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•  Populasi dan Sampel

Definisi Populasi
Populasi adalah keseluruhan dari objek penelitian
 
Definisi Sampel
Sampel adalah bagian dari populasi
 
Sampel yang baik adalah sampel yang representatif, yaitu sampel yang dapat mewakili populasinya. Agar representatif, maka pengambilan sampel dari populasi harus menggunakan teknik pengambilan sampel (sampling) yang benar.
 
Ada 2 teknik pengambilan sampel :
1. Teknik sampling berdasarkan peluang.
Teknik sampling berdasarkan peluang adalah sebuah teknik pengambilan sampel dimana setiap unit observasi dalam populasi mempunyai kesempatan yang sama untuk terpilih menjadi sampel.
Ada 3 teknik sampling berdasarkan peluang :
•  Sampling Acak Sederhana
Sampling acak sederhana adalah teknik pengambilan sampel dimana sampel diambil berdasarkan tabel bilangan acak
•  Sampling Klasifikasi
Sampling klasifikasi adalah sebuah teknik pengambilan sampel dimana populasi terlebih dahulu di bagi-bagi menjadi sub-sub populasi yang antar sub populasi homogen. Karena sub populasi homogen, salah satu sub populasi diambil sebagai sampel
•  Sampling Stratifikasi
Sampling stratifikasi adalah sebuah teknik pengambilan sampel dimana populasi terlebih dahulu di bagi-bagi menjadi sub-sub populasi yang antar sub populasi heterogen. Karena sub populasi heterogen, pada setiap sub polulasi ada yang diambil sebagai sampel

Research Methods (EDU 5900)/UPM sesi 2012/2013

Nama Ahli Kumpulan  6

1. Wanitah

2.  Anusha Devi

3.  Kavitha

4.  Kiran

Types of data and Data Collection

Statistical Data: 

A sequence of observation, made on a set of objects included in the sample drawn from population is known as statistical data.   

(1) Ungrouped Data:
          Data which have been arranged in a systematic order are called raw data or ungrouped data.

(2) Grouped Data:
          Data presented in the form of frequency distribution is called grouped data. 

Collection of Data:
            The first step in any enquiry (investigation) is collection of data. The data may be collected for the whole population or for a sample only. It is mostly collected on sample basis. Collection of data is very difficult job. The enumerator or investigator is the well trained person who collects the statistical data. The respondents (information) are the persons whom the information is collected.

Types of Data:
            There are two types (sources) for the collection of data.
            (1) Primary Data (2) Secondary Data     

(1) Primary Data:
          The primary data are the first hand information collected, compiled and published by organization for some purpose. They are most original data in character and have not undergone any sort  of statistical treatment.
Example: Population census reports are primary data because these are collected, complied and published by the population census organization.
   
(2) Secondary Data:
          The secondary data are the second hand information which are already collected by some one (organization) for some purpose  and are available for the present study. The secondary data are not  pure in character and have undergone some treatment at least once.
Example: Economics survey of England is secondary data because these are collected by more than one organization like Bureau of statistics, Board of Revenue, the Banks etc…

Methods of Collecting Primary Data:
            Primary data are collected by the following methods:

• Personal Investigation: The researcher conducts the survey him/herself and collects data from it. The data collected in this way is usually accurate and reliable. This method of collecting data is only applicable in case of small research projects.

• Through Investigation: Trained investigators are employed to collect the data. These investigators contact the individuals and fill in questionnaire after asking the required information. Most of the organizing implied this method.

• Collection through Questionnaire: The researchers get the data from local representation or agents that are based upon their own experience. This method is quick but gives only rough estimate.

• Through Telephone: The researchers get information through telephone this method is quick and give accurate information. 
  Methods of Collecting Secondary Data:
  The secondary data are collected by the following sources:

  • Official: e.g. The publications of the Statistical Division, Ministry of Finance, the Federal Bureaus of Statistics, Ministries of Food, Agriculture, Industry, Labor etc…

  • Semi-Official: e.g. State Bank, Railway Board, Central Cotton Committee, Boards of Economic Enquiry etc…

  • Publication of Trade Associations, Chambers of Commerce etc…

  • Technical and Trade Journals and Newspapers.

  • Collection of Statistical Data
    

    Statistical Data:
                A sequence of observation, made on a set of objects included in the sample drawn from population is known as statistical data.   

    (1) Ungrouped Data:
              Data which have been arranged in a systematic order are called raw data or ungrouped data.

    (2) Grouped Data:
              Data presented in the form of frequency distribution is called grouped data.

    Collection of Data:
                The first step in any enquiry (investigation) is collection of data. The data may be collected for the whole population or for a sample only. It is mostly collected on sample basis. Collection of data is very difficult job. The enumerator or investigator is the well trained person who collects the statistical data. The respondents (information) are the persons whom the information is collected.

    
    

    Types of Data: 

     There are two types (sources) for the collection of data.
                (1) Primary Data (2) Secondary Data   

      

    (1) Primary Data:
              The primary data are the first hand information  collected, compiled and published by organization for some purpose. They are most original data in character and have not undergone any sort of statistical treatment.
    Example: Population census reports are primary data because these are collected, complied and published by the population census organization.
       
    (2) Secondary Data:
              The secondary data are the second hand information which are already collected by some one (organization) for some purpose and are available for the present study. The secondary data are not pure in character and have undergone some treatment at least once.
    Example: Economics survey of England is secondary data because these are collected by more than one organization like Bureau of statistics, Board of Revenue, the Banks etc…      

    Methods of Collecting Primary Data:
                Primary data are collected by the following methods:

    • Personal Investigation: The researcher conducts the survey him/herself and collects data from it. The data collected in this way is usually accurate and reliable. This method of collecting data is only applicable in case of small research projects.

    • Through Investigation: Trained investigators are employed to collect the data. These investigators contact the individuals and fill in questionnaire after asking the required information. Most of the organizing implied this method.

    • Collection through Questionnaire: The researchers get the data from local representation or agents that are based upon their own experience. This method is quick but gives only rough estimate.

    • Through Telephone: The researchers get information through telephone this method is quick and give accurate information.

    Methods of Collecting Secondary Data:
     The secondary data are collected by the following sources:

    • Official: e.g. The publications of the Statistical Division, Ministry of Finance, the Federal Bureaus of Statistics, Ministries of Food, Agriculture, Industry, Labor etc…

    • Semi-Official: e.g. State Bank, Railway Board, Central Cotton Committee, Boards of Economic Enquiry etc…

    • Publication of Trade Associations, Chambers of Commerce etc…

    • Technical and Trade Journals and Newspapers.

    • Research Organizations such as Universities and other institutions.

    
    

    Difference between Primary and Secondary Data:
                The difference between primary and secondary data is only a change of hand. The primary data are the first hand data information which is directly collected form one source. They are most original data in character and have not undergone any sort of statistical treatment while the secondary data are obtained from some other sources or agencies. They are not pure in character and have undergone some treatment at least once.
    For Example: Suppose we interested to find the average age of MS students. We collect the age’s data by two methods; either by directly collecting from each student himself personally or getting their ages from the university record. The data collected by the direct personal investigation is called primary data and the data obtained from the university record is called secondary data. 

    Editing of Data:
                After collecting the data either from primary or secondary source, the next step is its editing. Editing means the examination of collected data to discover any error and mistake before presenting it. It has to be decided before hand what degree of accuracy is wanted and what extent of errors can be tolerated in the inquiry. The editing of secondary data is simpler than that of primary data.

Data Collection

SOURCES OF DATA
The sources of data may be classified into (a) primary sources and (b) secondary sources.

Primary Sources
Primary sources are original sources from which the researcher directly collects data that have not been previously collected, e.g., collection of data directly by the researcher on brand awareness, brand preference, brand loyalty and other aspects of consumer behaviour from a sample of consumers by interviewing them. Primary data are first-hand information collected through various methods such as observation, interviewing, mailing etc.

Secondary Sources
These are sources containing data that have been collected and compiled for another purpose. The secondary sources consist of readily available compendia and already compiled statistical statements and reports whose data may be used by researches for their studies, e.g., census reports, annual reports and financial statements of companies, Statistical statements, Reports of Government Departments, Annual Reports on currency and finance published by the National Bank for Ethiopia, Statistical Statements relating to Cooperatives, Federal Cooperative Commission, Commercial Banks and Micro Finance Credit Institutions published by the National Bank for Ethiopia, Reports of the National Sample Survey Organisation, Reports of trade associations, publications of international organisations such as UNO, IMF, World Bank, ILO, WHO, etc., Trade and Financial Journals, newspapers, etc.
Secondary sources consist of not only published records and reports, but also unpublished records. The latter category includes various records and registers maintained by firms and organisations, e.g., accounting and financial records, personnel records, register of members, minutes of meetings, inventory records, etc.
Features of Secondary Sources: Though secondary sources are diverse and consist of all sorts of materials, they have certain common charac-teristics.
First, they are readymade and readily available, and do not require the trouble of constructing tools and administering them.
Second, they consist of data over which a researcher has no original control over collection and classification. Others shape both the form and the content of secondary sources. Clearly, this is a feature, which can limit the research value of secondary sources.
Finally, secondary sources are not limited in time and space. That is, the researcher using them need not have been present when and where they were gathered.

USE OF SECONDARY DATA
Uses

The secondary data may be used in three ways by a researcher. First, some specific information from secondary sources may be used for refer-ence purposes.
Second, secondary data may be used as bench marks against which the findings of a research may be tested.
Finally, secondary data may be used as the sole source of information for a research project. Such studies as Securities Market Behaviour, Financial Analysis of Companies, and Trends in credit allocation in commercial banks, Sociological Studies on crimes, historical studies, and the like depend primarily on secondary data. Year books, Statistical reports of government departments, reports of public organisations like Bureau of Public Enterprises, Census Reports etc. serve as major data sources for such research studies.
Advantages

1. Secondary data, if available, can be secured quickly and cheaply.
2. Wider geographical area and longer reference period may be covered without much cost. Thus the use of secondary data extends the researcher's space and time reach.
3. The use of secondary data broadens the database from which scientific generalizations can be made.
4. The use of secondary data enables a researcher to verify the findings based on primary data.

Disadvantages/limitations

1. The most important limitation is the available data may not meet, our specific research needs.
2. The available data may not be as accurate as desired.
3. The secondary data are not up-to-date and become obsolete when they appear in print, because of time lag in producing them.
4. Finally information about the whereabouts of sources may not be available to all social scientists.

METHODS OF COLLECTING PRIMARY DATA: GENERAL
The researcher directly collects primary data from their original sources. In this case, the researcher can collect the required data precisely according to his research needs, he can collect them when he wants them and in the form he needs them. But the collection of Primary data is costly and time consuming. Yet, for several types of social science research such as socio-economic surveys, social anthropological studies of rural communities and tribal communities, sociological studies of social problems and social institutions, marketing research, leadership studies, opinion polls, attitudinal surveys, readership, radio listening and T.V. viewing surveys, knowledge-awareness practice (KAP) studies, farm management studies, business management studies, etc., required data are not available from secondary sources and they have to be directly gathered from the primary sources.
In all cases where the available data are inappropriate, inadequate or obsolete, primary data have to be gathered.

Methods of Primary Data Collection
There are various methods of data collection. A ‘Method’ is different from a ‘Tool’. While a method refers to the way or mode of gathering data, a tool is an instrument used for the method. For example, a schedule is used for interviewing. The important methods are (a) observation, (b) interviewing, (c) mail survey, (d) experimentation, (e) simulation, and (f) projective technique.
Observation involves gathering of data relating to the selected research by viewing and/or listening. Interviewing involves face-to-face con-versation between the investigator and the respondent. Mailing is used for collecting data by getting questionnaires completed by respondents. Ex-perimentation involves a study of independent variables under controlled conditions. Experiment may be conducted in a laboratory or in field in a natural setting. Simulation involves creation of an artificial situation similar to the actual life situation. Projective methods aim at drawing inferences on the characteristics of respondents by presenting to them stimuli. Each method has its advantages and disadvantages.

Choice of Methods of Data Collection
Which of the above methods of data collection should be selected for a proposed research project? This is one of the questions to be considered while designing the research plan. One or More methods has/have to be chosen. No method is universal. Each method's unique features should be compared with the needs and conditions of the study and thus the choice of the methods should be decided.

OBSERVATION
Meaning and Importance
Observation means viewing or seeing. We go on observing some thing or other while we are awake. Most of such observations are just casual and have no specific purpose. But observation as a method of data collection is different from such casual viewing.
Observation may be defined as a systematic viewing of a specific phenomenon in its proper setting or the specific purpose of gathering data for a particular study. Observation as a method includes both 'seeing' and 'hearing.' It is accompanied by perceiving as well.
Observation also plays a major role in formulating and testing hypothesis in social sciences. Behavioural scientists observe interactions in small groups; anthropologists observe simple societies, and small com-munities; political scientists observe the behaviour of political leaders and political institutions.

Types of Observation
Observation may be classified in different ways. With reference to the investigator’s role, it may be classified into (a) participant observation, and (b) non-participant observation. In terms of mode of observation, it may be classified into (c) direct observation, and (d) indirect observation. With reference to the rigour of the system adopted, observation is classified into (e) controlled observation, and (f) uncontrolled observation

EXPERIMENTATION
Experimentation is a research ‘process’ used to study the causal relationships between variables. It aims at studying the effect of an inde-pendent variable on a dependent variable, by keeping the other inde-pendent variables constant through some type of control. For example, a -social scientist may use experimentation for studying the effect of a method of family planning publicity on people's awareness of family plan-ning techniques.

Why Experiment?
Experimentation requires special efforts. It is often extremely difficult to design, and it is also a time consuming process. Why should then one take such trouble? Why not simply observe/survey the phenomenon? The fundamental weakness of any non-experimental study is its inability to specify causes and effect. It can show only correlations between variables, but correlations alone never prow causation. The experiment is the only method, which can show the effect of an independent variable on dependent variable. In experimentation, the researcher can manipulate the independent variable and measure its effect on the dependent variable. For example, the effect of various types of promotional strategies on the sale of a given product can be studies by using different advertising media such as T.V., radio and Newspapers. Moreover, experiment provides “the opportunity to vary the treatment (experimental variable) in a systematic manner, thus allowing for the isolation and precise specification of important differences.”

Applications
The applications of experimental method are ‘Laboratory Experiment’, and ‘Field Experiment’.

SIMULATION
Meaning
Simulation is one of the forms of observational methods. It is a process of conducting experiments on a symbolic model representing a phenomenon. Abelson defines simulation as “the exercise of a flexible imitation of processes and outcomes for the purpose of clarifying or explaining the underlying mechanisms involved.” It is a symbolic abstrac-tion, simplification and substitution for some referent system. In other words, simulation is a theoretical model of the elements, relations and processes which symbolize some referent system, e.g., the flow of money in the economic system may be simulated in a operating model consisting of a set of pipes through which liquid moves. Simulation is thus a techni-que of performing sampling experiments on the model of the systems. The experiments are done on the model instead of on the real system, because the latter would be too inconvenient and expensive.
Simulation is a recent research technique; but it has deep roots in history. Chess has often been considered a simulation of medieval warfare.

INTERVIEWING
Definition
Interviewing is one of the major methods of data collection. It may be defined as two-way systematic conversation between an investigator and an informant, initiated for obtaining information relevant to as a specific study.
It involves not only conversation, but also learning from the respondents’ gestures, facial expressions and pauses, and his environment. Interviewing requires face-to-face contact or contact over telephone and calls for interviewing skills. It is done by using a structured schedule or an unstructured guide.

Importance
Interviewing may be us either as a main method or as a supplemen-tary one in studies of persons. Interviewing is the only suitable method for gathering information from illiterate or less educated respondents. It is useful for collecting a wide range of data from factual demographic data to highly personal and intimate information relating to a person's opinions, attitudes, values, beliefs, past experience and future intentions. When qualitative information is required or probing is necessary to draw out fully, then interviewing is required. Where the area covered for the survey is a compact, or when a sufficient number of qualified interviewers are available, personal interview is feasible.
Interview is often superior to other data-gathering methods. People are usually more willing to talk than to write. Once rapport is established, even confidential information may be obtained. It permits probing into the context and reasons for answers to questions.
Interview can add flesh to statistical information. It enables the inves-tigator to grasp the behavioural context of the data furnished by the respondents. It permits the investigator to seek clarifications and brings to the forefront those questions, that, for one reason or another, respondents do not want to answer.

Types of Interviews
The interviews may be classified into: (a) structured or directive interview, (b) unstructured or non-directive interview, (c) focused inter-view, and (d) clinical interview and (e) depth interview.

Telephone Interviewing
Telephone interviewing is a non-personal method of data collection.

Group Interviews
Group interview may be defined as a method of collecting primary data in which a number of individuals with a common interest interact with each other. In a personal interview, the flow of information is multidimensional.

Interviewing Process
The interviewing process consists of the following stages:

• Preparation.
• Introduction
• Developing rapport
• Carrying the interview forward
• Recording the interview, and
• Closing the interview

PANEL METHOD
The panel method is a method of data collection, by which data is collected from the same sample respondents at intervals either by mail or by personal interview. This is used for longitudinal studies on economic conditions, expenditure pattern; consumer behaviour, recreational pattern, effectiveness of advertising, voting behaviour, and so on. The period, over which the panel members are contacted for information may spread over several months or years. The time interval at which they are contacted repeatedly may be 10 or 15 days, or one or two months depending on the nature of the study and the memory span of the respondents.

Characteristics
The basic characteristic of the panel method is successive collection of data on the same items from the same persons over a period of time. The type of information to be collected should be such facts that can be accurately and completely furnished by the respondent without any reservation. The number of item should be as few as possible so that they could be furnished within a few minutes, especially when mail survey is adopted. The average amount of time that a panel member has to spend each time for reporting can be determined in a pilot study. The panel method requires carefully selected and well-trained field workers and effective supervision over their work.-

Types of Panels
The panel may be static or dynamic. A static or continuous panel is one in which the membership remains the same throughout the life of the panel, except for the members who drop out. The dropouts are not replaced.

MAIL SURVEY
Definition
The mail survey is another method of collecting primary data. This method involves sending questionnaires to the respondents with a request to complete them and return them by post. This can be used in the case of educated respondents only. The mail questionnaire should be simple so that the respondents can easily understand the questions and answer them. It should preferably contain mostly closed-end and multiple-choice questions so that it could be completed within a few Minutes.
The distinctive feature of the mail survey is that the questionnaire is self-administered by the respondents themselves and the responses are recorded by them, and not by the investigator as in the case of personal interview method. It does not involve face-to-face conversation between the investigator and the respondent. Communication is carried out only in writing and this requires more cooperation from the respondents than does verbal communication.
Alternative modes of sending questionnaires
There are some alternative methods of distributing questionnaires to the respondents. They are: (1) personal delivery, (2) attaching question-naire to a, product, (3) advertising questionnaire in a newspaper or magazine, and (4) newsstand inserts.

PROJECTIVE TECHNIQUES
The direct methods of data collection, viz., personal interview, telephone interview and mail survey rely on respondents' own report of their behaviour, beliefs, attitudes, etc. But respondents may be unwilling to discuss controversial issues or to reveal intimate information about themselves or may be reluctant to express their true views fearing that they are generally disapproved. In order to overcome these limitations, indirect methods have been developed. Projective Techniques are such indirect methods. They become popular during 1950s as a part of motivation research.

Meaning
Projective techniques involve presentation of ambitious stimuli to the respondents for interpretation. In doing so, the respondents reveal their inner characteristics. The stimuli may be a picture, a photograph, an inkblot or an incomplete sentence. The basic assumption of projective techniques is that a person projects his own thoughts, ideas and attributes when he perceives and responds to ambiguous or unstructured stimulus materials. Thus a person's unconscious operations of the mind are brought to a conscious level in a disguised and projected form, and the person projects his inner characteristics.

Types of Projective Techniques
Projective Techniques may be divided into three broad categories: (a) visual projective techniques (b) verbal projective techniques, and (c) Expressive techniques.

Research Exercise: Sampling Plan

Use the Problem sheet to describe, as fully as you can, your sample-that is, the subjects you will include in your study. Describe the type of sample you plan to use and how you will obtain the sample. Indicate whether you expect your study to have population generalizability. If so, to what population? if not, why not? Then indicate whether the study would have ecological generalizability. If so, to what setting? if not, why would it not? 

Sampling Plan 

1. My intended sample(participants in your study) consists of(state who and how many):

_________________________________________________________________________________

2. Key demographics (characteristics of the sample) are as follows(eg..age, range, sex distribution, ethnic breakdown, socioeconomic status, location etc):

__________________________________________________________________________________________________________________________________________________________________

3. State what type of sample you plan to use( eg..convenience, purposive, simple random, cluster, systematic, stratified cluster):

__________________________________________________________________________________________________________________________________________________________________

4. I will gain access to and/or get contact information for my sample through the following steps:

__________________________________________________________________________________________________________________________________________________________________

5. What, if any, are the inclusion/exclusion criteria for participation in your study?

__________________________________________________________________________________________________________________________________________________________________

6. External validity:

a. To whom do you think yo can generalize the results of your study? Explain.

_________________________________________________________________________________

b. If applicable, to what settings/conditions could you generalize the results of your study (ecological validity)?

_________________________________________________________________________________

c. If results are not generalizable, why not?

__________________________________________________________________________________________________________________________________________________________________ 

External Validity

• The term external validity, as used in research refers to the extent that the results of a study can be generalized from a sample to a population.
• The term population generalizability refers to the extent to which the results of a study can be generalized to the intended population.
• The term ecological generalizability refers to the extent to which the results of a study can be generalized to conditions or settings other than those that prevailed in a particular study. 

Sample Size

Samples should be as large as a researcher can obtain with a reasonable expenditure of time and energy. A recommended minimum number of subjects is 100 for a descriptive study, 50 for a correctional study and 30 in each group for experimental and causal-comparative studies. Some differences between the sample and the population are bound to exist, but if the sample is randomly selected and of sufficient size, these differences are likely to be relatively insignificant and incidental.

Reflection of a situation: Suppose a target population consists of 1,000 eighth-graders in a given school district. Some samples sizes, of course are obviously too small. Samples with 1 or 2 or 3 individuals, for example, are so small that they cannot possibly be representative. Probably any sample that has less than 20 to 30 individuals is too small, since that would only be 3 or 3 percent of the population. On the other hand, a sample can be too large, given the amount of time and effort the researcher must put into obtaining it. In this example, a sample of 250 or more individuals would probably be needlessly large, as that would constitute a quarter of the population. But what about samples of 50 or 100 or even 200? The question arise is the the size of sample would be too large? The best answer is that a sample should be as large as the researcher can obtain with a reasonable expenditure of time and energy.

Sampling Method -Random Vs Nonrandom Sampling-

Sampling Methods

1) Probability Sampling Method

2) Non-Probability Sampling Method

Probability Sampling Method

1) A simple random sample

A simple random sample is obtained by choosing elementary units in search a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias. However, using a random number table to choose the elementary units can be cumbersome. If the sample is to be collected by a person untrained in statistics, then instructions may be misinterpreted and selections may be made improperly. Instead of using a least of random numbers, data collection can be simplified by selecting say every 10th or 100th unit after the first unit has been chosen randomly. Such a procedure is called systematic random sampling.The larger a random sample is in size, the more likely it is to represent the population. 

How to use a random number table.

1. Let's assume that we have a population of 185 students and each student has been assigned a number from 1 to 185. Suppose we wish to sample 5 students (although we would normally sample more, we will use 5 for this example).
2. Since we have a population of 185 and 185 is a three digit number, we need to use the first three digits of the numbers listed on the chart.
3. We close our eyes and randomly point to a spot on the chart. For this example, we will assume that we selected 20631 in the first column.
4. We interpret that number as 206 (first three digits). Since we don't have a member of our population with that number, we go to the next number 899 (89990). Once again we don't have someone with that number, so we continue at the top of the next column. As we work down the column, we find that the first number to match our population is 100 (actually 10005 on the chart). Student number 100 would be in our sample. Continuing down the chart, we see that the other four subjects in our sample would be students 049, 082, 153, and 005

Microsoft Excel has a function to produce random numbers.

The function is simply:
=RAND()
Type that into a cell and it will produce a random number in that cell. Copy the formula throughout a selection of cells and it will produce random numbers between 0 and 1.
If you would like to modify the formula, you can obtain whatever range you wish. For example.. if you wanted random numbers from 1 to 250, you could enter the following formula:
=INT(250*RAND())+1
The INT eliminates the digits after the decimal, the 250* creates the range to be covered, and the +1 sets the lowest number in the range. 

2) Stratified Sample

 A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into different groups may be based on some characteristic or variable like income of education. Like anybody with ten years of education will be in group A, between 10 and 20 group B and between 20 and 30 group C. These groups are referred to as strata. You can then randomly select from each stratum a given number of units which may be based on proportion like if group A has 100 persons while group B has 50, and C has 30 you may decide you will take 10% of each. So you end up with 10 from group A, 5 from group B and 3 from group C. 
The advantage of this sampling is that it increase the likelihood of representativeness, especially if one's sample is not very large.  
The disadvantage is that it requires more effort on the part of the researcher. 


3) Cluster Sample

A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. For example, a cluster may be something like a village or a school, a state. So you decide all the elementary schools in New Delhi are clusters. You want 20 schools selected. You can use simple or systematic random sampling to select the schools, and then every school selected becomes a cluster. This method is similar to simple random sampling except that groups rather than individuals are randomly selected.
The advantages is that it can be used when it is difficult or impossible to select a random sample of individuals and frequently less time consuming.
The disadvantage is that there is a far greater chance of selecting a sample that is not representative of the population. 

4) Systematic Sampling

Every nth individual in the population list is selected for inclusion in the sample. For example, in a population list of 5,000 names, to select a sample of 500, a researcher would select every tenth name on the list until reaching a total of 500 names.

Non Probability Sampling Method

1) Convenience Sampling

 Where the researcher questions anyone who is available. This method is quick and cheap. However we do not know how representative the sample is and how reliable the result.

2) Purposive Sampling

Purposive sampling is different from convenience sampling in that researchers do not simply study whoever is available but rather use their judgement to select a sample that they believe, based on prior information, will provide the data they need. 
The major disadvantage is that the researcher's judgement may be in error-might not be correct in estimating the representativeness of a sample or their expertise regarding the information needed.

Populations and Samples

The study of statistics revolves around the study of data sets. This lesson describes two important types of data sets - populations and samples. Along the way, we introduce simple random sampling, the main method used in this tutorial to select samples.

Populations versus Samples

The main difference between populations and samples has to do with how observations are assigned to the data set.

• A population includes each element from the set of observations that can be made.
• A sample consists only of observations drawn from the population.

Depending on the sampling method, a sample can have fewer observations than the population, the same number of observations, or more observations. More than one sample can be derived from the same population.

Other differences have to do with nomenclature, notation, and computations. For example,

• A a measurable characteristic of a population, such as a mean or standard deviation, is called a parameter; but a measurable characteristic of a sample is called a statistic.
• We will see in future lessons that the mean of a population is denoted by the symbol μ; but the mean of a sample is denoted by the symbol x.
• We will also learn in future lessons that the formula for the standard deviation of a population is different from the formula for the standard deviation of a sample.

Simple Random Sampling

A sampling method is a procedure for selecting sample elements from a population. Simple random sampling refers to a sampling method that has the following properties.

• The population consists of N objects.
• The sample consists of n objects.
• All possible samples of n objects are equally likely to occur.

An important benefit of simple random sampling is that it allows researchers to use statistical methods to analyze sample results. For example, given a simple random sample, researchers can use statistical methods to define a confidence interval around a sample mean. Statistical analysis is not appropriate when non-random sampling methods are used.

There are many ways to obtain a simple random sample. One way would be the lottery method. Each of the N population members is assigned a unique number. The numbers are placed in a bowl and thoroughly mixed. Then, a blind-folded researcher selects n numbers. Population members having the selected numbers are included in the sample.

Random Number Generator

In practice, the lottery method described above can be cumbersome, particularly with large sample sizes. As an alternative, use Stat Trek's Random Number Generator. With the Random Number Generator, you can select n random numbers quickly and easily. This tool is provided at no cost - free!! To access the Random Number Generator, simply click on the button below. It can also be found under the Stat Tools tab, which appears in the header of every Stat Trek web page.

Random Number Generator

Sampling With Replacement and Without Replacement

Suppose we use the lottery method described above to select a simple random sample. After we pick a number from the bowl, we can put the number aside or we can put it back into the bowl. If we put the number back in the bowl, it may be selected more than once; if we put it aside, it can selected only one time.

When a population element can be selected more than one time, we are sampling with replacement. When a population element can be selected only one time, we are sampling without replacement.

Population and Sample by Using Statistics

Population and Sample 

• The major use of inferential statistics is to use information from a sample to infer something about a population. A population is a collection of data whose properties are analyzed. 
• The population is the complete collection to be studied, it contains all subjects of interest. 
• A sample is a part of the population of interest, a sub-collection selected from a population. 
• A parameter is a numerical measurement that describes a characteristic of a population, while a sample is a numerical measurement that describes a characteristic of a sample. 
• In general, we will use a statistic to infer something about a parameter. Ex. Joe D. Politician is running for President. He calls you on the phone and asks you to find out what percentage of the registered voters in the country will vote for him. 
• There are a few things you could try. 
• Option I : Call all registered voters on the phone and ask them who they will vote for. Although this would provide a very accurate result, it would be a very tedious and time consuming project. All registered voters represent the population of interest here, and a better approach would be to use a sample. 
• Option II : Call 4 registered voters, 1 in each time zone, and ask them who they will vote for. Although this is a very easy task, the results would not be very reliable. To use a sample to make inferences about a population, the sample should be representative of the population. How likely is it that these 4 registered voters would represent the population of all registered voters? Not very! The sample needs to look just like the population, but smaller. 
• Option III : Somewhere between Option I and Option II. 
• We want to use a method that will be easier than Option I, but more reliable than Option II. 
• So, you randomly select 2000 registered voters and poll them. 1,120 (56%) tell you that they will vote for Joe. 
• The population of interest here is all registered voters, and the parameter is the percentage of them that will vote for Joe. 
• The sample is the 2000 registered voters that were polled, and the statistic is the percentage of them that will vote for Joe. 
• You can tell Joe that approximately 56% of all registered voters will vote for him. Ex. In a Statistics class of 40 students, 24 had a credit card with them. 
•  The statement "60% of the students in this Statistics class had a credit card with them" is a descriptive statement. 
• The population is the 40 students in this Statistics class. 
• The 60% represents a parameter. The statement "60% of the students in all classes have a credit card with them" is an inferential statement. 
• The 40 students in this Statistics class represent a sample of students in all classes. The 60% represents a statistic.

Samples and Populations

What is a Sample? 

Most people, we think, base their conclusions about a group of people (students, actors, football players and so on) on the experiences they have with a fairly small number or sample of individual members. One of the most important steps in the research process is the selection of the sample of individuals who will participate (be observed or questioned). Sampling refers to the process of selecting these individuals. 

Samples and Populations 

A sample in a research is the group on which information is obtained. The larger group to which one hopes to apply the results is called the population. 

Example:

All 700 students at State University who are majoring in mathematics, constitute a population; 50 of those students constitute a sample. Students who own automobiles make up another population, as do students who live in the campus dormitories. Notice that a group may be both a sample in one context and a population in another context. All State University students who own automobiles constitute a sample of all automobile owners at state universities across the Unites States. 

When it is possible, researchers would prefer to study the entire population of interest. Most populations of interest are large, diverse and scattered over a large geographic area. 

Defining the Population 

The first task in selecting a sample is to define the population of interest. In what group, exactly is the researcher interested? To whom does the results of the study to apply? Below are some examples of population. 

• All high school principles in the United States
• All students attending Central High School in Omaha, during the academic year 2005-2006
• All students in Ms. Brown's third grade class at Wharton Elementary School

The above examples shows that a population can be any size and that it will have at least one or several characteristic(s) that sets it off from any other population. 

 

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Welcome to How to Design and Evaluate Research in Education

This comprehensive introduction to research methods was designed to present the basics of educational research in as interesting and understandable a way as possible. To accomplish this, we've created the following features for our chapter which is more focused on the sampling and data collection. 

Objectives of this chapter will enable you to:

• Distinguish between a sample and population
• Explain what is meant by "random sampling" and describe three ways of obtaining a random sample
• use a table of random numbers to select a random sample from a population
• Explain how stratified random sampling differs from cluster random sampling
• Explain what is meant by "systematic sampling, convenience sampling and purposive sampling"
• Explain what is meant by sample size
• Explain what is meant by "external validity"