A Study on the Consumer Perception Regarding the Success of Big Bazaar

EXECUTIVE SUMMARY

Indian retail sector is witnessing one of the most hectic Marketing activities of all times. The companies are fighting to win the hearts of customer who is God said by the business tycoons. There is always a ‘first mover advantage’ in an upcoming sector. In India, that advantage goes to “BIG BAZAAR”. It has brought about many changes in the buying habits of people. It has created formats, which provide all items under one roof at low rates, or so it claims. In this project, we will study the perception of consumers regarding the success of Big Bazaar.

The research titled ‘To study the consumer perception on the success of Big bazaar’ helps to understand the contributing elements which has won the hearts of consumers and led to the success if Big Bazaar stores. The study of perception, attitude and satisfaction level of those people is taken into consideration who is the actual consumers or buyers at Big Bazaar stores. The research was carried out as per the steps of Marketing Research. The well supportive objectives were set for the study. To meet the objectives primary research was undertaken.

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The data collection approach adopted was survey research. The instrument used for the data collection was questionnaire. The target respondents were the consumers of BIG BAZAAR, with the sample size of 50 for the research. Tables & charts were used to translate responses into meaningful information to get the most out of the collected data. Based on those the inferences have been drawn with peer supportive data.

OBJECTIVE OF THE PROJECT

To study the consumer perception on the success of Big bazaar The present study is undertaken to understand the impact of customer services Provided by Big Bazaar.

The study will be helpful in finding out the profile, perception And satisfaction level of the consumers which contribute to the success story of big bazaar. Thus the main objective can be further divided into following subheads:

• To find out the factors that affect the buyers decision in Big Bazaar -Which of the factors-like price, schemes, ambience etc affect the consumers’ decision in making a purchase at Big Bazaar?
• To evaluate the strength of these factors in buying decision of the customers.
• To find out which of these factors are majorly responsible in turning into actual purchase.
• To understand customer’s level of satisfaction with Big Bazaar outlet.
• Overall customer satisfaction will add up to the successfulness of big Bazaar

INTRODUCTION - BIG BAZAAR

• Big Bazaar: Brand's Identity, Personality & Symbolism Big Bazaar is a chain of shopping malls in India currently with 29 outlets, owned by the Pantaloon Group. It works on same the economy model as Wal-Mart and has had considerable ssuccess in many Indian cities and small towns. The idea was pioneered by entrepreneur Kishore Biyani; the head of Pantaloon Retail India Ltd.

Big Bazaar stores in Metros have a gaming area and kids play area for entertainment. Cities where stores are located are, Agra, Ahmadabad, Allahabad, Ambala, Asansol, Bangalore, Bhubaneswar, Chennai, Coimbatore,Palakkad, Kolkata, Delhi, Durgapur, Ghaziabad, Gurgaon,Hyderabad, Indore, Lucknow,Kanpur, Mangalore, Mumbai, Nagpur, Nasik, Panipat, Pune, Rajkot, Surat, Thane, Thiruvananthapuram, Vishakhapatnam. Big Bazaar is Indian personification of retail. It's like an Indian bazaar or mandi or mela, the environment created by traders to give shoppers a sense of moment.

Its personality is of being an entity away from fancy or pretty and being authentically "no-frills". Kishore Biyani never hired any foreign consultant for Big Bazaar which is evident from Indian-specific personality of the brand. The brand's personality is self-explanatory by its tag-line only. This statement places Big Bazaar at the top of customer's mind. It reflects that entrepreneurship and simplicity are the essence of character of Big Bazaar. To use predatory pricing is not in the personality of Big Bazaar, they never sell goods below the price they have purchased it.

Big Bazaar, the "Indian Wal-Mart", is the modern Indian family's favorite store. Big Bazaar symbolizes modern retail, the business which isn't looked up to in our country, is now in the eyes of many multi-national biggies. Big Bazaar has shown a robust growth in recent years. Big Bazaar is not just another hypermarket. It caters to every need of a family. Where Big Bazaar scores over other stores is its value for money proposition for the Indian customers. At Big Bazaar, one can get the best products at the best prices – that is what they guarantee.

With the ever increasing array of private labels, it has opened the doors into the world of fashion and general merchandise including home furnishings, utensils, crockery, cutlery, sports goods and much more at prices that will surprise you. And this is just the beginning. Big Bazaar plans to add Much more to complete the shopping experience. Food is the main shopped for category in this store. Parent Company Pantaloon Retail (India) Limited, is India’s leading retailer that operates multiple retail formats in both the value and lifestyle segment of the Indian consumer market.

Headquartered in Mumbai (Bombay), the company operates over 12 million square feet of retail space, has over 1000 stores across 71 cities in India and employs over 30,000 people. The company’s leading formats include Pantaloons, a chain of fashion outlets, Big Bazaar, a uniquely Indian hypermarket chain, Food Bazaar, a supermarket chain, blends the look, touch and feel of Indian bazaars with aspects of modern retail like choice, convenience and quality and Central, a chain of seamless destination malls. Some of its other formats include Brand Factory, Blue Sky, a LL, Top 10 and Star and Sitara.

The company also operates an online portal: futurebazaar. com. A subsidiary company, Home Solutions Retail (India) Limited, operates Home Town, a large-format home solutions store, Collection i, selling home furniture products and eZone focussed on catering to the consumer electronics segment. Pantaloon Retail was recently awarded the International Retailer of the Year 2007 by the US-based National Retail Federation (NRF) and the Emerging Market Retailer of the Year 2007 at the World Retail Congress held in Barcelona.

Pantaloon Retail is the flagship company of Future Group, a business group scatering to the entire Indian consumption space. 7P Analysis of Big Bazaar 7P Marketing Mix is more useful for services industries and knowledge intensive industries. Successful marketing depends on number of key issues. The seven keys issues are explained as: - Product Big Bazaar offers a wide range of products which range from apparels, food, farm products, furniture, child care, toys, etc. . Products of all the major brands are available at Big Bazaar . Also, there are many in house brands promoted by Big Bazaar.

Big Bazaar sold over 300,000 pairs of jeans, 50,000 DVD-players and 25,000 microwave-ovens. In all, the fashion, electronics and travel segments made up about 70% of sales. Last year, these categories made up only about 60%. Price The tag-line is "Is se sasta aur accha aur kahin nahi". They work on the model of economics of scale. There pricing objective is to get "Maximum Market Share". The various techniques used at Big Bazaar are

• Value Pricing (EDLP - Every Day Low Pricing): Big Bazaar promises consumers the lowest available price without coupon clipping, waiting for discount promotions, or comparison shopping. Promotional Pricing: Big Bazaar offers financing at low interest rate. The concept of psychological discounting (Rs. 99, Rs. 49, etc. ) is used as promotional tool. Big Bazaar also caters on Special Event Pricing (Close to Diwali, Gudi Padva, and Durga Pooja).
• Differentiated Pricing: Time pricing, i. e. , difference in rate based on peak and non-peak hours or days of shopping is also a pricing technique used in Indian retail, which is aggressively used by Big Bazaar.
• Bundling: Selling combo-packs and offering discount to customers. The combo-packs add value to customer. Place Big Bazaar stores are located in 50 cities with 75 outlets. Big Bazaar has presence in almost all the major Indian cities. They are aggressive on their expansion plans. Promotion Big Bazaar started many new and innovative cross-sell and up-sell strategies in Indian retail market. The various promotion techniques used at Big Bazaar include "saal ke sabse saste teen din", Future Card (the card offers 3% discount), Shakti Card, Brand Endorsement by M. S. Dhoni, Exchange Offer - ‘Junk Swap Offer', Point-of-Purchase Promotions. Advertising has played a crucial role in building of the brand.

Big Bazaar advertisements are seen in print media, TV, Radio (FM) and road-side bill-boards. People They are one of the key assets for any organization. The salient features of staff of Big Bazaar are:

• Well-trained staff, the staff employed by Big-Bazaar is well-suited for modern retail.
• Well-dressed staff improves the overall appearance of store.
• Employees are motivated to think out-of-the-box.
• Retail sector is in growth stage, so staff is empowered to take innovative steps.
• Employs close to 10,000 people and recruits nearly 500 people every month. Use of technology like scenario planning for decision making.
• Multiple counters for payment, staff at store to keep baggage and security guards at every gate, makes for a customer-friendly atmosphere.

Process The goods' dispatch and purchasing area has certain salient features which include:

• Multiple counters with trolleys to carry the items purchased.
• Proper display / posters of the place like (DAL, SOAP, etc. ).
• Home delivery counters also started at many places.
• Physical Evidence - It deals with the final deliverable or the display of written facts. This includes the current system and available facilities.

THEORTICAL UNDERSTANDING OF THE RESEARCH FRAMEWORK

Depending on the existing state of knowledge about a problem that is being studied, different types of questions may be asked which require different study designs.

Several classifications of study types are possible, depending on what research strategies are used. The table below categorizes studies, based on the combination of research strategies they use, including: Non-intervention studies in which the researcher just observes and analyses researchable objects or situations but does not intervene; and Intervention studies in which the researcher manipulates objects or situations and measures the outcome of his manipulations

NON-INTERVENTION STUDIES

• Exploratory studies
• Descriptive studies
• Comparative (analytical) studies.

AN EXPLORATORY STUDY

is a small-scale study of relatively short duration, which is carried out when little is known about a situation or a problem. It may include description as well as comparison. Exploratory studies gain in explanatory value if we approach the problem from different angles at the same time. This is called triangulation. Information from different independent sources can be cross-checked.

A DESCRIPTIVE STUDY

involves describing the characteristics of a particular situation, event or case.

Descriptive studies can be carried out on a small or larger scale.

AN ANALYTICAL STUDY

attempts to establish causes or risk factors for certain problems. This is done by comparing two or more groups, some of which have or develop the problem and some of which have not.

INTERVENTION STUDIES

In intervention studies, the researcher manipulates a situation and measures the effects of this manipulation. Usually (but not always) two groups are compared, one group in which the intervention takes place and another group that remains ‘untouched’ (e. . treatment with a placebo). The two categories of intervention studies are: experimental studies and Quasi-experimental studies.

• Experimental studies - An experimental design is a study design that gives the most reliable proof for causation. in an experimental study, individuals are randomly allocated to at least two groups. One group is subject to an intervention, or experiment, while the other group(s) is not. The outcome of the intervention (effect of the intervention on the dependent variable/problem) is obtained by comparing the two groups.
• Quasi-experimental studies - In a quasi-experimental study, one characteristic of a true experiment is missing, either randomization or the use of a separate control group. A quasi-experimental study, however, always includes the manipulation of an independent variable which is the intervention. One of the most common quasi-experimental designs uses two (or more) groups, one of which serves as a control group in which no intervention takes place. Both groups are observed before as well as after the intervention, to test if the intervention has made any difference.

THEORY OF QUESTIONNAIRE

The design of a questionnaire will depend on whether the researcher wishes to collect exploratory information (i. e. qualitative information for the purposes of better understanding or the generation of hypotheses on a subject) or quantitative information (to test specific hypotheses that have previously been generated).

• Exploratory questionnaires: If the data to be collected is qualitative or is not to be statistically evaluated, it may be that no formal questionnaire is needed. For example, in interviewing the female head of the household to find out how decisions are made within the family when purchasing breakfast foodstuffs, a formal questionnaire may restrict the discussion and prevent a full exploration of the woman's views and processes. Instead one might prepare a brief guide, listing perhaps ten major open-ended questions, with appropriate probes/prompts listed under each.
• Formal standardized questionnaires: If the researcher is looking to test and quantify hypotheses and the data is to be analyzed statistically, a formal standardized questionnaire is designed. Such questionnaires are generally characterized by: · prescribed wording and order of questions, to ensure that each respondent receives the same stimuli · prescribed definitions or explanations for each question, to ensure interviewers handle questions consistently and can answer respondents' requests for clarification if they occur · prescribed response format, to enable rapid completion of the questionnaire during the interviewing process.

1. Given the same task and the same hypotheses, six different people will probably come up with six different questionnaires that differ widely in their choice of questions, line of questioning, use of open-ended questions and length. There are no hard-and-fast rules about how to design a questionnaire, but there are a number of points that can be borne in mind:
2. A well-designed questionnaire should meet the research objectives. This may seem obvious, but many research surveys omit important aspects due to inadequate preparatory work, and do not adequately probe particular issues due to poor understanding. To a certain degree some of this is inevitable. Every survey is bound to leave some questions unanswered and provide a need for further research but the objective of good questionnaire design is to 'minimize' these problems.
3. It should obtain the most complete and accurate information possible. The questionnaire designer needs to ensure that respondents fully understand the questions and are not likely to refuse to answer, lie to the interviewer or try to conceal their attitudes. A good questionnaire is organized and worded to encourage respondents to provide accurate, unbiased and complete information.
4. A well-designed questionnaire should make it easy for respondents to give the necessary information and for the interviewer to record the answer and it should be arranged so that sound analysis and interpretation are possible. It would keep the interview brief and to the point and be so arranged that the respondent(s) remain interested throughout the interview. Each of these points will be further discussed throughout the following sections.

It emphasizes that writing of the questionnaire proper should not begin before an exploratory research phase has been completed. The steps preceding questionnaire design Even after the exploratory phase, two key steps remain to be completed before the task of designing the questionnaire should commence. The first of these is to articulate the questions that research is intended to address. The second step is to determine the hypotheses around which the questionnaire is to be designed. It is possible for the piloting exercise to be used to make necessary adjustments to administrative aspects of the study.

This would include, for example, an assessment of the length of time an interview actually takes, in comparison to the planned length of the interview; or, in the same way, the time needed to complete questionnaires. Moreover, checks can be made on the appropriateness of the timing of the study in relation to contemporary events such as avoiding farm visits during busy harvesting periods. Preliminary decisions in questionnaire design There are nine steps involved in the development of a questionnaire:

• Decide the information required.
• Define the target respondents.
• Choose the method(s) of reaching your target respondents.
• Decide on question content.
• Develop the question wording.
• Put questions into a meaningful order and format.
• Check the length of the questionnaire.
• Pre-test the questionnaire.
• Develop the final survey form.

Deciding on the information required, It should be noted that one does not start by writing questions. The first step is to decide 'what are the things one needs to know from the respondent in order to meet the survey's objectives? These, as has been indicated in the opening chapter of this textbook, should appear in the research brief and the research proposal. One may already have an idea about the kind of information to be collected, but additional help can be obtained from secondary data, previous rapid rural appraisals and exploratory research. In respect of secondary data, the researcher should be aware of what work has been done on the same or similar problems in the past, what factors have not yet been examined, and how the present survey questionnaire can build on what has already been discovered.

Further, a small number of preliminary informal interviews with target respondents will give a glimpse of reality that may help clarify ideas about what information is required. Define the target respondents At the outset, the researcher must define the population about which he/she wishes to generalize from the sample data to be collected. For example, in marketing research, researchers often have to decide whether they should cover only existing users of the generic product type or whether to also include non-users. Secondly, researchers have to draw up a sampling frame.

Thirdly, in designing the questionnaire we must take into account factors such as the age, education, etc. of the target respondents. Choose the method(s) of reaching target respondents It may seem strange to be suggesting that the method of reaching the intended respondents should constitute part of the questionnaire design process. However, a moment's reflection is sufficient to conclude that the method of contact will influence not only the questions the researcher is able to ask but the phrasing of those questions. The main methods available in survey research are: · Personal interviews · group or focus interviews mailed questionnaires · telephone interviews. Within this region the first two mentioned are used much more extensively than the second pair. However, each has its advantages and disadvantages. A general rule is that the more sensitive or personal the information, the more personal the form of data collection should be. Decide on question content Researchers must always be prepared to ask, "Is this question really needed? " The temptation to include questions without critically evaluating their contribution towards the achievement of the research objectives, as they are specified in the research proposal, is surprisingly strong.

No question should be included unless the data it gives rise to is directly of use in testing one or more of the hypotheses established during the research design. There are only two occasions when seemingly "redundant" questions might be included: · Opening questions that are easy to answer and which are not perceived as being "threatening", and/or are perceived as being interesting, can greatly assist in gaining the respondent's involvement in the survey and help to establish a rapport. This, however, should not be an approach that should be overly used.

It is almost always the case that questions which are of use in testing hypotheses can also serve the same functions. "Dummy" questions can disguise the purpose of the survey and/or the sponsorship of a study. For example, if a manufacturer wanted to find out whether its distributors were giving the consumers or end-users of its products a reasonable level of service, the researcher would want to disguise the fact that the distributors' service level was being investigated. If he/she did not, then rumors would abound that there was something wrong with the distributor.

• Opening questions: Opening questions should be easy to answer and not in any way threatening to THE respondents. The first question is crucial because it is the respondent's first exposure to the interview and sets the tone for the nature of the task to be performed. If they find the first question difficult to understand, or beyond their knowledge and experience, or embarrassing in some way, they are likely to break off immediately. If, on the other hand, they find the opening question easy and pleasant to answer, they are encouraged to continue.
• Question flow: Questions should flow in some kind of psychological order, so that one leads easily and naturally to the next. Questions on one subject, or one particular aspect of a subject, should be grouped together. Respondents may feel it disconcerting to keep shifting from one topic to another, or to be asked to return to some subject they thought they gave their opinions about earlier. Question variety:. Respondents become bored quickly and restless when asked similar questions for half an hour or so.

It usually improves response, therefore, to vary the respondent's task from time to time. An open-ended question here and there (even if it is not analyzed) may provide much-needed relief from a long series of questions in which respondents have been forced to limit their replies to pre-coded categories. Questions involving showing cards/pictures to respondents can help vary the pace and increase interest. Closing questions It is natural for a respondent to become increasingly indifferent to the questionnaire as it nears the end.

Because of impatience or fatigue, he may give careless answers to the later questions. Those questions, therefore, that are of special importance should, if possible, be included in the earlier part of the questionnaire. Potentially sensitive questions should be left to the end, to avoid respondents cutting off the interview before important information is collected. In developing the questionnaire the researcher should pay particular attention to the presentation and layout of the interview form itself.

The interviewer's task needs to be made as straight-forward as possible. Questions should be clearly worded and response options clearly identified. · Prescribed definitions and explanations should be provided. This ensures that the questions are handled consistently by all interviewers and that during the interview process the interviewer can answer/clarify respondents' queries. Ample writing space should be allowed to record open-ended answers, and to cater for differences in handwriting between interviewers. Physical appearance of the questionnaire a significant effect upon both the quantity and quality of marketing data obtained. The quantity of data is a fu

nction of the response rate. Ill-designed questionnaires can give an impression of complexity, medium and too big a time commitment. Data quality can also be affected by the physical appearance of the questionnaire with unnecessarily confusing layouts making it more difficult for interviewers, or respondents in the case of self-completion questionnaires, to complete this task accurately.

In general it is best for a questionnaire to be as short as possible. A long questionnaire leads to a long interview and this is open to the dangers of boredom on the part of the respondent (and poorly considered, hurried answers), interruptions by third parties and greater costs in terms of interviewing time and resources. In a rural situation an interview should not last longer then 30-45 minutes. Piloting the questionnaires Even after the researcher has proceeded along the lines suggested, the draft questionnaire is a product evolved by one or two minds only.

Until it has actually been used in interviews and with respondents, it is impossible to say whether it is going to achieve the desired results. For this reason it is necessary to pre-test the questionnaire before it is used in a full-scale survey, to identify any mistakes that need correcting. The purpose of pretesting the questionnaire is to determine: · Whether the questions as they are worded will achieve the desired results · Whether the questions have been placed in the best order · Whether the questions are understood by all classes of respondent · Whether dditional or specifying questions are needed or whether some questions should be eliminated · whether the instructions to interviewers are adequate. Usually a small number of respondents are selected for the pre-test. The respondents selected for the pilot survey should be broadly representative of the type of respondent to be interviewed in the main survey. If the questionnaire has been subjected to a thorough pilot test, the final form of the questions and questionnaire will have evolved into its final form.

All that remains to be done is the mechanical process of laying out and setting up the questionnaire in its final form. This will involve grouping and sequencing questions into an appropriate order, numbering questions, and inserting interviewer instructions. A well designed questionnaire is essential to a successful survey. However, the researcher must develop his/her own intuition with respect to what constitutes 'good design' since there is no theory of questionnaires to guide him/her.

A good questionnaire is one which helps directly achieve the research objectives, provides complete and accurate information; is easy for both interviewers and respondents to complete, is so designed as to make sound analysis and interpretation possible and is brief. There are at least nine distinct steps: decide on the information required; define the target respondents, select the method(s) of reaching the respondents; determine question content; word the questions; sequence the questions; check questionnaire length; pre-test the questionnaire and develop the final questionnaire.

THEORY OF SAMPLING TECHNIQUE

What is a sample? A sample is a finite part of a statistical population whose properties are studied to gain information about the whole(Webster, 1985). When dealing with people, it can be defined as a set of respondents(people) selected from a larger population for the purpose of a survey. A population is a group of individuals persons, objects, or items from which samples are taken for measurement for example a population of presidents or professors, books or students what is sampling?

Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. What is the purpose of sampling? To draw conclusions about populations from samples, we must use inferential statistics which enables us to determine a population`s characteristics by directly observing only a portion (or sample) of the population. We obtain a sample rather than a complete enumeration (a census ) of the population for many reasons.

Obviously, it is cheaper to observe a part rather than the whole, but we should prepare ourselves to cope with the dangers of using samples. In this tutorial, we will investigate various kinds of sampling procedures. Some are better than others but all may yield samples that are inaccurate and unreliable. We will learn how to minimize these dangers, but some potential error is the price we must pay for the convenience and savings the samples provide. There would be no need for statistical theory if a census rather than a sample was always used to obtain information about populations.

But a census may not be practical and is almost never economical. There are six main reasons for sampling instead of doing a census. These are:

• Economy
• Timeliness
• The large size of many populations
• Inaccessibility of some of the population
• Destructiveness of the observation
• Accuracy

The economic advantage of using a sample in research Obviously, taking a sample requires fewer resources than a census. For example, let us assume that you are one of the very curious students around. You have heard so much about the famous Cornell and now that you are there, you want to hear from the insiders.

You want to know what all the students at Cornell think about the quality of teaching they receive, you know that all the students are different so they are likely to have different perceptions and you believe you must get all these perceptions so you decide because you want an in-depth view of every student, you will conduct personal interviews with each one of them and you want the results in 20 days only, let us assume this particular time you are doing your research Cornell has only 20,000 students and those who are helping are so fast at the interviewing art that together you can interview at least 10 students per person per day in addition to your 18 credit hours of course work. You will require 100 research assistants for 20 days and since you are paying them minimum wage of $5. 00 per hour for ten hours ($50. 00) per person per day, you will require $100000. 00 just to complete the interviews, analysis will just be impossible. You may decide to hire additional assistants to help with the analysis at another $100000. 00 and so on assuming you have that amount on your account. As unrealistic as this example is, it does illustrate the very high cost of census.

For the type of information desired, a small wisely selected sample of Cornell students can serve the purpose. You don`t even have to hire a single assistant. You can complete the interviews and analysis on your own. Rarely does a circumstance require a census of the population, and even more rarely does one justify the expense. A sample may provide you with needed information quickly. For example, you are a Doctor and a disease has broken out in a village within your area of jurisdiction, the disease is contagious and it is killing within hours nobody knows what it is.

You are required to conduct quick tests to help save the situation. If you try a census of those affected, they may be long dead when you arrive with your results. In such a case just a few of those already infected could be used to provide the required information. The very large populations Many populations about which inferences must be made are quite large. For example, consider the population of high school seniors in United States of America, group numbering 4,000,000. The responsible agency in the government has to plan for how they will be absorbed into the different departments and even the private sector.

The employers would like to have specific knowledge about the student`s plans in order to make compatible plans to absorb them during the coming year. But the big size of the population makes it physically impossible to conduct a census. In such a case, selecting a representative sample may be the only way to get the information required from high school seniors. The partly accessible populations There are some populations that are so difficult to get access to that only a sample can be used. Like people in prison, like crashed aero planes in the deep seas, presidents e. t. c.

The inaccessibility may be economic or time related. Like a particular study population may be so costly to reach like the population of planets that only a sample can be used. In other cases, a population of some events may be taking too long to occur that only sample information can be relied on. For example natural disasters like a flood that occurs every 100 years or take the example of the flood that occurred in Noah`s days. It has never occurred again. The destructive nature of the observation sometimes the very act of observing the desired characteristic of a unit of the population destroys it for the intended use.

Good examples of this occur in quality control. For example to test the quality of a fuse, to determine whether it is defective, it must be destroyed. To obtain a census of the quality of a lorry load of fuses, you have to destroy all of them. This is contrary to the purpose served by quality-control testing. In this case, only a sample should be used to assess the quality of the fuses Accuracy and sampling A sample may be more accurate than a census. A sloppily conducted census can provide less reliable information than a carefully obtained sample.

Bias and error in sampling A sample is expected to mirror the population from which it comes, however, there is no guarantee that any sample will be precisely representative of the population from which it comes. Chance may dictate that a disproportionate number of untypical observations will be made like for the case of testing fuses, the sample of fuses may consist of more or less faulty fuses than the real population proportion of faulty cases. In practice, it is rarely known when a sample is unrepresentative and should be discarded.

Sampling error What can make a sample unrepresentative of its population? One of the most frequent causes is sampling error. Sampling error comprises the differences between the sample and the population that are due solely to the particular units that happen to have been selected. For example, suppose that a sample of 100 American women are measured and are all found to be taller than six feet. It is very clear even without any statistical prove that this would be a highly unrepresentative sample leading to invalid conclusions.

This is a very unlikely occurrence because naturally such rare cases are widely distributed among the population. But it can occur. Luckily, this is a very obvious error and can be detected very easily. The more dangerous error is the less obvious sampling error against which nature offers very little protection. An example would be like a sample in which the average height is overstated by only one inch or two rather than one foot which is more obvious. It is the unobvious error that is of much concern. There are two basic causes for sampling error.

One is chance: That is the error that occurs just because of bad luck. This may result in untypical choices. Unusual units in a population do exist and there is always a possibility that an abnormally large number of them will be chosen. For example, in a recent study in which I was looking at the number of trees, I selected a sample of households randomly but strange enough, the two households in the whole population, which had the highest number of trees (10,018 and 6345 ) were both selected making the sample average higher than it should be. The average with these two extremes removed was 828 trees.

The main protection against this kind of error is to use a large enough sample. The second cause of sampling is sampling bias. Sampling bias is a tendency to favor the selection of units that have particular characteristics. The other main cause of unrepresentative samples is non sampling error. This type of error can occur whether a census or a sample is being used. Like sampling error, non sampling error may either be produced by participants in the statistical study or be an innocent by product of the sampling plans and procedures.

A non sampling error is an error that results solely from the manner in which the observations are made. The simplest example of non sampling error is inaccurate measurements due to malfunctioning instruments or poor procedures. For example, consider the observation of human weights. If persons are asked to state their own weights themselves, no two answers will be of equal reliability. The people will have weighed themselves on different scales in various states of poor calibration. An individual`s weight fluctuates diurnally by several pounds, so that the time of weighing will affect the answer.

The scale reading will also vary with the person`s state of undress. Responses therefore will not be of comparable validity unless all persons are weighed under the same circumstances. Biased observations due to inaccurate measurement can be innocent but very devastating. A story is told of a French astronomer who once proposed a new theory based on spectroscopic measurements of light emitted by a particular star. When his collogues discovered that the measuring instrument had been contaminated by cigarette smoke, they rejected his findings.

In surveys of personal characteristics, unintended errors may result from:

• The manner in which the response is elicited
• The social desirability of the persons surveyed
• The purpose of the study
• The personal biases of the interviewer or survey writer

No two interviewers are alike and the same person may provide different answers to different interviewers. The manner in which a question is formulated can also result in inaccurate responses. Individuals tend to provide false answers to particular questions. For example, some people want to feel younger or older for some reason known to themselves.

If you ask such a person their age in years, it is easier for the individual just to lie to you by over stating their age by one or more years than it is if you asked which year they were born since it will require a bit of quick arithmetic to give a false date and a date of birth will definitely be more accurate. The respondent effect Respondents might also give incorrect answers to impress the interviewer. This type of error is the most difficult to prevent because it results from out right deceit on the part of the responded.

An example of this is what I witnessed in my recent study in which I was asking farmers how much maize they harvested last year (1995). In most cases, the men tended to lie by saying a figure which is the recommended expected yield that is 25 bags per acre. The responses from men looked so uniform that I became suspicious. I compared with the responses of the wives of these men and their responses were all different. To decide which one was right, whenever possible I could in a tactful way verify with an older son or daughter.

It is important to acknowledge that certain psychological factors induce incorrect responses and great care must be taken to design a study that minimizes their effect. Knowing the study purpose Knowing why a study is being conducted may create incorrect responses. A classic example is the question: What is your income? If a government agency is asking, a different figure may be provided than the respondent would give on an application for a home mortgage. One way to guard against such bias is to camouflage the study`s goals; Another remedy is to make the questions very specific, allowing no room for personal interpretation.

For example, "Where are you employed? " could be followed by "What is your salary? " and "Do you have any extra jobs? " A sequence of such questions may produce more accurate information. Induced bias Finally, it should be noted that the personal prejudices of either the designer of the study or the data collector may tend to induce bias. In designing a questionnaire, questions may be slanted in such a way that a particular response will be obtained even though it is inaccurate. For example, an agronomist may apply fertilizer to certain key plots, knowing that they will provide more favorable yields than others.

To protect against induced bias, advice of an individual trained in statistics should be sought in the design and someone else aware of search pitfalls should serve in an auditing capacity.

SELECTING THE SAMPLE

The preceding section has covered the most common problems associated with statistical studies. The desirability of a sampling procedure depends on both its vulnerability to error and its cost. However, economy and reliability are competing ends, because, to reduce error often requires an increased expenditure of resources.

Of the two types of statistical errors, only sampling error can be controlled by exercising care in determining the method for choosing the sample. The previous section has shown that sampling error may be due to either bias or chance. The chance component (sometimes called random error) exists no matter how carefully the selection procedures are implemented, and the only way to minimize chance sampling errors is to select a sufficiently large sample (sample size is discussed towards the end of this tutorial). Sampling bias on the other hand may be minimized by the wise choice of a sampling procedure.

There are three primary kinds of samples: the convenience, the judgment sample, and the random sample. They differ in the manner in which the elementary units are chosen. The convenient sample A convenience sample results when the more convenient elementary units are chosen from a population for observation. The judgment sample A judgment sample is obtained according to the discretion of someone who is familiar with the relevant characteristics of the population. The random sample This may be the most important type of sample. A random sample allows a known probability that each elementary unit will be chosen.

For this reason, it is sometimes referred to as a probability sample. This is the type of sampling that is used in lotteries and raffles. For example, if you want to select 10 players randomly from a population of 100, you can write their names, fold them up, mix them thoroughly then pick ten. In this case, every name had any equal chance of being picked. Random numbers can also be used.

TYPES OF RANDOM SAMPLES

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 as discussed below. such a procedure is called systematic random sampling. A systematic random sample

A systematic random sample is obtained by selecting one unit on a random basis and choosing additional elementary units at evenly spaced intervals until the desired number of units is obtained. For example, there are 100 students in your class. You want a sample of 20 from these 100 and you have their names listed on a piece of paper may be in an alphabetical order. If you choose to use systematic random sampling, divide 100 by 20, you will get 5. Randomly select any number between 1 and five. Suppose the number you have picked is 4, that will be your starting number.

So student number 4 has been selected. From there you will select every 5th name until you reach the last one, number one hundred. You will end up with 20 selected students. A 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 any body 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. A 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 some thing like a village or a school, a state.

So you decide all the elementary schools in Newyork State 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. If you interest is to interview teachers on their opinion of some new program which has been introduced, then all the teachers in a cluster must be interviewed. Though very economical cluster sampling is very susceptible to sampling bias. Like for the above case, you are likely to get similar responses from teachers in one school due to the fact that they interact with one another.

PURPOSEFUL SAMPLING

Purposeful sampling selects information rich cases for in-depth study. Size and specific cases depend on the study purpose. There are about 16 different types of purposeful sampling. They are briefly described below for you to be aware of them. The details can be found in Patton (1990) Pg 169-186. Extreme and deviant case sampling This involves learning from highly unusual manifestations of the phenomenon of interest, such as outstanding successes, notable failures, top of the class, dropouts, exotic events, crises.

Intensity sampling This is information rich cases that manifest the phenomenon intensely, but not extremely, such as good students, poor students, above average/below average. Maximum variation sampling This involves purposefully picking a wide range of variation on dimensions of interest. This documents unique or diverse variations that have emerged in adapting to different conditions. It also identifies important common patterns that cut across variations. Like in the example of interviewing Cornell students, you may want to get students of different nationalities, professional backgrounds, cultures, work experience and the like.

Homogeneous sampling This one reduces variation, simplifies analysis, and facilitates group interviewing. Like instead of having the maximum number of nationalities as in the above case of maximum variation, it may focus on one nationality say Americans only.

• Typical case sampling. It involves taking a sample of what one would call typical, normal or average for a particular phenomenon, Stratified purposeful sampling This illustrates characteristics of particular subgroups of interest and facilitates comparisons between the different groups.
• Critical case sampling. This permits logical generalization and maximum application of information to other cases like "If it is true for this one case, it is likely to be true of all other cases. You must have heard statements like if it happened to so and so then it can happen to anybody. Or if so and so passed that exam, then anybody can pass. Snowball or chain sampling This particular one identifies cases of interest from people who know people who know what cases is information rich, that is good examples for study, good interview subjects. This is commonly used in studies that may be looking at issues like the homeless households.

What you do is to get hold of one and he/she will tell you where the others are or can be found. When you find those others they will tell you where you can get more others and the chain continues. Criterion sampling Here, you set a criteria and pick all cases that meet that criteria for example, all ladies six feet tall, all white cars, all farmers that have planted onions. This method of sampling is very strong in quality assurance. Theory based or operational construct sampling. Finding manifestations of a theoretical construct of interest so as to elaborate and examine the construct.

• Confirming and disconfirming cases. Elaborating and deepening initial analysis like if you had already started some study, you are seeking further information or confirming some emerging issues which are not clear, seeking exceptions and testing variation. Opportunistic Sampling: This involves following new leads during field work, taking advantage of the unexpected flexibility. Random purposeful sampling: This adds credibility when the purposeful sample is larger than one can handle. Reduces judgment within a purposeful category. But it is not for generalizations or representativeness.
• Sampling politically important cases. This type of sampling attracts or avoids attracting attention undesired attention by purposisefully eliminating from the sample political cases. These may be individuals, or localities. Convenience sampling It is useful in getting general ideas about the phenomenon of interest. For example you decide you will interview the first ten people you meet tomorrow morning. It saves time, money and effort. It is the poorest way of getting samples, has the lowest credibility and yields information-poor cases.
• Combination or mixed purposeful sampling. This combines various sampling strategies to achieve the desired sample. This helps in triangulation, allows for flexibility, and meets multiple interests and needs. When selecting a sampling strategy it is necessary that it fits the purpose of the study, the resources available, the question being asked and the constraints being faced. This holds true for sampling strategy as well as sample size.

SAMPLE SIZE

Before deciding how large a sample should be, you have to define your study population. For example, all children below age three in Tomkins’s County.

Then determine your sampling frame which could be a list of all the children below three as recorded by Tomkins’s County. You can then struggle with the sample size. The question of how large a sample should be is a difficult one. Sample size can be determined by various constraints. For example, the available funding may prespecify the sample size. When research costs are fixed, a useful rule of thumb is to spend about one half of the total amount for data collection and the other half for data analysis. This constraint influences the sample size as well as sample design and data collection procedures.

In general, sample size depends on the nature of the analysis to be performed, the desired precision of the estimates one wishes to achieve, the kind and number of comparisons that will be made, the number of variables that have to be examined simultaneously and how heterogeneous a universe is sampled. For example, if the key analysis of a randomized experiment consists of computing averages for experimental and controls in a project and comparing differences, then a sample under 100 might be adequate, assuming that other statistical assumptions hold.

In non-experimental research, most often, relevant variables have to be controlled statistically because groups differ by factors other than chance. More technical considerations suggest that the required sample size is a function of the precision of the estimates one wishes to achieve, the variability or variance, one expects to find in the population and the statistical level of confidence one wishes to use. The sample size N required to estimate a population mean (average) with a given level of precision is: The square root of N=(1. 6)*(&)/precision Where & is the population standard deviation of the for the variable whose mean one is interested in estimating. Precision refers to width of the interval one is willing to tolerate and 1. 96 reflects the confidence level. For details on this please see Salant and Dillmans (1994). For example, to estimate mean earnings in a population with an accuracy of $100 per year, using a 95% confidence interval and assuming that the standard deviation of earnings in the population is $1600. 0, the required sample size is 983:[(1. 6)(1600/100)] squared. Deciding on a sample size for qualitative inquiry can be even more difficult than quantitative because there are no definite rules to be followed. It will depend on what you want to know, the purpose of the inquiry, what is at stake, what will be useful, what will have credibility and what can be done with available time and resources. With fixed resources which are always the case, you can choose to study one specific phenomenon in depth with a smaller sample size or a bigger sample size when seeking breadth.

In purposeful sampling, the sample should be judged on the basis of the purpose and rationale for each study and the sampling strategy used to achieve the studies purpose. The validity, meaningfulness, and insights generated from qualitative inquiry have more to do with the information-richness of the cases selected and the observational/analytical capabilities of the researcher than with sample size.

CONCLUSION

In conclusion, it can be said that using a sample in research saves mainly on oney and time, if a suitable sampling strategy is used; appropriate sample size selected and necessary precautions taken to reduce on sampling and measurement errors, then a sample should yield valid and reliable information. Details on sampling can be obtained from the references included below and many other books on statistics or qualitative research which can be found in libraries.

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