MARKETING RESEARCH

Subject Paper Submitted By

Indradeep Guha(Roll no: BIMS/AC/2/2008/6005)

*Answers to: Q1 (a/b), Q2 (a/b), Q3 (a/b), Q5(a/b) & Q7 (1/2/3/5/6)

Q1. (a) Define Marketing Research. Explain the concept with nature, scope and principles of importance.

A. Marketing research is the systematic gathering, recording, and analysis of data about issues relating to marketing products and services. The term is commonly interchanged with market research; however, expert practitioners may wish to draw a distinction, in that market research is concerned specifically with markets, while marketing research is concerned specifically about marketing processes.

Marketing research is often partitioned into two sets of categorical pairs, either by target market:

Consumer marketing research , and Business-to-business (B2B) marketing research

Or, alternatively, by methodological approach:

Qualitative marketing research , and Quantitative marketing research

Marketing research may also be described as the systematic and objective identification, collection, analysis, and dissemination of information for the purpose of assisting management in decision making related to the identification and solution of problems and opportunities in marketing. The goal of marketing research is to identify and assess how changing elements of the marketing mix impacts customer behavior.

The task of marketing research is to provide management with relevant, accurate, reliable, valid, and current information. Competitive marketing environment and the ever-increasing costs attributed to poor decision making require that marketing research provide sound information. Sound decisions are not based on gut feeling, intuition, or even pure judgment.

Marketing managers make numerous strategic and tactical decisions in the process of identifying and satisfying customer needs. They make decisions about potential opportunities, target market selection, market segmentation, planning and implementing marketing programs, marketing performance, and control. Marketing research helps the marketing manager link the marketing variables with the environment and the consumers. It helps remove some of the uncertainty by providing relevant information about the marketing variables, environment, and consumers. Ongoing marketing research programs provide information on controllable and non-controllable factors and consumers; this information enhances the effectiveness of decisions made by marketing managers.

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Traditionally, marketing researchers were responsible for providing the relevant information and marketing decisions were made by the managers. However, the roles are changing and marketing researchers are becoming more involved in decision making, whereas marketing managers are becoming more involved with research. The role of marketing research in managerial decision making is explained further using the framework of the "DECIDE" model:

D Define the marketing problem E Enumerate the controllable and uncontrollable decision factors C Collect relevant information I Identify the best alternative D Develop and implement a marketing plan E Evaluate the decision and the decision process

The DECIDE model conceptualizes managerial decision making as a series of six steps. The decision process begins by precisely defining the problem or opportunity, along with the objectives and constraints. Next, the possible decision factors that make up the alternative courses of action (controllable factors) and uncertainties (uncontrollable factors) are enumerated. Then, relevant information on the alternatives and possible outcomes is collected. The next step is to select the best alternative based on chosen criteria or measures of success. Then a detailed plan to implement the alternative selected is developed and put into effect. Last, the outcome of the decision and the decision process itself are evaluated.

Marketing Research has 3 basic principles or guidelines for generating information useful to managers.

Attend to the timeliness and relevance of research. Define research objectives carefully and clearly.

Do not conduct research to support decisions already made.

These principles are simple and perhaps even intuitive. Nevertheless, they are crucial to the proper and successful application and marketing research.

First, marketing research is systematic. Thus systematic planning is required at all the stages of the marketing research process. The procedures followed at each stage are methodologically sound, well documented, and, as much as possible, planned in advance. Marketing research uses the scientific method in that data are collected and analyzed to test prior notions or hypotheses.

Marketing research is objective. It attempts to provide accurate information that reflects a true state of affairs. It should be conducted impartially. While research is always influenced by the researcher's research philosophy, it should be free from the personal or political biases of the researcher or the management. Research which is motivated by personal or political gain involves a breach of professional standards. Such research is deliberately biased so as to result in predetermined findings. The motto of every researcher should be, "Find it and tell it like it is." The objective nature of marketing research underscores the importance of ethical considerations, which are discussed later in the chapter.

Marketing research involves the identification, collection, analysis, and dissemination of information. Each phase of this process is important. We identify or define the marketing research problem or opportunity and then determine what information is needed to investigate it., and inferences are drawn. Finally, the findings, implications and recommendations are provided in a format that allows the information to be used for

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management decision making and to be acted upon directly. It should be emphasized that marketing research is conducted to assist management in decision making and is not: a means or an end in itself. The next section elaborates on this definition by classifying different types of marketing research.

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(b) What’s Marketing Research Process? Illustrate your answer with examples.

A. Marketing research process is a set of five - six steps which defines the tasks to be accomplished in conducting a marketing research study. These include problem definition, developing an approach to problem, research design formulation, field work, data preparation and analysis, and report generation and presentation.

Stages of marketing research process

Step 1: Problem Definition

The first step in any marketing research project is to define the problem. In defining the problem, the researcher should take into account the purpose of the study, the relevant background information, what information is needed, and how it will be used in decision making. Problem definition involves discussion with the decision makers, interviews with industry experts, analysis of secondary data, and, perhaps, some qualitative research, such as focus groups. Once the problem has been precisely defined, the research can be designed and conducted properly.

Step 2: Development of an Approach to the Problem

Development of an approach to the problem includes formulating an objective or theoretical framework, analytical models, research questions, hypotheses, and identifying characteristics or factors that can influence the research design. This process is guided by discussions with management and industry experts, case studies and simulations, analysis of secondary data, qualitative research and pragmatic considerations.

'Step 3: Research Design Formulation'

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A research design is a framework or blueprint for conducting the marketing research project. It details the procedures necessary for obtaining the required information, and its purpose is to design a study that will test the hypotheses of interest, determine possible answers to the research questions, and provide the information needed for decision making. Conducting exploratory research, precisely defining the variables, and designing appropriate scales to measure them are also a part of the research design. The issue of how the data should be obtained from the respondents (for example, by conducting a survey or an experiment) must be addressed. It is also necessary to design a questionnaire and a sampling plan to select respondents for the study.

More formally, formulating the research design involves the following steps:

1.Secondary data analysis 2.Qualitative research 3.Methods of collecting quantitative data (survey, observation, and experimentation) 4.Definition of the information needed 5.Measurement and scaling procedures 6.Questionnaire design 7.Sampling process and sample size 8.Plan of data analysis

Step 4: Field Work or Data Collection

Data collection involves a field force or staff that operates either in the field, as in the case of personal interviewing (in-home, mall intercept, or computer-assisted personal interviewing), from an office by telephone (telephone or computer-assisted telephone interviewing), or through mail (traditional mail and mail panel surveys with pre-recruited households). Proper selection, training, supervision, and evaluation of the field force help minimize data-collection errors.

Step 5: Data Preparation and Analysis

Data preparation includes the editing, coding, transcription, and verification of data. Each questionnaire or observation form is inspected, or edited, and, if necessary, corrected. Number or letter codes are assigned to represent each response to each question in the questionnaire. The data from the questionnaires are transcribed or key-punched on to magnetic tape, or disks or input directly into the computer. Verification ensures that the data from the original questionnaires have been accurately transcribed, while data analysis, guided by the plan of data analysis, gives meaning to the data that have been collected. Univariate techniques are used for analyzing data when there is a single measurement of each element or unit in the sample, or, if there are several measurements of each element, each RCH variable is analyzed in isolation. On the other hand, multivariate techniques are used for analyzing data when there are two or more measurements on each element and the variables are analyzed simultaneously.

Step 6: Report Preparation and Presentation

The entire project should be documented in a written report which addresses the specific research questions identified, describes the approach, the research design, data collection, and data analysis procedures adopted, and presents the results and the major findings. The findings should be presented in a comprehensible format so that they can be readily used in the decision making process. In addition, an oral presentation should be made to management using tables, figures, and graphs to enhance clarity and impact.

For these reasons, interviews with experts are more useful in conducting marketing research for industrial firms and for products of a technical nature, where it is relatively easy to identify and approach the experts. This method is also helpful in situations where little information is available from other sources, as in the case of radically new products.

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Secondary data analysis

Secondary data are data collected for some purpose other than the problem at hand. Primary data, on the other hand, are originated by the researcher for the specific purpose of addressing the research problem. Secondary data include information made available by business and government sources, commercial marketing research firms, and computerized databases. Secondary data are an economical and quick source of background information. Analysis of available secondary data is an essential step in the problem definition process: primary data should not be collected until the available secondary data have been fully analyzed.

Qualitative research

Information, industry experts, and secondary data may not be sufficient to define the research problem. Sometimes qualitative research must be undertaken to gain a qualitative understanding of the problem and its underlying factors. Qualitative research is unstructured, exploratory in nature, based on small samples, and may utilize popular qualitative techniques such as focus groups (group interviews), word association (asking respondents to indicate their first responses to stimulus words), and depth interviews (one-on-one interviews which probe the respondents' thoughts in detail). Other exploratory research techniques, such as pilot surveys with small samples of respondents, may also be undertaken.

Q2. (a) Explain various types of Marketing Research.

A. Marketing research techniques come in many forms, including: Ad Tracking – also known as post-testing or ad effectiveness tracking is in-market research that monitors a

brand’s performance including brand and advertising awareness, product trial and usage, and attitudes about the brand versus their competition.

Today, most ad tracking studies are conducted via the Internet. Some ad tracking studies are conducted continuously and others are conducted at specific points in time (typically before the advertising appears in market, and then again after the advertising has been running for some period of time). The two approaches use different types of analyses, although both start by measuring advertising awareness. Typically, the respondent is either shown a brief portion of a commercial or a few memorable still images from the TV ad. Other media typically are cued using either branded or de-branded visual of the ad. Then, respondents answer three significant questions.

1. Do you recognize this ad? (recognition measure) 2. Please type in the sponsor of this ad. (unaided awareness measure) 3. Please choose from the following list, the sponsor of this ad. (aided awareness measure)

The continuous tracking design analyzes advertising awareness over time, in relation to ad spending; separately, this design tracks brand awareness, and then develops indices of effectiveness based on the strength of the correlations between ad spending and brand awareness.

The most popular alternate approach to the continuous tracking design is the Communicus System longitudinal design, in which the same people are interviewed at two points in time. Changes in brand measures (for example, brand purchasing and future purchase intentions) exhibited among those who have seen the advertising are compared to the changes in brand measures that occurred among those unaware of advertising. By means of this method, the researchers can isolate those marketplace changes that were produced by advertising versus those that would have occurred without advertising.

Advertising Research – a specialized form of marketing research conducted to improve the efficiency of advertising. According to MarketConscious.com, “It may focus on a specific ad or campaign, or may be

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directed at a more general understanding of how advertising works or how consumers use the information in advertising. It can entail a variety of research approaches, including psychological, sociological, economic, and other perspectives.”

Brand equity research - refers to the marketing effects or outcomes that accrue to a product with its brand name compared with those that would accrue if the same product did not have the brand name. And, at the root of these marketing effects is consumers' knowledge. In other words, consumers' knowledge about a brand makes manufacturers/advertisers respond differently or adopt appropriately adept measures for the marketing of the brand. The study of brand equity is increasingly popular as some marketing researchers have concluded that brands are one of the most valuable assets that a company has. Brand equity is one of the factors which can increase the financial value of a brand to the brand owner, although not the only one.

In the early 2000's in North America, the Ford Motor Company made a strategic decision to brand all new or redesigned cars with names starting with "F". This aligned with the previous tradition of naming all sport utility vehicles since the Ford Explorer with the letter "E". The Toronto Star quoted an analyst who warned that changing the name of the well known Windstar to the Freestar would cause confusion and discard brand equity built up, while a marketing manager believed that a name change would highlight the new redesign. The aging Taurus, which became one of the most significant cars in American auto history, would be abandoned in favor of three entirely new names, all starting with "F", the Five Hundred, Freestar and Fusion. By 2007, the Freestar was discontinued without a replacement. The Five Hundred names were thrown out and Taurus was brought back for the next generation of that car in a surprise move by Alan Mulally. "Five Hundred" was recognized by less than half of most people, but an overwhelming majority was familiar with the "Ford Taurus".

Brand association research – refers to researches on “what do consumers associate with the brand”?

Brand attribute research – refers to researches on “what are the key traits that describe the brand promise”?

Brand name testing – refers to researches on “what do consumers feel about the names of the products”?

The annual list of the world’s most valuable brands, published by Interbrand and Business Week, indicates that the market value of companies often consists largely of brand equity. Research by McKinsey & Company, a global consulting firm, in 2000 suggested that strong, well-leveraged brands produce higher returns to shareholders than weaker, narrower brands. Taken together, this means that brands seriously impact shareholder value, which ultimately makes branding a CEO responsibility.

Commercial eye tracking research - examine advertisements, package designs, websites, etc by analyzing visual behavior of the consumer. Eye tracking is commonly used in a variety of different advertising media. Commercials, print ads, online ads and sponsored programs are all conducive to analysis with current eye tracking technology. Analyses focus on visibility of a target product or logo in the context of a magazine, newspaper, website, or televised event. This allows researchers to assess in great detail how often a sample of consumers fixates on the target logo, product or ad. In this way, an advertiser can quantify the success of a given campaign in terms of actual visual attention.

Concept testing - to test the acceptance of a concept by target consumers. is the process of using quantitative methods and qualitative methods to evaluate consumer response to a product idea prior to the introduction of a product to the market. It can also be used to generate communication designed to alter consumer attitudes toward existing products. These methods involve the evaluation by consumers of product concepts having certain rational benefits, such as "a detergent that removes stains but is gentle on fabrics," or non-rational benefits, such as "a shampoo that lets you be yourself." Such methods are commonly referred to as concept testing and have been performed using field surveys, personal interviews

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and focus groups, in combination with various quantitative methods, to generate and evaluate product concepts.

Today, with the advent of the Internet, concept testing has experienced resurgence. Armed with the ability to show thousands of respondents images of an actual concept, many market researchers, and organizations, have had their faith restored in this once questionable method. Online survey takers now have the ability to view a potential product in a similar manner to how they would view the same product in a retail environment. In addition, with online retailing become increasingly prominent, many online respondents are also online consumers. Thus, they are able to easily place themselves in the mindset of a consumer looking to buy goods or services. Since the arrival of these methods, market researchers have been able to make better, more accurate, suggestions to their clients regarding the decision to move forward, revise, or start over with a product concept. Online Choice Modeling for example can produce detailed econometric models of demand for various attributes of the new product such as feature, packaging and price.

Cool hunting - to make observations and predictions in changes of new or existing cultural trends in areas such as fashion, music, films, television, youth culture and lifestyle. In this they resemble the intuitive fashion magazine editors of the 1960s such as Nancy White (Harper's Bazaar 1958–1971). Cool hunters operate most notably in the world of street fashion and design, but their work also blurs into that of futurists such as Faith Popcorn. Many web loggers now serve as online cool hunters, in a variety of cultural and technological areas.

Buyer decision processes research - to determine what motivates people to buy and what decision-making process they use

Copy testing – is a specialized field of marketing research, it is the study of television commercials prior to airing them. It is defined as research to determine an ad’s effectiveness based on consumers’ responses to the ad and covers all media including print, TV, radio, Internet etc. Although also known as copy testing, pre-testing is considered the more accurate, modern name (Young, p.4) for the prediction of how effectively an ad will perform, based on the analysis of feedback gathered from the target audience. Each test will either qualify the ad as strong enough to meet company action standards for airing or identify opportunities to improve the performance of the ad through editing.

In 1982, a consortium of 21 leading advertising agencies including N.W.Ayers, D’Arcy, Grey, McCann-Erikson, Needham Harper & Steers, Ogilvy & Mather, J.Walter Thompson, Young & Rubicam etc released a public document where they laid out the PACT (Positioning Advertising Copy Testing) Principles on what constitutes a good copy testing system. According to PACT, a good copy testing system is one that meets the following criteria:

1. Provides measurements which are relevant to the objectives of the advertising 2. Requires agreements about how the results will be used in advance of each specific test. 3. Provides multiple measurements – because single measurements are generally inadequate to assess the performance of an advertisement 4.Based on a model of human response to communications – the reception of a stimulus, the comprehension of the stimulus and the response to the stimulus. 5. Allows for consideration of whether the advertising stimulus should be exposed more than once. 6. Recognizes that the more finished a piece of copy is, the more soundly it can be evaluated and requires, as a minimum, that alternative executions be tested in the same degree of finish. 7. Provides controls to avoid the biasing effects of the exposure context. 8. Takes into account basic considerations of sample definition. 9. Demonstrates reliability and validity.

Customer satisfaction research - quantitative or qualitative studies that yields an understanding of a customer's of satisfaction with a transaction

Demand estimation - to determine the approximate level of demand for the product Distribution channel audits - to assess distributors’ and retailers’ attitudes toward a product, brand, or

company

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Internet strategic intelligence - searching for customer opinions in the Internet: chats, forums, web pages, blogs... where people express freely about their experiences with products, becoming strong "opinion formers"

Marketing effectiveness and analytics - Building models and measuring results to determine the effectiveness of individual marketing activities.

Mystery Consumer or Mystery shopping - is a tool used by market research companies to measure quality of retail service or gather specific information about products and services. Mystery shoppers posing as normal customers perform specific tasks—such as purchasing a product, asking questions, registering complaints or behaving in a certain way – and then provide detailed reports or feedback about their experiences.

Mystery shopping was standard practice by the early 1940s as a way to measure employee integrity. Tools used for mystery shopping assessments range from simple questionnaires to complete audio and video recordings. Many mystery shopping companies are completely administered through the Internet, allowing potential mystery shoppers to use the Internet to register for participation, find mystery shopping jobs and receive payment.

The most common venues where mystery shopping is used are retail stores, movie theaters, restaurants, fast food chains, banks, gas stations, car dealerships, apartments and health clubs, as well as health care facilities. In the UK, mystery shopping is increasingly used to provide feedback on customer services provided by local authorities and other non-profit organizations, such as housing associations and churches.

Positioning research - how does the target market see the brand relative to competitors? - What does the brand stand for?

Price elasticity testing - to determine how sensitive customers are to price changes Sales forecasting - to determine the expected level of sales given the level of demand. With respect to other

factors like Advertising expenditure, sales promotion etc. Segmentation research - to determine the demographic, psychographic, and behavioral characteristics of

potential buyers A focus group - is a form of qualitative research in which a group of people are asked about their attitude

towards a product, service, concept, advertisement, idea, or packaging. Questions are asked in an interactive group setting where participants are free to talk with other group members.

The first focus groups were created at the Bureau of Applied Social Research by associate director, sociologist Robert K. Merton. The term itself was coined by psychologist and marketing expert Ernest Dichter.

Store audit - to measure the sales of a product or product line at a statistically selected store sample in order to determine market share, or to determine whether a retail store provides adequate service

Test marketing - a small-scale product launch used to determine the likely acceptance of the product when it is introduced into a wider market. in the field of business and marketing, is a geographic region or demographic group used to gauge the viability of a product or service in the mass market prior to a wide scale roll-out. The criteria used to judge the acceptability of a test market region or group include:

(1) A population that is demographically similar to the proposed target market; and

(2) Relative isolation from densely populated media markets so that advertising to the test audience can be efficient and economical.

Viral Marketing Research - refers to marketing research designed to estimate the probability that specific communications will be transmitted throughout an individuals Social Network. Estimates of Social

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Networking Potential (SNP) are combined with estimates of selling effectiveness to estimate ROI on specific combinations of messages and media.

All of these forms of marketing research can be classified as either problem-identification research or as problem-solving research.

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(b) How is Geographic Information System used for Marketing Research?

A. A Geographic Information System (GIS), or Geographical Information System is any system that captures, stores, analyzes, manages, and presents data that is linked to location. Technically, a GIS is a system that includes mapping software and its application to remote sensing, land surveying, aerial photography, mathematics, photogrammetry, geography, and tools that can be implemented with GIS software. Still, many refer to "Geographic Information System" as "GIS" even though it doesn't cover all tools connected to topology.

In the strictest sense, the term describes any information system that integrates, stores, edits, analyzes, shares, and displays geographic information. In a more generic sense, GIS applications are tools that allow users to create interactive queries (user-created searches), analyze spatial information, edit data, maps, and present the results of all these operations. Geographic Information Science is the science underlying the geographic concepts, applications and systems, taught in degree and GIS Certificate programs at many universities.

In the simplest terms, GIS is the merging of cartography and database technology. Consumer users would likely be familiar with applications for finding driving directions, like a GPS program on their hand-held device. GPS (Global Positioning System) is the real-time location component that uses satellites to show your current position, "where am I now" on your device. GPS technology is discussed in more detail later in this article.

The value of location as a business measure is fast becoming an important consideration for organizations. GIS (Geographical Information Systems), with its capability to manage, display, analyze business information spatially, is emerging as a powerful location intelligence tool. In the US, Starbucks, Blockbuster, Hyundai, and thousands of other businesses use census data and GIS software to help them understand what types of people buy their products and services, and how to better market to these consumers. For example, McDonald’s in Japan uses a GIS system to overlay demographic information on maps to help identify promising new store sites.

More than 80% of organization data are location-related – locations where transactions are done, where retailers are found, and where the customers are to buy their products. Recently, there has been a growing interest in the business community to use GIS to enhance decision making processes at both strategic and operational levels. It can be used for marketing research or to identify locations for new outlets -- what we call site selection. It can even be used for sales territory planning, meaning that a business will know how to deploy its sales staff so they don’t overlap with each others’ territories. GIS can also help optimize their “catchment” area. Understanding location is even more important when businesses go into new location such as China and Vietnam.

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In Shanghai, there was recently a project to carry out a market research study using GIS. The client, a retailer, had previously engaged a market research company to study how the market in Shanghai operates. The director wanted to know where exactly the relevant channel stores that would help distribute their products were located, so they could then decide on how many to start building relationships with. The study identified ‘mama’ stores and other possible channels of product distribution and how they performed. This is a classical way of doing market research. They now have a better idea of where the hot spots are located, and can focus their resources on the high concentration areas. They can also prioritize in the sense that they can find out the locations of the top 10% sales volume stores. If they have limited resources, they can zoom in on these stores or areas first.

The use of GIS in business has greatly enhanced the efficiency in a number of areas, especially marketing research. Examples of the use of GIS in business include: locating potential competitors, mapping market thresholds for retailers, providing computerized hazard information classifications, aiding risk management decisions in insurance companies, and enabling real estate agents to handle property data more efficiently. Delivery services also utilize GIS in aspects such as navigation & monitoring of their fleets, routing optimization for shipping and deliveries, geocoding address matching, and location searches. Typical data input into this category include road networks, street addresses, business profiles, and socioeconomic profiles.

Q3. (a) Define and explain Data Collection with its types and methods.

A. Data collection is a term used to describe a process of preparing and collecting data - for example as part of a process improvement or similar project. The purpose of data collection is to obtain information to keep on record, to make decisions about important issues, to pass information on to others. Primarily, data is collected to provide information regarding a specific topic.

Data collection usually takes place early on in an improvement project, and is often formalized through a data collection Plan which often contains the following activity.

1.Pre collection activity – Agree goals, target data, definitions, methods2.Collection – data collection3.Present Findings – usually involves some form of sorting analysis and/or presentation.

Prior to any data collection, pre-collection activity is one of the most crucial steps in the process. It is often discovered too late that the value of their interview information is discounted as a consequence of poor sampling of both questions and informants and poor elicitation techniques. After pre-collection activity is fully completed, data collection in the field, whether by interviewing or other methods, can be carried out in a structured, systematic and scientific way.

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A formal data collection process is necessary as it ensures that data gathered is both defined and accurate and that subsequent decisions based on arguments embodied in the findings are valid. The process provides both a baseline from which to measure from and in certain cases a target on what to improve.

Data collection is a way of gathering information for use in various studies or decision making situations. Depending on the required outcome or information needed methods of data collection can vary and even be combined to achieve needed results.

All data collection methods boil down to five basic types:

Registration Questionnaires Interviews Direct Observations Reporting

Each method of data collection has its uses, advantages and disadvantages. Most often using more than one method of data collection will gain better results.

Registration

Registration is a data collection method mainly used to gather information about a certain group or demographic population. This method is primarily used in the following ways:

Drivers licenses Welfare programs School programs Voter records

Questionnaires

This type of data collection method is one of the inexpensive ways to gain information. Most of the information gathered is from co-operative and highly literate people such as college graduates or people in professional fields. Many times questionnaires will be used by service providers to gain needed information. Such providers would include:

Medical Surveys Insurance Applications Higher paying job applications Scientific Research

Interviews

Interviews are more expensive than questionnaires as a method of data collection because of the labor involved. The tradeoff is that an interview can contain more complex questions. Interviews are more useful with lower literacy rates and less co-operative participants. The following fields tend to use the interview method of data collection as a main resource.

Government agencies such as the IRS or Welfare Department Census takers Law enforcement

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Direct Observation

This type of data collection method is the most accurate way of gathering information, and can be the most cost effective over a long time frame. This method is mainly used in institutional and professional settings such as:

Medical analysis Corrections Facilities Psychology and Sociology clinical settings Indirect research

Reporting

Reporting is a direct opposite to the interview and questionnaire where the study group is required to provide information without being asked specific questions. This type of data collection method is most frequently used for:

Tracking parolees and ex-offenders Government tracking and analysis of community needs Field teams gathering information using other methods

Data analysis depends on the method of data collection used. While some analysis will be simple statistics, other analysis will be far more complex depending on the information and combination of data collection methods used.

The data collection process can be relatively simple depending on the type of data collection tools required and used during the research. Data collection tools are instruments used to collect information for performance assessments, self-evaluations, and external evaluations. The data collection tools need to be strong enough to support what the evaluations find during research. Here are a few examples of data collection tools used within three main categories.

There are 3 main tools for data collection as follows:

Secondary Participation

Data collection tools involving secondary participation require no direct contact to gather information. Examples of secondary data collection tools would include:

Postal mail Electronic mail Telephone Web-based surveys

These data collection tools do not allow the researcher to truly gauge the accuracy of the information given by the participants who responded.

In-Person Observations

Data collection tools used in personal contact observations are used when there is face to face contact with the participants. Some examples of this type of data collection tool would include:

In-person surveys – used to gain general answers to basic questions Direct or participatory observations – where the researcher is directly involved with the study group Interviews – used to gain more in depth answers to complex questions Focus groups – where certain sample groups are asked their opinion about a certain subject or theory

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These data collection tools not only allow for a true measurement of accuracy but also let the researcher obtain any unspoken observations about the participants while conducting research.

Case Studies And Content Analysis

Case studies and content analysis are data collection tools which are based upon pre-existing research or a search of recorded information which may be useful to the researcher in gaining the required information which fills in the blanks not found with the other two types during the data collection process. Some examples of this type of data collection tool would include:

Expert opinions – leaders in the field of study Case studies – previous findings of other researchers Literature searches – research articles and papers Content analysis of both internal and external records – documents created from internal origin or other

documents citing occurrences within the research group

These three data collection tools are the primary sources for gaining information during research. The most effective being the In-Person Observations with the use of Case Studies and analysis for verification resources. While each type of data collection tool can be used alone, most often they are used in either combination or conjunction with each other in various ways.

Other main types of collection include census, sample survey, and administrative by-product and each with their respective advantages and disadvantages. A census refers to data collection about everyone or everything in a group or population and has advantages, such as accuracy and detail and disadvantages, such as cost and time. A sample survey is a data collection method that includes only part of the total population and has advantages, such as cost and time and disadvantages, such as accuracy and detail. Administrative by-product data is collected as a byproduct of an organization’s day-to-day operations and has advantages, such as accuracy, time simplicity and disadvantages, such as no flexibility and lack of control.

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(b) What is the significance of measurement instruments and sampling in data collection?

A. Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.

Measurement is the process observing and recording the observations that are collected as part of a research effort. There are two major issues that will be considered here.

First, you have to understand the fundamental ideas involved in measuring. Here we consider two of major measurement concepts. In Levels of Measurement, I explain the meaning of the four major levels of measurement: nominal, ordinal, interval and ratio. Then we move on to the reliability of measurement, including consideration of true score theory and a variety of reliability estimators.

Second, you have to understand the different types of measures that you might use in social research. We consider four broad categories of measurements. Survey research includes the design and implementation of interviews and questionnaires. Scaling involves consideration of the major methods of developing and implementing a scale. Qualitative research provides an overview of the broad range of non-numerical measurement approaches. An unobtrusive measure presents a variety of measurement methods that don't intrude on or interfere with the context of the research.

By the time you get to the analysis of your data, most of the really difficult work has been done. It's much more difficult to: define the research problem; develop and implement a sampling plan; conceptualize, operationalize

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and test your measures; and develop a design structure. If you have done this work well, the analysis of the data is usually a fairly straightforward affair.

In most social research the data analysis involves three major steps, done in roughly this order:

Cleaning and organizing the data for analysis (Data Preparation) Describing the data (Descriptive Statistics) Testing Hypotheses and Models (Inferential Statistics)

Data Preparation involves checking or logging the data in; checking the data for accuracy; entering the data into the computer; transforming the data; and developing and documenting a database structure that integrates the various measures.

Descriptive Statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. With descriptive statistics you are simply describing what is, what the data shows.

Inferential Statistics investigate questions, models and hypotheses. In many cases, the conclusions from inferential statistics extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population thinks. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

In most research studies, the analysis section follows these three phases of analysis. Descriptions of how the data were prepared tend to be brief and to focus on only the more unique aspects to your study, such as specific data transformations that are performed. The descriptive statistics that you actually look at can be voluminous. In most write-ups, these are carefully selected and organized into summary tables and graphs that only show the most relevant or important information. Usually, the researcher links each of the inferential analyses to specific research questions or hypotheses that were raised in the introduction, or notes any models that were tested that emerged as part of the analysis. In most analysis write-ups it's especially critical to not "miss the forest for the trees." If you present too much detail, the reader may not be able to follow the central line of the results. Often extensive analysis details are appropriately relegated to appendices, reserving only the most critical analysis summaries for the body of the report itself.

Q5. (a) Define Data Analysis. How is an initial analysis of data conducted?

A. Data analysis is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data analysis is a practice in which raw data is ordered and organized so that useful information can be extracted from it. The process of organizing and thinking about data is key to understanding what the data does and does not contain. There are a variety of ways in which people can approach data analysis, and it is notoriously easy to manipulate data during the analysis phase to push certain conclusions or agendas. For this reason, it is important to pay attention when data analysis is presented, and to think critically about the data and the conclusions which were drawn.

Raw data can take a variety of forms, including measurements, survey responses, and observations. In its raw form, this information can be incredibly useful, but also overwhelming. Over the course of the data analysis

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process, the raw data is ordered in a way which will be useful. For example, survey results may be tallied, so that people can see at a glance how many people answered the survey, and how people responded to specific questions.

In the course of organizing the data, trends often emerge, and these trends can be highlighted in the write-up of the data to ensure that readers take note. In a casual survey of ice cream preferences, for example, more women than men might express a fondness for chocolate, and this could be a point of interest for the researcher. Modeling the data with the use of mathematics and other tools can sometimes exaggerate such points of interest in the data, making them easier for the researcher to see.

Charts, graphs, and textual write-ups of data are all forms of data analysis. These methods are designed to refine and distill the data so that readers can glean interesting information without needing to sort through all of the data on their own. Summarizing data is often critical to supporting arguments made with that data, as is presenting the data in a clear and understandable way. The raw data may also be included in the form of an appendix so that people can look up specifics for themselves

When people encounter summarized data and conclusions, they should view them critically. Asking where the data is from is important, as is asking about the sampling method used to collect the data, and the size of the sample. If the source of the data appears to have a conflict of interest with the type of data being gathered, this can call the results into question. Likewise, data gathered from a small sample or a sample which is not truly random may be of questionable utility. Reputable researchers will always provide information about the data gathering techniques used, the source of funding, and the point of the data collection in the beginning of the analysis so that readers can think about this information while they review the analysis.

Initial data analysis

The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that are aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:

Quality of data

The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analyses: frequency counts, descriptive statistics (mean, standard deviation, and median), normality (skew ness, kurtosis, frequency histograms, normal probability plots), associations (correlations, scatter plots).

Other initial data quality checks are:

Checks on data cleaning: have decisions influenced the distribution of the variables? The distribution of the variables before data cleaning is compared to the distribution of the variables after data cleaning to see whether data cleaning has had unwanted effects on the data.

Analysis of missing observations: are there many missing values, and are the values missing at random? The missing observations in the data are analyzed to see whether more than 25% of the values are missing, whether they are missing at random (MAR), and whether some form of imputation (statistics) is needed.

Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution.

Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.

The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.

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Quality of measurements

The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.There are two ways to assess measurement quality:

Confirmatory factor analysis Analysis of homogeneity (internal consistency), which gives an indication of the reliability of a

measurement instrument, i.e., whether all items fit into a unidimensional scale. During this analysis, one inspects the variances of the items and the scales, the Cronbach's α of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale.

Initial transformations

After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase. Possible transformations of variables are:

Square root transformation (if the distribution differs moderately from normal) Log-transformation (if the distribution differs substantially from normal) Inverse transformation (if the distribution differs severely from normal) Make categorical (ordinal / dichotomous) (if the distribution differs severely from normal, and no

transformations help)

Did the implementation of the study fulfill the intentions of the research design?

One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.If the study did not need and/or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in sample.Other possible data distortions that should be checked are:

dropout (this should be identified during the initial data analysis phase) Item nonresponse (whether this is random or not should be assessed during the initial data analysis phase) Treatment quality (using manipulation checks).

Characteristics of data sample

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase.The characteristics of the data sample can be assessed by looking at:

Basic statistics of important variables Scatter plots Correlations Cross-tabulations

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Final stage of the initial data analysis

During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.Also, the original plan for the main data analyses can and should be specified in more detail and/or rewritten.In order to do this, several decisions about the main data analyses can and should be made:In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?

In the case of missing data: should one neglect or impute the missing data; which imputation technique should be used?

In the case of outliers: should one use robust analysis techniques? In case items do not fit the scale: should one adapt the measurement instrument by omitting items, or rather

ensure comparability with other (uses of the) measurement instrument(s)? In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use

small sample techniques, like exact tests or bootstrapping? In case the randomization procedure seems to be defective: can and should one calculate propensity scores

and include them as covariates in the main analyses?

Analyses

Several analyses can be used during the initial data analysis phase:

Univariate statistics Bivariate associations (correlations) Graphical techniques (scatter plots)

It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:

Nominal and ordinal variables

o Frequency counts (numbers and percentages) o Associations

circumambulations (crosstabulations) hierarchical loglinear analysis (restricted to a maximum of 8 variables) loglinear analysis (to identify relevant/important variables and possible confounders)

o Exact tests or bootstrapping (in case subgroups are small) o Computation of new variables

Continuous variables

o Distribution Statistics (M, SD, variance, skewness, kurtosis) Stem-and-leaf displays Box plots

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(b) What is the importance of Tabulation in Marketing Research?

A. Social research involves many weird and wonderful methods over which debate, often bitter, rages continuously. However, at some stage even the most virulently anti-positivist and anti-empiricist will need to be

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able to name, sort and count things, or to read, understand or even act on, reports based on things which have been named, sorted and counted. Perhaps the easiest way of explaining one of the most basic skills in statistics is to try to make sense of raw data through a process of naming, sorting and counting. For instance, take the following data relating to 20 sixth form students. Information is provided on their sex and on their intentions towards higher education.

Student   Sex          H.E.?          1    Male         Yes          2    Male         No          3    Female    Yes          4    Female    No          5    Female    No          6    Male         No          7    Female    No          8    Male         No          9    Female    No          10   Female   Yes          11   Male        Yes          12   Male        No          13   Male        Yes          14   Female    No          15   Male        Yes          16   Male        No          17   Female   No          18   Female   No          19   Male        No          20   Male       No

It is not easy to tell from these data how many males and females there are, let alone make any meaningful statement about the relationship between sex and plans for higher education. What can we do to make them easier to understand?

The first thing we need to do is to sort them into some kind of order. We can do this by arranging all the males in one group and the females in another, or we can do it by sorting all those with H.E. Plans into one group and the rest into another.   

Thus by sex:        Female  Yes         Female  No          Female  No          Female  No          Female  No          Female  No          Female  Yes         Female  No          Female  No           Total Females = 9

        Male    Yes         Male    No          Male    Yes         Male    No          Male    Yes         Male    No          Male    Yes         Male    No          Male    No 

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        Male    No          Male    No           Total Males = 11

 ...and by college plans:        Male       No          Female  No          Male       No          Female  No          Male       No          Female  No          Male       No          Female  No          Male       No          Male       No          Female  No          Female  No          Male       No          Female  No           Total with no college plans = 14       

        Male       Yes         Male       Yes         Female  Yes         Male       Yes         Female  Yes         Male       Yes          Total with college plans = 6

If we want to look at both distributions together we can sort on both variables to yield:

By sex and college plans:        Female  No          Female  No          Female  No          Female  No          Female  No          Female  No          Female  No      Total females with no college plans = 7

        Female  Yes         Female  Yes     Total females with college plans = 2

        Male    No          Male    No          Male    No          Male    No          Male    No          Male    No          Male    No      Total males with no college plans = 7

        Male    Yes         Male    Yes         Male    Yes         Male    Yes     Total males with college plans = 4

These data can be summarized by tabulating one variable at a time in frequency distributions.

Sex:          Female    9       45%          Male      11       55%

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                    -----------          Total     20      100%    

College:          No        14       70%          Yes       6         30%                    -----------          Total     20      100%

If we want to summarize data from both variables at the same time we need to construct a contingency table.   We do this by constructing a blank table with the same number of rows as there are categories in one of the variables, and the same numbers of columns as there are categories in the other.  Let us take “Sex" as the column variable and "College plans" as the row variable.  In this case both variables have only two categories, and so the table will have 2 rows and 2 columns, and therefore 4 cells.

 Sex             Male            Female                   -----------------------------                   I             I             I     No         I             I             I                   I             I             ICollege      -----------------------------                   I             I             I     Yes       I             I             I                   I             I             I                   -----------------------------                                 

These four cells form the body of the table into which we can now enter the counts from the list sorted on both variables at once. At the same time we enter outside the table the row-totals and column-totals from the original frequency distributions for each variable and the grand total for the number of cases in the whole table. Thus:

Sex     (Raw data)                   Male  Female      Row Total                -----------------------------               I             I             I       No   I      7     I      7    I     14               I             I             ICollege  -----------------------------               I             I             I      Yes  I      4     I      2     I      6               I             I             I                -----------------------------

Column total 11      9          20

This  is at least a little easier to interpret than the  original sorted  lists, but it is still difficult to answer a question  as to  whether males are more likely to want to go college than  are females,  or vice versa. To answer this question we need to ask not,”How many?", but, "What proportion?"  Of each sex have college plans. One further operation is now necessary - to standardize the data by converting the raw counts for each sex into percentages - to enable direct comparison between sexes.

                           Sex     (% data)                 Male     Female   Row Total                -----------------------------               I             I             I       No   I    63.6 I    77.8  I    70.0 College -----------------------------

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               I             I             I       Yes  I    36.4 I    22.2 I    30.0                -----------------------------Column total 100.0  100.0  100.0(Base for %)   (11)     (9)       (20)

From this table we can now state that female sixth-formers are less likely to have plans for Higher Education. From the above example we can state the importance of tabulation in Market Research.

Q7. Write short notes on:

(1) Questionnaire Format

A. A questionnaire is a research instrument consisting of a series of questions and other prompts for the purpose of gathering information from respondents. Although they are often designed for statistical analysis of the responses, this is not always the case. The questionnaire was invented by Sir Francis Galton.

Questionnaires are frequently used in quantitative marketing research and social research. They are a valuable method of collecting a wide range of information from a large number of individuals, often referred to as respondents. Good questionnaire construction is critical to the success of a survey. Inappropriate questions, incorrect ordering of questions, incorrect scaling, or bad questionnaire format can make the survey valueless. A useful method for checking a questionnaire and making sure it is accurately capturing the intended information is to pretest among a smaller subset of target respondents.

Types of questions

1.Contingency questions - A question that is answered only if the respondent gives a particular response to a previous question. This avoids asking questions of people that do not apply to them (for example, asking men if they have ever been pregnant).

2.Matrix questions - Identical response categories are assigned to multiple questions. The questions are placed one under the other, forming a matrix with response categories along the top and a list of questions down the side. This is an efficient use of page space and respondents’ time.

3.Closed ended questions - Respondents’ answers are limited to a fixed set of responses. Most scales are closed ended. Other types of closed ended questions include:

o Yes/no questions - The respondent answers with a “yes” or a “no”. o Multiple choices - The respondent has several option from which to choose. o Scaled questions - Responses are graded on a continuum (example: rate the appearance of the product

on a scale from 1 to 10, with 10 being the most preferred appearance). Examples of types of scales include the Likert scale, semantic differential scale, and rank-order scale (See scale for a complete list of scaling techniques.).

4.Open ended questions - No options or predefined categories are suggested. The respondent supplies their own answer without being constrained by a fixed set of possible responses. Examples of types of open ended questions include:

o Completely unstructured - For example, “What is your opinion of questionnaires?” o Word association - Words are presented and the respondent mentions the first word that comes to

mind. o Sentence completion - Respondents complete an incomplete sentence. For example, “The most

important consideration in my decision to buy a new house is . . .” o Story completion - Respondents complete an incomplete story. o Picture completion - Respondents fill in an empty conversation balloon. o Thematic apperception test - Respondents explain a picture or make up a story about what they think

is happening in the picture

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Question sequence

Questions should flow logically from one to the next. The researcher must ensure that the answer to a question is not influenced by previous questions. Questions should flow from the more general to the more specific. Questions should flow from the least sensitive to the most sensitive. Questions should flow from factual and behavioral questions to attitudinal and opinion questions. Questions should flow from unaided to aided questions. According to the three stages theory (also called the sandwich theory), initial questions should be screening

and rapport questions. Then in the second stage you ask all the product specific questions. In the last stage you ask demographic questions.

Basic rules for questionnaire item construction

Use statements which are interpreted in the same way by members of different subpopulations of the population of interest.

Use statements where persons that have different opinions or traits will give different answers. Use only one aspect of the construct you are interested in per item. Use positive statements and avoid negatives or double negatives. Do not make assumptions about the respondent. Use clear and comprehensible wording. Use correct spelling, grammar and punctuation.

Questionnaire administration modes

Main modes of questionnaire administration are:

Face-to-face questionnaire administration, "where an interviewer presents the items orally." Paper-and-pencil questionnaire administration, "where the items are presented on paper." Computerized questionnaire administration, "where the items are presented on the computer." Adaptive computerized questionnaire administration, "where a selection of items is presented on the

computer, and based on the answers on those items, the computer selects following items optimized for the testee's estimated ability or trait."

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(2) Co-relation

A. The correlation coefficient, denoted by r, is a measure of the strength of the straight-line or linear relationship between two variables. The correlation coefficient takes on values ranging between +1 and -1. The following points are the accepted guidelines for interpreting the correlation coefficient:

1.0 indicates no linear relationship. 2.+1 indicates a perfect positive linear relationship: as one variable increases in its values, the other variable

also increases in its values via an exact linear rule. 3.-1 indicates a perfect negative linear relationship: as one variable increases in its values, the other variable

decreases in its values via an exact linear rule. 4.Values between 0 and 0.3 (0 and -0.3) indicate a weak positive (negative) linear relationship via a shaky

linear rule. 5.Values between 0.3 and 0.7 (0.3 and -0.7) indicate a moderate positive (negative) linear relationship via a

fuzzy-firm linear rule. 6.Values between 0.7 and 1.0 (-0.7 and -1.0) indicate a strong positive (negative) linear relationship via a firm

linear rule.

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7.The value of r squared is typically taken as “the percent of variation in one variable explained by the other variable,” or “the percent of variation shared between the two variables.”

8.Linearity Assumption. The correlation coefficient requires that the underlying relationship between the two variables under consideration is linear. If the relationship is known to be linear, or the observed pattern between the two variables appears to be linear, then the correlation coefficient provides a reliable measure of the strength of the linear relationship. If the relationship is known to be nonlinear, or the observed pattern appears to be nonlinear, then the correlation coefficient is not useful, or at least questionable.

The calculation of the correlation coefficient for two variables, say X and Y, is simple to understand. Let zX and zY be the standardized versions of X and Y, respectively. That is, zX and zY are both re-expressed to have means equal to zero, and standard deviations (std) equal to one. The re-expressions used to obtain the standardized scores are in equations (3.1) and (3.2):

zXi = [Xi - mean(X)]/std(X)                                                            (3.1)

zYi = [Yi - mean(Y)]/std(Y)                                                            (3.2)

The correlation coefficient is defined as the mean product of the paired standardized scores (zXi, zYi) as expressed in equation (3.3).

rX,Y = sum of [zXi * zYi]/(n-1), where n is the sample size              (3.3)

For a simple illustration of the calculation, consider the sample of five observations in Table 1. Columns zX and zY contain the standardized scores of X and Y, respectively. The last column is the product of the paired standardized scores. The sum of these scores is 1.83. The mean of these scores (using the adjusted divisor n-1, not n) is 0.46. Thus, rX,Y = 0.46. 

Obs X Y zX zY zX*zY

1 12 77 -1.14 -0.96 1.11

2 15 98 -0.62 1.07 -0.66

3 17 75 -0.27 -1.16 0.32

4 23 93 0.76 0.58 0.44

5 26 92 1.28 0.48 0.62

Mean 18.6 87 Sum 1.83

Std 5.77 10.32

n 5 r 0.46

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(3) Regression

A. A regression equation allows us to express the relationship between two (or more) variables algebraically. It indicates the nature of the relationship between two (or more) variables. In particular, it indicates the extent to which you can predict some variables by knowing others, or the extent to which some are associated with others.

A linear regression equation is usually written Y = a + bX + e where Y is the dependent variable a is the intercept b is the slope or regression coefficient

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X is the independent variable (or covariate) e is the error term

The equation will specify the average magnitude of the expected change in Y given a change in X.

The regression equation is often represented on a scatter plot by a regression line.

A regression line is a line drawn through the points on a scatter plot to summarize the relationship between the variables being studied. When it slopes down (from top left to bottom right), this indicates a negative or inverse relationship between the variables; when it slopes up (from bottom right to top left), a positive or direct relationship is indicated.

The regression line often represents the regression equation on a scatter plot.

Simple linear regression aims to find a linear relationship between a response variable and a possible predictor variable by the method of least squares.

Multiple linear regression aims is to find a linear relationship between a response variable and several possible predictor variables.

Nonlinear regression aims to describe the relationship between a response variable and one or more explanatory variables in a non-linear fashion.

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(5) Qualitative and Quantitative Research

A. In the social sciences, quantitative research refers to the systematic empirical investigation of quantitative properties and phenomena and their relationships. The objective of quantitative research is to develop and employ mathematical models, theories and/or hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships.

Quantitative research is used widely in social sciences such as sociology, anthropology, and political science. Research in mathematical sciences such as physics is also 'quantitative' by definition, though this use of the term differs in context. In the social sciences, the term relates to empirical methods, originating in both philosophical positivism and the history of statistics, which contrast qualitative research methods.

Quantitative research is generally made using scientific methods, which can include:

The generation of models, theories and hypotheses The development of instruments and methods for measurement Experimental control and manipulation of variables Collection of empirical data Modeling and analysis of data Evaluation of results

Qualitative research is a method of inquiry appropriated in many different academic disciplines, traditionally in the social sciences, but also in market research and further contexts. Qualitative researchers aim to gather an in-depth understanding of human behavior and the reasons that govern such behavior. The qualitative method investigates the why and how of decision making, not just what, where, when. Hence, smaller but focused samples are more often needed, rather than large samples.

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Qualitative research was one of the first forms of social studies, but in the 1950s and 1960s – as quantitative science reached its peak of popularity (The Quantitative Revolution) – it was diminished in importance and began to regain recognition only in the 1970s. The phrase 'qualitative research' was until the 1970s used only to refer to a discipline of anthropology or sociology, and terms like were used instead. During the 1970s and 1980s qualitative research began to be used in other disciplines, and became a significant type of research in the fields of education studies, social work studies, women's studies, disability studies, information studies, management studies, nursing service studies, human service studies, psychology, communication studies, and many other fields. Qualitative research occurred in the consumer products industry during this period, with researchers investigating new consumer products and product positioning/advertising opportunities. The earliest consumer research pioneers including Gene Reilly of The Gene Reilly Group in Darien, CT, Jerry Schoenfeld of Gerald Schoenfeld & Partners in Tarrytown, NY and Martin Calle of Calle & Company, Greenwich, CT, also Peter Cooper in London, England, and Hugh Mackay in Mission, Australia. There continued to be disagreement about the proper place of qualitative versus quantitative research. In the late 1980s and 1990s after a spate of criticisms from the quantitative side, new methods of qualitative research evolved, to address the perceived problems with reliability and imprecise modes of data analysis.[2] During this same decade, there was a slowdown in traditional media advertising spending, so there was heightened interest in making research related to advertising more effective.

In the last thirty years the acceptance of qualitative research by journal publishers and editors has been growing. Prior to that time many mainstream journals were prone to publish research articles based upon the natural sciences and which featured quantitative analysis.

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(6) Experimentation in Market Research

A. An experiment involves the creation of a contrived situation in order that the researcher can manipulate one or more variables whilst controlling all of the others and measuring the resultant effects. For instance, when United Fruits were considering replacing their Gros Michel variety of banana with the Valery variety, a simple experiment was first carried out. In selected retail outlets, the two varieties were switched on different days of the week and sales data examined to determine what effect the variety had on sales volumes. That is, the variety was being manipulated whilst all other variables were held constant. United Fruits found that the switch back and forth between Gros Michel and Valery had no effect upon sales. United Fruit were therefore able to replace Gros Michel with Valery.

Boyd and Westfall have defined experimentation as:

"...that research process in which one or more variables are manipulated under conditions which permit the collection of data which show the effects, if any, in unconfused fashion."

Experiments can be conducted either in the field or in a laboratory setting. When operating within a laboratory environment, the researcher has direct control over most, if not all, of the variables that could impact upon the outcome of the experiment. For example, an agricultural research station may wish to compare the acceptability of a new variety of maize. Since the taste characteristics are likely to have a major influence on the level of acceptance, a blind taste panels might be set up where volunteers are given small portions of maize porridge in unmarked bowls. The participants would perhaps be given two porridge samples and the researcher would observe whether they were able to distinguish between the maize varieties and which they preferred. In addition to taste testing, laboratory experiments are widely used by marketing researchers in concept testing, package testing, advertising research and test marketing.

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Experimentation offers the possibility of establishing a cause and effective relationship between variables and this makes it an attractive methodology to marketing researchers. An experiment is a contrived situation that allows a researcher to manipulate one or more variables whilst controlling all of the others and measuring the resultant effects on some independent variable.

Experiments are of two types: those conducted in a laboratory setting and those which are executed in natural settings; these are referred to as field experiments. Laboratory experiments give the researcher direct control over most, if not all, of the variables that could affect the outcome of the experiment. The evidence for drawing inferences about causal relationships takes three forms: associative variation, consistent ordering of events and the absence of alternative causes.

There are a number of potential impediments to obtaining valid results from experiments. These may be categorised according to whether a given confounding factor has internal validity, external validity, or both. Internal validity is called into question when there is doubt that the experimental treatment is actually responsible for changes in the value of the dependent variable. External validity becomes an issue when there is uncertainty as to whether experimental findings can be generalised to a defined population. The impediments to internal validity are history, pre-testing, maturation, instrumentation, sampling bias and mortality. Impediments to external validity are: the interactive effects of testing, the interactive effects of sampling bias and errors arising from making use of contrived situations.

The main forms of experimental design differ according to whether or not a measure is taken both before and after the introduction of the experimental variable or treatment, and whether or not a control group is used alongside the experimental group. The designs are: after-only, before-after, before-after with control group, after-only with control group and ex post facto designs.

END

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