probability sampling methods. In this context, the clusters are referred to
as primary sampling units (PSU) and units within clusters are referred to as secondary
sampling units (SSU). When there are more than two stages, tertiary sampling units (TSU) are
selected within SSE, and the process continues until there is a final sample.
• Example;
1
-
‫في‬ ‫مكاتب‬ ‫الشركة‬ ‫تمتلك‬
10
‫مماثلة‬ ‫وظائف‬ ‫في‬ ‫الموظفين‬ ‫عدد‬ ‫بنفس‬ ‫ا‬ً‫ب‬‫تقري‬ ‫جميعها‬ ،‫البالد‬ ‫أنحاء‬ ‫جميع‬ ‫في‬ ‫مدن‬
.
‫لديك‬ ‫ليس‬
‫لتحديد‬ ‫العشوائية‬ ‫العينات‬ ‫أخذ‬ ‫تستخدم‬ ‫لذلك‬ ،‫بك‬ ‫الخاصة‬ ‫البيانات‬ ‫لجمع‬ ‫مكتب‬ ‫كل‬ ‫إلى‬ ‫السفر‬ ‫على‬ ‫القدرة‬
3
‫مكاتب‬
-
‫هي‬ ‫هذه‬
‫مجموعاتك‬
.
2
-
‫عنقودية‬ ‫عينة‬ ‫ستختار‬
100
‫الصف‬ ‫في‬ ‫طالب‬ ‫كل‬ ‫مع‬ ‫مقابلة‬ ‫تجري‬ ‫ثم‬ ‫مدرسة‬
11
‫المدارس‬ ‫تلك‬ ‫من‬
.
‫يمكنك‬ ، ‫ذلك‬ ‫من‬ ً‫ال‬‫بد‬
‫الصف‬ ‫طالب‬ ‫بجميع‬ ‫قائمة‬ ‫على‬ ‫والحصول‬ ، ‫المدارس‬ ‫من‬ ‫المزيد‬ ‫تحديد‬
11
‫عشوائية‬ ‫عينة‬ ‫واختيار‬ ‫المختارة‬ ‫المدارس‬ ‫هذه‬ ‫من‬
‫الصف‬ ‫طالب‬ ‫من‬
11
‫مدرسة‬ ‫كل‬ ‫من‬
.
‫مرحلتين‬ ‫من‬ ‫العينات‬ ‫ألخذ‬ ‫ا‬ً‫م‬‫تصمي‬ ‫هذا‬ ‫سيكون‬
.
‫هي‬ ‫المدارس‬ ‫ستكون‬ PSU ‫والطالب‬
‫في‬ SSU. ‫الصف‬ ‫فصول‬ ‫بجميع‬ ‫قائمة‬ ‫على‬ ‫الحصول‬ ‫ا‬ً‫ض‬‫أي‬ ‫يمكنك‬
11
‫واختيار‬ ، ‫المحددة‬ ‫المدارس‬ ‫في‬
‫الفصول‬ ‫في‬ ‫الطالب‬ ‫بجميع‬ ‫قائمة‬ ‫على‬ ‫والحصول‬ ، ‫مدرسة‬ ‫كل‬ ‫من‬ ‫الفصول‬ ‫من‬ ‫عشوائية‬ ‫عينة‬
‫عينة‬ ‫تحديد‬ ‫ثم‬ ، ‫المختارة‬

44. ‫دراسي‬ ‫صف‬ ‫محددة‬ ‫مجموعة‬ ‫كل‬ ‫من‬ ‫الطالب‬ ‫من‬ ‫عشوائية‬
.
‫مراحل‬ ‫ثالث‬ ‫من‬ ‫العينات‬ ‫ألخذ‬ ‫ا‬ً‫م‬‫تصمي‬ ‫هذا‬ ‫سيكون‬
.
‫ستكون‬
‫هي‬ ‫المدارس‬
PSU
‫هي‬ ‫الفصول‬ ‫وستكون‬ ،
SSU
‫سيكونون‬ ‫والطالب‬
TSU.
‫تصبح‬ ، ‫مرحلة‬ ‫إضافة‬ ‫فيها‬ ‫يتم‬ ‫مرة‬ ‫كل‬ ‫في‬
‫ًا‬‫د‬‫تعقي‬ ‫أكثر‬ ‫العملية‬
.

45. Non-probability
sampling methods

46. https://www.questionpro.com/blog/non-probability-sampling
/

47. Advantages of non-probability sampling
 More conducive and practical method for researchers deploying surveys in the
real world. Although statisticians prefer probability sampling because it yields
data in the form of numbers, however, if done correctly, it can produce similar if
not the same quality of results.
 Faster and more cost-effective than probability sampling because the sample is
known to the researcher. The respondents respond quickly as compared to people
randomly selected as they have a high motivation level to participate.
• This type of sample is easier to access.

48. When to use non-probability sampling?
 If a particular trait or characteristic exists in a population.
 When they aim at conducting qualitative research, pilot studies, or exploratory research.
 When they have limited time to conduct research or have budget constraints.
 When the researcher needs to observe whether a particular issue needs in-depth analysis, he
applies this method.
 Use it when you do not intend to generate results that will generalize the entire population.

49. Characteristics
• There is no way of estimating the probability of an element’s being included in a sample.
• In a non-probability sample, individuals are selected based on non-random criteria, and
not every individual has a chance of being included.
• But it has a higher risk of sampling bias.
• That means the inferences you can make about the population are weaker than with
probability samples, and your conclusions may be more limited.
• If you use a non-probability sample, you should still aim to make it as representative of the
population as possible.
• Non-probability sampling techniques are often used in exploratory and qualitative
research.
• In these types of research, the aim is not to test a hypothesis about a broad population,
but to develop an initial understanding of a small or under-researched population.

50. Convenience sampling

51. https://www.scribbr.com/methodology/sampling-methods
/

52. • It is the most common non-probability sampling method.
• A convenience sample simply includes the individuals who happen to be most conveniently
accessible to the researcher.
• Because of its speed, cost-effectiveness, easy to recruit, and availability to gather initial data
(sample).
• But there is no way to tell if the sample is representative of the population, so it can’t
produce generalizable results.
• It is good to test a sample that represents the population. But, in some research, the population
is too large to examine and consider the entire population.
• Example; Survey
•
‫إكم‬ ‫الطالب‬ ‫زمالئك‬ ‫من‬ ‫تطلب‬ ،‫دراسي‬ ‫فصل‬ ‫كل‬ ‫بعد‬ ‫لذلك‬ ،‫جامعتك‬ ‫في‬ ‫الطالب‬ ‫دعم‬ ‫خدمات‬ ‫حول‬ ‫آراء‬ ‫عن‬ ‫تبحث‬ ‫أنت‬
‫ال‬
‫الذ‬ ‫الطالب‬ ‫آراء‬ ‫باستطالع‬ ‫فقط‬ ‫قمت‬ ‫ألنك‬ ‫ا‬ً‫نظر‬ ‫ولكن‬ ،‫البيانات‬ ‫لجمع‬ ‫مناسبة‬ ‫طريقة‬ ‫هذه‬ ،‫الموضوع‬ ‫حول‬ ‫استطالع‬
‫يحضرون‬ ‫ين‬
‫جامعتك‬ ‫في‬ ‫الطالب‬ ‫جميع‬ ‫تمثل‬ ‫ال‬ ‫العينة‬ ‫فإن‬ ،‫المستوى‬ ‫نفس‬ ‫في‬ ‫مثلك‬ ‫الدراسية‬ ‫الفصول‬ ‫نفس‬
.

53. Quota sampling

54. https://www.voxco.com/blog/quota-sampling
//

55. • Quota sampling is a two-stage non-probability sampling method that assigns quotas to the
population in order to ensure that when elements of the population are selected, the sample group
is representative of the population’s characteristics.
• In this technique, the participants are chosen based on a predetermined quota falling under a
specific category, or data is collected from a homogeneous group. This technique is quite
popular in market research agencies.
• Interviewers are given a quota of subjects of a specified type to attempt to recruit.
• Quota sampling is a simple yet effective way to do research in the initial phases.
• But it is done from one category which some researchers feel could be unreliable.
• Quota sampling involves the selection of a certain percentage of individuals
from specified subgroups of the population when the population is large and lists
of

56. members are not available. Many polling organizations use this technique.
• Quota sampling can also be used at times when detailed accuracy is not important.
• Types of Quota Sampling;
1. Controlled quota sampling: It imposes certain limitations on the researcher’s choice of samples.
2. Uncontrolled quota sampling: It does not impose any limitations or restrictions on the researcher’s choice of
samples.
• Example;
• ‫الوظيفية‬ ‫وحالتهم‬ ‫جنسهم‬ ‫على‬ ‫ا‬ً‫د‬‫اعتما‬ ‫نيويورك‬ ‫في‬ ‫لألشخاص‬ ‫الشراء‬ ‫عادات‬ ‫دراسة‬ ‫يريد‬ ‫الباحث‬ ‫أن‬ ‫لنفترض‬
.
‫في‬
‫الوظيفي‬ ‫والوضع‬ ‫الجنس‬ ‫سيكون‬ ، ‫المثال‬ ‫هذا‬
"
‫الصلة‬ ‫ذات‬ ‫التحكم‬ ‫خصائص‬
"
‫الحصص‬ ‫تحديد‬ ‫خاللها‬ ‫من‬ ‫سيتم‬ ‫والتي‬ ، .
‫يلي‬ ‫ما‬ ‫ا‬ً‫ض‬‫أي‬ ‫لنفترض‬: ‫الوظيفية‬ ‫الحالة‬10 ‫يعمل‬ ‫العمل‬ ‫عن‬ ‫عاطلة‬ ‫نيويورك‬ ‫في‬ ‫العاملة‬ ‫القوى‬ ‫من‬ ٪
90
‫القوى‬ ‫من‬ ٪
‫عرف‬ُ‫ي‬ ،‫نيويورك‬ ‫في‬ ‫العاملة‬
40
‫ذكور‬ ‫بأنهم‬ ‫نيويورك‬ ‫سكان‬ ‫من‬ ٪ . 60 ‫إناث‬ ‫بأنهم‬ ‫عرفون‬ُ‫ي‬ ‫نيويورك‬ ‫سكان‬ ‫من‬٪.
‫الذكور‬ ‫من‬ ‫متشابهة‬ ‫نسب‬ ‫لتعكس‬ ‫المعلومات‬ ‫هذه‬ ‫ذلك‬ ‫بعد‬ ‫الباحثون‬ ‫سيستخدم‬
/
‫والموظفين‬ ‫اإلناث‬
/
‫عن‬ ‫العاطلين‬
‫بهم‬ ‫الخاصة‬ ‫العينة‬ ‫مجموعة‬ ‫في‬ ‫العمل‬. ‫لنفترض‬ ، ‫الدراسة‬ ‫لهذه‬ ‫بالنسبة‬

57. ‫من‬ ‫عينة‬ ‫حجم‬ ‫تحديد‬ ‫تم‬ ‫أنه‬
100
‫شخص‬
.
‫واإل‬ ‫الذكور‬ ‫عدد‬ ‫لتحديد‬ ‫حصص‬ ‫لها‬ ‫التي‬ ‫السوق‬ ‫أبحاث‬ ‫أدوات‬ ‫الباحثون‬ ‫سيستخدم‬
‫ناث‬
‫الوظيفي‬ ‫بوضعهم‬ ‫يتعلق‬ ‫فيما‬ ‫اختيارهم‬ ‫يتم‬ ‫الذين‬
.
‫إدراج‬ ‫يختارون‬ ‫قد‬ ، ‫لذلك‬
60
‫و‬ ‫أنثى‬
40
، ‫ا‬ً‫ذكر‬
10
‫العمل‬ ‫عن‬ ‫عاطلون‬ ‫منهم‬
.
‫الحكم‬ ‫أو‬ ‫المالءمة‬ ‫أساس‬ ‫على‬ ‫الباحث‬ ‫قبل‬ ‫من‬ ‫العناصر‬ ‫هذه‬ ‫اختيار‬ ‫سيتم‬

58. Purposive sampling

59. https://www.scribbr.com/methodology/sampling-methods
/
• This type of sampling, also known as judgment sampling, involves the researcher using their expertise to select

60. a sample that is most useful to the purposes of the research.
• It is often used in qualitative research, where the researcher wants to gain detailed knowledge
about a specific phenomenon rather than make statistical inferences, or where the population is
very small and specific.
• An effective purposive sample must have clear criteria and rationale for inclusion.
• Example;
‫الدع‬ ‫احتياجات‬ ‫ذوي‬ ‫الطالب‬ ‫من‬ ‫ًا‬‫د‬‫عد‬ ‫قصد‬ ‫عن‬ ‫تختار‬ ‫لذلك‬ ،‫جامعتك‬ ‫في‬ ‫المعاقين‬ ‫الطالب‬ ‫وتجارب‬ ‫آراء‬ ‫عن‬ ‫المزيد‬ ‫معرفة‬ ‫تريد‬
‫المختلفة‬ ‫م‬
‫الطالب‬ ‫خدمات‬ ‫مع‬ ‫تجاربهم‬ ‫حول‬ ‫البيانات‬ ‫من‬ ‫متنوعة‬ ‫مجموعة‬ ‫جمع‬ ‫أجل‬ ‫من‬
.

61. Snowball sampling

62. https://www.scribbr.com/methodology/sampling-methods
/

63. • If the population is hard to access, snowball sampling can be used to recruit
participants via other participants. The number of people you have access to
“snowballs” as you get in contact with more people.
• Example;
‫مدينتك‬ ‫في‬ ‫التشرد‬ ‫تجارب‬ ‫عن‬ ‫تبحث‬ ‫أنت‬
.
‫العينات‬ ‫أخذ‬ ‫فإن‬ ، ‫المدينة‬ ‫في‬ ‫المشردين‬ ‫األشخاص‬ ‫بجميع‬ ‫قائمة‬ ‫وجود‬ ‫لعدم‬ ‫ا‬ً‫نظر‬
‫ممكن‬ ‫غير‬ ‫االحتمالية‬
.
‫مشر‬ ‫بأشخاص‬ ‫اتصال‬ ‫على‬ ‫تجعلك‬ ‫وهي‬ ،‫البحث‬ ‫في‬ ‫المشاركة‬ ‫على‬ ‫يوافق‬ ‫ًا‬‫د‬‫واح‬ ‫ا‬ً‫ص‬‫شخ‬ ‫تقابل‬
‫آخرين‬ ‫دين‬
‫المنطقة‬ ‫في‬ ‫تعرفهم‬
.

64. Voluntary response
sample

65. • Similar to a convenience sample, a voluntary response sample is mainly based on ease of
access.
• Instead of the researcher choosing participants and directly contacting them, people
volunteer themselves (e.g. by responding to a public online survey).
• Voluntary response samples are always at least somewhat biased, as some people will
inherently be more likely to volunteer than others.
• Voluntary response sampling is not advantageous or applicable in most studies as it is highly
susceptible to bias and yields unreliable results.
• Characteristics of Voluntary Response Sample;
• Easy to gather data
• Biased responses
• Errors in data quality
• Lack of coverage
• Advantages of Voluntary Response Sample;
• Inexpensive to conduct.

66. • Requires little effort on the part of the researcher.
• Easily feasible as volunteers can be obtained in a fast and inexpensive manner.
• Example:
•
،‫أيدول‬ ‫أمريكان‬ ‫مثل‬ ،‫للمسابقات‬ ‫التلفزيونية‬ ‫البرامج‬ ‫مقدمو‬ ‫يطلب‬ ‫عندما‬ ‫هو‬ ‫تطوعية‬ ‫استجابة‬ ‫عينة‬ ‫على‬ ‫مثال‬
‫مشاهديهم‬ ‫من‬
‫بالمسابقة‬ ‫يفوز‬ ‫أن‬ ‫يجب‬ ‫أنه‬ ‫يعتقدون‬ ‫لمن‬ ‫ردودهم‬ ‫إرسال‬
.
‫من‬ ‫هم‬ ‫فقط‬ ‫يفوز‬ ‫أن‬ ‫يجب‬ ‫من‬ ‫حول‬ ‫قوية‬ ‫آراء‬ ‫لديهم‬ ‫الذين‬ ‫المشاهدون‬
‫أصواتهم‬ ‫يرسلون‬
.
‫أصواتهم‬ ‫يرسلوا‬ ‫لن‬ ‫ومنافسيه‬ ‫بالعرض‬ ‫المبالين‬ ‫غير‬ ‫المشاهدون‬
.

67. Consecutive sampling

68. https://www.simplypsychology.org/convenience-sampling.html

69. • This non-probability sampling method is very similar to convenience sampling, with a
slight variation.
• Here, the researcher picks a single person or a group of a sample, conducts research over a
period, analyzes the results, and then moves on to another subject or group if needed.
• Consecutive sampling technique gives the researcher a chance to work with many topics
and fine-tune his/her research by collecting results that have vital insights.

70. Difference between non-
probability sampling and
probability sampling

71. Table 1 Difference between non-probability sampling and probability sampling
https://www.questionpro.com/blog/non-probability-sampling/

72. https://www.studocu.com/ph/document/isabela-state-university/bsed-math/ch7-sampling-techniques/11054884

73. Frequently asked questions
about sampling

74. • What is sampling?
• A sample is a subset of individuals from a larger population. Sampling means selecting the
group that you will actually collect data from in your research. For example, if you are
researching the opinions of students in your university, you could survey a sample of 100
students.
• In statistics, sampling allows you to test a hypothesis about the characteristics of a
population.
• Why are samples used in research?
• Samples are used to make inferences about populations. Samples are easier to collect data
from because they are practical, cost-effective, convenient and manageable.
• What is probability sampling?
• Probability sampling means that every member of the target population

75. has a known chance of being included in the sample.
• Probability sampling methods include simple random sampling, systematic sampling,
stratified sampling, and cluster sampling.
• What is non-probability sampling?
• In non-probability sampling, the sample is selected based on non-random criteria, and not
every member of the population has a chance of being included.
• Common non-probability sampling methods include convenience sampling, voluntary
response sampling, purposive sampling, snowball sampling, and quota sampling.
• What is multistage sampling?
• In multistage sampling, or multistage cluster sampling, you draw a sample

76. from a population using smaller and smaller groups at each stage.
• This method is often used to collect data from a large, geographically spread group of people
in national surveys, for example. You take advantage of hierarchical groupings (e.g., from
state to city to neighborhood) to create a sample that’s less expensive and time-consuming to
collect data from.
• What is sampling bias?
• Sampling bias occurs when some members of a population are systematically more likely to
be selected in a sample than others.

77. References
1. McCombes S. Sampling Methods | Types and Techniques Explained. https://www.scribbr.com/methodology/sampling-methods/ Published on
September 19, 2019. Revised on May 3, 2022.
2. Isabela State University. Bsed Math (MAT). Chapter 7. Sampling Techniques. https://www.studocu.com/ph/document/isabela-state-
university/bsed-math/ch7-sampling-techniques/11054884. 2020/2021
3. University of Calicut. B.COM. https://www.studocu.com/in/document/university-of-calicut/bcom/q-2g-a-guide-to-sampling-techniques-
wf/21290541. 2016/2017
4. Hossain S. A complete guide to sampling techniques. https://hotcubator.com.au/research/a-complete-guide-to-sampling-techniques/ July 21,
2020
5. Bryan F.J. Manly. Randomization. in Encyclopedia of Social Measurement, 2005. https://www.sciencedirect.com/topics/nursing-and-health-
professions/randomization
6. Canada. 3.2.2 Pr
obability sampling. https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch13/prob/5214899-eng.htm Date modified:2021-09-02
7. QuestionPro. Non-Probability Sampling: Definition, types, Examples, and advantages. https://www.questionpro.com/blog/non-probability-
sampling/
8. Quota Sampling . https://www.voxco.com/blog/quota-sampling/
9. Simkus S. Convenience Sampling: Definition, Method and Examples. https://www.simplypsychology.org/convenience-sampling.html
published Jan 30, 2022

78. Thank you