POPULATION AND SAMPLING
In the quantitative research, moreover if it’s designed as the survey research. The existences of population and sample as the main sources to get needed data. In revealing a phenomena or reality that can be a focus of the research. To achieve the accuracy and validity data that resulted, Population and Sample can be made as the object of the research that must have a transparent of the scope, size and characteristic .
A. BASIC CONCEPT OF THE POPULATION AND SAMPLE OF THE RESEARCH
Population, also called Universe is an overall or totality of the object will be researched, its features will be estimated or interpreted as well. the feature of the Population called Parameter. Therefore, The Population is also meant as the collection of the research objects where data will be collected from. Population of the research, for example a communication research could be people ( Individual, group, organization, community or society ) or things. For instance, the amounts of the publishing mass media, articles in a mass media, and rubrics. Especially, if the research uses a content analysis.
A.1 SAMPLING POPULATION AND TARGET POPULATION
Population of the research consists of Sampling Population and Target Population. Sampling Population is an overall of the researched objects. Target Population is a population that used as the original data. for example, we research “ How is the average of an academic achievement level of the English Education faculty at STKIP Muhammadiyah ”. We just focus on our research to the active students in the students’ institutions. So, all of the English Education Faculty students are the Sampling Population. And all students who are active in the students’ Institutions are The Target Population.

A.2 POPULATION NUMBERS AND POPULATION SIZE

Population numbers is a quantity of population category that made as the research objects, notified with the letter of K. for example, when we researched How is the average of an academic achievement level of the English Education faculty at STKIP Muhammadiyah, hence, the numbers of the Population is one ( 1 ). Namely, the category of students. Meanwhile, if we researched the behavior of the Academicians of the English Education faculty at STKIP Muhammadiyah toward a Rector’s policy about the college fee, for instance categories of Students, Lecturers and Administration Staff. so, the Population Numbers is three ( 3 ).
Size Population is the amounts of element or unit that contain in a certain category of population. symbolized with a letter of N. for example, we researched “ How is the average of an academic achievement level of the English Education faculty at STKIP Muhammadiyah “. Then, the Population numbers is one ( 1 ), and The Size population is 120 students ( based on the officially registered at STKIP Muhammadiyah ).
If we use all elements of population as a data source, then our research is called Census.
Census is a research that regarded able to reveal the population’s features ( Parameter ) accurately and comprehensively. Because by using all elements of population as the data source, then the description about coherently and comprehensively population will be obtained. Therefore, the best research is the Census. However, in a certain limitations, Census sometimes is ineffective an inefficient. Especially, if it is connected with the limitation of the researcher’ qualifications ( research focus, time limitation, man power and cost of the research ).
B. SAMPLE
In a certain condition for the researcher who is impossible to conduct a Census. Then, the researcher may take a partial of Population unsure that can be made as the research objects of the data sources. Partial Population unsure that made as the research object is called as Sample. Sample is also said as Example is a representation of population that its features will be stated and used to estimate the population’s features. the population’s feature is Statistic. There are two concepts of Sample, they are : Sampling Numbers and Sampling Size.
B.1 Sampling Numbers and Sampling Size
Sampling Numbers is the amounts of the sample categories that examined or researched, symbolized with a letter of k, the amounts equal to the Population Numbers ( k= K ). Whereas, the Sampling Size is symbolized with a letter of n. the Sampling Size is the big amounts of a population unsure that made as a Sample that its numbers is always less than a Population Size (n
As a researcher, why we have to completely understand about the Sampling Numbers and the Sampling Size ,because the researched of the Sampling Numbers and the Sampling Size ( especially, for the explicative research that used correlative research approaches ), it will determine the examined of interference statistic which should be used to estimate a hypothesis that will be formulated on our research. The accurateness of choosing the examined of interference statistic is a key to determine a validity of the research.
An obtained data from Sample must be a able to estimate a population. Then, whenever we take a Sample from a certain population, we must take a Sample that represents its population ( Representative Sample ). Representative Sample is a Sample which has same characteristics or relatively closed to the features of its Population Characteristic.
The level of the Sample representation depends on :
• Kinds of Sample will be used
• Size of Sample will be taken
• How to take a Sample from Population.
The way or procedure is used to take a Sample from a certain Population called as Sampling Technique.





B.2 THE SIZE OF SAMPLE

Before determining how big the size of a Sample that taken from certain population, there are several aspects should be considered ( Singarimbun masri and Effendi Sofyan, Metode penelitian Survei, 1989 ) :
1. Degree of homogeneity.
If the Degree of Population homogeneity is high or almost perfect, so the Sampling Size that taken may be less. Contrary, if the degree of population homogeneity is low ( heterogeneity is high ), so the sampling size must be bigger. To determine the degree of population in homogeneity, it supposed to do a Homogeneity Examination, by estimating a certain Statistic.
2. The using of the precisions level. It is used in the Explicative Research on a Correlative Research, it is a statement of the research about the accuracy level of the research result.
3. Analysis Frame.
It is connecting with managing , presented, analyzing data and interpreting data that will be encountered in the research.
4. Certain reasons dealing with appearing limitations of the researcher. Such as, the limitation of time, power, cost and so on.
Moreover, considering the factors above, we can take several sample from population by using the formulation of Slovin :

N
n = ———
1 + Ne²

Explanations :
n = sampling size
N = population size
e = tolerance of careless because of mistaken in taking tolerated sample. example 5 %.

The tolerated mistaken limitations is not same in every population. ( 1%, 2%,3%,4%,5%, or 10 % ).
If the sampling size is quite big can be obtained from a population proportion presuming, so The formulation of Yamane must be used :

N
n = ———–
Nd² + 1

d = the limitation of a mistaken sample taken tolerance that used.

Example : a researcher wants to presume a proportion of the newspaper readers from population ( 4.000 people ). Stating Precision between 5 % with the trusty level 95 %, so the amount of sample is :

4000
n = ————————- = 364
4000 x (0,05)² + 1


C. SAMPLING FRAME

Among the use of sampling techniques , there is requirement of the sampling frame. The sampling frame is a list that contains of data about all unit or unsure of the sampling that exist in the population sampling. In simple way, the researcher says it as the list of names that containing in the population if the research.


D. KINDS OF SAMPLING DAN SAMPLING TECHNIQUE

Based on procedure or way is used in taking sample from population ( Sampling Technique ), we can identify two kind of sample, they are : Probability Sampling and Non Probability Sampling.
Probability Sampling ( Random Sample ) is the taking of the sample based on Chance Theory Principal, namely : a principal to give same chances to all unit of the population to be chosen as Sample.
Non probability Sample ( Non random Sample ) is the taking of the sample based on certain considerations.


D.1 SAMPLING PROBABILITY TECHNIQUE ( RANDOM SAMPLING TECHNIQUE )

a. Simple Random Sampling

Simple Random Sampling is the taking of the sample originally until all unit of the research or element of unit from population has the same chance to be chosen as sample. The opportunity that owned by every unit of the research to be chosen as Sample is n/N, it is an expected sample size divided by population size.
In using the Simple Random Sampling, there are qualifications should be fulfilled, namely ( Singarimbun and Effendy, 1989 ) :
1. Providing of the sampling frame
2. Characteristics of population must be in homogeneous. Otherwise, it will happen bias.
3. Limited population size
4. Population condition isn’t too spread.


Techniques to do, are :

1. By speculating the unsure of the research or the element of unit in population.
2. By using the Random number Table




b. Systematic Random Sampling

If the population size is too large that is impossible done a choice of the sample by the speculation way. In this condition, the research has to use Systemic Random Sampling. It has same conditions just like the Simple Random Sampling.
The way of the Systemic Random Sampling is similar to Simple Random Sampling. The difference is the speculation is done once. When determined first unsure of the taking sample. The determination of the sampling unsure will be continued by using Interval of Sample ( Ratio Sampling ). Interval of Sample is the number or statistic that showed interval between listed numbers that containing in the sampling frame that will be used as an indicator in determining or choosing the second sampling unsure and continued until unsure of n. Interval of sample is usually symbolized with the letter of k.

c. Stratified Random Sampling

This Sampling technique is used if the population isn’t homogeneous ( Heterogeneous ). The more heterogeneous of a population, the more differences of the characteristics among the populations. In order to describe precisely about the characteristics of the heterogeneous population, then population is divided into the homogeneous strata and each of strata can be taken as sample in random.
To use the Stratified Random Sampling technique, there are conditions should be fulfilled, they are ( Singarimbun and Effendi, 1`989 : 162 – 163 ) :
1. There must be clear criteria that will be used as the basic to stratify the population into stratum.
2. There must be a preface data of the population about the criteria that will be used to stratify.
3. The amount of the element unit of each strata ( the size of each sub population ) must be certainly known. To determine the target sample or respondent must be continued by using Simple Random Sampling and Systemic Random Sampling, after making the sample frame for each of the sub population.

The Stratified Random Sampling consists of two kinds, they are :
1. Proportional Strata Sampling
2. Disproportional Strata Sampling

Proportional Strata Sampling technique is used if the proportion of the sub population size or the amount of the element unit of each strata is relatively balanced and equaled. In each of the Proportional Strata Sampling, each of strata is taken a sample that equals to the size of strata by having indication to the Sampling fraction that altogether used. The Sampling Fraction is the number or statistic that showed percentage of the sample size that will be taken from a certain population size.



d. Cluster Random Sampling

This technique is used if the population size is certainly unknown. So that, it’s impossible to make the sampling frame and the existence is spreading geographically or clustered in different clusters. However, if the cluster and geography area are large, then the sample taking isn’t sufficient to conduct one stage, but has to conduct several stages. In this instance, the researcher has to use multistage cluster sampling.

d. Nonrandom Technique Sampling

in determining sample that doesn’t use random principal ( change theory principal ). The basic of determining is certain considerations from the researcher or the research. as the consequence, Technique Sampling Nonrandom can’t be used the frame of the research as the explicative research that will examine certain hypothesis. For example, correlation research. because the formulation of inferential statistic examined can’t be applied the data that belong to Nonrandom Sample. This technique of sampling broadly used for the exploitative and descriptive research.
There are kinds of Nonrandom Sample that used in the social or communication research, as follow :
1. Accidental Sampling
It is said a coincidence Sample ( Convenience Sampling )that taken based on the consideration of easiness to the researcher.
2. Quota Sampling
It’s almost same like the Stratified Sampling Technique. The difference is when the researcher took a sample from each of the strata, the researcher didn’t use random ways but use convenience / easiness ways.
3. Purposeful Sampling
This technique is called as the Judgmental Sampling or Purposed Consideration Sampling. Determining basic Sample is the purpose. This sample is used for getting the researching data about phenomena or problem required the specific qualified respondents. Like : experts, lecturers and professional ones.



E. SEVERAL PROBLEMS OF THE RESEARCH THAT RELATED TO SAMPLE THAT POSSIBLY FACED BY THE RESEARCHER

In the research, problems or distortions possibly could happen depending on the characteristic of the research itself. In the research, there are two distortions :
1. Distortion by using Error Sampling
2. Distortion by using non-Sampling Error
This distortion can be caused by, as follows :
• The failure of planning
• The changing of Sampling
• Misinterpretation by a data collector or respondent.
• Error of answering from the respondent
• Error of managing data

F. THE PROBLEMS OF MAKING THE FRAMEWORK OF THE SAMPLING

•Blank Foreign Elements
The obtaining of the population data didn’t fit with the reality in the field.
•Incomplete Frame
incompleteness of the Samp0ling Frame happened, because of unsure of population ( people ) weren’t recorded as the data.
•Cluster of Elements
The Sampling Frame that the researcher had, not completely same with the necessary.

CLASSIFICATION AND INSTRUMENT OF DATA
A. Data based on how the way to collect
1. Primary Data
Primary data is a data that taken directly from the object of the research by the researcher. For example : Interviewing the viewer of the cinema directly, to research a movie preference of the viewers.
2. Secondary Data
Secondary data is data that collected indirectly from the object of the research .the researcher collected the existing data that belongs to others with the various way or method in commercial or non- commercial. for example : the researcher used the researching statistic data from newspaper or magazine


B. Data Based on the Source of Data
1. Internal Data
Internal Data is a data that describe situation and condition in one of organization internally. For example : Financial data, Employees data, Production data.
2. External Data
External data is a data that describe situation and condition outside of the organization. For example : a product users data, customers preference.
C. Classifying data based on the kind or variety
1. Quantitative Data
Quantitative data is a data that described in the form of numbers ( statistic ). For example : the amounts of buyers , the height of students.
2. Qualitative Data
Qualitative data is a data that presented in the form of the words that containing of meaning. For example : the perception of the consumers to a product.
D. Classifying Data based on the Data Characteristic
1. Discreet Data
Discreet data is a data that has original value. For example : the weight of mothers of PKK Sumber Ayu, the currency value of Rupiah.
2. Continue Data
Continue Data is a data that has value on a certain interval or stay in one point to another point. For example : the usage words of about, more than, average.

E. Kind of Data based on the time of collecting
1. Cross Section Data
Cross Section Data is a data that show a certain time point. For example :the financial report per December 31, 2009, Customer data of PT. Angin Ribut in May 2010
2.Time Series Data
Time Series Data is a data that its data describe something from time to time or periodical historically. For example : the fluctuation of the currency US Dollar to EURO from 2006 – 2009.

high technology and science: high technology and science: What is a public page?

high technology and science: high technology and science: What is a public page?

high technology and science: What is a public page?

high technology and science: What is a public page?

What is a public page?

What is a public page?