PAPER

INTRODUCTION TO RESEARCH
”Population and Sample and Sampling Technique and the Size of Sample”

BY GROUP VII :
1. RISMAWATI (170220057)
2. NUR AISAH IBRAHIM (170230049)

ENGLISH DEPARTMENT
TEACHER TRAINING AND EDUCATION FACULTY
SEMBILANELAS NOVEMBER UNIVERSITY
KOLAKA
2020
 LIST OF CONTENT

PAPER ........................................................................................................................... i
LIST OF CONTENT ..................................................................................................... ii
CHAPTER I INTRODUCTION
A. Background ....................................................................................................... 1
B. Problem Formulation ....................................................................................... 2
C. Purpose .............................................................................................................. 2
CHAPTER II DISCUSSION
A. The Definition of Population and Sample....................................................... 5
B. Sampling Technique ......................................................................................... 6
C. The Size of Sampling ........................................................................................ 11
CHAPTER III CLOSING
A. Conclusion ......................................................................................................... 15
REFERENCES............................................................................................................... 16
 CHAPTER I
INTRODUCTION

A. Background
Research is scientific work that intends to reveal the secrets of science
objectively, with complete and solid evidence. Research is a creative process for
expressing a symptom in separate ways so that information is obtained. Basically,
this information is the answer to the problems that were asked before.
One part of the research design is determining the population and research
sample. Most research activities are carried out by sampling, because the sampling
method is more practical, more cost effective, and requires less time and effort
compared to the method of sampling.
Research that uses samples to examine or investigate the characteristics of
the object of research is carried out for several reasons, including the object under
study is easily damaged, the object under study is homogeneous, it is impossible
the physically examine all the objects in the population, to save costs, to save time
and effort, and the accuracy of the sampling results.
In a research that uses the sample as a unit of analysis, both in research
with a quantitative approach and research with a qualitative approach, at least there
are two problems or problems faced. First, the sampling problem is the process of
getting a sample from a population. Here the sample must truly reflect the state of
the population, meaning that the conclusions of the research results drawn from the
sample must be the conclusions of the population. So the problem faced is how to
obtain a representative sample, namely a sample that can represent other element
in the population or reflect the state of the population. Second, the problem faced
in research that uses the sample as a unit of analysis is how the sampling process
is and how many units of analysis will be taken. So that the problems faced include
which sampling technique matches the characteristics of the population, the
objectives and research problems to be studied. Apart from that, how many units
of analysis or sample size will be involved in research activities. Based on the
above understanding, this paper discusses material regarding population and
samples in quntitative research.
B. Problem Formulation
1. What is definition of sample and population ?
2. What is the sampling technique ?
3. How to determine the size of sample ?

C. Purpose
1. To know the definition of sample and population
2. To know the sampling technique
3. To know the way to determine the size of sample
 CHAPTER II
DISCUSSION

A. Definition of Population and Sample

1. Population
Population is a generalization area consisting of objects/subjects that
have certain qulities and characteristics that are determined by the resercher
for study and the draw conclusions. In simple terms, the population is all the
subjects or objects of research target.
The population is not only people, but also other natural objects.
Population is also not just the number that exists in the object or subject being
studied, but also includes all the characteristics or properties possessed by that
subject or object. The subject's form varies, it can be: humans, animals, plants,
products (products). handicraft products, industrial products, etc.), non-
products (stone, sand, soil, water, etc.), and lingual forms or verbal expressions
(words, phrases, sentences, paragraphs, texts), or documents and printed
materials.
Researcher's treatment of the subject or object can allow two
alternative population status. First, the research population has the status of
being the object of research if the population is not a source of information,
but is the substance under study, such as production (milk, paint, masks, etc.).
Second, the research population has the status of a source of information, such
as humans and documents. In a social survey, a person or group of people
commonly functions as a source of information about things related to
themselves or social phenomena related to them. In certain studies, the research
population can have a dual status, as the research object whose information is
also from that population. Research on "differences in learning methods
between science students and social students" suggests that the research
population will have a dual status: as a research object as well as a source of
research data.
According to S. Margono, Population is all data that becomes our
attention within a specified scope and time. So, population is related to data,
not people. If humans provide some data, then the number or population size
will be as much as the size of humans.
 The population has a parameter that is a measured quantity that
indicates the characteristics of the population. The quantities that we are
familiar with include: average shape, average deviation, variance, standard
deviation as population parameters. The parameters of a population are fixed
in value, if the value changes, then the population changes. The data used in
the study (research materials) can be in the form of a population (universe) or
a sample.

2. Sample
The sample is a part of the population. This means that there will be
no samples if there is no population. In a study, it is not always necessary to
study all individuals in the population because it will take a lot of time and
money. Therefore, sampling is carried out, where the sample taken is a sample
that is truly representative or that represents the entire population.
In a study which becomes the basis for consideration of sampling is
taking into account the problem of efficiency (time and cost) and the problem
of accuracy where research with sampling can increase accuracy because if the
research on the population cannot be done carefully. A researcher in a study
must take into account and pay attention to the relationship between time, cost
and energy to be expended with the precision (level of accuracy) that will be
obtained as a consideration in determining the sampling method to be used.
The sample is part of the population which is expected to be able to represent
the population in the study.

B. Sampling Technique
In general, there are two types of sampling techniques namely, random
sampling or random sampling / probability sampling, and non-random or non-
probability sampling. What is meant by random sampling is a method of taking
samples that provide an equal opportunity to be taken to each element of the
population. This means that if there are 100 population elements and 25 will be
sampled, then each of these elements has a probability of 25/100 to be selected as
the sample.
Meanwhile, what is meant by nonrandom sampling or nonprobability
sampling, each element of the population does not have the same possibility of
being sampled. Five elements of the population were selected as the sample
because they were close to the house of the researcher, while the others, because
they were far away, were not selected; meaning that the probability is 0 (zero).
The two types of sampling techniques above have different purposes. If
the researcher wants the results of his research to be used as a measure to estimate
the population, or the term is to generalize, then the sample should be
representative and randomly drawn. However, if the researcher does not have the
will to generalize the results of the study, the sample can be taken non-randomly.
A non-random sample is usually also taken if the researcher does not have definite
data on population size and complete information about each element of the
population

1. Probability/random sampling
The first condition that must be done to take samples randomly is to
obtain or create a sample frame or what is known as a "sampling frame". What
is meant by a sampling frame is a list that contains each element of the
population that can be taken as a sample. Population elements can be data
about people / animals, about events, about places, or about things. If the
research population is college student "A", the researcher should be able to
have a complete list of all students enrolled in the college "A". Name, NRP,
gender, address, age, and other information useful for research. From this list,
the researcher will be able to ascertain the population size (N). If the population
is households in a city, the researcher must have a list of all the households in
that city. If the population is West Java, the researcher must have a complete
map of the West Java area. Regency, District, Village, Village. Then each place
is given a code (numbers or symbols) which is different from each other
In addition to the sampling frame, the researcher must also have an
instrument that can be used as a sample determinant. Of the population
elements, which elements can be selected as samples? Commonly used tools
are the Random Number Table, calculator, or lottery. Random sample
selection can be done via a lottery system if there are not so many population
elements. but if there are hundreds, the method of the lottery can interfere with
the concept of "random" or "random" itself
a. Simple Random Sampling
This method or technique can be done if the research analysis
tends to be descriptive and general in nature. Character differences that
may exist in each element or population element are not important for the
analysis plan. For example, in the population there are women and men, or
there are rich and poor, there are managers and not managers, and other
differences. As long as gender differences, affluence status, and position
in the organization, as well as other differences are not something
important and have a significant effect on the research results, the
researcher can take a simple random sample. Thus each element of the
population must have the same opportunity to be selected as a sample.
There are 2 opinions regarding the simple random sampling method. The
first opinion states that each selected number must be returned again so
that each sample has the same chance percentage. The second opinion
states that there is no need to return to sampling using this method.
However, the method most often used is simple random sampling with
returns.
The advantages of this method are that it can reduce bias and can
find out research standard error. Meanwhile, the disadvantage is that there
is no guarantee that the selected sample can truly represent the intended
population.
Example of Simple Random Sampling Method: In a study, 30 samples are
needed, while the population of the study is 100 people. Then the
researcher makes a lottery to get the first sample. After getting the first
sample, the selected name is returned again so that the population remains
intact so that the probability of the next respondent remains the same as
the first respondent. This step was carried out again so that the number of
samples met the research needs.
b. Systematic Sampling
If the researcher is faced with a large population size and does not
have a random data collection tool, a systematic sampling method can be
used. This method requires researchers to systematically select population
elements, namely the population elements that can be sampled are the
"how much"
 For example, members of a population of 100 people. All
members are given serial numbers, namely number 1 to number 100.
Sampling can be done only with odd numbers, even numbers, or multiples
of certain numbers, for example multiples of the number five. For that,
taken as samples are 5, 10, 15, 20 and so on to 100.
c. Stratified Random Sampling
Since population elements are heterogeneous in character, and this
heterogeneity has a significant meaning in achieving the research
objectives, the researcher can take samples in this way. The stratified
random sampling method takes samples based on a certain level. For
example, research on work motivation at the top level, middle level
managers and lower level managers. The randomization process was taken
from each of these groups
d. Cluster Sampling
Cluster sampling is a sampling technique in groups. This type of
sampling is carried out based on certain groups / areas. The purpose of the
Cluster Random Sampling method, among others, is to examine a matter
in different parts of an institution, for example, research on patient
satisfaction in the inpatient room, emergency room, and poly room at
Hospital A and so on.
e. Area Sampling
This technique is used when the researcher is faced with a situation
that the research population is spread across various regions. This type of
sampling process is carried out in stages. Either two, three or more stories
2. Nonprobability or Nonrandom Sampling
As previously described, this sample type was not randomly selected.
Not all elements or population elements have the same opportunity to be
selected as samples. The elements of the population selected for the sample
could be due to coincidence or due to other factors that the researcher had
previously planned.
a. Purposive Sampling
Purposive Sampling is a sampling technique that is used quite
often. This method uses the criteria selected by the researcher in selecting
the sample. The sample selection criteria are divided into inclusion and
 exclusion criteria. The inclusion criteria are the sample criteria that the
researcher wants based on the research objectives. While the exclusion
criteria are special criteria that cause prospective respondents who meet
the inclusion criteria must be excluded from the research group. For
example, potential respondents may experience comorbidities or
psychological disorders that can affect the results of the study
b. Accidental Sampling
In this accidental sampling method, the researcher took a sample
that he happened to meet at that time. This study is suitable for examining
the types of rare disease cases whose samples are difficult to obtain.
This sampling technique is also suitable for general research, for example
a researcher wants to examine the cleanliness of the city of Bandung.
Furthermore, he asked about the cleanliness of Bandung City to the
Bandung residents he met at that time
c. Quota Sampling
This sampling technique is a form of proportionally stratified
sample, but not randomly selected but by chance.
For example, in an office there are 60% male employees and 40% female
employees. If a researcher wants to interview 30 employees of both sexes,
he must take a sample of 18 male employees while 12 female employees.
Once again, the technique of taking the thirty samples was not done
randomly, but by chance
d. Snowball Sampling
Snowball Sampling is a sampling technique based on interviews
or correspondence. This method asks for information from the first sample
to get the next sample, so continuously so that all the research sample
needs can be met.
Snowball sampling method or this snowball is very suitable for research
on matters that are sensitive and require a high level of privacy, for
example research on transgender women, people with HIV, and other
special groups
 e. Saturated Sampling Technique
Saturated Sampling technique is a sampling technique that takes
all members of the population as a sample. with the condition that the
existing population is less than 30 people

C. The Size of Sampling

To determine the sample from the population used calculations and table
references developed by experts. In general, for correlational research the
minimum number of samples to get good results is 30, while in experimental
studies the minimum sample size is 15 from each group and for survey research
the minimum sample size is 100.
Roscoe (1975) quoted by Uma Sekaran (2006) provides a general reference for
determining sample size: The size of a sample of more than 30 and less than 500
is appropriate for most studies.
If the sample is broken down into subsamples (male / female, junior / senior, etc.),
a minimum sample size of 30 for each category is appropriate.
In multivariate studies (including multiple regression analysis), the sample
size should be 10x greater than the number of variables in the study. For simple
experimental studies with strict experimental control, successful studies are
possible with small sample sizes between 10 and 20.
The size of this sample really depends on the degree of accuracy or error
desired by the researcher. However, in terms of the error rate, in social research the
maximum error rate is 5% (0.05). The greater the error rate, the smaller the sample
size. However, what needs to be considered is that the larger the number of samples
(the closer the population is), the smaller the chance of generalization error and
vice versa, the smaller the number of samples (away from the population), the
greater the chance of generalization error.
There are several formulas for determining the number of samples, including:
1. SLOVIN THEORY (1960)
One of the most widely used literatures is the determination of sample size
using the Slovin (1960) formula. An expert whose name is slovin, it turns out
that until now the original name has not been known, it has even been debated
about the publication year of the manuscript written by slovin, namely 1960
and 1843.
 In Riduwan's (2005) writing, with the research title "easy learning research for
teachers", he cites the Slovin formula with the following formula:
SAMPLE FORMULA : SLOVIN FORMULA
n= N
1 +Ne2
n : total of sample
N : population size or number of elements in the population
e : a predetermined value of precision or significance level. Generally, in
research the significance level is determined at 95% or 0.05.
because our sample must be a whole number and person, then we do the
rounding following the standard rounding rule, that is, if ≥ 0.5 then we round
up and vice versa
2. GAY, LR AND DIEHL, PL (1992)
Research results from Gay, LR and Diehl, PL (1992), with the research
title "Research Methods for Business and Management, it is stated that the
research sample size should be as large as possible. The assumptions made by
Gay and Diehl are based on the larger the sample taken, the more it represents
the shape and character of the population and is more generalizable. However,
the exact size of the sample to be taken depends to a large extent on the type
of research being undertaken.
Here are some conditions that need to be considered:
 If the research being carried out is descriptive, then the sample size is
at least 10% of the total population elements
 If the research carried out is a correlational or related research, then
the sample size is at least 30 subjects (sample units)
 If the research carried out is a comparative study, the recommended
research sample size is 30 subjects
 If the research being carried out is experimental in groups, the
recommended sample size is 15 samples per group
3. WIRATNA SUJARWENI (2008)
In the writings of Wiratna Sujarweni (2008) about "Easy SPSS learning
for theses, theses, dissertations & general", there is no specific amount or value
required. Sujarweni argues that the number of samples expected to 100%
represent the population is the entire population itself
 I think this point gives us a deeper understanding that it is almost impossible
to get a picture of 100% of the population from the sample data. For this reason,
care is needed in choosing the sampling method, determining the number of
samples, and the need to take into account the error rate
Sujarweni also added that if the size of a population is very large, the research
can be carried out with a sample survey. Determination of sample size may use
the Slovin formula.
4. JACOB COHEN (In ARIKUNTO SUHARSIMI, 2010: 179)
Sample formula Jacob Cohen:
N = LF² + u + 1
N = sample size
F² = effect size
u = the number of changes involved in the research
L = the power function of u = 0
5. ISAAC and MICHAEL
Determining the sample size of the study using the Isaac and Michael
tables is a little easier, where the error rates for 1%, 5% and 10% have been
determined. With this table, the researcher can directly determine the sample
size based on the number of population and the desired error rate.
The way to determine the number of research samples using tables and based on the level
of research confidence.
Cohen Manion and Marrison table :
 CHAPTER III
CLOSING

A. Conclusion
The sample is a part of the population. This means that there will be no
samples if there is no population. Population is the whole element or elements that
we will examine. Research carried out on all elements is called a census. Ideally,
to make the results of their research more reliable, a researcher should conduct a
census. But because of one thing the researcher could not examine all of these
elements, that is what he could do
 REFERENCES

Suharsimi, Arikunto. 2010. Prosedur Penelitian : Suatu Pendekatan Praktis, Edisi Revisi
2010. Jakarta : Rineka Cipta

Riduwan . 2005. Belajar Mudah Penelitian Untuk Guru, Karyawan dan Peneliti Pemula.
Bandung : Alfabeta

Sekaran, Uma. 2006. Metode Penelitian Bisnis. Jakarta : Salemba Empat

Sugiyono. 2007. Metode Penelitian Administrasi. Bandung : Alvabeta

http://la-banara.blogspot.com/2012/06/teknik-pengambilan-sampel-penelitian.html

https://www.eurekapendidikan.com/2015/09/defenisi-sampling-dan-teknik-sampling.html

http://sandimilzam.blogspot.com/2015/06/v-behaviorurldefaultvmlo_71.html