3. Basic concepts
Research Population
It means all observations under study.
Or it is all the individuals of the study
population
Research Sample
A sample can be defined as a subset of the study
population that is selected in an appropriate
manner and studied. Then, use those results, and
circulate it to the entire original study population.
Comprehensive inventory method
It is sometimes called the census
method for each population individual.
4. Why use samples?
1. Homogeneity of the original research community.
2. High cost, time, and effort: if the study population is large and geographically far apart.
3. Poor control and supervision: when the study population is large.
4. It is not possible to enumerate the entire individuals of the original research population.
5. It is not possible to conduct the study on the entire individuals of the original research community
5. Phases of sample selection
1. Identify the original population of the study: The researcher must determine from the outset the objective of the
study, its type, and the individuals included and not included in the study.
2. Prepare a list of members of the original population of the research.
3. Selection of a representative sample
4. Selection of a sufficient number of individuals in the sample.
6. Samples Types
Group II: Non-Probability Samples
It is selected non-randomly and is not done according to different
probability bases, but rather according to certain bases,
estimates, and criteria set by the researcher, in which the
researcher intervenes in the selection of the sample and
estimates who chooses and who does not choose from the
members of the original research community.
Group I: Probability (random) samples
Equal or known opportunities are given to each individual in the
study population in the probability of being selected in the study
sample. The use of this type of sample is a guarantee to obtain a
representative unbiased sample that the researcher has no
involvement in the selection of its vocabulary
8. The simple random sampling
Used when two conditions are met:
1.All members of the research community should be known.
2.To have homogeneity between these individuals.
The simple random sample is selected according to the following methods:
1.Draw style
2.Table of random numbers
Random choice of retrospection can be from a limited population, and without
retrospection. However, this method of retrospection is impractical and rarely used in
social research.
9. Systematic Random Sampling
A regular sample is usually selected by listing the
individuals of the original study population and then
each individual is given a sequential number. Then
the number of individuals of the research population
is divided by the size of the required sample and
produces the number that will be separated
10. Stratified Random sampling
This type of sampling is used in heterogeneous populations whose vocabulary varies
according to certain characteristics, such as educational level.
Selection steps:
1.The division of society into homogeneous categories or groups according to a
certain characteristic.
2.Determine the number of individuals of the total sample.
3.Determine the ratio of each level in the selected sample to the total size of the
original population.
4.Determine the number of individuals per level in the selected sample
11. If we assume that there is a population consisting of three classes, the upper class of
1,000, the middle class of 4,000, and the lower class of 5,000, it is required to select a
random stratified sample of 100 people using the proportional distribution method.
The answer is in the following table:
Stratified Random sampling, example:
Sample
Percentage
No.
Classes
10
10
%
1000
Upper
40
40
%
4000
Middle
50
50
%
5000
Lower
100
100
%
10000
Total
12. Cluster sampling
Example: If we want to study the annual household income in Gaza City, we may
choose a cluster sample in two stages as follows:
1.In the first stage, we consider clusters as city neighborhoods, and we may
divide the city into neighborhoods and take a sample of them in an
appropriate size with the size of the neighborhood.
2.We divide each of the selected neighborhoods into condominiums and
choose from each of them an appropriate number of apartments and then
14. Purposive Sample
It is known as a purposeful, intentional, or judgmental sample.
Individuals with particular characteristics are selected.
For example, if a researcher wanted to study consumer reviews about
a type of instant coffee (Nescafe).
15. Quota Sampling
The difference between quota sampling and the stratified sample is the method of
selecting the members of each class, as the random method is not used in the
selection in the quota sample, but rather the method of chance and intention is
used. This type of sample is used in the study of public opinion and in educational
and social studies.
16. Accidental Sample
The sample consists of individuals whom the researcher meets by chance. If the
researcher wants to measure the public opinion about an issue, he chooses a number
of people he meets by chance, whether on the street or on the bus.
This sample is only self-represented, but it is easy to use and gives an idea of what
individuals think about the issue in question quickly. The larger the sample size, the
17. Sample size and representativeness of the study
population
There is no given percentage of the size of the study population that can be applied
to all cases. There are a group of factors that affect the size of the study sample,
which are the following:
1. The degree of accuracy and confidence to be achieved:
• The degree of accuracy is the proximity of the sample results to the actual
reality.
• Confidence degree is the extent to which the results of the study are likely to
not match the actual results.
2. The homogeneity of the study population
3. The size of the study population: There is a direct relationship between the sample
size and the size of the study population.
4- The degree of generalization sought by the researcher.
5. The used research method: Does the researcher want to use the survey or
experimental method? And what kind of experimental method will he use? Surveys
18. Sample size and representativeness of the study
population
The following table shows the appropriate sample size at different levels of the
original study population:
Appropriate
sample size
Indigenous
community
size
Appropriate
sample size
Indigenous
community
size
226
550
10
10
242
650
28
30
269
900
59
70
322
2000
118
170
20. Sampling error, coincidence error, or random
error
This error is due to the nature of the random selection of the sample members,
so we find that the results of the sample differ from the results of the original
population 0 Selecting samples with the best sampling methods do not guarantee
that the selected sample is representative of the population 0 It is not possible to
obtain a sample whose composition matches the composition of the community
completely, so this type of error occurs when the values of the real community
features diverge as a result of random sampling from the values we obtained
from the sample 0
21. Bias error
It is due to the researcher, in which there is a tendency
to prefer units with certain characteristics over others to
join the sample, and this causes the basic
characteristics of the original community to not be
represented 0
22. If the population of secondary school students consists of (50% males and 50% females) and the
researcher obtained a sample of males (20% and females 80%), this may result in biased results. Then, the
sample will not be accurately represented, and thus the type of individual in the sample becomes an
influential variable.
The bias error often occurs as a result of poor planning when selecting sample 0 this error is due to the
following reasons: The inefficiency of researchers in calculating estimates, the ambiguity of questions,
inaccurate responses of examiners, failure to collect data from some individuals or collect data more than
once from the same individuals, and lack of a sound framework when selecting sample 0.
Example
23. How to minimize selection bias errors
• Randomly select all sample units using one of the random selection methods
• Do not replace any selected unit with another
• Complete answers to all questions
• Conduct empirical research (survey sample) to detect intentional and unintentional bias
• Train researchers well to collect data and adhere to instructions
24. Bias Estimation Error
It is the mistake we make which is related to the method of estimation or appropriate methods of analysis
Bias error caused by misidentification of the sampling module
When we define the sampling unit, it must be clearly defined in a way that minimizes bias errors that result if the
unit is not clearly defined
Other common errors in samples
Unresponsive errors (attributions to not updating the frame).
Categorization and data processing errors.
Printing errors.
Errors in interpreting results on the number from the correctness of estimation methods and analysis methods
25. In the light of what I studied
Through groups select the type and sample size in your
research with the logical justification for it
