It is rightly said that the difference between primary data and secondary data is of degree. Data collected by X will be primary for X and secondary for everyone else. The one who collects any data, data are primary in his hands and as soon as any other person wants to use it, it becomes secondary. For example, Population Census is conducted by the government of India every 10 years, it is primary for the government but when a book writer takes data of demographic condition of India from the reports of these census, it is secondary for him. Therefore, it is rightly said first hand data. Only in first hand, it is primary. When it goes to second hand, it becomes secondary.

There are three primary types of nonrandom sampling methods:
1. Quota Sampling
Quota sampling is designed to overcome the most obvious flaw of availability sampling. Rather than taking just anyone, you set quotas to ensure that the sample you get represents certain characteristics in proportion to their prevalence in the population. Note that for this method, you have to know something about the characteristics of the population ahead of time. Say you want to make sure you have a sample proportional to the population in terms of gender - you have to know what percentage of the population is male and female, then collect sample until yours matches. Marketing studies are particularly fond of this form of research design. The primary problem with this form of sampling is that even when we know that a quota sample is representative of the particular characteristics for which quotas have been set, we have no way of knowing if sample is representative in terms of any other characteristics. If we set quotas for gender and age, we are likely to attain a sample with good representativeness on age and gender, but one that may not be very representative in terms of income and education or other factors. Moreover, because researchers can set quotas for only a small fraction of the characteristics relevant to a study quota sampling is really not much better than availability sampling. To reiterate, you must know the characteristics of the entire population to set quotas; otherwise there's not much point to setting up quotas. Finally, interviewers often introduce bias when allowed to self select respondents, which is usually the case in this form of research. In choosing males 18-25, interviewers are more likely to choose those that are better dressed, seem more approachable or less threatening. That may be understandable from a practical point of view, but it introduces bias into research findings.
2. Judgment Sampling! Purposive Sampling/Deliberate Sampling
Purposive sampling is a sampling method in which elements are chosen based on purpose of the study. Purposive sampling may involve studying the entire population of some limited group (sociology faculty at Columbia) or a subset of a population (Columbia faculty who have won Nobel Prizes). As with other non-probability sampling methods, purposive sampling does not produce a sample that is representative of a larger population, but it can be exactly what is needed in some cases - study of organization, community, or some other clearly defined and relatively limited group.
3. Convenience Sampling/Availability Sampling
Convenience sampling is a method of choosing subjects who are available or easy to find. This method is also sometimes referred to as haphazard, accidental, or convenience sampling. The primary advantage of the method is that it is very easy to carryout, relative to other methods. A researcher can merely stand out on his/her favorite street comer or in his/her favorite tavern and hand out surveys. One place this used to show up often is in university courses. Years ago, researchers often would conduct surveys of students in their large lecture courses. For example, all students taking introductory sociology courses would have been given a survey and compelled to fill it out. There are some advantages to this design - it is easy to do, particularly with a captive audience, and in some schools you can attain a large number of interviews through this method. The primary problem with Convenience sampling is that you can never be certain what population the participants in the study represent. The population is unknown, the method for selecting cases is haphazard, and the cases studied probably don't represent any population you could come up with. However, there are some situations in which this kind of design has advantages - for example, survey designers often want to have some people respond to their survey before it is given out in the "real" research setting as a way of making certain the questions make sense to respondents. For this purpose, Convenience sampling is not a bad way to get a group to take a survey, though in this case researchers care less about the specific responses given than whether the instrument is confusing or makes people feel bad.

There are two methods of random sampling.
(a) Unrestricted Random Sampling
(b) Restricted random sampling.
Unrestricted Random Sampling: It is also called simple random sample. A simple random sample is one in which each and every item of the universe has an equal chance of selection. Selection of all items is purely on chance. So each and every item of the universe has an equal chance of being selected or rejected. There are two most popular methods of taking sample from this method
Lottery Method: It is the simplest method of selecting a random sample. Under this method, all items of the population are numbered or nailed and these numbers or names are written on small slips. These slips are put in a bowl and requisite numbers of slips are withdrawn at random. It is to be noted that all slips must be of same size and colour. It is also recommended that a non party should be asked to pick the slips. This method is widely used in quiz contests for selecting a question for the team, government departments make use of this method for allotment of benefits under schemes etc.
Use of Random Number Tables: When the population size is very large, it becomes a cumbersome task to make so many slips and using lottery method. To save time and energy, random number tables are used. A random number table is a simply a table which is created by scrambling the digits 0-9.The most popular random table is Tippet's Random Number Table which consists of 10,400 four digited random numbers, giving in all 41,600 digits (10,400x 4). For example, if we have to select 100 persons from a group of 800 then we can select first 100 from Triplett's table which are below 800.There are some other random number tables also. These are:
(i) Fisher and Yates Table: It is a table of 15,000 random digits written in the form of  1,500 groups.
(ii) Rand Corporation: It consists of one million random digits consisting of 2, 00,000random numbers of 5 digits each.
(iii) M.G. Kendall and B.B. Smith Table: this table consists of  1,00,000 digits grouped into 25,000 set of 4-digit random number.
Stratified Sampling/Mixed Sampling: In this form of sampling, the population is first divided into two or more mutually exclusive segments based on some categories of variables of interest in the research. It is designed to organize the population into homogenous subsets before sampling, then drawing a random sample within each subset. With stratified random sampling the population of N units is divided into subpopulations of units respectively. These sub populations, called strata, are non-overlapping and together they comprise the whole of the population. When these have been determined, a sample is drawn from each, with a separate draw for each of the different strata. The sample sizes within the strata are denoted by respectively. If a SRS is taken within each stratum, then the whole sampling procedure is described as stratified random sampling. The primary benefit of this method is to ensure that cases from smaller strata of the population are included in sufficient numbers to allow comparison.
Cluster Sampling: In some instances the sampling unit consists of a group or cluster of smaller units that we call elements or subunits. There are two main reasons for the widespread application of cluster sampling. Although the first intention may be to use the elements as sampling units, it is found in many surveys that no reliable list of elements in the population is available and that it would be prohibitively expensive to construct such a list. In many countries there are no complete and updated lists of the people, the houses or the farms in any large geographical region.
Systematic Sampling: This method of sampling is at first glance very different from SRS. In practice, it is a variant of simple random sampling that involves some listing of elements - every nth element of list is then drawn for inclusion in the sample. Say you have a list of 10,000 people and you want a sample of 1,000.
Creating such a sample includes three steps:
1. Divide number of cases in the population by the desired sample size. In this example, dividing 10,000 by 1,000gives a value of 10.
2. Select a random number between one and the value attained in Step 1. In this example, we choose a number between 1 and 10 - say we pick 7.
3. Starting with case number chosen in Step 2, take every tenth record (7,17,27, etc.).

Stratified Sampling: In this form of sampling, the population is first divided into two or more mutually exclusive segments based on some categories of variables of interest in the research. It is designed to organize the population into homogenous subsets before sampling, then drawing a random sample within each subset. With stratified random sampling the population of N units is divided into subpopulations of units respectively. These subpopulations, called strata, are non-overlapping and together they comprise the whole of the population. When these have been determined, a sample is drawn from each, with a separate draw for each of the different strata. The sample sizes within the strata are denoted by respectively. If a SRS is taken within each stratum, then the whole sampling procedure is described as stratified random sampling. The primary benefit of this method is to ensure that cases from smaller strata of the population are included in sufficient numbers to allow comparison.
Its Merits include:
1. Administrative convenience may dictate the use of stratification, for example, if an agency administering a survey may have regional offices, which can supervise the survey for a part of the population.
2. Stratification may improve the estimates of characteristics of the whole population. It may be possible to divide a heterogeneous population into sub-populations, each of which is internally homogenous. If these strata are homogenous, i.e., the measurements vary little from one unit to another; a precise estimate of any stratum mean can be obtained from a small sample in that stratum. The estimate can then be combined into a precise estimate for the' whole population.
3. There is also a statistical advantage in the method, as a stratified random sample nearly always results in a smaller variance for the estimated mean or other population parameters of interest.
Demerits
Sampling problems may be inherent with certain sub populations, such as people living in institutions (e.g. hotels, hospitals, prisons).
Systematic Sampling
This method of sampling is at first glance very different from SRS. In practice, it is a variant of simple random sampling that involves some listing of elements - every nth element of list is then drawn for inclusion in the sample. Say you have a list of 10,000 people and you want a sample of 1,000.
Creating such a sample includes three steps:
1. Divide number of cases in the population by the desired sample size. In this example, dividing 10,000by 1,000 gives a value of 10.
2. Select a random number between one and the value attained in Step 1. In this example, we choose a number between 1 and 10 - say we pick 7.
3. Starting with case number chosen in Step 2, take every tenth record (7, 17,27, etc.). More generally, suppose that the N units in the population are ranked 1to N in some order (e.g., alphabetic). To select a sample of 11 units, we take a unit at random, from the 1st k units and take every k-th unit thereafter.
The advantages of systematic sampling method over simple random sampling include:
1. It is easier to draw a sample and often easier to execute without mistakes. This is a particular advantage when the drawing is done in the field.
2. It stratifies the population into n strata, consisting of the 1st k units, the 2nd k units, and so on. Thus, we might expect the systematic sample to be as precise as a stratified random sample with one unit per stratum. The difference is that with the systematic one the units occur at the same relative position in the stratum whereas with the stratified, the position in the stratum is determined separately by randomization within each stratum.

In statistics population has a different meaning than what it is in general usage. Generally speaking, population refers to the number of people living in a geographical area but in statistics, it refers to the collection of all possible observations of specified characteristic of interest. It is also called universe. For example, if we wish to know likelihood of olay effects amongst women then women in age group of 30and above who belong to upper middle class families will constitute our population. Sample refers to a part of population selected for analysis and drawing inferences about the population. The process of drawing samples is called sampling. If we selected 1000 women by using any method of sampling, it will be sample. When population consists of definite number of items, it is called definite population. On the other hand, when it consists of infinite number of items, it is called infinite population. For example, number of students in a class is a definite population but number of pollution creating vehicles is practically indefinite.
Census Method
It is also called complete enumeration method. In this method data are collected of reach and every item of population.
Advantages:
(a) It provides information about each and every item of the population.
(b) Element of bias is eliminated as results are based on each and every item.
(c) It is an exhaustive survey which can be used for other purposes later as secondary data.
(d) It helps to study diversity in the universe. It is especially useful in case of diverse population.
(e) When items in a universe are of complex nature then it is necessary to use census method.
Disadvantages:
1. Census method requires a lot of expenditure in terms of time, effort and money.
2. It is possible only for definite population.
3. It is very time consuming.
4. It is not possible in some cases like we can't use census method to find infection in blood of a person. A lady can't taste entire food to judge if it is cooked or not.
Suitability:
Census method is useful when:
1. Population size is limited.
2. Population consists of diverse items.
3. High degree of accuracy and reliability is required.
4. Enough time and funds are available.
Sampling Method
In this method, a group of items are selected from population either randomly or otherwise and these items are studies to draw inferences about the entire population. Samples are a subset of population. 
Advantages of Sample Method:
1. Less Costly: Sample method is more economical than census method. It needs to collect data from a part of population and hence costs less.
2. Less Time consuming: It is far less time consuming as compared to census method. Due to smaller volume of data, it takes lesser time.
3. Greater Accuracy: Accuracy of sampling data is more because it involves lesser calculations and lesser handling of data.
4. Scientific Approach: It is a scientific method of data collection because it does not take all items but scientifically selects such a part of population which is representative of entire population.
5. Greater Scope: When we want to collect data in greater details, this method is more useful. It is also more useful when enumerators are required to collect data and help of experts is required.
6. Mathematical Convenience: Sample data is mathematically easier to handle as it is small in volume.
7. Exceptional Applicability: In many cases, we have only one option which is sampling method. When we want to test blood, we can take only a sample and not entire blood. Similarly, for testing your knowledge, teacher can't give the complete chapter in test but takes some questions at random which are sample for her.
Disadvantages of Sample Method:
1. Difficult to achieve complete accuracy: It is almost impossible that there are no difference between results of census method and results of sampling methods. There always exist some difference which is called sampling errors.
2. Bias in Sample Selection: Many a times, sample is selected in a biased way. He might have some interest in a particular interest and may modify the data accordingly. For example, if principal ask teacher to give five copies of her class, teachers used biased selection of sampling.
3. Samples are not representative of entire population: The accuracy of the samples drawn depends on whether the sample is representative of the characteristics of population. Practically, no sample is proper represents entire population.
4. Not possible in case of Heterogeneous Population: Sampling method cannot be used in case of heterogeneous population.
5. Lack of expertise: For drawing a sample scientifically, specialized knowledge and expertise is required which is not so easily available.
Suitability:
Sample method is useful when:
1. Population is homogeneous
2. Experts are available to withdraw sample scientifically.
3. Less time and money is available.
4. Accuracy is not so important.

Primary data are those which are collected for a specific purpose directly from the field of enquiry, and thus original in nature. The procedure adopted for collection of data may either be complete enumeration or sampling.(i.e. sample survey). Primary data provides a statistician with detailed information. Such data are published by the authorities themselves as Government, Civil bodies, Trade Associations etc. Individuals such as Economists, and institutions like Banks and other allied bodies can collect primary data for a specific purpose, by engaging trained investigators. The collection of primary data is a laborious and time consuming process. It is also expensive. Secondary data are such numerical information which has been already collected by some agency for a specific purpose and are subsequently compiled from that source for application in a different connection. For example the Census figures published will be primary data, whereas the same data reproduced in another publication will come under the category of secondary data. The chief sources of secondary data are: Publications of State Governments, of Foreign governments, and international bodies like ILO, UNO, UNESCO, WHO, etc. In short, when time and funds are less and accuracy is not so important then secondary method in better, otherwise Primary method in better.

Telephonic Interviews
In this method, investigator collects information from respondents over telephone. He collects telephonic data of his respondents and calls them for getting information.
Advantages:
(a) It is very cheap and quick method of collecting data.
(b) This method provides information from distant places.
(c) This method involves less labor and time.
Disadvantages:
(a) In this method, person may be unwilling to respond and may disconnect the phone.
(b) This method is not so reliable and accurate as we can't read facial expressions and body language.
(c) It lacks uniformity.
Questionnaires Filled By Enumerators According into this method enumerators are appointed who go to the informants with the questionnaire and help them in recording the answer. Here the enumerators explain the background, aim and object of the problem under investigation and emphasize the necessity of giving correct answer. They also help the informants in understanding some t4echnical terms of question the concept of which is not clear to the informants. Thus the questionnaire is filled by the informants in the presence and help of the enumerators.
Advantages:
1. The information collected by this method is reliable and accurate
2. It is a good method for intensive investigation.
3. This method is flexible. Enumerator can make requisite changes in the language of the question or otherwise as per the comfort of respondent.
4. Data obtained by this method are more uniform and homogeneous.
5. This method gives a satisfactory result provided the scope of inquiry is narrow.
6. This method can be used when respondents are illiterate.
Disadvantages:
1. This methods is not suitable for extensive inquiry
2. Its required a lot of expenses and time as enumerators need to be paid for their services.
3. The bias on the part of enumerator can damage the whole inquiry
4. Sometimes the informant may be reluctant to answer the question.

When investigator uses the data which is used by someone else, it is called secondary data. Sources fo secondary data are as follows:
Published Printed Sources: There are a variety of published printed sources. Their credibility depends on many factors. For example, on the writer, publishing company and time and date when published. New sources are preferred and old sources should be avoided as new technology and researches bring new facts into light.
1. Books: Books are available today on any topic that you want to research. The use of books start before even you have selected the topic. After selection of topics books provide insight on how much work has already been done on the same topic and you can prepare your literature review. Books are secondary source but most authentic one in secondary sources.
2. Journals/periodicals: Journals and periodicals are becoming more important as far as data collection is concerned. The reason is that journals provide up-to-date information which at times books cannot and secondly, journals can give information on the very specific topic on which you are researching rather talking about more general topics.
3. Magazines/Newspapers: Magazines are also effective but not very reliable. Newspaper on the other hand are more reliable and in some cases the information can only be obtained from newspapers as in the case of some political studies.
Published Electronic Sources: As internet is becoming more advance, fast and reachable to the masses; it has been seen that much information that is not available in printed form is available on internet. In the past the credibility of internet was questionable but today it is not. The reason is that in the past journals and books were seldom published on internet but today almost every journal and book is available online. Some are free and for others you have to pay the price.
4. E-journals: e-journals are more commonly available than printed journals. Latest journals are difficult to retrieve without subscription but if your university has an e-library you can view any journal, print it and those that are not available you can make an order for them.
5. General Websites: Generally websites do not contain very reliable information so their content should be checked for the reliability before quoting from them.
6. Weblogs: Weblogs are also becoming common. They are actually diaries written by different people. These diaries are as reliable to use as personal written diaries.
Unpublished Personal Records: Some unpublished data may also be useful in some cases.
Diaries: Diaries are personal records and are rarely available but if you are conducting a descriptive research then they might be very useful. The Anne Franks diary is the most famous example of this. That diary contained the most accurate records of Nazi wars.
Letters: Letters like diaries are also a rich source but should be checked for their reliability before using them.
Government Records: There are two major government agencies which provide data. These are CSO and NSSO.
Central Statistical Office (CSO) It is responsible for coordination of statistical activities in the country and for evolving and maintaining statistical standards. Its activities include compilation of National Accounts; conduct of Annual Survey of Industries and Economic Censuses, compilation of Index of Industrial Production, as well as Consumer Price Indices. It also deals with various social statistics, training, international cooperation, Industrial Classification etc. The CSO is headed by a Director-General who is assisted by 5 Additional Director-Generals looking after the National Accounts Division, Social Statistics Division. Economic Statistics Division, Training Division and the Coordination and Publication Division. CSO is located in the Sardar Patel Bhawan, Parliament Street, New Delhi. The Industrial Statistics Wing of CSO is located in Kolkata. The Computer Centre also under the CSO is located in R K Puram, New Delhi.
 National Sample Survey Office (NSSO) It has four divisions
(i) Survey Design and Research Division (SDRD) - Kolkata
(ii) Field Operations Division (FOD) - New Delhi - Data Processing Division (DPD) - Kolkata
(iii) Co-ordination and Publication Division (CPD) - New Delhi The surveys on Consumer Expenditure, Employment - Unemployment, Social Consumption (Health, Education etc.), Manufacturing Enterprises, Service Sector Enterprises are carried out once in 5years. And survey of Land and Livestock Holding and Debt and Investment are carried out once in 10 years.

Primary data refers to the data collected by investigator himself.
Methods of Collection of Primary Data: The primary data are collected by the following methods.
1. Direct personal investigation.
2. Indirect personal investigation
3. Information through correspondents
4. Telephonic Interviews
5. Mailed Questionnaire Method
6. Questionnaire filled by Enumerators
Information from Correspondents According to this method the collection of data is neither through the questionnaire nor through the enumerators but through local correspondents. This method of collecting the data is not reliable and it should be used only at those places where the purpose the investigation is served by rough estimates.
Advantages:
(a) It is very cheap and quick method of collecting data.
(b) This method provides information on regular basis from distant places.
(c) This method involves less labor and time.
Disadvantages:
(a) In this method, correspondent may use any method of collecting data which investigator is relying on.
(b) This method is not so reliable and accurate.
(c) Data may be biased.
(d) It lacks uniformity.
Suitability:
 This method is suitable when scope of enquiry is large and information is required on regular basis.

Statistical enquiry refers to an investigation on a topic by any agency or an individual which involves quantitative facts. In other words, statistical enquiry implies search of truth by using statistical tools. All statistical enquiries involve collection of data, their organization, presentation, analysis and interpretation. Collection of data is the foundation of all other exercises involved in undertaking a statistical enquiry. Organization, presentation and analysis all will become futile if there is any defect in data collected. Therefore it is of utmost importance in a statistical enquiry. An investigator whether it be an individual or an agency has to plan all stages of data collection much in advance. He has to move step by step. Given below are some of the major steps in collection of data.
(a) Develop a complete plan for survey: It is important to draw a complete plan for a statistical survey before we start collecting data actually. The statistician has to plan three things.
(i) What data to be obtained;
(ii) From whom data are to be obtained
(iii) By what methods the data are to be obtained-primary or secondary He also has to decide the expenditure to be incurred and time availability. The expenditure and the time period will determine most of the subsequent steps he has to take.
(b) Decide whether to adopt census method or sample method: For conducting survey on any issue, the investigator has two options: sample and census. Which method will depend on budget, availability of time and accuracy requirement?
(c) Preparing Questionnaire: Designing the questionnaire is influenced by many considerations like number of questions to be included, language of questions, types of questions, ordering of the questions etc. we shall discuss it in detail later in this chapter under the heading "construction of a questionnaire".
(d) Mode of Distribution of Questionnaire: There are different ways in which questionnaire can be sent. It can be personal or by post or by email. In personal it can be filled either by respondent or enumerator. These methods have also been discussed in detail later in the chapter.
(e) Check the filled in forms for completeness and consistency: It is the last stage in collection of data. This stage comes when duly filled forms are returned by the respondents. Each questionnaire must be examined and edited if necessary.