Quota sampling may be viewed as two-stage restricted judgmental sampling, The first stage consists of developing control categories, or quotas, of population elements, To develop these quotas, the researcher lists relevant control characteristics and determines the distribution of these characteristics in the target population, The relevant control characteristics, which may include sex, age, and race, are identified on the basis of judgment. Often, the quotas are assigned so that the proportion of the sample elements possessing the control characteristics is the same as the proportion of population elements with these characteristics, In other words, the quotas ensure that the composition of the sample is the same as the composition of the population with respect to the characteristics of interest, In the second stage, sample elements are selected based on convenience or judgment, Once the quotas have been assigned, there is considerable freedom in selecting the elements to be included in the sample, The only requirement is that the elements selected fit the control characteristics. 12
Does Metropolitan Magazine Readership Measure Up?
A study is undertaken to determine the readership of certain magazines by the adult population of a metropolitan area with a population of 350.000, A quota sample of adult is selected, The control characteristics are sex, age, and race. Based on the composition of the adult population of the community, the quotas are assigned as follows.
In this example, quotas are assigned such that the composition of the sample is the same as that of the population. In certain situations, however, it is desirable either to under- or over sample elements with certain characteristics. To illustrate, it may be desirable to over sample heavy users of a product so that their behavior can be examined in detail. Although this type of sample is not representative, it may nevertheless be very relevant.
Even if the sample composition mirrors that of the population with respect to the control characteristics, there is no assurance that the sample is representative. If a characteristic that is relevant to the problem is overlooked, the quota sample will not be representative. Relevant control characteristics are often omitted, because there are practical difficulties associated with including many control characteristics. Because the elements within each quota are selected based on convenience or judgment, many sources of selection bias are potentially present, The interviewers may go to selected areas where eligible respondents are more likely to be found, Likewise, they may avoid people who look unfriendly, are not well dressed, or live in undesire-able locations, Quota sampling does not permit assessment of sampling error.
Quota sampling attempts to obtain representative samples at a relatively low cost. Its advantages are the lower costs and greater convenience to the interviewers in selecting elements for each quota. Recently, tighter controls have been imposed on interviewers and interviewing procedures that tend to reduce selection bias, and guidelines have been suggested for.improving the quality of mall-intercept quota samples. Under certain conditions, quota sampling obtains results close to those for conventional probability sampling.
In snowball sampling, an initial group of respondents is selected, usually at random, After being interviewed, these respondents are asked to identify others who belong to the target population of interest, Subsequent respondents are selected based on the referrals, This process may be carried out in waves by obtaining referrals from referrals, thus leading to a snowballing effect, Even though probability sampling is used to select the initial respondents, the final sample is a non probability sample, The referrals will-have demographic and psychographic characteristics that are more similar to the persons referring them than would occur by chance, 14 A major objective of snowball sampling is to estimate characteristics that are rare in the population, Examples include users of particular government or social services, such as food stamps, whose names cannot be revealed; special census groups, such as widowed males under 35; and members of a scattered minority population. Snowball sampling is used in industrial buyer-seller research to identify buyer-seller pairs. The major advantage of snowball sampling is that it substantially increases the likelihood of locating the desired characteristic in the population, It also results in relatively low sampling variance and costs. i5
Knowledge Is Power
It is estimated that in 2009, every minute someone somewhere in the world was infected with HIV.A study was undertaken to examine the risk behavior of Indo-Chinese drug users (JDUs) in Australia, A structured questionnaire was administered to 184 JDUs age 15 to 24, Respondents were recruited using snowball sampling techniques “based on social and street networks.” This technique was used because drug users know other drug users and can easily provide referrals for research purposes. Respondents were asked numerous questions regarding their drug use, injection-related risk behaviors, and perceived susceptibility to 11V, Interviews were held in Melbourne and in Sydney, Locations of interviews varied from on the streets, to restaurants and coffee shops, and even in people’s homes, The results showed that heroin was the first drug injected for 98 percent of the respondents, and 86 percent of them stated they smoked the drug prior to intravenous use. Age for the first injection varied from II years to 23 years, averaging 17 years, Thirty-six percent “ever shared” a needle, 23 percent of those shared with a close friend, and I percent shared with a partner or lover, The awareness of blood borne viruses and related complications was low, Based on these results, the public health officials in Australia decided to launch a vigorous campaign to educate the lDUs of the risks they faced and what they could do to reduce them.16
In this example, snowball .sampling was more efficient than random selection. In other cases, random selection of respondents through probability sampling techniques is more appropriate.
Probability Sampling Techniques
Probability sampling techniques vary in terms of sampling efficiency, Sampling efficiency is a concept that reflects a trade-off between sampling cost and precision, Precision refers to the level of uncertainty about the characteristic being measured, Precision is inversely related to sampling errors but positively related to cost, The greater the precision, the greater the cost, and most studies require a trade-off, The researcher should strive for the most efficient sampling design, subject to the budget allocated, The efficiency of a probability sampling technique may be assessed by comparing it to that of simple random sampling, presents a graphical illustration of the various probability sampling techniques, As in the case of non-probability sampling, the population consists of 25 elements and we have to select a-sample of size 5. A, B. C, D, and E represent groups and can also be viewed as strata or clusters.
Simple Random Sampling
In simple random sampling (SRS), each element in the population has a known and equal probability of selection. Furthermore, each possible sample of a given size (n) has a known and equal probability of being the sample actually selected, This simple that every element is selected independently of every other element, The sample is drawn by . random procedure from a sampling frame. This method is equivalent to a lottery system in which names are placed in a container, the container is shaken. and the names of the winners are then drawn out in an unbiased manner, To draw a simple random sample, the researcher first compiles a sampling frame in which each element is assigned a unique identification number, Then random numbers are generated to determine which elements to include in the sample, The random numbers may be generated with a computer routine or a table Suppose that a sample of size 10 is to be selected from a sampling frame containing 800 elements, This could be done by starting with row 1 and column 1 of Table I. considering the three right most digits, and going down the column until to numbers between 1 and 800 have been selected. Numbers outside this range are ignored. The elements corresponding to the random numbers generated constitute the sample. Thus. in our example, elements 480. 368, 130. 167.570,562,301,579,475, and 553 would be selected. Note that the last three digits (It row 6 (921) and row II (918) were ignored, because they were out of range, SRS has many desirable features. It is easily understood The sample results may be projected to the target population Most approaches to statistical inference assume that the data have been collected by simple random sampling, However SRS suffers from at least four significant limitations, First, it is often difficult to construct a sampling frame that will permit a simple random sample to be drawn. Second, SRS can result in samples that are very large or spread over large geographic areas, thus increasing the time and cost of data collection, Third SRS often results in lower precision with larger standard errors than other probability sampling techniques, Fourth, SRS mayor may not result in a representative sample, Although samples drawn will represent the population well on average, a given simple random sample may seriously misrepresent the target population, This is more likely if the size of the sample is small, For these reasons SRS is not widely used in marketing research, Procedures· such as systematic sampling are more popular.