Classification of Samples in Marketing Researches

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Table of Contents

Introduction
Classification of Samples
Probability vs. Non-Probability Sampling
References

Introduction

Marketing researchers aim at attaining the most accurate data which will be helpful for the dilemmas that the study relates to and can present a conclusion that will be the basis for the firm’s decision making. Problems such as the irregularities of consumer’s feedback toward a certain product may come at hand and developing a new concept for the marketing plan needs to consider the factors for the researches.

Sampling is used as a vital part of a statistical population to further study the information that relates to them. It can be described as the set of people that are chosen from a larger population to gather necessary data. The population then accounts for the group of persons, objects, or individuals wherein samples are collected for the evaluation and segmenting into each class where the individuals belong such as the population of students, professors, medical professionals, and the like.

Primarily, a specific problem that should be addressed in this paper points out the determination of consumer insights toward a product that will be launched by a firm. This will hypothetically be referred to as the introductory stage of the product for a company. The target population is set to a state and involves different respondents of different ages. Marketing research should be conducted to answer specific areas such as the demography of the target market, its reception toward the product, and the competitors. Thus, a good sampling technique should be chosen according to what the research study requires to come up with an analysis that will benefit the company. Determining the proper sampling technique should first discuss the different types of samples and decide the best sampling method to be used on the problem.

Classification of Samples

Samples can be classified into three different perspectives namely random, judgment, and the convenience type of samples. A convenient sample points out to the simplest form of the sample wherein the outcomes prevail when simple units have opted from the population through observations. Judgment sample on the other hand can be attained through the good judgment of a person who does not profoundly recognize the important details of a population.

The third one is the random sample which is the most important type of sample. It allows a determined probability that each vital unit will opt. This is also called a probability sample and is then classified into different approaches; simple random sample, systematic random sample, stratified sample, and cluster sampling (Lapin, 1987). However, in a broader perspective, sampling is viewed in two ways which are non-random and random sampling or non-probability and probability sampling.

Simple Random Sample

A simple random sample can be attained through the selection of basic units in search whereby each unit in the population has equal chances of being selected. This type of sample is also viewed to be free from any biased sampling. In a given size, all the sets of considerations are given fair probability. Although, using an illustration or diagram for the simple random sample, can sometimes be a hassle, and even though this type of referred to as the simplest form, this may still commit mistakes most especially if this will be conducted by an untrained person (Lapin, 1987). This is seen to be preferred by many researchers because of its convenience and simple strategies for executing the process of sampling, unlike other methods.

Systematic Random Sample

This sample can be acquired by selecting one unit on a chance basis and picking more basic units in a consistent spaced distance until it meets the desired number of units. To be more specific, conducting marketing research about the consumer’s behavior and preferences towards a certain product using a systematic random sample may need to evaluate a random selection in a data. For example, the researcher will be selecting every 10th name in a gourmet’s store list of valued customers as one way of conducting a systematic random sample wherein a non-biased selection will be given to the customers since the target population accords to all the customers of the business.

Considerations of looking into the idea that not every first name should be the first one to be selected as the start of the counting depend on the variables that the researchers will use as part of the method (Lapin, 1987). Thus, this may somehow appear to be used by the researchers that look forward to the identification of data in a large and uncertain population.

Stratified Sample

In this sample, it is attained through selecting a segmented simple random separately from the level of each population. A population can then be divided into different groups which depend on the determinants of social status such as age, gender, education, income, job titles, and other demographic factors. Taking for example group A comprises of the college students in their 1st year, group B comprises of those who are on their senior levels and group C comprises of those who are on the junior levels. This approach is then referred to as the group’s strata. In each stratum, the researchers can choose a percentage to determine the number of respondents per group (Lapin, 1987).

For example, group A has 70 persons, B has 60 and C has 100 persons. The researchers can then assign a specific percentage such as 10% for each stratum and use it as the basis of determining how many respondents are included per group. Thus, this will give you figures of; 7 respondents for group A, 6 for B, and 10 for C. This sample is viewed to be more visible on researches which include various large population and driving forces account for its suitable strategy on saving time and effort for the researches.

Cluster Sample

A cluster sample is distinguished through the selection of clusters from the population which also depends on simple random sampling. It specifically covers a specific area in which the respondents are included. For example, a marketer will be researching a state wherein the gourmet store is located and the respondents include all the school canteens in that area. Hence, the researcher will be looking for all the schools located in the village, and those are determined as clusters. However, a problem is seen on cluster sampling with its methods because of covering a wide range to get the desired units of the sample. Because there are lots of schools in a state and a biased result may be acquired (Hubbard and Lindsay, 2002, p.387).

Driving forces for each method include a general consideration of the variables involves in the marketing research. For example, a consumer behavior marketing research will need to identify its target area or market before conducting research. Researchers may opt to choose the method that relates to the topic of the research and the considerations of appropriate data that may be gathered from the method to be used.

A simple random method may be chosen by the researchers for a simple data collection and analysis of a market structure. A stratified random sample may depict the researcher’s driving force of attaining the opinions and necessary information from various classes of respondents without any bias. Driving forces for systematic and cluster samples may relate to the researcher’s deliberation of a broad population and minimizing the effort and cost of conducting a long-term survey (Hubbard and Lindsay, 2002, p.387).

Upon reviewing the different types of samples that can be used in the given problem, a suggestion of using a stratified random sampling is therefore recommended to come out with a profound analysis of the market and complete the aim of the study. Stratified sampling is chosen because it will be effective for a wide- range of target audiences and identify the views of each class for a determined population.

Probability vs. Non-Probability Sampling

The distinction for the probability and non-probability sampling shows the advantages and disadvantages of each area. Probability sampling has been particularly evaluated on the previous discussion and thus it generally means selecting among the respondents with the considerations of all involved individuals have the chance of being selected. In addition to, a probability sampling is the method that uses a randomized form of selection (Flyvbjerg, 2006, p. 220).

To be bale to attain such selection, it is required that processes should be set up accordingly and guarantee that various units in the population have equal chances of being chosen as respective respondents for the study. People in different fields have applied a variety of random selection using its classifications or types prevailed in the earlier part of the discussion. However, most researches today have come into innovations of using modern technologies to facilitate the random selection (Frambach and Schillewaert, 2002, p.169). In the case’s perspective, the research is primarily set on the people’s responses towards the product that the firm will make available through the specified target population.

In a probability sampling, advantages are seen through the determination of the sampling errors. Sampling error is the term used to identify a degree in which a sample appears to be different from the population. Concluding for a population, the outcomes are stated in addition to or less the sampling error (Verdugo, 1998). However, a disadvantage may be viewed through having biased results from the methods used in probability sampling and its inaccuracy for having various perspectives gathered from the wide- ranged respondents (Verdugo, 1998).

A non-probability sampling on the other hand does not include a random selection unlike probability sampling. Probability samples can not be based on the concept of a probability theory as well. Probability sampling gives specifications for its methods such as the snowball sampling, purposive sampling, deviant case, accidental, case studies and ad hoc quotas. Snowball sampling takes place when the first respondent refers a friend and a series of network appears on each referral.

Purposive sampling accounts for the choice of the researcher on whom he or she thinks would fit the study. A deviant case in the same sense may be related to a purposive sampling though it profoundly differs for some grounds. In an accidental sampling, individuals in a population are opted according to the ease of accessibility for the researchers such as friends of the researchers, co- workers and the like. For the case study and ad hoc quotas, restrictions for a certain group that has the same characteristics are referred and a specific percentage for the ad hoc method is allotted by the researcher to choose among the covered respondents (Hubbard and Lindsay, 2002, p.389).

Non- probability sample is uncertain in representing the population well and usually appears to be difficult to determine the state of the research process. Generally, researchers choose probability sampling than non- probability sampling and take them as more accurate and thorough. Also, non- probability sampling is said to be unsatisfactory in meeting the criteria of a probability sample and this should be utilize with carefulness. Also, it can not be used to assume from the sample with a wide population.

Conclusions attained from a non- probability sample should be empirically studied through the knowledge of the researcher and extensively consider the topic the marketing research. Conducting such non- probability sampling procedures can be less costly than a probability sampling though but the outcomes are sometimes restricted from being accurate (Verdugo, 1998). Thus, studying the significant areas in marketing should consider the suitable sampling techniques that will fit the target market or respondents.

Apparently, a random sampling or probability sampling is well viewed as a suitable tool in order to analyze the consumer behavior and the probable reception of the product. This is chosen because a more fair result can be prevailed at the end and a comprehensive study will help the researchers to conclude for the collected data.

Indeed, marketing research can be described in various areas. It is systematic and requires a thorough investigation of the data gathered from the respondents. This serves as a tool to analyze the preceding ideas for the business and settle its objectives. An objective tries to give definite information which foresees its environment and primarily considers its effects for the target market. However, this should be done without bias.

A marketing research should also include proper identification of the data related to the structure of the business and its external environment and be able to use the information gathered in the analysis and dissemination processes (Kotler & Armstrong, 2007). In a marketing research, terms should be considered to help evaluate whether a strategic planning needs more development and elaboration for a better result. Tools for marketing research attributes to the conceptualization of the ideas into an action and emerge into operating such concepts so measurable results should then be assessed.

Sampling accounts for the processes or techniques that are use for the selection of a best sample or a part of the population to be able to know the considerations and characteristics of the whole population (Gronhauge, 2002, p.370). And this entails a good point for studying the population of the proposed dilemma in order to complete and assessed the market research for an introductory product. In addition to, the main purpose of sampling is to give an evaluation about the populations from the given samples and respond to the questions regarding the objectives of the study through the opinions of the respondents.

This will help the researchers to study the characteristics of a population directly through observing a specific segment whether the strategy is well- defined. It will also obtain information from the sample which represents the population with a huge number and varieties of ideas (Gronhauge, 2002, p.368). Thus, using this kind of sampling method will not be too costly to study a part of the population rather than observe the whole though there may be instances that danger may arise. These dangers accounts for the probable errors, inaccurate and unreliable data that may be gathered (Verdugo, 1998). Product launching may be a difficult one because marketers will be risking for all the elements that comprise the whole strategy and determine the success of the business.

References

Flyvbjerg, B (2006) “Five Misunderstandings About Case Study Research.” Qualitative Inquiry, vol. 12, no. 2, pp. 219-245.

Frambach, R.T. and Schillewaert, N. (2002) ‘Organizational Innovation Adoption: A Multi-Level Framework of Determinants and Opportunities for Future Research’s, Journal of Business Research 55(2): 163–76.

Gronhaug, K. (2002) ‘Is Marketing Knowledge Useful?’, European Journal of Marketing, 36: 364–72.

Hubbard, R. and R.M. Lindsay (2002) ‘How the Emphasis on “Original” Empirical Marketing Research Impedes Knowledge Development’, Marketing Theory 2: 381–402.

Kotler, P. & Armstrong, G. (2007). Principles of Marketing Pearson, Prentice Hall, New Jersey.

Lapin, L. L. (1987). Statistics for modern business decisions. Harcourt Brace Jovanovich, Inc.

Verdugo, E.D. (1998). Practical Problems in Research Methods. Pyrczak Publishing: Los Angeles.

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