Sampling and Methodes. (Business Research) Study Material.

Business Research Methods Sampling

1.         Errors in Sampling— Probability and Non probability Sampling Techniques— Types of Sampling— Sampling – Meaning, Steps in Sampling process—Topics to be covered

2.         The process of selecting sample for the purpose of research study is called sampling.— A sample is a subgroup of the population selected for participation in the study. Sample characteristics, called statistics, are then used to make inferences about the population parameters.—Sampling - Meaning

3.         A census the elements of a population. A sample, on the other hand, is a subgroup of the population selected for participation in the study.When is a Census Appropriate ?1. A census is appropriate if the size of population is small. For eg. A researcher may be interested in contacting firms in iron and steel industry. These industries are limited in number, so a census will be suitable.2. Sometimes, the researcher is interested in gathering information from every individual. Eg. Quality of food served in a mess.—Distinction between Census andSampling involves a complete enumeration of

4.        When is Sample Appropriate?1. When the size of population is large.2. When time and cost are the main considerations in research.3. If the population is homogeneous.4. Circumstances when a census is not possible. Eg. Reactions to global advertising by a company.

5.         A perfect mix of all the population elements.— Sample should be chosen properly by appropriate sampling technique.— Appropriate sample size— True representative of the whole population.—Characteristics of a good sampledesign

6.        Steps in Sampling DesignProcess Define the target population Determine the sampling frame Specify the sampling unit Selection of sampling method Determine the sample size Execute the sampling process

7.        Define the target population – The target population is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made. The target population should be defined in terms of elements, sampling units, extent and time.An element is the object about which or from which the information is desired.A sampling unit is an element or a unit containing the element, that is available for selection at some stage of the sampling process. Suppose that Revlon wanted to assess consumer response to new line of lipsticks and wanted to sample females over 18 years of age.Here the sampling unit would be households and all females over 18 in each selected household would be population element.—

8.        Extent refers to the geographical boundaries, and the time factor is the time period under consideration.For eg., For a study of Departmental Store:Elements : male or female head of the household responsible for most of the shopping at Departmental storesSampling units : householdsExtent : Bangalore CityTime : 2011

9.         Determine the Sampling Frame : A sampling frame is a representation of the elements of the target population. It consists of a list or set of directions for identifying the target population. Examples of a sampling frame include the telephone directory, an association directory listing the firms in an industry, a mailing list purchased from an organization, a city directory or a map. EG. You want to learn about scooter owners in a city. The RTO will be the frame which provides you names, addresses and the types of vehicles possessed.—

10.      Selection of sampling method – The researcher must decide whether to use Bayesian or traditional sampling approach, to sample with or without replacement, and to use nonprobability or probability sampling.In the Bayesian approach, the elements are selected sequentially. After each element is added to the sample, the data are collected, sample statistics computed and sampling costs determined. In the traditional approach, the entire sample is selected before— Specify the Sampling Unit – Individuals who are to be contacted are the sampling units. If retailers are to be contacted in a locality, they are the sampling units.—

11.     In sampling with replacement, an element is selected from the sampling frame and appropriate data are obtained. Then the element is placed back in the sampling frame. In sampling without replacement, once an element is selected for inclusion in the sample, it is removed from the sampling frame and therefore cannot be selected again. In probability sampling technique, sampling units are selected by chance. There is no biasness involved at the time of selecting the sample and each element is getting a fair chance to be a part of the sample.In non probability sampling technique, the sample is selected based on personal judgment of the researcher and not chance. Thus some biasness is involved.

12.      Determine the sample size – Sample size refers to the number of elements to be included in the study. The sample size depends upon the type of study that is being conducted. If it is an exploratory research, the sample size will be generally small and for descriptive research, the sample size will be large. The sample size also depends on the resources available with the company. It depends on the accuracy required in the study and the permissible errors allowed.—

13.      Execute the sampling process – A detailed specification of the sampling design decisions with respect to the population, sampling frame, sampling unit, sampling technique and sample size are to be implemented. If households are the sampling unit, an operational definition whether household includes husband or wife or both, and procedure should be specified if household is not available whether to call back or eliminate it from the sample.—

14.      Non probability sampling – The units in the population have unequal or negligible chances for being selected as a sample unit. Sampling relies on the personal judgment of the researcher rather than chance to select sample elements. The researcher can arbitrarily or consciously decide what elements to include in the sample.— Probability sampling – Every unit in the population has equal chances for being selected as a sample unit. The sampling units are selected by chance and no biasness is involved,—Types of Sampling

15.     Classification of Sampling Techniques Sampling Techniques Probability Non ProbabilitySimple Systematic Area Stratified ClusterRando Sampling Sampling Sampling Sampling m &Sampli Multistage ng Sampling Disproportionat Proportionate e Snowbal Panel Convenience Judgmental Quota l Sampling Sampling Sampling Sampling Samplin g

16.      Simple Random Sampling – Each element in the population has a known and equal probability of selection. This implies that every element is selected independently of every other element. The sample is drawn by a random procedure from a sampling frame. This method is equivalent to a lottery system – Take a population containing four departmental stores: A,B, C—Probability Sampling Techniques & D. Suppose we need to pick a sample of two stores from the population. We write down all possible combinations AB, AC, AD, BC, BD, CD on pieces of papers and fold the pieces. Put them in a box. Mix them

17.      Systematic Sampling – Sample is chosen by selecting a random starting point and then picking every Kth element in succession from the sampling frame. There are three steps:1. Sampling interval K is determined by the foll formula: K = No. of units in the population No. of units desired in the sample2. One unit between the first and Kth unit in the population list is randomly chosen.3. Add Kth unit to the randomly chosen number.Example – Consider 1000 households from which we want to select 50 units. Calculate K = 1000 = 20 50To select the first unit, we randomly pick one number between 1 to 20, say 17. So our sample begins with 17, 37, 57 …… Only the first item is randomly selected and rest are systematically selected.—

18.      Stratified Random Sampling – It is a two step process:1. Population to be sampled is divided into groups or strata. The strata should be mutually exclusive and collectively exhaustive, i.e. every population element should be assigned to assigned to one and only one stratum and no population elements should be omitted. Strata are more or less equal on some characteristics.2. Elements are selected from each stratum by a random procedure.Stratified sampling are of two types:1. Proportionate stratified Sampling – The number of sampling units drawn from each stratum is in proportion to the population size of that stratum.2. Disproportionate stratified Sampling – The number of sampling units drawn from each stratum is based on analytical consideration, but not in proportion to the size of the population of that stratum.—

19.     Example of Stratified SamplingSuppose a researcher wants to study the retail sales of tea in India of 1000 grocery stores. The researcher can first divide India into three strata based on the size of the store. Size of stores No. of stores Percentage of stores Large stores 2000 20 Medium stores 3000 30 Small stores 5000 50Suppose we need 12 stores, then choose four from each strata, at random. Or choose two large stores (20% of 12), four medium stores (30% of 12 and six small stores (50% of 12).

20.      Cluster Sampling – The following steps are followed:1. The population is divided into mutually exclusive and collectively exhaustive clusters.2. A simple random sample of few clusters is selected. For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically.3. All the units in the selected cluster are studied.If all the elements in each selected cluster are included in the sample, the procedure is called one-stage cluster sampling. If a sample of elements is drawn probabilistically from each selected cluster, the procedure is two-stage cluster sampling.—

21.      Area Sampling – It is a form of cluster sampling, in which the clusters consist of geographic areas, such as counties, housing tracts or blocks. Area sampling is of two types : a) One-stage area sampling – If only one level of sampling takes place in selecting the basic elements (for eg. The researcher samples blocks and then all the households within the selected blocks are included in the sample), the design is called one-stage area sampling. If two or more levels of sampling take place before the basic elements are selected (the researcher samples blocks, and then samples households within selected blocks), the design is called two-stage area sampling.—

22.      Study all the units in the sub area which has been selected. For eg. Retailers or wholesalers or households in a particular sub-area selected.— Select sub areas randomly.— Segmenting the total area (state or country) into sub areas.—Area sampling involves the following procedure:

23.      An Illustration:The management of a newly-opened club solicits new membership. During the first rounds, all corporates were sent details so that those who are interested may enroll. The second round concentrates on how many are interested to enroll for various entertainment activities that club offers such as billiards, indoor sports, swimming and gym etc. After obtaining this information, you might stratify the interested repondents. This will also tell you the reaction of new members to various activities.— Multistage Sampling – Sampling is done in several stages.—

24.      More resources are required to design and execute than in non-probability design.— It is costly.— It takes time.— Less knowledge of universe is sufficient.Disadvantages of Probability Sampling— Quantification is possible in probability sampling.— It is unbiased.—Advantages of ProbabilitySampling

25.      Convenience Sampling – Attempts to obtain a sample of convenient elements. The selection of sampling units is left primarily to the interviewer. Often, respondents are selected because they happen—Non-Probability SamplingTechniques  Least expensive and least time-consuming of all sampling techniques.—to be in the right place at the right time. Examples of convenience sampling include: 1) use of students, church groups and members of social organizations, 2) mall intercept interviews without qualifying the respondents, 3) department stores, 4) tear- out questionnaires in a magazine, 5) people on the street interviews.

26.      Judgmental Sampling –It is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher. The researcher exercising judgment or expertise, chooses the elements to be included in the sample, because he or she believes that they are representative of the population of interest or are otherwise appropriate. Common examples of judgmental sampling include 1) test markets selected to determine the potential of a new product, 2) purchase engineers selected in industrial marketing research because they are considered to be representative of the company, 3) expert witnesses used in court.—

27.      Quota Sampling – It involves the fixation of certain quotas, which are to be fulfilled by the interviewers. It involves two stages:1. 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 control characteristics include sex, age and race.2. In the second stage, Sample elements are selected based on convenience or judgment.—

28.      Suppose 200, 000 students are appearing for a competitive examination. We need to select 1% of them based on quota sampling. The classification of quota may be as follows: Category Quota General Merit 1000 Sport 600 NRI 100 SC/ST 300—Example of Quota Sampling

29.      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 opinion or referrals provided by the initial respondents. This process may be carried out in waves by obtaining referrals from referrals, thus leading to a snowballing effect. Examples include special census groups such as widowed males under 35 and members of a scattered minority population. College students bring in more students on the consumption of Pepsi.—

30.      For eg. Suppose that one is interested in knowing the change in the consumption pattern of households. A sample of households are drawn. These households are contacted to gather information on the pattern of consumption. Subsequently, say after a period of six months, the same households are approached once again and the necessary information on their consumption is collected.— Panel Sampling – A sampling technique where the same sample group is being contacted on a regular basis and the necessary information is gathered.—

31.      Non-sampling error – Occurs in some systematic way, which is other than sampling.— Sampling Error – Error due to inappropriate selection of sample size. It can be minimized by choosing the appropriate sample size. As the sample keeps on increasing, the sampling error decrease. Eg. If a study is done amongst Maruti-car owners in a city to find the average monthly expenditure on the maintenance of car, it can be done by including all Maruti-car owners.—Errors in Sampling

32.     Sampling Frame Error – Errors in the specific list of population units, from which the sample for a study is being chosen.Eg. Assume that a bank wants to contact the people belonging to a particular profession over phone to market a home loan product. The sampling frame in this case is the telephone directory. This sampling frame may pose several problems:1) People might have migrated. 2) Numbers have changed. 3) Many numbers were not yet listed. Residents who are included in the directory likely to differ from those who are not included.—

33.     Non-response error – The two major nonresponse issues in sampling are improving response rates and adjusting for nonresponse. Nonresponse error arises when some of the potential respondents included in the sample do not respond.The primary causes of low response rates are refusals and not-at-homes.Refusals, which result from the unwillingness or inability of people included in the sample to participate, result in lower response rates.—

34.     Attempts to lower refusal rates:1. Prior notification – Potential respondents are sent a letter notifying them of the imminent mail, telephone, personal or internet survey.2. Motivating the respondents – The interviewer starts with a small request such as ‘Will you please take five minutes to answer five questions?’ which is followed by a larger request (foot-in-the-door strategies). In the reverse strategy, the initial request is relatively large, followed by a smaller request (door-in- the-face strategy).3. Incentives – Offering monetary as well as nonmonetary incentives to potential respondents.

35.      Data Error – This occurs during the data collection, analysis of data or interpretation. Respondents sometimes give distorted answers unintentionally for questions which are difficult, or if the question is exceptionally long and the respondent may not have answer.—4.Questionnaire design and administration – A well designed questionnaire can decrease the overall refusal rate as well as refusals to specific questions.5.Follow-up – Contacting the non respondents periodically after the initial contact or by sending a letter to remind non respondents to complete and return the questionnaire.