Research is a systematic and objective process of gathering, recording and analyzing data for aid in making business decisions. Business research is a systematic and organized effort to investigate a specific problem encountered in the work setting that needs a solution

Research inculcates scientific thinking: Research inculcates scientific and inductive thinking and it promotes the development of logical habits of thinking and organization.
Increasing role of research: The role of research in several fields of applied economics, whether related to business or to the economy as a whole, has greatly increased in modern times. The increasingly complex nature of business and government focused attention on the use of research in solving operational problems.
Research provides the basis for nearly all government policies in economic system.
Solving operational and planning problems: Research has its special significance in solving various operational and planning problems of business and industry. Operations research and market research, along with motivational research, are considered crucial and their results assist, in more than one way in taking business decisions.
Important for social scientists: Research is equally important for social scientists in studying social relationships and in seeking answers to various social problems. It provides the intellectual satisfaction knowing a few things just for sake of knowledge and also has practical utility for the social scientists to know the sake of being able to do something better or in a more efficient manner.
Significance of research can also be understood keeping in view the following points:
1) To those students who are write to master’s or ph. D thesis, research, may mean a careerism or a way to attain a high position in the social structure;
2) To professionals in research methodology, research may mean a source of livelihood;
3) To philosophers and thinkers, research may mean the outlet for new ideas and insights;
4) To literary men and women, research may mean the development of new styles and creative work;
5) To analysis and intellectuals, research may mean the generalization of new theories
Formulating the research problem
The formulation of a general topic into a specific research problem thus constitutes the first step in a scientific enquiry. Essentially two steps are involved in formulating the research problem, viz., understanding the problem thoroughly, and rephrasing the same into meaningful terms from an analytical point of view.
Extensive Literature survey
The abstracting and indexing journals and published or unpublished bibliographies
Formulating the working hypotheses
After extensive literature survey, researcher should state in clear terms the working hypothesis. It is tentative assumption made in order to draw out and test its logical or empirical consequences.
Preparing the research design
Research design includes the means of obtaining the information, explanation of the way in which selected means of obtaining information will be organized and the reasoning leading to the selection.
Determining Sample design
A sample design is a definite plan determined before nay data are actually collected for obtaining a sample from a given population.
Collecting the data
Data are two types – Primary data and Secondary data. Primary data can be collected by observation, through personal interview, through telephone interviews, by mailing of questionnaires, through schedules.
Analysis of data
The analysis of data requires a number of closely related operations such as establishment of categories, the application of these categories to raw data through coding, tabulation and then drawing statistical inferences.
Interpret and report.
Finally, the researcher has to prepare the report of what has been done by him. The main text of the report should have the following parts
1. Introduction
2. Summary of findings
3. Main report
4. Conclusion
1.3 Types of research
Pure Research is undertaken for the sake of knowledge without any intention to apply it in practice
* Not necessarily problem oriented
* Discovery of new theory / refinement of existing theory.
* Ex : inventions like steam engine, EDP, telecomm.
Applied research is carried on to find solution to a real life problem requiring an action or policy decision.
* Problem oriented
* Action directed
* It seeks an immediate and practical result
* Ex: Marketing research carried on for developing a new market
Exploratory Research analyses the data and explores the possibility of obtaining as many relationships as possible.
* It is a preliminary study of an unfamiliar problem about which the researcher has little or no knowledge.
* ” To see what is there rather than to predict the relationships that will be founded”
* EX: Doctor’s initial investigation of a patient suffering from an unfamiliar disease.
Descriptive research
It is a fact finding investigation with adequate interpretation.
* It focuses on particular aspects or dimensions of the problem studied
* Ex: Consumption behavior of people in a village
Diagnostic study
It is to discover what is happening, Why it is happening and What can be done
– It aims in identifying the cause of the problem and the possible solution for it
Evaluation studies
• It is for assessing the effectiveness of social or economic programmes implemented
– Ex: (Polio drops)
• For assessing the impact of development projects
– Ex: (irrigation projects)
Action Research
• It is a concurrent evaluation study of an action programme launched for solving a problem for improving existing situation
– Ex: (Creating awareness about HIV)
Experimental Research
• It is to assess the effects of particular variables on a phenomenon by keeping the other variables constant or controlled
– To determine whether and in what manner variables are related to each other
– The factor , which is influenced , by other factors is called a dependent variable, and the other factors , which influence it are known as independent variables
– EX: agricultural productivity (i.e) is a dependent variable and the factors such as soil fertility, irrigation, quality of seed etc. which influences the yield are independent variables.
Analytical studies
• It is a system of procedures and techniques of analysis applied to quantitative data
• It consists of mathematical model
• It aims in testing hypothesis and specifying and interpreting relationship
• Used to measure variables, comparing groups and examining association with factors
Historical research
• Study of past record and other information sources
• Its main objective is to draw explanations and generalizations from the past trends in order to understand the present and to anticipate the future.
• It is fact finding study
• It involves collection of data directly from a population
• It requires expert and imaginative planning, careful analysis and rational interpretation of the findings
Case Study
• It is an in-depth comprehensive study of a person, a social groups , an episode, a process
• Ex: a study of the financial health of a business undertaking
– A study of labor participation in management in a particular enterprises
– A study of life style of working women
Field studies
It is a scientific enquiries aimed at discovering the relations and interactions among sociological, psychological and educational variables in social institutions and actual life situations like communities, school, factories etc
• A social or institutional situation is selected and the relations among the attitudes, values, perceptions and behaviors of individuals and groups in the selected situation are studied.
1.5 On the basis of extent theory research are two types:
* Theoretical Research
* Empirical Research
Theoretical research: Theoretical research generally uses the findings from existing works to develop new ideas through analyzing existing theory and explanations. These new ideas are not tested through collecting evidence in the form of primary data. Theoretical research is held to be a classical way of adding something to the value of the body of knowledge.
In the business and management studies world theoretical research is not always well received. In fact some academic researchers would argue that the process described as theoretical research should not be regarded as “proper” academic research. The basis of such a claim is that this type of theoretical research does not have a test component. This fact is used by those who are not enthusiastic about theoretical research, to imply that theories can postulated without any “proof”. However this type of thinking is a misunderstanding of the nature of research. All research processes requires conceptualization. One of the primary roles of theoretical research is to rework already established ideas in order to improve insights into the subject matter. Such improvements could well-constitute adding something of value to the body of knowledge.
Evaluating theoretical research: theoretical research does not rely on data or evidence, collection, analysis and synthesis it is sometimes often said to be more difficult. Theoretical research relies heavily on creativity and imagination. Al though these attributes are still required for empirical research they are often required to a greater extent in theoretical research.
Empirical research: empirical means based upon observation or measurement rather than theoretical reasoning. It supports the development of new ideas through the collection of data. The researcher who develops a theory of spot fan violence through visiting a library and developing their own explanation through reading existing work will be undertaking theoretical research. The researcher to take this one step further and collects data test their explanation will be undertaking empirical research. For example, computer simulations generate scores from random number routines. The cases and measures are not involved. Analytical researchers use mathematical operations to work from initial assumptions to conclusions there are no cases, measures, or scores.
Empirical research involves three activities, as which are as follows:
Measurement: it involves activities associated with measuring the factors that from the expected relationship. In other situations, a researcher may begin with measures already developed and assess their suitability for a study at hand.
Research design: it establishes procedures to obtain cases for study and to determine how scores will be obtained from those cases.
Analysis: empirical research also involves analysis of scores. Analyses are performed to describe crosses on single measures and, especially, to identify relationships that may exist between scores across different measures.
Benefits of empirical research
1) Understand and respond to dynamics of situations.
2) Respect contextual differences.
3) Build upon what is already known to work
4) Meet accepted professional standards of research.
5) Integrate professional knowledge with empirical data to inform instructional development decisions.
6) Establish relationship between intervention and behavioral response.
Limitations of Empirical Research
1) Time: Since empirical research requires soliciting participation and “data gathering” from various off campus of researchers.
2) Cost: Field research requires on-sites visits by researchers may be require cash outlays for travel, lodging, and other expenses not required in conceptual research, which can usually be accomplished in the local academic setting.
3) Access to firms: they cannot gain access to the types of the firms necessary for their studies.
4) Access to data: even if they gain access to business firms, such firms may be reluctant to release any or all the data necessary for the studies.
5) Skills: they do not possess the requisite skills necessary to design such empirically based studies, to gather and analyze the oftentimes huge data efficiently, or two interpret the results in a manner meaningful to and rewarded by both the business and academic worlds.
1.6 On the basis of time dimension:
Two types:
* Cross-sectional Research
* Longitudinal Research

Cross-sectional research: in this research, researchers observe at one point in time. Cross-sectional research is usually the simplest and least costly alternative. A cross- sectional designs a snapshot of the variables included in the study, at one particular point in time. It may reveal how those variables are related.
Longitudinal Research: Researchers using longitudinal research examine features of people or other units at more than one time it is usually more complex and costly than cross sectional research, but it is also more powerful, especially where researchers seek answers to questions about social change. Three types of longitudinal research which as follows;
1) Time-series research
2) Panel study
3) Cohort study
Time series research: the time design collects data on the same variable at regular intervals (weeks, months, year) etc
Time series designs are useful for:
* Establishing a baseline measure,
* Describing charges over time,
* Keeping track of trends and
* Forecasting future trends.
Panel study: it is a powerful type of longitudinal research. It is more difficult to conduct than time series research. In panel study, researchers observe exactly the same people, group, or organization across time period. Participants who are examined over repeated time points may be affected by having previously completed the measure being used. (This is known as sensitization)
Cohort study: it is similar to the panel study, but rather than observing the exact same people, a category of people who share a similar life experienced in a specified time period is studied.

1.7 Research Problem
* The term ‘problem’ means a question or issue to be examined.
* Research Problem refers to some difficulty /need which a researcher experiences in the context of either theoretical or practical situation and wants to obtain a solution for the same.
* The first step in the research process – definition of the problem involves two activities:
* Identification / Selection of the Problem
* Formulation of the Problem
* This step involves identification of a few problems and selection of one out of them, after evaluating the alternatives against certain selection criteria.
* Formulation is the process of refining the research ideas into research questions and objectives.
* Formulation means translating and transforming the selected research problem/topic/idea into a scientifically researchable question. It is concerned with specifying exactly what the research problem is.
* The selection of one appropriate researchable problem out of the identified problems requires evaluation of those alternatives against certain criteria. They are:
* Internal / Personal criteria – Researcher’s Interest, Researcher’s Competence, Researcher’s own Resource: finance and time.
* External Criteria or Factors – Researchability of the problem, Importance and Urgency, Novelty of the Problem, Feasibility, Facilities, Usefulness and Social Relevance, Research Personnel.
* Reading
* Academic Experience
* Daily Experience
* Exposure to Field Situations
* Consultations
* Brainstorming
* Research
* Intuition
• Problem Formulation
For a researcher, the problem formulation means converting the management problem to a research problem.
• Management problem- Want to increase the sale of product A
• Research problem- What is the current standing of the product A?
• While problem is formulated, the following should be considered
1. Determine the objectives
2. Consider environmental factors
3. Nature of the problem
4. Stating the alternatives
• Determine the objectives
To increase the sales or does it means it has improved the knowledge of the audience.
If the advertisement by the company was indeed ineffective, what course of action does the company intend to take?
Increase the budget for the next Ad
Use different appeal
Change the media
Go to a new agency.
Consider environmental factors
• If a company introduce a new product
Purchasing habits of consumers
Presently, who are the competitors in the market with similar product?
What is the perception of the people about other products of the company?
Size of the market and target audience.
• Nature of the problem
Initial investigation could be carried by using a focus group of consumers or sales representatives
Did the customer ever include this company’s product in his mental map?
If the customer is not buying, the reasons for that
Why did the customer turn to the competitor’s product?
• Stating the alternatives
The researcher would be better served by generating as many alternatives as possible during the problem formulation.
For every alternative, a hypothesis has to be developed and data to be collected and to be proved whether it is best alternative or not.
1.8 Research objective
It’s not long term goal, but is the step towards the long term goal.
It defines the purpose of the proposed research. It should be phrased in such a way that central hypothesis clearly grows out of it
An ideal research objective
-Hypothesis driven
-To study mechanism
-Realistic & focused
-Doable in the requested budge and time
1.9 Hypothesis Testing
It considered as a principal instrument in research. Hypothesis is a mere assumption to be proved or disproved.
Hypothesis is defined as the proposition or a set of proposition set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide some investigation or accepted as highly probable in the right of established facts.
Characteristics of hypothesis
1) Hypothesis should be clear and precise
2) A good hypothesis is assumption or explanation of why or how something occurs
3) Hypothesis should be capable of being tested.
4) Hypothesis should state relationship between variables.
5) Hypothesis should be limited in scope and must be specific.
6) Hypothesis should be tested in most simple terms so that the same is easily understandable by all concerns.
7) Hypothesis should be consistent with most known facts, in other words, it should be the one which judges accept as being the most likely.
8) Hypothesis should be agreeable to testing with a reasonable time.
9) Hypothesis must explain the facts what it claims to explain. It should have empirical reference.
Eg: Companies manufacturing washing machines spend at least 10% of their annual profits on advertising.
Testing Hypothesis: This is a statement or proposition that we would like to verify whether it is true or not.
Concept of Null and alternative Hypothesis
A Null hypothesis is a statement about a population parameter (such as mu) and the test is used to decide whether or not to accept the hypothesis.
It is identified by the symbol Ho
It is always stated that “There is no significant difference between the samples.
If the H0 is false, something else must be true. That is called alternative hypothesis
It is identified by the symbol H1.
It should be clear that both Null and alternative hypotheses cannot be true and only one of them must be true.
For any exercise, our conclusion must result into the acceptance of one hypothesis and rejection of the other.

Eg: Suppose a person is facing a legal trial for committing a crime. The judge look into all the evidence for and against it listens very carefully the prosecutions and defendants arguments and then decides the case and gives his verdict.

The verdict could be
H0: The person has not committed the crime
H1: The person has committed the crime

Procedure in Hypothesis testing
There are five steps involved in testing a hypothesis
1. Formulate a hypothesis. We have to set the null and alternative hypothesis (H0 and H1)
2. Set up a suitable significance level
3. Significance level means the confidence with which a null hypothesis is rejected or accepted depends upon the significance level used for the purpose.
4. Select Test criterion: There are many techniques from which one is to be chosen.

Eg: If sample size > 30- Z test
If sample size <30- t-test.
5. Compute
This step involves various computations necessary for the application of that particular test. These computations include the testing statistic as also its standard error.
6. Make decisions: This step involves in the process of accepting or rejecting the null hypothesis at a given level of significance.
Two types of Errors
Type I error: it occurs when one rejects the null hypothesis and accepts the alternative, when it fact the null hypothesis is true.
Type II error: it occurs when one accepts the null hypothesis when in fact the null hypothesis is false.

2.1 Research Design Meaning & Types
A research project conducted scientifically has a specific framework of research from the problems identification to the presentation of the research report. This framework of conducting research is known as the research design.
A research designs simply the framework or plan for a study that is used as a guide in collecting and analyzing the data. It is blueprint that is followed in completing study”.
1) Exploratory Research
i) Literate Research/Study of Secondary Data
ii) Survey of knowledgeable persons or experience survey
iii) Case Groups
iv) Focus Groups
v) Two-Stage Design
2) Conclusive Research
i) Descriptive
a) Longitudinal Study
b) Cross-sectional Study
ii) Experimental or Casual Research
2.2 Exploratory Research Design
Exploratory research studies are also termed as formulative research studies. The main purpose of such studies is that of formulating a problem for more precise investigation or of developing the working hypotheses from an operational point of view. The major emphasis in such studies is on the discovery of ideas and insights. As such the research design appropriate for such studies must be flexible enough to provide opportunity for considering different aspects of a problem under study.
Objectives of Exploratory Research
1) Precise formulation of the problem
2) Provide more knowledge to the researcher about the problem environment
3) Establishes priorities for further research
4) To design appropriate information collection procedure for the given situation.
5) To determine nature of relationship between various factors associated in the problem.
6) Gathering information on the problems associated with doing conclusive research.
1. Study of Secondary Data: The quickest and most economical way is to find possible hypotheses from the available literature. The past researches may be suitable sources of information to develop new hypotheses. The findings of marketing research are generally published in trade and professional journals, which can be fruitful sources of information.
2) Depth Interview: Experience survey means the survey of people who have had practical experience with the problem to be studied. These individuals can be top executives, sales managers/executives, wholesalers and retailers possessing valuable knowledge and information about the problem environment.
3) Case Study: The third general type of exploratory research is the case method. This research method has long been considered “soft” or nonscientific, but with the modern surge in qualitative research the case method has received more attention. Indeed, the case method might be considered one variation of the survey of individuals with ideas. It involves the comprehensive study of one, or a few, specific situations and lends itself particularly to the study of complex situations in which the interrelations of several individuals are import – for example, the effective management of distributor relations or what constitutes good marketing management.
4) Focus Group: Focus group originates from sociology studies. They have been extensively used in marketing research. Focus groups studied are generally conducted to evaluate the potential of a new product idea or concept. A focus group comprises several persons, who are led by a trained moderator. The moderator’s task is to lead the team in generating and exchanging ideas on a particular issue. The process starts by issuing a topic for discussion among participants by the moderator. In such discussions, the moderator’s role will be to silently watch the proceedings and ensure that the discussion is going on as expected. However, the moderator needs to intervene to ensure that all individuals in the group participate. Once the focus group’s observations and recommendations are obtains, the information is evaluated by the moderator,. This forms the basis for further research.
5) Two-Stage Design: A two-stage design is beneficial approach for designing research. In this method, the exploration is conducted in two stages. The first stage consists of clearly defining the research problem, while the second stage comprises developing the research design. A two-stage design is beneficial, when the problem is vaguely defined and the researcher is not clear about the particular topic that has to be studied.
2.3 Conclusive Research Design
Conclusive research provides information, which helps the executive to make a rational decision. The marketing executive has to arrive at a suitable decision from the various alternative decisions. The various alternative conclusions and selecting the most suitable conclusion may be done by descriptive research design or experimental research design.
i. Descriptive Research
Descriptive studies, as their name implies, are designed to describe something – for example, the characteristics of users of a given product; the degree to which product use varies with income, age, sex, or other characteristics, or the number who saw a specific television commercial. A majority of marketing studies are of this type.
Objectives of Descriptive Research
1) To describe the characteristics of relevant groups
2) To estimate the percentage of units in a specified population exhibiting a certain behavior.
3) To determine the perceptions of product characteristics.
4) To determine the degree to which marketing variable are associated.
Types of Descriptive Studies
1) Case Method: Case studies are more appropriate to exploratory research than descriptive research. They are not widely used in descriptive research, but they are worth some comment in the descriptive context and perhaps should be used more than they have been in the past.
2) Statistical Method: The statistical method is the most widely used method in marketing research and is the method usually implied when a “survey” is referred to. The name comes from the statistical techniques that are used in analyzing the data collected – techniques that vary from simple means and percentage to very sophisticated techniques that require computers to manipulate the data.

Uses of Descriptive Research
1) Consumer profiles
2) Market potential studied
3) Product usages studies
4) Attitude surveys
5) Sales analysis
6) Media research
7) Price surveys
Descriptive Research Analysis
1) Longitudinal Design/Panel Analysis: Longitudinal studies are based on panel data and panel methods. A panel is a sample of respondents who are interviewed and then re-interviewed from time to time. Generally panel data relate to the repeated measurements of the same variables. Each family included in the panel, records its purchases of a number of product at regular intervals, say, weekly, monthly or quarterly. Over a period of time, such data will reflect changes in the buying behavior or families.
2) Cross-Sectional Design: A cross-sectional study is concerned with a sample of elements from a population. Thus, it may deal with households, dealers, retail stores, or other entitles. Data on a number of characteristics from the sample elements are collected and analyzed. The cross-sectional study is the most frequently used descriptive design in marketing research. Cross-sectional design involves the collection of information from any given sample of population elements only one. They may be either single cross- sectional or multiple cross-sectional.
Types of Cross-Sectional Design
i. Field Studies ii.Survey Research
2.4 Casual or Experimental Research
Although descriptive research in identifying co-variation between variables (e.g., blue packages outsell red ones, consumption rate varies by education level) it cannot truly indicate causality (e.g., color causes sales, education causes consumption). When we are in need of determining whether two or more variables are causally related we must turn to casual research procedures. While there might be a tendency to see many research objectives from a casual perspective (“We really want to know what causes consumers to act that way”), there is a difference between causality in the vernacular and how it is defined by scientists.
Types of experimental research design
* Before after without control
* Before-after with control
* After only with control
* Ex-post facto design
* Panel design
* Simple time series experiments
* Recurrent time series experiments
* Completely randomized design
* Randomized block design
* Latin square design
* Factorial design

Informal design
Before and after without control
In such a design a design a single test group or area is selected and the dependent variable is measured before the introduction of the treatment. The treatment is then introduced and the dependent variable is measured again after the treatment has been introduced.
After only with control design
In this design two groups or areas (test are and control area) are selected and the treatment is introduced into the test area only. Treatment impact is assessed by subtracting the dependent variable in the control area from its value in the test area.
Before and after with control design
In this design two areas are selected and the dependent variable is measured in the both the area for an identical time period before the treatment. The treatment is then introduced into the test area only, and the dependent variable is measured in both for an identical time period after the introduction of the treatment.
Ex post facto design
One variable of the “after only “design is called the ex post facto design. This differs from the “after only” design because the experimental and control groups are selected after the experimental variable is introduced instead of before. One advantage is that the test subject cannot be influenced, pro or con, toward the subject by knowing they are being tested, since they are exposed to the experimental variable before being selected for the sample.
Panel design
A permanent set of experimental units used in market research investigation is known as a ‘panel’. Panel can be used both for exploratory and conclusive research. In such an experimental observation are recorded at some pre d determined intervals of time and experimental variables can be introduced if and when desired. Here any two set of successive measurements between which some experimental variable is introduced can be considered as before and after experimental measurements.
Simple test series experiments
Some dealers or retailers are selected for recording observation over certain period of time. Eg: to study the impact of some advertising policy on sales, one may select some dealer or retailers. the sales during a certain period before the advertisement and after the advertisement are recorded.
Recurrent time series design
Here the advertising policy is introduced, removed and introduced over different period of time and sales over these periods are recorded.
Formal design
Completely randomized design
The main feature of this design is that the experimental treatments are assigned to the test units completely at random. No prior precaution is needed to some extraneous variable before the assignment is randomly made.
Randomized block design
It is an improvement over the CR design. In this design the principal of local control can be applied along with the other two principles of experimental design. In this design subjects are first divided into groups, known as blocks, such that within that group the subjects are relatively homogeneous in respect to some selected variables
Latin square design
This design suggest that test will from a square because there will be as many tests units are treatments. This design is used to control important extraneous influence. It is assumed that each treatment occurs once with each store on a block. There are several stores in a block, there must be as many blocks as there are treatments. It may be three, four and so on.
2.5 Variables in research
Dependent and Independent variables
An independent variable is the presumed cause of the dependent variable- the presumed effect. When it can say A cause B , it means A is independent variable and B is dependent variable. The independent variable thus is one which explains or accounts variation in the dependent variable.
Experimental and measured variable
The experimental variable spell out the detail of the investigator’s manipulation while the measured variable refer to measurement. For example, rural development(measured variable) may be assessed in terms of increase in income, literacy, infrastructure……
Qualitative and quantitative variables
The Quantitative variable is one whose values or categories consist of number and differences between it’s categories can be expressed numerically. Eg: age, income, size……The qualitative variable is one which consist of discreet categories rather than numerical units
Categorical and numerical variables
Numerical variables are broken down into units in which the numbers used carry mathematical meaning. The numbers may be either discrete (1,2,3,4..)which cannot be broken down into smaller fractional quantities(no. Of children)or continuous.
2.6 Measurement and scaling
By measurement we mean the process of assigning numbers to objects or observations, the level of measurement being a function of the rules under which the numbers are assigned.
According to kenneth D. Bailey: “Measurement is the process of determining the value or level, either qualitative or quantitative, of a particular attribute for a particular unit of analysis”.
Basic process of measurement
I. Selecting observable empirical events
II. Developing a set of mapping rules- a scheme for assigning numbers or symbols to represent aspect of the event being measured.
III. Applying the mapping rules to each observation of that event
Mapping rules
In measuring, one devices some mapping rule and then translate the observation of property indicants using this rule. For each concept or construct, several types of measurement are possible; the appropriate choice depends on what you assume about the mapping rules. Each one has its own set of underlying assumption about how the numerical symbols correspond to real world observation.
Importance of measurement
1) Measurement allows researchers to quantify abstract construct and variables.
2) The level of statistical sophistication used to analyze data derived from a study is directly dependent on the scale of measurement used to quantify the variables of interest.
Functions of measurement
Empirical description: it facilitates empirical description of social and psychological phenomena. Eg:-in a study of a tribal community, the researcher has to classify and categorize the cultural patterns and behaviors.
1) Facilitates statistical treatment: measurement renders data amenable to statistical manipulation and treatment. The statistical techniques for comparing groups, studying relationship between variables.
2) Aids testing of hypothesis: measurement facilitates testing of theories and hypothesis.
3) Provide differentiation in objects: measurement enables researchers to differentiate between objects or people in terms of specific properties they possess.

Measurement scales
1) Nominal scale
2) Ordinal scale
3) Interval scale
4) Ratio scale
Nominal scale
It represents the most elementary level of measurement. a nominal scale assigns a value to an object for identification or classification purposes. The value can be a number because no quantities are being represented. In this sense, a nominal scale is truly a qualitative scale. Nominal scales are extremely useful even though they can be considered elementary.
Marketing researchers use this scale quite often. For example, suppose three old drinks were experimented with taste. The researcher would like the experiment to be blind, so when subject were asked to taste one of the three cold drink, the drinks were labeled A,B or C.
Ordinal scaling
Ordinal scales have nominal properties, but they also allow things to be arranged based on how much of some concept they possess. In other words, an ordinal scale is a ranking scale. The ordinal scale indicates the relative position of two or more objects or some characteristics. The consumers are asked to rank preference for several brands, flavor or package designs. The measures of such preference are ordinal in nature.
Interval scale
The interval scale has all characteristics of the ordinal scale and in addition, the units of measures or intervals between successive positions are equal.
Eg:- a researcher scaled brand A,B and C on an interval scale regarding the buyers ‘ degree of liking of the brands. Brand A receives the highest liking score 6, B received 3 and C receives 2. First the liking for brand A is more favorable than that for brand B. second the degree of liking between A and B is three times greater than the liking between B and C.
Ratio scale
Ratio scale represents the highest form of measurement. They have all the properties of interval scale with the additional attribute of representing absolute quantities. Interval scale represents only relative meaning, whereas ratio scale represents absolute meaning. In other words, ratio scale provides iconic measurement. Zero, therefore, has meaning in that it represents an absence of some concept. An absolute zero is a defining characteristic in determining between ratio and interval scale.
Classification of scaling ttechniques
Comparative scales Non-comparative scales
Paired comparison Continuous rating scales Itemized rating scales
Rank order Likert
Constant sum Semantic differential
Types of scaling Techniques
• Involve the respondent directly comparing stimulus objects.
• e.g. How does Pepsi compare with Coke on sweetness
• Respondent scales each stimulus object independently of other objects
• e.g. How would you rate the sweetness of Pepsi on a scale of 1 to 10

Paired comparison items
Please indicate which of the following airlines you prefer by circling your more preferred airline in each pair:
Air Canada WestJet
Air Transat Air Canada
Horizon Air WestJet
Comparative constant sum scales
• Allocate a total of 100 points among the following soft-drinks depending on how favorable you feel toward each; the more highly you think of each soft-drink, the more points you should allocate to it. (Please check that the allocated points add to 100.)
• Coca-Cola _____ points
• 7-Up _____ points
• Mirinda ____ points
• Fanta _____ points
• Pepsi-Cola _____ points
Total 100 points
Comparative rank order scales
Rank the following soft-drinks from 1 (best) to 5 (worst) according to your taste preference:
Coca-Cola _____
7-Up _____
Fanta _____
Pepsi-Cola _____
Mountain Dew _____
Semantic differential scale
Here are a number of statements that could be used to describe Tesco. For each statement tick ( ) the box that best describes your feelings about Tesco.
Modern store Old fashioned store
Low prices High prices
Unfriendly staff Friendly staff
Stapel Scale
+5 +5 +5
+4 +4 +4
+3 +3 +3
+2 +2 +2
+1 +1 +1
-1 -1 -1
-2 -2 -2
-3 -3 -3
-4 -4 -4
-5 -5 -5
Likert scale or 5 points scale
Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree
Characteristics of good mmeasurement scales
1. Reliability
• The degree to which a measure accurately captures a true outcome without error
• synonymous with repetitive consistency
2. Validity
• The degree to which a measure faithfully represents the underlying concept (it asks the right questions)
3. Sensitivity
The ability to discriminate meaningful differences between attitudes. The more categories the more sensitive (but less reliable
* Reliability can be more easily determined than validity
* If it is reliable, it may or may not be valid
* If a measure is valid, it may or may not be reliable
* If it is not reliable, it cannot be valid
* If it is not valid, it may or may not be reliable
Example of low validity, high reliability
* Scale is perfectly accurate, but is capturing the wrong thing; for example, it measures consumers’ interest in creative writing rather than preference for kinds of stationery.
* Example of modest validity, low reliability
* Scale genuinely measures consumers’ interest in kinds of stationery, but poorly worded items, sloppy administration, data entry errors lead to random errors in data
2.6 Reliability
• Reliability refers to how consistent a measuring device is. A measurement is said to be reliable or consistent if the measurement can produce similar results if used again in similar circumstances.
• Validity
This refers to whether a study measures or examines what it claims to measure or examine. Valid measures will ALWAYS be reliable…but reliable measures are not necessarily valid.
• Reliability’ of any research is the degree to which it gives an accurate score across a range of measurement. It can thus be viewed as being ‘repeatability’ or ‘consistency’. In summary:
• Inter-rater: Different people, same test.
• Test-retest: Same people, different times.
• Parallel-forms: Different people, same time, different test.
• Internal consistency: Different questions, same construct.
2 measures yield identical (or similar) results at 2 different times. The test-retest reliability method is one of the simplest ways of testing the stability and reliability of an instrument overtime. For example, a group of respondents is tested for IQ scores: each respondent is tested twice – the two tests are, say, a month apart. Then, the correlation coefficient between two sets of IQ-scores is a reasonable measure of the test-retest reliability of this test.
Parallel forms reliability is used to assess the consistency of the results of two tests constructed in the same way using the same content. To create the parallel forms a large pool of test questions that measure the same quality are created and then randomly divided into two separate tests. Each test is given to the same sample of people and the correlation between the two parallel forms is used as an estimate of the reliability.
Content (Face) validity
Is the degree to which a test measures an intended content area, e.g., achievement tests. Example: to measure knowledge of parenting skills could be obtained by consulting experts such as social workers, parents. Judgment is dependent upon the knowledge of the experts
Construct validity
Is the degree to which a test measures an intended hypothetical construct? i.e., a non-observable trait, such as intelligence, which explains behavior
Criterion validity
Describes the extent to which a correlation exists between the measuring instrument & standard – empirical evidence. E.g., the relationship between College Board examination and student academic success in college. Two measures need to be taken: the measure of the test itself & the criterion to which the test is related
Difference between reliability and validity
Reliability: the degree to which a measurement procedure produces similar outcomes when it is repeated. E.g., gender, birthplace, mother’s name should be the same always.
Validity: tests for determining whether a measure is measuring the concept that the researcher thinks is being measured, i.e., “Am I measuring what I think I am measuring”?

3.1 TYPES OF DATA: The task of data collection begins after a research problem has been defined and researches design/chalked out. While deciding about method of data collection to be used for the study, the researcher should keep in mind two types of data viz ,primary and secondary data.
The primary data are those data, which are collected afresh and for the first time, and those happen to be original in character. The secondary data, on the other hand, are those which have already been collected by someone else and which have already been passed through the statistical process.
Statistical data can be classified under two categories
1) primary data
2) secondary data
Primary data
Primary data is the one, which is collected by the investigator himself for the purpose of a specific inquiry or study. Such data is original in character and is generated by survey conducted by individuals or research institutions or any organization.
The objectives of primary data are formulated on the basis of research objectives. Objectives set the guidelines and direction research planning. Formulating the objectives offers the best feasible means of solution.
Significance of primary data.
* Reliability
* Availability of a wide range of techniques
* Addresses specific research issues
* Greater control
* Efficient spending for information
> Time consuming
> High cost
> Not always feasible
> Large volume of data
> Reluctance of respondents

Secondary data
Secondary data are those data which have been already collected and analyzed by some earlier agency for its own use; and later the same data are used by a different agency.
Secondary data are statistics that already exist. It can be classified as
– Internal secondary data
-External secondary data
Internal secondary data is a part of the company’s record, for which research is already conducted.
Eg: Daily production report, monthly collection report.
External Secondary data
The data collected by the researcher from outside the company.
This can be divided into four parts:
* Census data
* Census of the whole sale trade
* Census of the retail trade
* Population Census
* Census of manufacturing industries
* Individual project report being published
* Encyclopedia of business information sources
* Syndicated data is an important form of secondary data which may be classified into
* Consumer purchase data
* Retailer and whole sale data
* Advertising data
Advantages of secondary data
* Economy
* Quickness
* Quality
* No need of measuring instruments
* Availability
* Bases for comparison
* Useful in exploratory research
* Generates feasible alternatives

Disadvantages of secondary data
* The data may not fit into the needs of investigation
* Less accuracy
* Existence of obsolete information
* Nondisclosure of research findings
* There may be difficulties in the identification of source
* Errors may be there in recording or transferring information from secondary sources
3.2 Primary data Vs secondary data
Basis of comparison Primary data Secondary data Object Originate with the specific research undertaking Gathered for some other purpose but are applicable to present investigation Cost involved Collection is expensive Collection is cheaper Time consumption Collection can take weeks or even months Collection time usually involves hours or days Nature of errors Errors can be there due to interviewer and respondent biases There may be inaccuracies due to errors in recording or transferring of the original data Accuracy and validity The information is more valid, reliable and relevant The validity of information should be judged/evaluated before using secondary data Mode of collection The information is to be generated either by questioning the people or by observing selected activities Information already exists in various sources. Data are obtained by searching these sources and then recording from various sources only. Need of auxiliary instruments Data collection instruments are to designed according to the need of investigation No need of data collection instruments.
3.3 Methods of primary data collection

Survey techniques
Survey techniques can be divided into three broad categories as in figure below.
Interview method
Interviewing is one of the prominent methods of collection. It may be defined as a two-way systematic conversation between an investigator and an informant, initiated for obtaining information relevant to a Specific study.
Interviewing requires face to face contact over telephone and calls for interviewing skills. It is done by using a structured schedule or an unstructured guide.
Characteristics of interview method
> Needs proper introduction
> Incorporates transitory relationships
> Caters to a specific purpose
> Verbal interaction
> Facilitates telephonic conversation
> Group studies possible
> Interactional process
> Simultaneous recording
Types of interview
* Personal interview
* Unstructured and direct interview
* Structured and direct interviews
* Unstructured and indirect interviews
* Telephone interview
* Panel interview
* Electronic interview
Limitations of interview method
* Expensive
* Subject to bias and personal traits
* Ineffective in some areas
* Recording complexities
* demands skilled interviewers
* subjective
Questionnaire method
The questionnaire is the list of questions to be asked from the respondents. It also contains a suitable space where the answers can be recorded.
A questionnaire is a method of obtaining specific information about a defined problem so that the data, after analysis and interpretation, results in a better appreciation of the problem. a questionnaire form ,which has to be completed by an interviewer, is often referred as schedule
Types of questionnaire
* Structured, non-disguised questionnaire
* Non-structured, non-disguised questionnaire
* Non-structured, disguised questionnaire
* Structured, disguised
Process of Questionnaire designing
* Determine what information is needed
* What type of questionnaire to be used
* Decide on the type of questions
* Decide on the wording of questions
* Deciding on the layout
* Pretest
* Revise and prepare final questionnaire
Validation of questionnaire
To achieve high quality survey result, a critical component is validating the instrument (questionnaire) reliability and validity. The validity of questionnaire is assessed by three components
1. Content validation: It often refers face validity. Face validity is determined by comparing the questionnaire with other similar questionnaire surveys.
2. Sampling validity: It is another component of validation. A large sample size can ensure low sampling errors and high sampling validity.
3. Empirical validity: It examines the survey result by comparison with other studies. The aim is to check consistency with previous results. Empirical validation of questionnaire reliability often involves two techniques:
i. Test-retest techniques: It determines stability of measured indicators.
ii. Construct validity : It is a score to determine internal consistency -reliability, measured by the Cronbach alpha
It is a device in social research, which is most frequently used in collecting field data especially where the survey method is employed. It is used in indirect interview. It contains questions and blank tables, which are to be filled in by the investigators themselves after getting information from the respondents.
Difference between Questionnaire and schedule
Basis of difference Questionnaire Schedules Mode The questionnaire is generally sent through mail to informants to be answered as specified a covering letter, but otherwise without future assistance from the sender. It is generally filled out the researcher, who can interpret questions when necessary. Economy To collect data through questionnaire is relatively cheap and economical. The data collection is relatively expensive. Chances of non-response Non-response is usually in case of questionnaire. Non-response is generally very low in case of schedules. Identification of respondent In case of questionnaire, it is not always clear as to who replies. In case of schedule the identity of respondent is known. Time consumption The questionnaire method is likely to be very slow since many respondents do not return the questionnaire, in time despite several reminders. In case of schedule the information is collected well in time as they are filled in by researcher. Personal contact Personal contact is generally not possible in case of questionnaire. In case of schedules direct personal contact is established with respondents. Influence of respondents literacy Questionnaire method can be used only when respondents are literate and co-operative. In case of schedule the information can be gathered even the respondents happen to be illiterate. Coverage and distribution Wider and more representative distribution of sample is possible under questionnaire method. In respect of schedule there usually reminds the difficulty in sending enumerators over a relatively wider area. Accuracy of information Risk of collecting complete and right information is relatively more under questionnaire method. In case of schedules, the information collected is generally complete and accurate. Success The success of questionnaire method lies more on the quality of questionnaire itself. In case of schedules much depends upon the honesty and competence of researcher. Appearance of questionnaire In order to attract the attention of respondents, the physical appearance of questionnaire must be quite attractive. This may not be so in case of schedules as they are to be filled in by enumerators and not by respondents
Observation Method
• Structured or unstructured method
• Disguised or undisguised method
• Direct-indirect observation
• Human-mechanical observation
Structured-Unstructured Observation
Structured Observation
How many of his customers visit the hotel with their families and how many come as single customers.
Unstructured Observation
How single customers and those with families behave and their attitude
Disguised-Undisguised Observation
In disguised observation, the respondents do not know that they are being observed
In non-disguised observation, the respondents are well aware that they are being observed.
Direct-Indirect Observation
In direct observation, the actual behavior or phenomenon of interest is observed.
In in-direct observation, the results of the consequences of the phenomenon are observed.
Eg: Inorder to know the soft drinks consumption, he may like to observe empty bottles dropped into the bin.
3.4 Survey Vs observation method
Basis of comparison Survey method Observation method Objective This method of collecting data is useful when population size is very large. This is decidedly superior to survey research, experimentation, or document study for collecting data in behavior research. Response Responds in survey method based on verbal answers to limited set of questions Response in observation is neither as restrictive nor as artificial as either the survey or experiment. Difficulties of quantification Measurement in survey studies generally takes the form of the observer’s quantitative measure. Measurement in observational studies generally takes the form of the observers un quantified perceptions. Sample size Survey studies conducted for large sample size. Observational studies tend to use a smaller sample than survey studies, but a larger sample than experiments.

Qualitative techniques of Data collection
There are four major techniques in Qualitative research. They are:
* Depth Interview
* Delphi Technique
* Focus Group
* Projective Technique

Depth interview
Unstructured, direct interview is known as a depth interview. It is free from restrictions imposed by a formal list of questions.
Eg: What did you mean by that statement? Why did you feel this way? What other reasons do you have
o It is its ability to discover motivations
o The second advantage of the depth interview procedure is that it encourages respondents to express any ideas they have.
o The third advantage is that it provides a lot of flexibility to the interviewer.
o Longer duration
o Difficult to find the qualified and trained people for conducting depth interview
o No quantifiable data is obtained in the depth interviewing process
Delphi technique
This is a process where a group of experts in the field gather together. The group members are asked to make individual judgments about a particular subject, these judgments are compiled and returned to the group members, so that they can compare with those of others and revise, then reach conclusion after 5 to 6 rounds.
Projective techniques
In projective techniques, respondents are asked to interpret the behavior of users, rather than describe their own behavior. In interpreting the behavior of others, respondents indirectly project their own motivation and feelings into the situation.
The general categories of projective techniques are:
1. Word association test
2. Completion technique
3. TAT and
4. Cartoon test
Word Association Test
This is consists of presenting a series of stimulus words to the respondent.
For eg: What brand of detergent comes to your mind first, when I mention washing of an expensive cloth?
Completion techniques
Sentence Completion
Eg: Earnings of software professional
Story Completion: A situation is described to a respondent who is asked to complete the story based on his opinion and attitude.
Thematic Apperception Test
TAT is a projective technique. It is used to measure the attitude and perception of the individual.
Some picture cards are shown to respondents. The respondent is required to tell the story by looking at the picture. When the subjects start telling the story, the researcher notices the respondents’ expression, pauses and emotions to draw the inference.

3.5 Sampling
A sample is a part of a target population, which is carefully selected to represent the population.
Sample Frame
Sampling frame is the list of elements from wh ich the sample is actually drawn. Actually, sampling frame is nothing but the correct list of population.
Eg: Telephone directory, Product finder, Yellow pages
Distinction between Census and Sampling
Census refers to complete inclusion of all elements in the population. A sample is a sub-group of the population
Sampling Process:
1. Define the population
2. Identify the sampling frame.
3. Specify the sampling unit
4. Selection of sampling method
5. Determination of sample size
6. Specify sampling plan
Define the population
* Elements- Company’s product
* Sampling unit-Retail outlet, super market
* Extent- Hyderabad and Secunderabad
* Time-April 10 to May 10
Identify the sampling frame
Sampling frame could be
* Telephone Directory
* Localities of a city using the municipal corporation listing
* Any other list consisting of all sampling units.

Specify the sampling unit
Individuals who are to be contacted are the sampling units. If retailers are to be contacted in a locality, they are the sampling units.
Selection of sampling method
This refers to whether a. Probability b. non-probability methods are used
Determine the sample size
We need to decide how many elements of the target population are to be chosen?
For eg: If it is an exploratory research, the sample size will be generally small. For conclusive research, such as descriptive research, the sample size will be large.
Specify the sampling plan
A sampling plan should clearly specify the target population. Improper defining would lead to wrong data collection.
3.6 Sampling Types/ Methods/ Techniques
? Sampling is divided into two types.
? Probability sampling: In a probability sample, every unit in the population has equal chances for being selected as a sample unit.
? Non-probability sampling: In the non-probability sampling, the units in the population have unequal or negligible, almost no chances for being selected as a sample unit.
Probability sampling techniques
* Random Sampling
* Stratified random sampling
* Systematic sampling
* Cluster sampling
* Multi-stage sampling
Random Sampling
? Simple random sample is a process in which every item of the population has an equal probability of being chosen.
? Lottery method:
We can now write down all the combination, put them in a box. Mix them and pull one at random.
Systematic Random sampling
? Sampling interval K is determined by the following formula
K=No. of units in the population
No. of units desired in the sample.
Stratified Random Sampling
? A probability sampling procedure in which simple random sub-samples are drawn from within different strata that are, more or less equal on some characteristics.
? Proportionate stratified sampling: The number of sampling units drawn from each stratum is in proportion to the population size of that stratum.
? Disproportionate stratified sampling: The number of sampling units drawn from each stratum is based on the analytical consideration, but not in proportion to the size of the population of that stratum.
Cluster Sampling
? The population is divided into clusters.
? A simple random sample of few clusters is selected.
? All the units in the selected cluster are studied
Multi-stage Sampling
The name implies that sampling is done in several stages. This is used with stratified cluster designs. The management of a newly opened club is solicits new membership. During the first rounds, all corporate were sent details so that those who are interested may enroll. Having enrolled, the second round concentrates on how many are interested to enroll for various entertainment activities that club offers such billiards, indoor sports. After obtaining this information, you might stratify the interested respondents
Area Sampling
If clusters happen to be some geographic subdivisions, in that case cluster sampling is better known as area sampling.
Eg: If someone wants to measure the sales of toffee in retail stores, one might choose a city locality and then audit toffee sales in retail outlets in those localities

Non-Probability Sampling

Judgment Sampling
The investigator uses his discretion in selecting sample observations from the universe.
Eg: Test market cities are being selected, based on the judgment sampling, because these cities are viewed as typical cities matching with certain demographical characteristics.

Sequential Sampling
This is a method in which the sample is formed on the basis of a series of successive decisions.
Eg: If the evidence is not conclusive after a small sample, more samples are required. If the position is still inconclusive, still larger samples are taken.

Quota Sampling
Quota sampling is quite frequently used in marketing research. Suppose, 2,00,000 students are appearing for a competitive examination. We need to select 1% of them based on quota sampling.
? Category Quota
? General merit` 1,000
? Sport 600
? NRI 100
? SC/ST 300
? Total 2000
Snow ball Sampling
In this method, the initial group of respondents is selected randomly. Subsequent respondents are being selected based on the opinion or referrals provided by the initial respondents

Panel Samples
To give an example, suppose that one is interested in knowing the change in the consumption pattern of households. A sample of households is drawn. These households are contacted to gather information on the pattern of consumption. Subsequently, say after a period of six months, the same households are approached once again and the necessary information on their consumption is collected.

Errors in Sampling/ Sampling bias
Sampling error is the gap between the sample mean and population mean.
An MNC bank wants to pick up a sample among the credit card holders. They can readily get a complete list of credit card holders, which forms their data bank. From this frame, the desired individuals can be chose. In this example, sample frame is identical to ideal population namely all credit card holders. There is no sampling error in this case

Assume that a bank wants to contact the people belonging to a particular profession over phone to market a home loan product. The sampling frame in this case is the telephone directory. Reasons may be People might have migrated, Numbers have changed, Numbers may not be listed. Thus in this case, there will be a sampling error

Non-Sampling Error/ Non-response Error
? This occurs, because the planned sample and final sample vary significantly.
? Eg: Marketers want to know about the television viewing habits across the country. They choose 500 households and mail the questionnaire. Assume that only 200 respondents reply. If there is no response, then its Non-response error.

Data Error
? This occurs during the data collection, analysis or interpretation. Respondents sometimes give distorted answers unintentionally for questions which are difficult, or if the question is exceptionally long and the respondent may not have answer.
How to reduce Sampling Error
? To choose appropriate sample size.
? Non-sampling error
– Provide incentives to collect data, against the golden rule of research.
– Do not ask sensitive questions
– Training the interviewer
– Pretest the questionnaire
– Modify the sampling frame to make it a representative of the population

How will you decide the sample size?
? First factor must be considered in estimating sample size, is the error permissible.
? Greater the desire precision, larger will be the sample size
? Higher the confidence level in the estimate, the larger the sample must be.
? The greater the number of sub-groups of interest within the sample, the greater its size must be.
? Cost is a factor that determines the size of the sample.
? The issue to be considered in deciding the necessary sample size is the actual number of questionnaires that must be sent out. Calculation wise, we may send questionnaires to the required number of people, but we may not receive the response.

4.1 Editing:
Data editing is the activity aimed at detecting and correcting errors (logical inconsistencies) in data.
The customary first step in analysis is to edit the raw data. Editing detects errors and omissions, corrects them when possible, and certifies that maximum data quality standards are achieved. Alternately, recorded raw data is normally less than perfect and the first phase through which this data must pass is editing. The editor’s purpose is to guarantee that data are:

1. Accurate
2. Consistent with the intent of the question and other information in the survey
3. Uniformly entered
4. Complete
5. Arranged to simplify coding and tabulation
Objectives of data editing
* To ensure the accuracy of data
* To establish the consistency of data
* To determine whether or not the data are complete
* To ensure the coherence of aggregated data; and
* To obtain the best possible data available
Different stages of Editing:
The editing may be done in two stages they are as follow:
Field Editing:
The Field editing is a preliminary editing done to detect the glaring omissions and inaccuracies in the data. It is useful to controlling the field force and removing misunderstanding.
For example:
If interviewers did not follow the correct patterns or if open ended responses reflect a lack of probing. When poor interview is detected; supervisor may train the interviewer.
Office Editing:
It is another type of editing job of data collection performed by a centralized office staff to perform. The researcher must set up a centralized office with all facilities for editing and coding purpose by which coordination can be accomplished.
The office editing is done after the field editing. This implies a complete and thorough scrutiny of the questionnaire. There should be expert editors in the office to evaluate and examine the completed returns of the respondents.
* “A systematic way in which to condense extensive data sets into smaller analyzable units through the creation of categories and concepts derived from the data.”
* “The process by which verbal data are converted into variables and categories of variables using numbers, so that the data can be entered into computers for analysis.”
* When testing a hypothesis (deductive), categories and codes can be developed before data is collected.
* When generating a theory (inductive), categories and codes are generated after examining the collected data.
o Content analysis
o How will the data be used?

The process transforming data from a research project, such as answers to a survey questionnaire, to computers is referred to as data entry. The process of entering data into a computerized database or spreadsheet.
4.2 Univariate Analysis
Only one variable (Eg: Blood types) Can calculate percentage (Eg. 30% have A blood group etc.)
4.3 Bivariate analysis is the simultaneous analysis of two variables (attributes). It explores the concept of relationship between two variables, whether there exists an association and the strength of this association, or whether there are differences between two variables and the significance of these differences. There are three types of bivariate analysis.
• Numerical & Numerical
• Categorical & Categorical
• Numerical & Categorical
• Tests the significance of group differences between two or more groups
• Tests with two or more categories only determines that there is a difference between groups, but doesn’t tell which is different
• eg: Do CAT scores differ for low- middle- and high-income students?
Analysis of Variance (ANOVA)
The ANOVA test assesses whether the averages of more than two groups are statistically different from each other. This analysis is appropriate for comparing the averages of a numerical variable for more than two categories of a categorical variable.
4.4 Multivariate Analysis
• Many statistical techniques focus on just one or two variables. Multivariate analysis (MVA) techniques allow more than two variables to be analysed at once.
• Imagine out of the five senses you only had sight. From your perspective you could see the world but you would not be able to hear the sounds around you, smell, and taste or feel things. Your understanding of the world would be more limited. Most of us use all of our senses to understand the world around us i.e. not just one “measurement” but the combination of several senses working together. In multivariate analysis we use the information from many sources simultaneously to get a better picture of our surroundings.
The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels of independent variables on their own or in combination with one another have an effect on the dependent variables.
4.5 Discriminate Analysis
In this analysis, two or more groups are compared. IN the final analysis, we need to find out whether the groups differ one from another.
Eg; where discriminate analysis is used:
1. Those who buy our brand and those who buy competitors’ brand.
2. Good salesman, poor salesman, medium salesman.
3. Those who go to Food world to buy and those who buy in a kirana shop.
4. Heavy user, medium user and light user of the product.
4.6 Factor analysis
In purpose of Factor analysis is to group large set of variable factors into fewer factors. Each factor will account for one or more component. Each factor a combination of many variables.
Customer feedback about a two-wheeler manufactured by a company.
The MR Manager prepares a questionnaire to study the customer feedback. The researcher has identified six variables or factors for this purpose. They are as follows:
1. Fuel efficiency (A)
2. Durability (B)
3. Comfort(c)
4. Spare parts availability(D)
5. Break down frequency(E)
6. Price(F)
A, B,D,E into Factor-1
F into Factor-2
C into Factor-3
Factor-1 can be termed as Technical factor
Factor-2 can be termed as price factor;
Factor-3 can be termed a s personal factor
For future analysis, while conducting a study to obtain customers’ opinion, three factors mentioned above would be sufficient. One basic purpose of using factor analysis is to reduce the number of independent variables in the study.
4.7 Cluster Analysis
It is used:
1. To classify personal or objects into small number of clusters or group.
2. To identify specific customer segment for the company’s brand.
Cluster analysis is a technique used for classifying objects into groups. This can be used to sort data (a number of people, companies, cities, brands or any other objects) into homogeneous groups based on their characteristics. The result of cluster analysis is a grouping of the data into groups called clusters. The researcher can analyze the clusters for their characteristics and give the cluster, names based on these.
A housing finance corporation wants to identify and cluster the basic characteristics, lifestyles and mindset of persons who would be availing housing loans. Clustering can be done based on parameters such as interest rates, documentation, processing fee, number of installments. Etc.
There are two ways in which Cluster Analysis can be carried out:
1. First, objects/respondents are segmented into a pre-decided number of clusters. In this case, a method called non-hierarchical method can be used, which partitions data into the specified number of clusters.
2. The second method is called the hierarchical method.
4.8 Conjoint Analysis
It is concerned with the measurement f the joint effect of two or more attributes that are important from the customers’ point of view. Eg: An airline would like to know, which is the most desirable combination of attributes to a frequent traveler: a) Punctuality b) Air fare c) Quality of food served on the flight, and d) Hospitality and empathy shown.

Design attributes for a product are first identified. For a shirt manufacturer, these could be design such as designer shirts vs. plain shirts, this price of Rs 400 versus Rs. 800. The outlets can have exclusive distribution or mass distribution. All possible combinations of these attribute levels are then listed out. Each design combination will be ranked by customers and used as input data for conjoint analysis. Then the utility of the products relative to price can be measured.
There are three steps in conjoint analysis
a. Identification of relevant products or service attributes.
b. Collection of data
c. Estimation/Evaluate the worth for the attribute chosen.
4.9 Multidimensional scaling (MDS)
It is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with a matrix of item-item similarities, and then assigns a location to each item in N-dimensional space, where N is specified a priori. For sufficiently small N, the resulting locations may be displayed in a graph or 3D visualization.
MDS algorithms fall into a taxonomy, depending on the meaning of the input matrix:
1. Classical multidimensional scaling
Also known as Torgerson Scaling or Torgerson-Gower scaling, takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.
2. Metric multidimensional scaling
A superset of classical MDS that generalizes the optimization procedure to a variety of loss functions and input matrices of known distances with weights and so on. A useful loss function in this context is called stress, which is often minimized using a procedure called stress majorization.
3. Non-metric multidimensional scaling
In contrast to metric MDS, non-metric MDS finds both a non-parametric monotonic relationship between the dissimilarities in the item-item matrix and the Euclidean distances between items, and the location of each item in the low-dimensional space. The relationship is typically found using isotonic regression.
Louis Guttman’s smallest space analysis (SSA) is an example of a non-metric MDS procedure.
4. Generalized multidimensional scaling
An extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In case when the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.
4.10 Application of SPSS
1. Start SPSS. Go to Windows Start menu and choose Programs, and the SPSS for Windows. Then the Data Editor window will open.
2. Step 2
Appearing in the list boxes will be the variable names. Often it is best when these variable titles are in alphabetical order so you may have to change the order. From the menu choose Edit then Options, then go to the General Tab and select Display labels in the Variables list group. Select Alphabetical and then click OK twice.
3. Step 3
Open a Data File. From the menu choose File, Open, Data. The Open File box will display. Double click Tutorial folder, double click sample file folders, click the file demo.sav, click Open. From the menus choose View and then Value Labels.
4. Step 4
Run an Analysis. From the menus choose Analyze, Descriptive Statistics, then Frequencies. The frequencies dialog box will be displayed and the icons will provide the information needed about the data type and level of measurement.
How to use SPSS
1. Open SPSS and import your data. You can import your data set from an Excel file or any other CSV file. To import your data, click “Open Another Type of File” and choose the file you would like to import. If you would like to enter new data, simply click “Type in Data”. When you have chosen a data source, click “Ok.”
2. Step 2
Edit your raw data in the variable view. There are two views in SPSS, (1) data view and (2) variable view. Click the “Variable View” tab located on the bottom of the application and edit your raw data. Here you can edit categories the name of your variable, type of variable and measurement category. It is important to properly edit your variables, as many statistical tests will rely upon properly formatted data.
3. Step 3
Choose a statistical test. Once you have entered and formatted your data, you will be ready to run a statistical test. The most common tests are found in the “Analyze” tab located across the top of the application.
4. Step 4
Paste your syntax. Before completing a statistical test, you should always paste your syntax by clicking “Paste” in the dialog box. This will copy your syntax into a separate file. If the application crashes, or you need to run the same test again, you can use the syntax file to initiate a statistical test.
5. Step 5
View your output. Once your statistical test has been run, you can view the results in an output file that will open in a separate window
Advantages of SPSS
SPSS is the statistical package most widely used by political scientists. There seem to be several reasons why:
* SPSS has been around since the late 1960s. Statistical Package for the Social Sciences,
* Of the major packages, it seems to be the easiest to use for the most widely used statistical techniques
* One can use it with either a Windows point-and-click approach or through syntax (i.e., writing out of SPSS commands
* Many of the widely used social science data sets come with an easy method to translate them into SPSS; this significantly reduces the preliminary work needed to explore new data.
Disadvantages of SPSS
There are also two important limitations that deserve mention at the outset:
* SPSS users have less control over statistical output than, But, once a researcher wants greater control over the equations or the output, she or he will need to either choose another package or learn techniques for working around SPSS
* Once a researcher begins wanting to significantly alter data sets, he or she will have to either learn a new package or develop greater skills at manipulating SPSS.
Overall, SPSS is a good first statistical package for people wanting to perform quantitative research in social science because it is easy to use and because it can be a good starting point to learn more advanced statistical packages.

* The research report is the compilation of findings from a piece of research
* A research report is a precise presentation of the work done by a researcher while investigating a particular problem
Report writing is the final stage of the business research and it is concerned with making the findings available to the readers with varied interests. It is important to understand as to how to write a report. Your final report should be in accordance with the writing style used at your university. Whatever style you adopt, the content of the research report never varies. The final report of a research exercise takes a variety of forms.
* A research report funded by an educational institution may be in the form of written document.
* A research report may also take the form of an article in a professional journal.
* The purpose of research is to search for knowledge. (It is just to analyze a particular situation and finding out some solution, that solution/result will be finished in the form of report.)
* Reporting is the process through which a basis ground is prepared for the exchange of ideas or thoughts.
* Reporting helps the researcher to make specific recommendation for a course of action over, the phenomena, he studied. This is what actually expected in case of any study.
1. Oral report
2. Written report
a) The Popular report
b) The report for the administration
c) The technical report
d) Formal report
The oral reporting is that the oral presentation in meetings. For example: seminars, conferences, symposia, etc. is mainly oral presentation.
When compare to oral report, the preparation and presentation of written is somewhat difficult because in case of oral report the presenter can talk in their own style, but in case of written they should be very careful about the alignment, meaning, words, language, etc.
Written reports themselves are different types. In the context of reporting to management of a company, reports are classified as: external and internal reports; routine and special reports; and operating and special reports.
A useful classification of research reports seems to be the one based on the audience, i.e., the people to whom the reports is meant. On this basis, written reports can be categorized as follows:
* The popular report
* The report for the administration
* The technical report
* Formal report
Popular report: This is the report meant to be read by public in the developments taking place around them. For example: a researcher has worked on denudation of forest and ecological balance (research on forest i.e., about cause and effects of cutting and destroying trees in forest).the public in this context would be interested in such facts as the extent of forests, denudation, impact of denudation on ecology and specific sectors like agriculture.
The report for the administration: Many of the business reports are of this type. They may be submitted to any level. Usually, Supervisors submit periodical reports about production, machinery maintenance, overtime, etc. Similar reports are also submitted by the middle level managers to the top level management
The technical reports: A technical report is written by an expert to be read by another expert. In this sense, a thesis is a technical report intended to be read by another researcher.
Formal report: A formal report is used to document the results of an experiment, a design, or to pass on any type of information in a formal style. When writing a formal report it is important to ensure good English use and to follow the correct format as like as follow:
* Abstract or summary
* Outline
* Introduction
* Discussion
* Conclusion
* Recommendations
* appendix
Research report format: The following outline is the suggested format for writing the research report
1. Title page
2. Letter of authorization
3. Summary of findings
4. Table of content
* List of tables
* List of figures
5. Introduction
* Background to the research problem
* Objectives
* Hypothesis
6. Methodology
7. Data collection
* Sample and sampling method
8. Statistical or qualitative methods used for data analysis
9. Sample description
10. Findings
11. Limitations
12. Results, interpretation and conclusions.
13. Recommendation
14. Appendices
15. Bibliography

* Problem definition
* Research objectives
* Background material
* Methodology
* Sampling design
* Research design
* Data collection
* Data analysis
* Limitation
* Findings
* Conclusions
* Recommendations
* Appendices
* Bibliography
* Index
* Conclusion
Executive summary is a term used in business for a short document that summarizes a longer report, in such a way that readers can rapidly become acquainted with a large body of material without having to read it all.
Steps to write an executive summary:
Step 1: Plan to create a summary each time you write a business report exceeding four pages. Write the summary after you write the main report, and make sure it is no more than 1/10 the length of the main report.
Step 2: List the main points the summary will cover in the same order they appear in the main report.
Step 3: write a simple declarative sentence for each of the main point
Step 4: Add supporting or explanatory sentences as needed, avoiding unnecessary technical material and jargon.
Step 5: Read the summary slowly and critically, making sure it conveys your purpose, message and key recommendations. You want readers to be able to skim the summary without missing the point of the main report.
Step 6: Check the errors of style, spelling, grammar and punctuation. Ask a fellow writer to proofread and edit the document.
Step 7: Ask a nontechnical person- for example, your parents or your spouse – to read the document. If it confuses or bores them, the summary probably will have the same effect on other nontechnical readers.
Chapterisation means scanning of the entire report taken by the researcher. The subject of the report is to be divided into different parts, arrange them in a systematic way and mention which aspects of them in a systematic way and mention which aspects of the research will be studied in which chapter. It should be planned that one chapter will seems to be a continuation of the previous one.
> Introduction
> Review of related literature
> Design of the study
> Analysis and interpretation of data
> Main findings and recommendation
> Summary
Writing a Report
During your studies you may be required to research a particular area and produce a report. For Instance depending on your area of study you might be asked to write a report on the performance.
Some of the reasons we write reports are to
• Inform
• Make proposals or recommendations for change
• Analyze and solve problems
• Present the findings of an investigation or project
• Record progress
Your lecturer or teacher will usually provide you with the following information
• The topic or subject of the report
• The required length and due date
• A clear idea of its purpose and who will read it
• The format headings to be used and their order
Audience Research is an important tool to study the characteristics of target audience for various media including demographic and psycho-graphic details of the audience, their exposure to various media, listening/viewing/reading habits, needs and tastes for various media contents and moreover, to estimate the size of audience for various programmes and programme ratings.
Audience Research on the one hand provides programme feedback to programme produces to prepare audience friendly programmes and on the others provides audience share for the various media contents to advertisers and marketers which in turn , helps in fixing rates for the various programmes and channels. Thus, this gives consumer insight to the stakeholders and works as eyes and ears for the media organizations.
Readability is the ease in which text can be read and understood. Various factors to measure readability have been used, such as “speed of perception,” “perceptibility at a distance,” “perceptibility in peripheral vision,” “visibility,” “the reflex blink technique,” “rate of work” (e.g., speed of reading), “eye movements,” and “fatigue in reading.”
Readability is distinguished from legibility which is a measure of how easily individual letters or characters can be distinguished from each other. Readability can determine the ease in which computer program code can be read by humans, such as through embedded documentation.
In general usage, and more specifically in reference to education and psychology, it has roughly the same meaning as understanding. Reading comprehension measures the understanding of a passage of text.
Reading comprehension
It is defined as the level of understanding of a text. This understanding comes from the interaction between the words that are written and how they trigger knowledge outside the text.
TONE IN WRITING: In written composition, tone is often defined as what the author (rather than the reader) feels about the subject. (What the reader feels about it, by contrast, is referred to as the mood.) Tone is also sometimes confused with voice, which can be explained as the author’s personality expressed in writing. Tone is established when the author answers a few basic questions about the purpose of the writing:
Why am I writing this?
Who am I writing it to?
What do I want the readers to learn, understand, or think about?
Tone depends on these and other questions. In expository, or informative, writing, tone should be clear and concise, confident but courteous. The writing level should be sophisticated but not pretentious, based on the reader’s familiarity with or expertise in the topic, and should carry an undertone of cordiality, respect, and, especially in business writing, an engagement in cooperation and mutual benefit.
After finishing the documentation, one is ready to proofread the report and to prepare final manuscript. Proofreading is the process of checking work for errors in spelling, grammar, usage, level of language, capitalization, punctuation, and documentation. Final editing of the report should be taken-up after completing the writing of research report. This helps in identifying mistakes, if any, better and correcting the mistakes.
* Double check the spellings of proper names, such as the names of people and places.
* Check to see that the quotations you have used fit grammatically into the sentences in which they appear.
* Check to see that your language is not too informal
* Check all titles of works to make sure that these rules have been followed.
* Check every sentence to make sure that it has an end mark. If the sentence ends with a parenthetical citation, make sure that the citations appears before the end mark. In the case of a long, indented quotation , the citation should follow the end mark
* Check every quotation in the body of the text to make sure that it begins and ends with quotation mark.
* Check to see that you have used points of ellipsis properly in edited quotations.
* Make sure that every citation corresponds to an entry in the works cited list.
* Make sure that quotation, summary, or paraphrase is followed by a parenthetical citation.

Although most people acquire their sense of right and wrong during childhood, moral development occurs throughout life and human beings pass through different stages of growth as they mature. Ethical norms are so ubiquitous that one might be tempted to regard them as simple commonsense.
The following is a rough and general summary of some ethical principles that various codes address*:
Honest: Strive for honesty in all scientific communications. Honestly report data, results, methods and procedures, and publication status. Do not fabricate, falsify, or misrepresent data. Do not deceive colleagues, granting agencies, or the public.
Objectivity: Strive to avoid bias in experimental design, data analysis, data interpretation, peer review, personnel decisions, grant writing, expert testimony, and other aspects of research where objectivity is expected or required. Avoid or minimize bias or self-deception. Disclose personal or financial interests that may affect research.
Integrity: Keep your promises and agreements; act with sincerity; strive for consistency of thought and action.
Carefulness: Avoid careless errors and negligence; carefully and critically examine your own work and the work of your peers. Keep good records of research activities, such as data collection, research design, and correspondence with agencies or journals.
Openness: Share data, results, ideas, tools, resources. Be open to criticism and new ideas.
Respect for Intellectual Property: Honor patents, copyrights, and other forms of intellectual property. Do not use unpublished data, methods, or results without permission. Give credit where credit is due. Give proper acknowledgement or credit for all contributions to research.
Confidentiality: Protect confidential communications, such as papers or grants submitted for publication, personnel records, trade or military secrets, and patient records.
Responsible Publication: Publish in order to advance research and scholarship, not to advance just your own career. Avoid wasteful and duplicative publication.
Competence: Maintain and improve your own professional competence and expertise through lifelong education and learning; take steps to promote competence in science as a whole.
Legality: Know and obey relevant laws and institutional and governmental policies.
Human Subjects Protection: When conducting research on human subjects, minimize harms and risks and maximize benefits; respect human dignity, privacy, and autonomy; take special precautions with vulnerable populations; and strive to distribute the benefits and burdens of research fairly.
SUBJECTIVITY refers to that the results are researcher -dependent. Different researchers may reach different conclusions based on same interview. In contrast, when a survey respondent provides a commitment score on a quantitative scale, it is thought to be more objective because the number will be the same no matter what researcher is involved in the analysis.
Subjectivity guides everything from the choice of topic that one studies, to formulating hypotheses, to selecting methodologies, and interpreting data.
OBJECTIVITY pre-supposes an independent reality that can be grasped. If there is no independent reality, or if reality cannot be apprehended, or if reality is mere the concoction of the observer, then the notion of objectivity is moot.
Objectivity is the first condition of research. It means willingness and ability to examine the evidence dispassionately. In other words, objectivity, means basing conclusion on facts without any bias judgment. This difficulty arises out of the adverse influences of:
1. Personal prejudices and bias,
2. Value judgments,
3. Ethic dilemma, and Complexity of social phenomenon