MARKETING RESEARCH UNIT-1Applications of Market ResearchMarketing research uses include: Advertising testing research, branding research, customer satisfaction research, pricing research, product positioning research, new product assessments, marketing due diligence, and segmentation research. We conduct these marketing research studies for firms of most sizes from venture funded start ups to middle-market and large enterprises.Applications of Market Research

Pricing Research

We provide pricing strategy consulting backed by strong pricing research capabilities. Our perspective is broad when dealing with pricing research and pricing strategy decisions, and focus on finding for your business optimum price-product-feature configurations in the context of market positioning opportunities. We employ both qualitative and quantitative pricing research tools.

Product Research

Product market research serves several goals: new product design and market validation research, or assessing existing product strength and line extension potential. We follow the product development cycle integrating research with creative positioning and technical product design efforts.

Concept Testing

Concept testing research evaluates advertising concepts, ad theme concepts and appeals, new product concepts, pricing, brand concepts, brand names, and positioning strategy concepts. We select techniques -- qualitative and quantitative -- to both develop concepts, refine, and screen to assess market potential.

Positioning Research

We offer experienced market positioning and creative branding research capabilities to define and go-to-market with a high-impact positioning strategy. First, it requires understanding the market positioning concept, your current and potential markets, and the process needed to generate brand name impact.

Marketing Due Diligence

We support venture investment firms with primary and secondary marketing research in a stand alone or component marketing due diligence study.

Customer Satisfaction Research

The buzz and interest around customer satisfaction research sometimes deflates if the research design does not lead to actionable results. Also, customer expectations generally rise overtime as advances in technology in many categories boost the consumer consciousness of what to expect. We build into our customer satisfaction study design "action indicators" to point to immediate use of customer satisfaction results.

Branding Research

Branding decisions drive branding marketing research strategy. Corporate, product and advertising brand development is amix of creativity and marketing informationto uncover brand positioning opportunities in cluttered market spaces.

Brand Equity Research

Brand equity research measures the breadth and depth of brand power in your target markets. We use both standard and custom tailored brand equity survey measurements. A key to research design is the goal of a brand equity measurement study.

Advertising Research

Advertising research design is determined by specific advertising goals and the stage of ad development, or campaign. We use a broad range of advertising research techniques including ad recall surveys, message and theme salience and impact measures, buying motivation and association with the ad message or positioning theme. We employ both qualitative and quantitative pricing research tools.

Market Segmentation

Market segmentation research maintains focus and delivers needed marketing information in today's moving economy where new markets and new product categories emerge and traditional market segments fade away. Market segmentation research is a way to keep 'your eye on the ball.' Often we start the market segmentation process withqualitative researchto the range and breadth of customers. Then we follow with quantitative research using appropriate multivariate analysis (cluster, k-means factor, etc) to define meaningful segments.

Sales Analysis

Data mining -- finding gems of insight from sophisticated or basic analysis of your internal customer and sales and margin trend data -- is a key first step in product and brand analysis. Simply put, a marketing analysis data mining effort searches for meaning and insight among the stacks of sales data and marketing data already within a sales and marketing organization. Through these tools we can better target your best customers, find which advertising and promotion methods are most efficient and effective.Limitations of Marketing ResearchFollowing are the mainlimitations of Marketing Research: Marketing Research (MR) is not an exact science though it uses the techniques of science. Thus, the results and conclusions drawn upon by using MR are not very accurate. The results of MR are very vague as MR is carried out on consumers, suppliers, intermediaries, etc. who are humans. Humans have a tendency to behave artificially when they know that they are being observed. Thus, the consumers and respondents upon whom the research is carried behave artificially when they are aware that their attitudes, beliefs, views, etc are being observed. MR is not a complete solution to any marketing issue as there are many dominant variables between research conclusions and market response. MR is not free from bias. The research conclusions cannot be verified. The reproduction of the same project on the same class of respondents give different research results. Inappropriate training to researchers can lead to misapprehension of questions to be asked for data collection. Many business executives and researchers have ambiguity about the research problem and its objectives. They have limited experience of the notion of the decision-making process. This leads to carelessness in research and researchers are not able to do anything real. There is less interaction between the MR department and the main research executives. The research department is in segregation. This all makes research ineffective. MR faces time constraint. The firms are required to maintain a balance between the requirement for having a broader perspective of customer needs and the need for quick decision making so as to have competitive advantage. Huge cost is involved in MR as collection and processing of data can be costly. Many firms do not have the proficiency to carry wide surveys for collecting primary data, and might not also able to hire specialized market experts and research agencies to collect primary data. Thus, in that case, they go for obtaining secondary data that is cheaper to obtain. MR is conducted in open marketplace where numerous variables act on research settings.Four Factors to Consider When Creating a Market Research Function. /Organising the Marketing Research Function Todays marketers need to remember that one of their primary jobs is providing the rest of the company with a window into the customer. This takes research.Research enables you to: Ask the questions you want to ask Begin with and test a hypothesis Predict what might happen in the futureIf improving your ability to retain and grow business with existing customers is important to you, then conduct research. Research in its most basic form is to inquire, to examine. There is a rigor to research. Research begins with a question and the question helps you formulate your approach or methodology.In addition to conducting research, a number of companies are exploring adding a market research function to their organization. Weve had a number of questions regarding what factors should be considered when assessing workload requirements. Here are four key factors to consider:Project complexity. The number and type of people you will need to staff your own research department is going to depend how complex the research projects are going to be. Secondary research projects require different skills sets than primary research efforts. Short, closed ended online web surveys that require little statistical analysis will require different skills and people than conducting in-person product validation research around the world.Frequency of research. A couple of research projects a year will require different resources than a couple of research projects a month. If you havent created a research calendar, this is a good starting point. At a minimum the calendar should identify the research topic, timing, methodology, participant profile, and purpose.Project alignment.Market research is frequently aligned by brand and the staff allocations for each brand are based on the products lifecycle and category. The number of brands and markets and the number of products and complexity of these products in each brand will affect the staff and budget requirements. The research calendar will help you determine staff and budget requirements.Market potential.A products market potential can influence the number of analysts assigned to a research project. It is common for companies to have more analysts assigned to research projects related to larger brands or products with the greatest potential. It helps to have a way to evaluate each research effort. One way to do this is to use a 2X2 grid, with one axis for business/marketing value/ impact (high to low) and one axis for effort or investment (high to low). Those research efforts in the high value/impact quadrant should be prioritized first. We often recommend tackling some of the easiest efforts first as a way to rack up some fast wins.What are the various types of research design?Various types of research design are as follow:1. Research design for exploratory or formulative studies In this type of design, a vague problem is selected and understood and is then followed by an exploratory research to find a new hypothesis and then carrying out conclusion research decisions to finally get new ideas. Aims at finding a new hypothesis. Individual surveys, referring to secondary sources of data etc. play an important role in such research designs. Reviewing related literature, following or surveying people having practical experience in the problem related field act as very important and most commonly used methods by an exploratory researcher.2. Research design for conclusive studiesAlso referred to as the research design for the descriptive studies and is further divided as follows a. Case Study method Finds extensive use in commerce and industry. Very respectable method of teaching and research in management. Helps greatly in knowing the causes and the results of the incident of the phenomenon.b. Statistical method Also trying to find its place in commerce and industry. Act as method of correlation and regressions, analysis, chi square etc. Has been made very rigorous and sophisticated by coming up of the computers.3. Research design for experimental studies Explains the structure of an experiment. Involve plans for the testing of the causal hypothesis. Decides the number of observations to be taken and also the order in which experiments are to be carried out. Which randomization method to be used. Which mathematical model to be used for explaining the experiment.This research design can be further categorized into the following 1. Informal experimental design After only design. After only with control design. Before and after without control design. Before and after with control design.2. Formal experimental design Completely randomized design. Randomized block design. Latin square design. Factorial design.UNIT-2Data Collection MethodsData Collection is an important aspect of any type of research study. Inaccurate data collection can impact the results of a study and ultimately lead to invalid results.Data collection methods for impact evaluation vary along a continuum. At the one end of this continuum are quantatative methods and at the other end of the continuum are Qualitative methods for data collection.Quantitative and Qualitative Data collection methodsTheQuantitative data collection methods, rely on random sampling and structured data collection instruments that fit diverse experiences into predetermined response categories. They produce results that are easy to summarize, compare, and generalize.Quantitative research is concerned with testing hypotheses derived from theory and/or being able to estimate the size of a phenomenon of interest.Depending on the research question, participants may be randomly assigned to different treatments.If this is not feasible, the researcher may collect data on participant and situational characteristics in order to statistically control for their influence on the dependent, or outcome, variable.If the intent is to generalize from the research participants to a larger population, the researcher will employ probability sampling to select participants.Typical quantitative data gathering strategies include: Experiments/clinical trials. Observing and recording well-defined events (e.g., counting the number of patients waiting in emergency at specified times of the day). Obtaining relevant data from management information systems. Administering surveys with closed-ended questions (e.g., face-to face and telephone interviews, questionnaires etc).InterviewsIn Quantitative research(survey research),interviews are more structured than in Qualitative research. In a structured interview,the researcher asks a standard set of questions and nothing more.(Leedy and Ormrod, 2001)Face -to -face interviewshave a distinct advantage of enabling the researcher to establish rapport with potential partiocipants and therefor gain their cooperation.These interviews yield highest response rates in survey research.They also allow the researcher to clarify ambiguous answers and when appropriate, seek follow-up information. Disadvantages include impractical when large samples are involved time consuming and expensive.(Leedy and Ormrod, 2001)Telephone interviewsare less time consuming and less expensive and the researcher has ready access to anyone on the planet who hasa telephone.Disadvantages are that the response rate is not as high as the face-to- face interview but cosiderably higher than the mailed questionnaire.The sample may be biased to the extent that people without phones are part of the population about whom the researcher wants to draw inferences.Computer Assisted Personal Interviewing (CAPI):is a form of personal interviewing, but instead of completing a questionnaire, the interviewer brings along a laptop or hand-held computer to enter the information directly into the database. This method saves time involved in processing the data, as well as saving the interviewer from carrying around hundreds of questionnaires. However, this type of data collection method can be expensive to set up and requires that interviewers have computer and typing skills.QuestionnairesPaper-pencil-questionnairescan be sent to a large number of people and saves the researcher time and money.People are more truthful while responding to the questionnaires regarding controversial issues in particular due to the fact that their responses are anonymous. But they also have drawbacks.Majority of the people who receive questionnaires don't return them and those who do might not be representative of the originally selected sample.(Leedy and Ormrod, 2001)Web based questionnaires: A new and inevitably growing methodology is the use of Internet based research. This would mean receiving an e-mail on which you would click on an address that would take you to a secure web-site to fill in a questionnaire. This type of research is often quicker and less detailed.Some disadvantages of this method include the exclusion of people who do not have a computer or are unable to access a computer.Also the validity of such surveys are in question as people might be in a hurry to complete it and so might not give accurate responses. (http://www.statcan.ca/english/edu/power/ch2/methods/methods.htm)Questionnaires often make use of Checklist and rating scales.These devices help simplify and quantify people's behaviors and attitudes.Achecklistis a list of behaviors,characteristics,or other entities that te researcher is looking for.Either the researcher or survey participant simply checks whether each item on the list is observed, present or true or vice versa.Arating scaleis more useful when a behavior needs to be evaluated on a continuum.They are also known as Likert scales. (Leedy and Ormrod, 2001)Qualitative data collection methodsplay an important role in impact evaluation by providing information useful to understand the processes behind observed results and assess changes in peoples perceptions of their well-being.Furthermore qualitative methods can beused to improve the quality of survey-based quantitative evaluations by helping generate evaluation hypothesis; strengthening the design of survey questionnaires and expanding or clarifying quantitative evaluation findings. These methods are characterized by the following attributes: they tend to be open-ended and have less structured protocols (i.e., researchers may change the data collection strategy by adding, refining, or dropping techniques or informants) they rely more heavily on iteractive interviews; respondents may be interviewed several times to follow up on a particular issue, clarify concepts or check the reliability of data they use triangulation to increase the credibility of their findings (i.e., researchers rely on multiple data collection methods to check the authenticity of their results) generally their findings are not generalizable to any specific population, rather each case study produces a single piece of evidence that can be used to seek general patterns among different studies of the same issueRegardless of the kinds of data involved,data collection in a qualitative study takes a great deal of time.The researcher needs to record any potentially useful data thououghly,accurately, and systematically,using field notes,sketches,audiotapes,photographs and other suitable means.The data collection methods must observe the ethical principles of research.The qualitative methods most commonly used in evaluation can be classified in three broad categories: indepth interview observation methods document reviewThe following link provides more information on the above three methods.

Primary Data Collection:In primary data collection, you collect the data yourself using qualitative and quantitative methods. The key point here is that the data you collect is unique to you and your research and, until you publish, no one else has access to it.There are many methods of collecting primary data. The main methods include: questionnaires interviews focus group interviews observation case-studies scientific experimentsTen Steps to Design a QuestionnaireDesigning a questionnaire involves 10 main steps:1. Write a study protocolThis involves getting acquainted with the subject, making a literature review, decide on objectives, formulate a hypothesis, and define the main information needed to test the hypothesis.2. Draw a plan of analysisThis steps determineshowthe information defined in step 1should be analysed. The plan of analysis should contain the measures of association andthe statistical tests that you intend to use. In addition, you should draw dummy tables with the information of interest. The plan of analysis willhelp you to determine which type of results you want to obtain. An example of a dummy table is shown below.Exposurenr Cases (%)TotalAttack RateRR (CI95%)

Tomato salad

Chicken breast

3. Draw a list of the information neededFrom the plan of analysis you can draw a list of the information you need to collect from participants. In this step you should determine the type and format of variables needed.4. Design different parts of the questionnaireYou can start now designing different parts of the questionnaire using this list of needed information.5. Write the questionsKnowing the education and occupation level of the study population, ethnic or migration background, language knowledge and special sensitivities at this step is crucial at this stage. Please keep in mind that the questionnaire needs to be adapted to your study population. Please see "Format of Questions" section for more details.6. Decide on the order of the questions askedYou should start from easy, general and factual to difficult, particular or abstract questions. Please consider carefully where to place the most sensitive questions. They should be rather placed in the middle or towards the end of the questionnaire. Make sure, however, not to put the most important item last, since some people might not complete the interview.7. Complete the questionnaireAdd instructions for the interviewers and definitions of key words for participants. Insure a smooth flow from one topic to the next one (ex. "and now I will ask you some questions about your own health..."). Insert jumps between questions if some questions are only targeted at a subgroup of the respondents.8. Verify the content and style of the questionsVerify that each question answers to one of the objectives and all your objectives are covered by the questions asked. Delete questions that are not directly related to your objectives. Make sure that each question is clear, unambiguous, simple and short. Check the logical order and flow of the questions. Make sure the questionnaire is easy to read and has an clear layout. Please see theHints to Design a good Questionnairesection for more details.9.Conduct a pilot studyYou should always conduct a pilot study among the intended population before starting the study.Please see thePiloting Questionnairessection for more details.10. Refine your questionnaireDepending on the results of the pilot study, you will need toamend the questionnairebefore the main survey starts.Guidelines on how to design a good questionnaire Good questionnaire should not be too lengthy. Simple English should be used and the question shouldnt be difficult to answer. A good questionnaire requires sensible language, editing, assessment, and redrafting.Questionnaire Design Process1. State the information required-This will depend upon the nature of the problem, the purpose of the study and hypothesis framed. The target audience must be concentrated on.2. State the kind of interviewing technique-interviewing method can be telephone, mails, personal interview or electronic interview. Telephonic interview can be computer assisted. Personal interview can be conducted at respondents place or at mall or shopping place. Mail interview can take the form of mail panel. Electronic interview takes place either through electronic mails or through the internet.3. Decide the matter/content of individual questions-There are two deciding factors for this-a. Is the question significant? - Observe contribution of each question. Does the question contribute for the objective of the study?b. Is there a need for several questions or a single question? - Several questions are asked in the following cases: When there is a need for cross-checking When the answers are ambiguous When people are hesitant to give correct information.4. Overcome the respondents inability and unwillingness to answer-The respondents may be unable to answer the questions because of following reasons-. The respondent may not be fully informed. The respondent may not remember. He may be unable to express or articulateThe respondent may be unwilling to answer due to-. There may be sensitive information which may cause embarrassment or harm the respondents image.. The respondent may not be familiar with the genuine purpose. The question may appear to be irrelevant to the respondent. The respondent will not be willing to reveal traits like aggressiveness (For instance - if he is asked Do you hit your wife, sister, etc.)To overcome the respondents unwillingness to answer:iv. Place the sensitive topics at the end of the questionnaireiv. Preface the question with a statementiv. Use the third person technique (For example - Mark needed a job badly and he used wrong means to get it - Is it right?? Different people will have different opinions depending upon the situation)iv. Categorize the responses rather than asking a specific response figure (For example - Group for income levels 0-25000, 25000-50000, 50000 and above)1. Decide on the structure of the question-Questions can be of two types:e. Structured questions-These specify the set of response alternatives and the response format. These can be classified into multiple choice questions (having various response categories), dichotomous questions (having only 2 response categories such as Yes or No) and scales (discussed already).e. Unstructured questions-These are also known as open-ended question. No alternatives are suggested and the respondents are free to answer these questions in any way they like.1. Determine the question language/phrasing-If the questions are poorly worded, then either the respondents will refuse to answer the question or they may give incorrect answers. Thus, the words of the question should be carefully chosen. Ordinary and unambiguous words should be used. Avoid implicit assumptions, generalizations and implicit alternatives. Avoid biased questions. Define the issue in terms of who the questionnaire is being addressed to, what information is required, when is the information required, why the question is being asked, etc.1. Properly arrange the questions-To determine the order of the question, take decisions on aspects like opening questions (simple, interesting questions should be used as opening questions to gain co-operation and confidence of respondents), type of information (Basic information relates to the research issue, classification information relates to social and demographic characteristics, and identification information relates to personal information such as name, address, contact number of respondents), difficult questions (complex, embarrassing, dull and sensitive questions could be difficult), effect on subsequent questions, logical sequence, etc.1. Recognize the form and layout of the questionnaire-This is very essential for self-administered questionnaire. The questions should be numbered and pre-coded. The layout should be such that it appears to be neat and orderly, and not clattered.1. Reproduce the questionnaire-Paper quality should be good. Questionnaire should appear to be professional. The required space for the answers to the question should be sufficient. The font type and size should be appropriate. Vertical response questions should be used, for example:Do you use brand X of shampoo?. Yes. No Pre-test the questionnaire-The questionnaire should be pre-tested on a small number of respondents to identify the likely problems and to eliminate them. Each and every dimension of the questionnaire should be pre-tested. The sample respondents should be similar to the target respondents of the survey. Finalize the questionnaire-Check the final draft questionnaire. Ask yourself how much will the information obtained from each question contribute to the study. Make sure that irrelevant questions are not asked. Obtain feedback of the respondents on the questionnaire.Secondary DataSecondary data is the data that have been already collected by and readily available from other sources. Such data are cheaper and more quickly obtainable than the primary data and also may be available when primary data cannot be obtained at all.Advantages of Secondary data1. It is economical. It saves efforts and expenses.2. It is time saving.3. It helps to make primary data collection more specific since with the help of secondary data, we are able to make out what are the gaps and deficiencies and what additional information needs to be collected.4. It helps to improve the understanding of the problem.5. It provides a basis for comparison for the data that is collected by the researcher.Disadvantages of Secondary Data1. Secondary data is something that seldom fits in the framework of the marketing research factors. Reasons for its non-fitting are:-a. Unit of secondary data collection-Suppose you want information on disposable income, but the data is available on gross income. The information may not be same as we require.b. Class Boundaries may be different when units are same.Before 5 YearsAfter 5 Years

2500-50005000-6000

5001-75006001-7000

7500-100007001-10000

c. Thus the data collected earlier is of no use to you.2. Accuracy of secondary data is not known.3. Data may be outdated.Evaluation of Secondary DataBecause of the above mentioned disadvantages of secondary data, we will lead to evaluation of secondary data. Evaluation means the following four requirements must be satisfied:-1. Availability-It has to be seen that the kind of data you want is available or not. If it is not available then you have to go for primary data.2. Relevance-It should be meeting the requirements of the problem. For this we have two criterion:-a. Units of measurement should be the same.b. Concepts used must be same and currency of data should not be outdated.3. Accuracy-In order to find how accurate the data is, the following points must be considered: -a. Specification and methodology used;b. Margin of error should be examined;c. The dependability of the source must be seen.4. Sufficiency-Adequate data should be available.Robert W Joselyn has classified the above discussion into eight steps. These eight steps are sub classified into three categories. He has given a detailed procedure for evaluating secondary data.1. Applicability of research objective.2. Cost of acquisition.3. Accuracy of data.

Measurement scalesA topic which can create a great deal of confusion in social and educational research is that of types of scales used in measuring behaviour.It is critical because it relates to the types of statistics you can use to analyse your data. An easy way to have a paper rejected is to have used either an incorrect scale/statistic combination or to have used a low powered statistic on a high powered set of data. Nominal Ordinal Interval Ratio

NominalThe lowest measurement level you can use, from a statistical point of view, is a nominal scale.A nominal scale, as the name implies, is simply some placing of data into categories, without any order or structure.A physical example of a nominal scale is the terms we use for colours. The underlying spectrum is ordered but the names are nominal.In research activities a YES/NO scale is nominal. It has no order and there is no distance between YES and NO.and statisticsThe statistics which can be used with nominal scales are in the non-parametric group. The most likely ones would be:modecrosstabulation - with chi-squareThere are also highly sophisticated modelling techniques available for nominal data.

OrdinalAn ordinal scale is next up the list in terms of power of measurement.The simplest ordinal scale is a ranking. When a market researcher asks you to rank 5 types of beer from most flavourful to least flavourful, he/she is asking you to create an ordinal scale of preference.There is no objective distance between any two points on your subjective scale. For you the top beer may be far superior to the second prefered beer but, to another respondant with the same top and second beer, the distance may be subjectively small.An ordinal scale only lets you interpret gross order and not the relative positional distances.and statisticsOrdinal data would use non-parametric statistics. These would include:Median and moderank order correlationnon-parametric analysis of varianceModelling techniques can also be used with ordinal data.

IntervalThe standard survey rating scale is an interval scale.When you are asked to rate your satisfaction with a piece of software on a 7 point scale, from Dissatisfied to Satisfied, you are using an interval scale.It is an interval scale because it is assumed to have equidistant points between each of the scale elements. This means that we can interpret differences in the distance along the scale. We contrast this to an ordinal scale where we can only talk about differences in order, not differences in the degree of order.Interval scales are also scales which are defined by metrics such as logarithms. In these cases, the distances are note equal but they are strictly definable based on the metric used.and statisticsInterval scale data would use parametric statistical techniques:Mean and standard deviationCorrelation - rRegressionAnalysis of varianceFactor analysisPlus a whole range of advanced multivariate and modelling techniquesRememberthat you can use non-parametric techniques with interval and ratio data. But non-paramteric techniques are less powerful than the parametric ones.

RatioA ratio scale is the top level of measurement and is not often available in social research.The factor which clearly defines a ratio scale is that it has a true zero point.The simplest example of a ratio scale is the measurement of length (disregarding any philosophical points about defining how we can identify zero length).The best way to contrast interval and ratio scales is to look at temperature. The Centigrade scale has a zero point but it is an arbitrary one. The Farenheit scale has its equivalent point at -32o. (Physicists would probably argue that Absolute Zero is the zero point for temperature but this is a theoretical concept.) So, even though temperture looks as if it would be a ratio scale it is an interval scale. Currently, we cannot talk aboutno temperature- and this would be needed if it were a ration scale.and statisticsThe same as for Interval dataComparative scaling techniques Pairwise comparisonscale a respondent is presented with two items at a time and asked to select one (example: Do you prefer Pepsi or Coke?). This is an ordinal level technique when a measurement model is not applied. Krus and Kennedy (1977) elaborated the paired comparison scaling within their domain-referenced model. TheBradleyTerryLuce (BTL) model(Bradley and Terry, 1952; Luce, 1959) can be applied in order to derive measurements provided the data derived from paired comparisons possess an appropriate structure. Thurstone'sLaw of comparative judgmentcan also be applied in such contexts. Rasch modelscaling respondents interact with items and comparisons are inferred between items from the responses to obtain scale values. Respondents are subsequently also scaled based on their responses to items given the item scale values. The Rasch model has a close relation to the BTL model. Rank-ordering a respondent is presented with several items simultaneously and asked to rank them (example: Rate the following advertisements from 1 to 10.). This is an ordinal level technique. Bogardus social distance scale measures the degree to which a person is willing to associate with a class or type of people. It asks how willing the respondent is to make various associations. The results are reduced to a single score on a scale. There are also non-comparative versions of this scale. Q-Sort Up to 140 items are sorted into groups based on rank-order procedure. Guttman scale This is a procedure to determine whether a set of items can be rank-ordered on a unidimensional scale. It utilizes the intensity structure among several indicators of a given variable. Statements are listed in order of importance. The rating is scaled by summing all responses until the first negative response in the list. The Guttman scale is related to Rasch measurement; specifically, Rasch models bring the Guttman approach within a probabilistic framework. Constant sum scale a respondent is given a constant sum of money, script, credits, or points and asked to allocate these to various items (example: If you had 100 Yen to spend on food products, how much would you spend on product A, on product B, on product C, etc.). This is an ordinal level technique. Magnitude estimation scale In apsychophysicsprocedure invented byS. S. Stevenspeople simply assign numbers to the dimension of judgment. The geometric mean of those numbers usually produces apower lawwith a characteristic exponent. Incross-modality matchinginstead of assigning numbers, people manipulate another dimension, such as loudness or brightness to match the items. Typically the exponent of the psychometric function can be predicted from the magnitude estimation exponents of each dimension.Non-comparative scaling techniques[edit] Continuous rating scale(also called the graphic rating scale) respondents rate items by placing a mark on a line. The line is usually labeled at each end. There are sometimes a series of numbers, called scale points, (say, from zero to 100) under the line. Scoring and codification is difficult. Likert scale Respondents are asked to indicate the amount of agreement or disagreement (from strongly agree to strongly disagree) on a five- to nine-point response scale (not to be confused with a Likert scale). The same format is used for multiple questions. It is the combination of these questions that forms the Likert scale. This categorical scaling procedure can easily be extended to amagnitude estimationprocedure that uses the full scale of numbers rather than verbal categories. Phrase completion scales Respondents are asked to complete a phrase on an 11-point response scale in which 0 represents the absence of the theoretical construct and 10 represents the theorized maximum amount of the construct being measured. The same basic format is used for multiple questions. Semantic differential scale Respondents are asked to rate on a 7 point scale an item on various attributes. Each attribute requires a scale with bipolar terminal labels. Stapel scale This is a unipolar ten-point rating scale. It ranges from +5 to 5 and has no neutral zero point. Thurstone scale This is a scaling technique that incorporates the intensity structure among indicators. Mathematically derived scale Researchers infer respondents evaluations mathematically. Two examples aremulti dimensional scalingandconjoint analysis.Types of Sampling DesignsWhen conducting research, it is almost always impossible to study the entire population that you are interested in. For example, if you were studying political views amongcollege students in the United States, it would be nearly impossible to survey every single college student across the country. If you were to survey the entire population, it would be extremely timely and costly. As a result, researchers usesamplesas a way to gather data.A sample is a subset of thepopulationbeing studied.It represents the larger population and is used to draw inferences about that population. It is a research technique widely used in the social sciences as a way to gather information about a population without having to measure the entire population.There are several different types and ways of choosing a sample from a population, from simple to complex.Non-probability Sampling TechniquesNon-probability samplingis a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.Reliance On Available Subjects.Relying on available subjects, such as stopping people on a street corner as they pass by, is one method of sampling, although it is extremely risky and comes with many cautions. This method, sometimes referred to as aconvenience sample, does not allow the researcher to have any control over the representativeness of the sample. It is only justified if the researcher wants to study the characteristics of people passing by the street corner at a certain point in time or if other sampling methods are not possible.The researcher must also take caution to not use results from a convenience sample to generalize to a wider population.Purposive or Judgmental Sample.A purposive, or judgmental, sample is one that is selected based on the knowledge of a population and the purpose of the study. For example, if a researcher is studying the nature of school spirit as exhibited at a school pep rally, he or she might interview people who did not appear to be caught up in the emotions of the crowd or students who did not attend the rally at all. In this case, the researcher is using a purposive sample because those being interviewed fit a specific purpose or description.Snowball Sample.A snowball sample is appropriate to use in research when the members of a population are difficult to locate, such as homeless individuals, migrant workers, orundocumented immigrants. A snowball sample is one in which the researcher collects data on the few members of the target population he or she can locate, then asks those individuals to provide information needed to locate other members of that population whom they know. For example, if a researcher wishes to interview undocumented immigrants from Mexico, he or she might interview a few undocumented individuals that he or she knows or can locate and would then rely on those subjects to help locate more undocumented individuals. This process continues until the researcher has all the interviews he or she needs or until all contacts have been exhausted.Quota Sample.A quota sample is one in which units are selected into a sample on the basis of pre-specified characteristics so that the total sample has the same distribution of characteristics assumed to exist in the population being studied. For example, if you a researcher conducting a national quota sample, you might need to know what proportion of the population is male and what proportion is female as well as what proportions of each gender fall into different age categories, race or ethnic categories, educational categories, etc. The researcher would then collect a sample with the same proportions as the national population.Probability Sampling TechniquesProbability sampling is a sampling technique where the samples are gathered in a process that gives all the individuals in the population equal chances of being selected.Simple Random Sample.Thesimple random sampleis the basic sampling method assumed instatistical methodsand computations. To collect a simple random sample, each unit of the target population is assigned a number. A set ofrandom numbersis then generated and the units having those numbers are included in the sample. For example, lets say you have a population of 1,000 people and you wish to choose a simple random sample of 50 people. First, each person is numbered 1 through 1,000. Then, you generate a list of 50 random numbers (typically with a computer program) and those individuals assigned those numbers are the ones you include in the sample.Systematic Sample.In asystematic sample, the elements of the population are put into a list and then everykth element in the list is chosen (systematically) for inclusion in the sample. For example, if the population of study contained 2,000 students at a high school and the researcher wanted a sample of 100 students, the students would be put into list form and then every 20th student would be selected for inclusion in the sample. To ensure against any possible human bias in this method, the researcher should select the first individual at random. This is technically called asystematic sample with a random start.Stratified Sample.Astratified sampleis a sampling technique in which the researcher divided the entire target population into different subgroups, or strata, and then randomly selects the final subjects proportionally from the different strata. Thistype of samplingis used when the researcher wants to highlight specificsubgroupswithin the population. For example, to obtain a stratified sample of university students, the researcher would first organize the population by college class and then select appropriate numbers of freshmen, sophomores, juniors, and seniors. This ensures that the researcher has adequate amounts of subjects from each class in the final sample.Cluster Sample.Cluster samplingmay be used when it is either impossible or impractical to compile an exhaustive list of the elements that make up the target population. Usually, however, thepopulation elementsare already grouped into subpopulations and lists of those subpopulations already exist or can be created. For example, lets say the target population in a study was church members in the United States. There is no list of all church members in the country. The researcher could, however, create a list of churches in the United States, choose a sample of churches, and then obtain lists of members from those churches.10 Interviewing RulesIn the current job market, you'd better have your act together, or you won't stand a chance against the competition. Check yourself on these 10 basic points before you go on that all-important interview.1. Do Your ResearchResearching the companybefore the interview and learning as much as possible about its services, products, customers and competition will give you an edge in understanding and addressing the company's needs. The more you know about the company and what it stands for, the better chance you have ofselling yourself in the interview. You also should find out about thecompany's cultureto gain insight into your potential happiness on the job.2. Look SharpSelectwhat to wear to the interview. Depending on the industry and position, get out your best interview clothes and check them over for spots and wrinkles. Even if the company has a casual environment, you don't want to look like you slept in your outfit. Above all, dress for confidence. If you feel good, others will respond to you accordingly.3. Be PreparedBring along a folder containing extra copies of your resume, a copy of yourreferencesand paper to take notes. You should also have questions prepared to ask at the end of the interview. For extra assurance, print a copy of Monster's handyinterview take-along checklist.4. Be on TimeNever arrive late to an interview. Allow extra time to arrive early in the vicinity, allowing for factors like getting lost. Enter the building 10 to 15 minutes before the interview.

5. Show EnthusiasmA firmhandshakeand plenty of eye contact demonstrate confidence. Speak distinctly in a confident voice, even though you may feel shaky.6. ListenOne of the most neglectedinterview skillsislistening. Make sure you are not only listening, but also reading between the lines. Sometimes what is not said is just as important as what is said.7. Answer the Question AskedCandidates often don't think about whether they are actually answering the questions their interviewers ask. Make sure you understand what is being asked, and get further clarification if you are unsure.8. Give Specific ExamplesOne specific example of your background is worth 50 vague stories. Prepare your stories before the interview.Give examplesthat highlight your successes and uniqueness. Your past behavior can indicate your future performance.9. Ask QuestionsMany interviewees don't ask questions and miss the opportunity to find out valuable information. Thequestions you askindicate your interest in the company or job.10. Follow UpWhetherit's through email or regular mail, theinterview follow-upis one more chance to remind the interviewer of all the valuable traits you bring to the job and company. Don't miss this last chance to market yourself.It is important to appear confident and cool for the interview. One way to do that is to be prepared to the best of your ability. There is no way to predict what an interview holds, but by following these important rules you will feel less anxious and will be ready to positively present yourself.UNIT-3Data processing

Data processingis, broadly, "thecollectionand manipulation of items of data to producemeaningfulinformation."[1]In this sense it can be considered a subset ofinformation processing, "the change (processing) of information in any manner detectable by an observer."[note 1]The term is often used more specifically in the context of a business or other organization to refer to the class of commercial data processing applications.[2]ContentsData processing functions[edit]Data processing may involve various processes, including: Validation Ensuring that supplied data is "clean, correct and useful" Sorting "arranging items in some sequence and/or in different sets." Summarization reducing detail data to its main points. Aggregation combining multiple pieces of data. Analysis the "collection, organization, analysis, interpretation and presentation of data.". Reporting list detail or summary data or computed information. Classification separates data into various categories.HistoryTheUnited States Census Bureauillustrates the evolution of data processing from manual through electronic procedures.Manual data processing[edit]Although widespread use of the termdata processingdates only from the nineteen-fifties[3]data processing functions have been performed manually for millennia. For examplebookkeepinginvolves functions such as posting transactions and producing reports like thebalance sheetand thecash flow statement. Completely manual methods were augmented by the application ofmechanicalor electroniccalculators. A person whose job it was to perform calculations manually or using a calculator was called a "computer."The1850 United States Censusschedule was the first to gather data by individual rather thanhousehold. A number of questions could be answered by making a check in the appropriate box on the form. From 1850 through 1880 the Census Bureau employed "a system of tallying, which, by reason of the increasing number of combinations of classifications required, became increasingly complex. Only a limited number of combinations could be recorded in one tally, so it was necessary to handle the schedules 5 or 6 times, for as many independent tallies."[4]"It took over 7 years to publish the results of the 1880 census"[5]using manual processing methods.Automatic data processing[edit]The termautomatic data processingwas applied to operations performed by means ofunit record equipment, such asHerman Hollerith's application ofpunched cardequipment for the1890 United States Census. "Using Hollerith's punchcard equipment, the Census Office was able to complete tabulating most of the 1890 census data in 2 to 3 years, compared with 7 to 8 years for the 1880 census. ... It is also estimated that using Herman Hollerith's system saved some $5 million in processing costs"[5](in 1890 dollars) even with twice as many questions as in 1880.Electronic data processing[edit]Computerized data processing, orElectronic data processingrepresents the further evolution, with the computer taking the place of several independent pieces of equipment. The Census Bureau first made limited use ofelectronic computersfor the1950 United States Census, using aUNIVAC Isystem,[4]delivered in 1952.Further evolution[edit]"Data processing (DP)" has also previously been used to refer to the department within an organization responsible for the operation of data processing applications.[6]The termdata processinghas mostly been subsumed under the newer and somewhat more general terminformation technology(IT).[citation needed]"Data processing" has acquired a negative connotation, suggesting use of older technologies. As an example, in 1996 theData Processing Management Association(DPMA) changed its name to theAssociation of Information Technology Professionals. Nevertheless, the terms are roughly synonymous.Applications[edit]Commercial data processing[edit]Main article:Electronic data processingCommercial data processing involves a large volume of input data, relatively few computational operations, and a large volume of output. For example, an insurance company needs to keep records on tens or hundreds of thousands of policies, print and mail bills, and receive and post payments.Data analysis[edit]Main article:Data analysisIn a science or engineering field, the termsdata processingandinformation systemsare considered too broad, and the more specialized termdata analysisis typically used. Data analysis makes use of specialized and highly accurate algorithms and statistical calculations that are less often observed in the typical general business environment.One divergence of culture between data processing and data analysis is shown by the numerical representations generally used; In data processing, measurements are typically stored asintegers,fixed-pointorbinary-coded decimalrepresentations of numbers, whereas the majority of measurements in data analysis are stored asfloating-pointrepresentations of rational numbers.For data analysis, packages likeSPSSorSAS, or their free counterparts such asDAP,gretlorPSPPare often used.RESEARCH METHODOLOGY

Processing and Analysis of Data

The data, after collection, has to be processed and analysed in accordance with the outline laid down for the purpose at the time of developing the research plan. This is essential for a scientific study and for ensuring that we have all relevant data for making contemplated comparisons and analysis. Technically speaking, processing implies editing, coding, classification and tabulation of collected data so that they are amenable to analysis. The term analysis refers to the computation of certain measures along with searching for patterns of relationship that exist among data-groups. Thus, in the process of analysis, relationships or differences supporting or conflicting with original or new hypotheses should be subjected to statistical tests of significance to determine with what validity data can be said to indicate any conclusions.1 But there are persons (Selltiz, Jahoda and others) who do not like to make difference between processing and analysis. They opine that analysis of data in a general way involves a number of closely related operations which are performed with the purpose of summarising the collected data and organising these in such a manner that they answer the research question(s). We, however, shall prefer to observe the difference between the two terms as stated here in order to understand their implications more clearly.PROCESSING OPERATIONS1.

Editing:Editing of data is a process of examining the collected raw data (specially in surveys) to detect errors and omissions and to correct these when possible. As a matter of fact, editing involves a careful scrutiny of the completed questionnaires and/or schedules. Editing is done to assure that the data are accurate, consistent with other facts gathered, uniformly entered, as completed as possible and have been well arranged to facilitate coding and tabulation.With regard to points or stages at which editing should be done, one can talk of field editing and central editing. Field editing consists in the review of the reporting forms by the investigator for completing (translating or rewriting) what the latter has written in abbreviated and/or in illegible form at the time of recording the respondents responses. This type of editing is necessary in view of the fact that individual writing styles often can be difficult for others to decipher. This sort of editing should be done as soon as possible after the interview, preferably on the very day or on the next day. While doing field editing, the investigator must restrain himself and must not correct errors of omission by simply guessing what the informant would have said if the question had been asked.Central editing should take place when all forms or schedules have been completed and returned to the office. This type of editing implies that all forms should get a thorough editing by a single editor in a small study and by a team of editors in case of a large inquiry. Editor(s) may correct the obvious errors such as an entry in the wrong place, entry recorded in months when it should have been recorded in weeks, and the like. In case of inappropriate on missing replies, the editor can sometimes determine the proper answer by reviewing the other information in the schedule. A t times, the respondent can be contacted for clarification. The editor must strike out the answer if the same is inappropriate and he has no basis for determining the correct answer or the response. In such a case an editing entry of no answer is called for. All the wrong replies, which are quite obvious, must be dropped from the final results, especially in the context of mail surveys.Editors must keep in view several points while performing their work:They should be familiar with instructions given to the interviewers and coders as well as with the editing instructions supplied to them for the purpose.While crossing out an original entry for one reason or another, they should just draw a single line on it so that the same may remain legible. They must make entries (if any) on the form in some distinctive colur and that too in a standardised form. They should initial all answers which they change or supply. Editors initials and the date of editing should be placed on each completed form or schedule.2. Coding:Coding refers to the process of assigning numerals or other symbols to answers so that responses can be put into a limited number of categories or classes. Such classes should be appropriate to the research problem under consideration. They must also possess the characteristic of exhaustiveness (i.e., there must be a class for every data item) and also that of mutual exclusively which means that a specific answer can be placed in one and only one cell in a given category set. Another rule to be observed is that of unidimensionality by which is meant that every class is defined in terms of only one concept.Coding is necessary for efficient analysis and through it the several replies may be reduced to a small number of classes which contain the critical information required for analysis. Coding decisions should usually be taken at the designing stage of the questionnaire. This makes it possible to precode the questionnaire choices and which in turn is helpful for computer tabulation as one can straight forward key punch from the original questionnaires. But in case of hand coding some standard method may be used. One such standard method is to code in the margin with a coloured pencil. The other method can be to transcribe the data from the questionnaire to a coding sheet. Whatever method is adopted, one should see that coding errors are altogether eliminated or reduced to the minimum level.3. Classification:Most research studies result in a large volume of raw data which must be reduced into homogeneous groups if we are to get meaningful relationships. This fact necessitates classification of data which happens to be the process of arranging data in groups or classes on the basis of common characteristics. Data having a common characteristic are placed in one class and in this way the entire data get divided into a number of groups or classes. Classification can be one of the following two types, depending upon the nature of the phenomenon involved: Classification according to attributes: As stated above, data are classified on the basis of common characteristics which can either be descriptive (such as literacy, sex, honesty, etc.) or numerical (such as weight, height, income, etc.). Descriptive characteristics refer to qualitative phenomenon which cannot be measured quantitatively; only their presence or absence in an individual item can be noticed. Data obtained this way on the basis of certain attributes are known as statistics of attributes and their classification is said to be classification according to attributes.Such classification can be simple classification or manifold classification. In simple classification we consider only one attribute and divide the universe into two classesone class consisting of items possessing the given attribute and the other class consisting of items which do not possess the given attribute. But in manifold classification we consider two or more attributes simultaneously, and divide that data into a number of classes (total number of classes of final order is given by 2n, where n = number of attributes considered). Whenever data are classified according to attributes, the researcher must see that the attributes are defined in such a manner that there is least possibility of any doubt/ambiguity concerning the said attributes. Classification according to class-intervals: Unlike descriptive characteristics, the numerical characteristics refer to quantitative phenomenon which can be measured through some statistical units. Data relating to income, production, age, weight, etc. come under this category. Such data are known as statistics of variables and are classified on the basis of class intervals. For instance, persons whose incomes, say, are within Rs 201 to Rs 400 can form one group, those whose incomes are within Rs 401 to Rs 600 can form another group and so on. In this way the entire data may be divided into a number of groups or classes or what are usually called, class-intervals. Each group of class-interval, thus, has an upper limit as well as a lower limit which are known as class limits. The difference between the two class limits is known as class magnitude. We may have classes with equal class magnitudes or with unequal class magnitudes. The number of items which fall in a given class is known as the frequency of the given class. All the classes or groups, with their respective frequencies taken together and put in the form of a table, are described as group frequency distribution or simply frequency distribution. Classification according to class intervals usually involves the following three main problems:How may classes should be there? What should be their magnitudes?There can be no specific answer with regard to the number of classes. The decision about this calls for skill and experience of the researcher. However, the objective should be to display the data in such a way as to make it meaningful for the analyst. Typically, we may have 5 to 15 classes. With regard to the second part of the question, we can say that, to the extent possible, class-intervals should be of equal magnitudes, but in some cases unequal magnitudes may result in better classification. Hence researchers objective judgement plays an important part in this connection. Multiples of 2, 5 and 10 are generally preferred while determining class magnitudes. Some statisticians adopt the following formula, suggested by H.A. Sturges, determining the size of class interval:i = R/(1 + 3.3 log N)wherei = size of class interval;R = Range (i.e., difference between the values of the largest item and smallest item among the given items);N = Number of items to be grouped.It should also be kept in mind that in case one or two or very few items have very high or very low values, one may use what are known as open-ended intervals in the overall frequency distribution. Such intervals may be expressed like under Rs 500 or Rs 10001 and over. Such intervals are generally not desirable, but often cannot be avoided. The researcher must always remain conscious of this fact while deciding the issue of the total number of class intervals in which the data are to be classified.How to choose class limits?While choosing class limits, the researcher must take into consideration the criterion that the mid-point (generally worked out first by taking the sum of the upper limit and lower limit of a class and then divide this sum by 2) of a class-interval and the actual average of items of that class interval should remain as close to each other as possible. Consistent with this, the class limits should be located at multiples of 2, 5, 10, 20, 100 and such other figures. Class limits may generally be stated in any of the following forms:Exclusive type class intervals: They are usually stated as follows:1020203030404050The above intervals should be read as under:10 and under 2020 and under 3030 and under 4040 and under 50Thus, under the exclusive type class intervals, the items whose values are equal to the upper limit of a class are grouped in the next higher class. For example, an item whose value is exactly 30 would be put in 3040 class interval and not in 2030 class interval. In simple words, we can say that under exclusive type class intervals, the upper limit of a class interval is excluded and items with values less than the upper limit (but not less than the lower limit) are put in the given class interval.Inclusive type class intervals: They are usually stated as follows:1120213031404150In inclusive type class intervals the upper limit of a class interval is also included in the concerning class interval. Thus, an item whose value is 20 will be put in 1120 class interval. The stated upper limit of the class interval 1120 is 20 but the real limit is 20.99999 and as such 1120 class interval really means 11 and under 21.When the phenomenon under consideration happens to be a discrete one (i.e., can be measured and stated only in integers), then we should adopt inclusive type classification. But when the phenomenon happens to be a continuous one capable of being measured in fractions as well, we can use exclusive type class intervals.How to determine the frequency of each class?This can be done either by tally sheets or by mechanical aids. Under the technique of tally sheet, the class-groups are written on a sheet of paper (commonly known as the tally sheet) and for each item a stroke (usually a small vertical line) is marked against the class group in which it falls. The general practice is that after every four small vertical lines in a class group, the fifth line for the item falling in the same group, is indicated as horizontal line through the said four lines and the resulting flower (IIII) represents five items. All this facilitates the counting of items in each one of the class groups. An illustrative tally sheet can be shown as under:An Illustrative Tally Sheet for Determining the Number of 70 Families in Different Income Groups

Alternatively, class frequencies can be determined, specially in case of large inquires and surveys, by mechanical aids i.e., with the help of machines viz., sorting machines that are available for the purpose. Some machines are hand operated, whereas other work with electricity. There are machines which can sort out cards at a speed of something like 25000 cards per hour. This method is fast but expensive.4. Tabulation:When a mass of data has been assembled, it becomes necessary for the researcher to arrange the same in some kind of concise and logical order. This procedure is referred to as tabulation. Thus, tabulation is the process of summarising raw data and displaying the same in compact form (i.e., in the form of statistical tables) for further analysis. In a broader sense, tabulation is an orderly arrangement of data in columns and rows. Tabulation is essential because of the following reasons.4. It conserves space and reduces explanatory and descriptive statement to a minimum.4. It facilitates the process of comparison.4. It facilitates the summation of items and the detection of errors and omissions.4. It provides a basis for various statistical computations.Tabulation can be done by hand or by mechanical or electronic devices. The choice depends on the size and type of study, cost considerations, time pressures and the availaibility of tabulating machines or computers. In relatively large inquiries, we may use mechanical or computer tabulation if other factors are favourable and necessary facilities are available. Hand tabulation is usually preferred in case of small inquiries where the number of questionnaires is small and they are of relatively short length. Hand tabulation may be done using the direct tally, the list and tally or the card sort and count methods. When there are simple codes, it is feasible to tally directly from the questionnaire. Under this method, the codes are written on a sheet of paper, called tally sheet, and for each response a stroke is marked against the code in which it falls. Usually after every four strokes against a particular code, the fifth response is indicated by drawing a diagonal or horizontal line through the strokes. These groups of five are easy to count and the data are sorted against each code conveniently. In the listing method, the code responses may be transcribed onto a large work-sheet, allowing a line for each questionnaire. This way a large number of questionnaires can be listed on one work sheet. Tallies are then made for each question. The card sorting method is the most flexible hand tabulation. In this method the data are recorded on special cards of convenient size and shape with a series of holes. Each hole stands for a code and when cards are stacked, a needle passes through particular hole representing a particular code. These cards are then separated and counted. In this way frequencies of various codes can be found out by the repetition of this technique. We can as well use the mechanical devices or the computer facility for tabulation purpose in case we want quick results, our budget permits their use and we have a large volume of straight forward tabulation involving a number of cross-breaks.Tabulation may also be classified as simple and complex tabulation. The former type of tabulation gives information about one or more groups of independent questions, whereas the latter type of tabulation shows the division of data in two or more categories and as such is deigned to give information concerning one or more sets of inter-related questions. Simple tabulation generally results in one-way tables which supply answers to questions about one characteristic of data only. As against this, complex tabulation usually results in two-way tables (which give information about two inter-related characteristics of data), three-way tables (giving information about three interrelated characteristics of data) or still higher order tables, also known as manifold tables, which supply information about several interrelated characteristics of data. Two-way tables, three-way tables or manifold tables are all examples of what is sometimes described as cross tabulation.Data analysisResearch ProcessDefinition:Researchers who are attempting to answer a research question employ the research process. Though presented in a liner format, in practice the process of research can be less straightforward. This said, researchers attempt to follow the process and use it to present their research findings in research reports and journal articles.Identifying research problemsResearch problems need to be researchable and can be generated from practice, but must be grounded in the existing literature. They may be local, national or international problems, that need addressing in order to develop the existing evidence base.Searching the existing literature baseA thorough search of the literature using data bases, internet, text and expert sources should support the need to research the problem. This should be broad and in depth, showing a comprehensive search of the problem area.Critical appraisal of the literatureA critical appraisal framework should be employed to review the literature in a systematic way.Developing the questions/ and or hypothesisA more specific research question and /or hypothesis may be developed from the literature review, that provides the direction for the research, which aims to provide answers to the question /hypothesis posed.Theoretical baseThe research may employ a theoretical base to examining the problem, especially seen in masters level research and in many research studies. In the health and social care field this might come from the social sciences, psychology or anthropology.Sampling strategiesSampling is the method for selecting people, events or objects for study in research. Non-probability and probability sampling strategies enable the researcher to target data collection techniques. These may need to be of a specific size (sometimes determined by a power calculation) or composition.Data collection techniquesThese are the tools and approaches used to collect data to answer the research question /hypothesis. More than one technique can be employed, the commonest are questionnaires and interviews.Approaches to qualitative and quantitative data analysisThis component is more fully explored in the site, but can involve qualitative and quantitative approaches, dependent on the type of data collected.Interpretation of resultsThe results are interpreted, drawing conclusions and answering the research question /hypothesis. Implications for practice and further research are drawn, which acknowledge the limitations of the research.Dissemination of researchThe research and results can be presented through written reports, articles, papers and conferences, both in print and electronic forms.5 Steps To Data ProcessingData is an integral part of all business processes. It is the invisible backbone that supports all the operations and activities within a business. Without access to relevant data, businesses would get completely paralyzed. This is because quality data helps formulate effective business strategies and fruitful business decisions.

Therefore, the quality of data should be maintained in good condition in order to facilitate smooth business proceedings. In order to enhance business proceedings, data should be made available in all possible forms in order to increase the accessibility of the same.

Data processing refers to the process of converting data from one format to another. It transforms plain data into valuable information and information into data. Clients can supply data in a variety of forms, be it .xls sheets, audio devices, or plain printed material. Data processing services take the raw data and process it accordingly to produce sensible information. The various applications of data processing can convert raw data into useful information that can be used further for business processes.

Companies and organizations across the world make use of data processing services in order to facilitate their market research interests. Data consists of facts and figures, based on which important conclusions can be drawn. When companies and organizations have access to useful information, they can utilize it for strategizing powerful business moves that would eventually increase the company revenue and decrease the costs, thus expanding the profit margins. Data processing ensures that the data is presented in a clean and systematic manner and is easy to understand and be used for further purposes.

Here are the 5 steps that are included in data processing:

EditingThere is a big difference between data and useful data. While there are huge volumes of data available on the internet, useful data has to be extracted from the huge volumes of the same. Extracting relevant data is one of the core procedures of data processing. When data has been accumulated from various sources, it is edited in order to discard the inappropriate data and retain relevant data.

CodingEven after the editing process, the available data is not in any specific order. To make it more sensible and usable for further use, it needs to be aligned into a particular system. The method of coding ensures just that and arranges data in a comprehendible format. The process is also known as netting or bucketing.

Data EntryAfter the data has been properly arranged and coded, it is entered into the software that performs the eventual cross tabulation. Data entry professionals do the task efficiently.

ValidationAfter the cleansing phase, comes the validation process. Data validation refers to the process of thoroughly checking the collected data to ensure optimal quality levels. All the accumulated data is double checked in order to ensure that it contains no inconsistencies and is utterly relevant.

TabulationThis is the final step in data processing. The final product i.e. the data is tabulated and arranged in a systematic format so that it can be further analyzed.

All these processes make up the complete data processing activity which ensures the said data is available for access.Data Analysisis the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data..While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase (Savenye, Robinson, 2004). The form of the analysis is determined by the specific qualitative approach taken (field study, ethnography content analysis, oral history, biography,unobtrusiveresearch) and the form of the data (field notes, documents, audiotape, videotape).An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well.Considerations/issues in data analysis

There are a number of issues that researchers should be cognizant of with respect to data analysis. These include: Having the necessary skills to analyze Concurrently selecting data collection methods and appropriate analysis Drawing unbiased inference Inappropriate subgroup analysis Following acceptable norms for disciplines Determiningstatistical significance Lack of clearly defined and objectiveoutcome measurements Providing honest and accurate analysis Manner of presenting data Environmental/contextual issues Data recording method Partitioning textwhen analyzing qualitative data Training of staff conducting analyses Reliability and Validity Extent of analysisHaving necessary skills to analyze

A tacit assumption of investigators is that they have received training sufficient to demonstrate a high standard of research practice. Unintentional scientific misconduct' is likely the result of poor instruction and follow-up. A number of studies suggest this may be the case more often than believed (Nowak, 1994; Silverman, Manson, 2003). For example, Sica found that adequate training of physicians in medical schools in the proper design, implementation and evaluation of clinical trials is abysmally small (Sica, cited in Nowak, 1994). Indeed, a single course in biostatistics is the most that is usually offered (Christopher Williams, cited in Nowak, 1994).A common practice of investigators is to defer the selection of analytic procedure to a research team statistician. Ideally, investigators should have substantially more than a basic understanding of the rationale for selecting one method of analysis over another. This can allow investigators to better supervise staff who conduct the data analyses process and make informed decisions

Concurrently selecting data collection methods and appropriate analysis

While methods of analysis may differ by scientific discipline, the optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought. According to Smeeton and Goda (2003), Statistical advice should be obtained at the stage of initial planning of an investigation so that, for example, the method of sampling and design of questionnaire are appropriate.

Drawing unbiased inference

The chief aim of analysis is to distinguish between an event occurring as either reflecting a true effect versus a false one. Any bias occurring in the collection of the data, or selection of method of analysis, will increase the likelihood of drawing a biased inference. Bias can occur when recruitment of study participants falls below minimum number required to demonstrate statistical power or failure to maintain a sufficient follow-up period needed to demonstrate an effect (Altman, 2001).

Inappropriate subgroup analysis

When failing to demonstrate statistically different levels between treatment groups, investigators may resort to breaking down the analysis to smaller and smaller subgroups in order to find a difference. Although this practice may not inherently be unethical, these analyses should be proposed before beginning the study even if the intent is exploratory in nature. If it the study is exploratory in nature, the investigator should make this explicit so that readers understand that the research is more of a hunting expedition rather than being primarily theory driven.Although a researcher may not have a theory-based hypothesis for testing relationships between previously untested variables, a theory will have to be developed to explain an unanticipated finding. Indeed, in exploratory science, there are no a priori hypotheses therefore there are no hypothetical tests. Although theories can often drive the processes used in the investigation of qualitative studies, many times patterns of behavior or occurrences derived from analyzed data can result in developing new theoretical frameworks rather than determineda priori(Savenye, Robinson, 2004).

It is conceivable that multiple statistical tests could yield a significant finding by chance alone rather than reflecting a true effect. Integrity is compromised if the investigator only reports tests with significant findings, and neglects to mention a large number of tests failing to reach significance. While access to computer-based statistical packages can facilitate application of increasingly complex analytic procedures, inappropriate uses of these packages can result in abuses as well.

Following acceptable norms for disciplines

Every field of study has developed its accepted practices for data analysis. Resnik (2000) states that it is prudent for investigators to follow these accepted norms. Resnik further states that the norms are based on two factors:(1) the nature of the variables used (i.e., quantitative, comparative, or qualitative),(2) assumptions about the population from which the data are drawn (i.e., random distribution, independence, sample size, etc.). If one uses unconventional norms, it is crucial to clearly state this is being done, and to show how this new and possibly unaccepted method of analysis is being used, as well as how it differs from other more traditional methods. For example, Schroder, Carey, and Vanable (2003) juxtapose their identification of new and powerful data analytic solutions developed to count data in the area of HIV contraction risk with a discussion of the limitations of commonly applied methods.

If one uses unconventional norms, it is crucial to clearly state this is being done, and to show how this new and possibly unaccepted method of analysis is being used, as well as how it differs from other more traditional methods. For example, Schroder, Carey, and Vanable (2003) juxtapose their identification of new and powerful data analytic solutions developed to count data in the area of HIV contraction risk with a discussion of the limitations of commonly applied methods.

Determining significance

While the conventional practice is to establish a standard of acceptability for statistical significance, with certain disciplines, it may also be appropriate to discuss whether attaining statistical significance has a true practical meaning, i.e.,clinical significance. Jeans (1992) defines clinical significance as the potential for research findings to make a real and important difference to clients or clinical practice, to health status or to any other problem identified as a relevant priority for the discipline.Kendall and Grove (1988) define clinical significance in terms of what happens when troubled and disordered clients are now, after treatment, not distinguishable from a meaningful and representative non-disturbed reference group. Thompson and Noferi (2002) suggest that readers of counseling literature should expect authors to report either practical or clinical significance indices, or both, within their research reports. Shepard (2003) questions why some authors fail to point out that the magnitude of observed changes may too small to have any clinical or practical significance, sometimes, a supposed change may be described in some detail, but the investigator fails to disclose that the trend is not statistically significant .

Lack of clearly defined and objective outcome measurements

No amount of statistical analysis, regardless of the level of the sophistication, will correct poorly defined objective outcome measurements. Whether done unintentionally or by design, this practice increases the likelihood of clouding the interpretation of findings, thus potentially misleading readers.

Provide honest and accurate analysis

The basis for this issue is the urgency of reducing the likelihood of statistical error. Common challenges include the exclusion ofoutliers, filling in missing data, altering or otherwise changing data, data mining, and developing graphical representations of the data (Shamoo, Resnik, 2003).

Manner of presenting data

At times investigators may enhance the impression of a significant finding by determining how to presentderived data(as opposed to data in its raw form), which portion of the data is shown, why, how and to whom (Shamoo, Resnik, 2003). Nowak (1994) notes that even experts do not agree in distinguishing between analyzing and massaging data. Shamoo (1989) recommends that investigators maintain a sufficient and accurate paper trail of how data was manipulated for future review.Environmental/contextual issues

The integrity of data analysis can be compromised by the environment or context in which data was collected i.e., face-to face interviews vs. focused group. Theinteractionoccurring within a dyadic relationship (interviewer-interviewee) differs from the group dynamic occurring within a focus group because of the number of participants, and how they react to each others responses. Since the data collection process could be influenced by the environment/context, researchers should take this into account when conducting data analysis.Data recording method

Analyses could also be influenced by the method in which data was recorded. For example, research events could be documented by:a. recording audio and/or video and transcribing laterb. either a researcher or self-administered surveyc. eitherclosed ended surveyoropen ended surveyd. preparing ethnographic field notes from a participant/observere. requesting that participants themselves take notes, compile and submit them to researchers.While each methodology employed has rationale and advantages, issues of objectivity and subjectivity may be raised when data is analyzed.Partitioning the text

During content analysis, staff researchers or raters may use inconsistent strategies in analyzing text material. Some raters may analyze comments as a whole while others may prefer to dissect text material by separating words, phrases, clauses, sentences or groups of sentences. Every effort should be made to reduce or eliminate inconsistencies between raters so that data integrity is not compromised.Training of Staff conducting analyses

A major challenge to data integrity could occur with the unmonitored supervision of inductive techniques. Content analysis requires raters to assign topics to text material (comments). The threat to integrity may arise when raters have received inconsistent training, or may have received previous training experience(s). Previous experience may affect how raters perceive the material or even perceive the nature of the analyses to be conducted. Thus one rater could assign topics or codes to material that is significantly different from another rater. Strategies to address this would include clearly stating a list of analyses procedures in the protocol manual, consistent training, and routine monitoring of raters.Reliability and Validity

Researchers performing analysis on either quantitative or qualitative analyses should be aware of challenges to reliability and validity. For example, in the area of content analysis, Gottschalk (1995) identifies three factors that can affect the reliability of analyzed data: stability , or the tendency for coders to consistently re-code the same data in the same way over a period of time reproducibility , or the tendency for a group of coders to classify categories membership in the same way accuracy , or the extent to which the classification of a text corresponds to a standard or norm statisticallyThe potential for compromising data integrity arises when researchers cannot consistently demonstrate stability, reproducibility, or accuracy of data analysisAccording Gottschalk, (1995), the validity of a content analysis study refers to the correspondence of the categories (the classification that raters assigned to text content) to the conclusions, and the generalizability of results to a theory (did the categories support the studys conclusion, and was the f