Research Methodology

MPRBA-307: RESEARCH METHODOLOGY The objective of this course is to familiarize the students with the concepts and the techniques of Research Methodology applicable to business arena UNIT-I: Introduction - Meaning, Importance of Research, Types of research, Research ProcessProblem of Identification-Formulation-Classification, Concept and Construction of HypothesisSteps in Testing Hypothesis. UNIT–II: Research Design – Meaning, Purpose and Principles –Types of Research DesignExploratory – Descriptive – Experimental, Data Collection- Sources of Data-Methods of Data Collection-Questionnaire Design and Pre Testing of Questionnaire. UNIT–III: Sampling & Sampling Designs- Determination of Sample Size-Census Survey Vs Sample Survey-Advantages of Sampling – Sampling Methods-Probability Sampling-Non Probability Sampling. UNIT–IV: Data Tabulation-Analysis and Interpretation: Editing, Decoding and Classification of Data-Preparation of Tables-Analysis of Data - Scaling Techniques - Graphic and Diagrammatic Representation of Data. UNIT-V: Research Analysis and Report Writing: Multiple Regression(General Linear Model), Principals of Component Analysis, Discriminate Analysis –Factor Analysis- Types of ReportsContents of Report-Formats of Reports-Presentation of Reports. Text Book Kothari, C.R., Research Methodology – Methods and Techniques, New Age International Publishers, New Delhi,2007. Reference Books 1. Boyd, Westfall and Stouch, Marketing Research, Text and cases, All India Travel Book Sellers, New Delhi, 2005. 2. Brayman, Research Methods, Oxford University Press, New Delhi, 2005. 3. Krishnaswami, O.R., Methodology of Research in Social Sciences, Himalaya Publishing House, Mumbai, 2006. 4. R.Pannersalvem, Research Methodology, Prentice-hall of India Pvt Ltd, New Delhi, 2004 5. R.S. Dwivedi, Research Methodology in Behavioral Science, Macmillan India ltd, New Delhi, 2005. 6. Wilkinsan and Bhandarkar – Methodology and Techniques of Social research, Himalaya Publishing house, New Delhi, 2005.

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UNIT-I: Introduction - Meaning, Importance of Research, Types of research, Research ProcessProblem of Identification-Formulation-Classification, Concept and Construction of HypothesisSteps in Testing Hypothesis.

Research Methodology  A search for knowledge  A scientific and systematic search for pertinent information on a specific topic  A systematized effort to gain new knowledge  Research as an academic activity comprises – defining and redefining problems, formulating hypothesis; collecting organizing and evaluating data; making deductions and reaching conclusions. Definition  Research is the systematic process of collecting and analyzing information (data) in order to increase our understanding of the phenomenon about which we are concerned or interested.  The search for knowledge through objective and systematic method of finding solution to a problem is research. Objectives of research The purpose of research is to discover answers to questions through the application of scientific procedures  To gain familiarity with a phenomenon or to achieve new insights into it (studies with this objective are known as explorative or formulative studies)  To portray accurately the characteristics of a particular individual, situation or a group (studies with this object in view are known as descriptive studies)  To determine the frequency with which something occurs or with which it is associated with something else (known as diagnostic studies)  To test a hypothesis of a causal relationship between variables (known as hypothesis testing) Types of Research 1. Descriptive vs. Analytical 2. Applied vs. Fundamental 3. Quantitative vs. Qualitative 4. Conceptual vs. Empirical 1. Descriptive vs. Analytical •

The major purpose of the descriptive research is description of the state of affairs as it exists; usually includes surveys and fact-finding enquiries.

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The main characteristic here is that the researcher has no control over the variables – he can only report what has happened or what is happening. Ex: survey methods to identify people’s preferences.

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In analytical research, the researcher has to use facts or information already available and analyze these for critical evaluation.

Conceptual vs. Empirical • Conceptual research is related to some abstract idea or theory. Used by philosophers and thinkers. • Empirical research relies on experience or observation alone; it is data based research;

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Quantitative vs. Qualitative Quantitative research is based on the measurement of quantity – it is applicable to phenomena that can be expressed in terms of quantity. Qualitative research is concerned with qualitative phenomena – relating to or involving quality or kind. Ex.-motivation research. 4. Applied vs. Fundamental

Research can either be applied (action) research or fundamental (basic or pure). Applied research aims at finding a solution for an immediate problem facing society or an organization, whereas, fundamental research is mainly concerned with generalizations and with formulation of a theory.

Research Process •

Research Process consists of series of actions or steps necessary to effectively carry out research. • The process consists of closely related activities; such activities overlap continuously rather than following a strictly prescribed sequence. The steps are as follows: 1. Formulating the research problem 2. Extensive literature survey 3. Development of working hypothesis 4. Preparing the research design 5. Determining sample design 6. Collecting the data 7. Execution of the project 8. Analysis of data 9. Hypothesis-testing 10. Generalization and Interpretation 11. Preparation of the Report 3

1. Formulating the Research Problem: Two types of problems:  Problems which related to state of nature  Problems which relate to relationships between variables The formulation of a general topic into a specific research problem is the first step in scientific enquiry Two steps in formulating the research problem:  Understanding the problem thoroughly  Rephrasing the same into meaningful terms from an analytical point of view Must review two types of literature:  The Conceptual literature concerning concepts and theories  The Empirical literature consisting of earlier studies, which are similar to the one proposed  Formulating/defining a research problem is of great importance and significance in the entire research process  The problem must be defined unambiguously  Must verify the objectivity and validity of background facts concerning the problem 2.      

Extensive Literature Review Abstracting/Indexing journals Published/Unpublished bibliographies Academic journals Conference proceedings Govt. Reports Books

3. Development of Working Hypothesis  The researcher should state, in clear terms the working hypothesis  Working Hypothesis is a tentative assumption made in order to draw out and test its logical or empirical assumptions  Hypothesis is the focal point of the research, for ex: “students who receive counseling will show a greater increase in creativity than students not receiving counseling” or “car A is performing as well as car B”

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Functions of a Hypothesis  It guides the direction of the study.  It identifies facts that are relevant and those that are not.  It suggests which form of research design is likely to be most appropriate.  It provides a framework for organizing the conclusions that result 4. Preparing the Research Design Need to prepare a research design – a conceptual structure within which the research would be conducted The primary objective of the research design is to collect the relevant data Research Purposes may be grouped into 1. Exploration 2. Description 3. Diagnosis 4. Experimentation Many research designs exist. 5. Determining the Sample Design  All the items under consideration in any field constitute a “Universe” or “Population”.  A complete enumeration of all the items in the “population” is known as a “census enquiry”.  Since a complete census enquiry is not possible generally, we select a ‘sample’ – a few items from the “universe” for our study.  Researcher selects the sample by using ‘sampling design’ – a definite plan determined before any data is actually collected Types of Sampling 1. Deliberate Sampling 2. Simple Random Sampling 3. Systematic Sampling 4. Quota Sampling 5. Stratified Sampling 6. Cluster/area Sampling 7. Multi-stage Sampling 8. Sequential Sampling

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6. Collecting the Data Need to collect appropriate data Primary data can be collected thru experiment or survey In experiment, he observes some quantitative measurements (data), with which the hypothesis is tested

i. ii. iii. iv. v.

In Survey, data can be collected by the following methods: Observation Personal Interview Telephone Interview Mailing Questionnaires Through Schedules

7. Execution of the Project The research study must be executed in a systematic manner to ensure that adequate and dependable data are collected. Should be rigorously methodological 8. Analysis of Data Requires that the data be necessarily condensed into manageable groups and tables for further analyses Should classify the new data into some purposeful and usable categories Coding is done at this stage Tabulation – classified data are put into tables Analysis, after tabulation is based on the computation of various percentages, coefficients, etc. by applying statistics Tests of significance would be applied wherever relevant 9. Hypothesis Testing Do the data support the hypothesis or they contrary? Chi Square test, t-test, f-test are normally used Hypothesis testing will result in either accepting the hypothesis or in rejecting it 10. Generalization & Interpretation To arrive at a generalization, that is, to build a theory Or to interpret the data in terms of existing state of knowledge (theories) 11. Preparation of Report/Thesis Has to prepare the report The layout of the report is as follows:  The prefatory part  The Main Body/Text  The Supplemental Part The Prefatory Part 6

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Title page Certification Acknowledgments Preface Contents page The Main Body Introduction Summary of Findings Main Report conclusion

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The Supplemental Part References, or Bibliography Appendices Index

Good Research Requires  The scope and limitations of the work to be clearly defined.  The process to be clearly explained so that it can be reproduced and verified by other researchers.  A thoroughly planned design that is as objective as possible.  Highly ethical standards are applied.  All limitations are documented.  Data be adequately analyzed and explained.  All findings are presented unambiguously and all conclusions be justified by sufficient evidence.

Problem Identification Defining the Research Problem: The first step in research is selecting and properly defining a research problem  “A research problem refers to some difficulty which a researcher experiences in the context of either a theoretical or conceptual situation and wants a solution for it”  “ A research problem exists when the individual or the group, having one or more desired outcomes, are confronted with two or more courses of action that have some but not equal efficiency for the desired objective(s) and are in doubt about which course of action is best” Components of Research Problem: 7

1. There must be an individual or a group which has some difficulty or the problem 2. There must be some objectives 3. There must be alternative means (courses of action) for obtaining the objectives 4. There must remain some doubt in the mind of the researcher regarding the selection of the alternatives 5. There must be some environment to which the difficulty pertains

Selecting the Problem The process of selecting the problem is the most difficult and crucial step in the entire research process. The following steps are suggested: 1. Subject, which is overdone, should not be normally chosen for it will be difficult to throw any light on it. 2. Controversial subject should not become the choice of the average researcher 3. Too narrow or vague problems should be avoided 4. The subject should be familiar and feasible so that related research material or sources are within reach 5. The selection of a problem must be preceded by a preliminary study Formulation of the Problem  “A problem clearly stated is a problem half solved”  A problem must be precisely DEFINED  Formulation of a problem is often more essential than its solution  It facilitates the working out of the research design and all the sequential steps involved in research. Defining a problem involves the task of laying down the boundaries within which a researcher shall study the problem, with a predetermined objective in view.  The following steps are helpful:  Statement of the Problem in a general way  Understanding the nature of the problem  Surveying the available literature  Developing ideas thru discussions  Developing the research problem An Illustration… Statement 1: “Why is the productivity in Japan so much higher than in India? Statement 2: “What factors are responsible for the higher labor productivity of Japan’s manufacturing industries, during the decade 1971 to 1980, relative to India’s manufacturing industries?” Statement 3: “To what extent did labor productivity in 1971 to 1980 in Japan exceed that of India in respect of 15 selected manufacturing industries?”

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Conclusion The task of defining a research problem follows a sequential pattern – the problem is stated in a general way, the ambiguities are resolved, thinking and rethinking results in a more specific formulation of the problem so that it may be a realistic one.

Hypothesis  Hypothesis is a principal instrument in research  Most research is carried out with the deliberate intention of testing hypothesis  Decision makers need to test hypothesis to take decisions regarding alternate courses of action  In Social Sciences, hypothesis testing is often used for deciding whether a sample data offers support for certain generalizations  Hypothesis-testing, thus, enables us to make probability statements about population parameters Meaning of Hypothesis  Simply, a mere assumption to be proved or disproved  But for a researcher, hypothesis is a formal question that he intends to resolve  Definition: “A proposition or a set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena asserted merely as a provisional conjecture to guide some investigation or accepted as highly probable in the light of established facts”  Often hypothesis is a predictive statement capable of being tested by scientific methods, that relates an independent variable to some dependent variable  Ex: students who receive counseling will show greater increase in creativity than students not receiving counseling; or Car A is performing as well as Car B  In sum, hypothesis is a proposition which can be put to test to determine its validity Characteristics of a Hypothesis o Should be clear and precise o Should be capable of being tested o Should be limited in scope and be specific o Should be stated in simple terms o Should state the relationship between variables o Should be consistent with most known facts o Should be amenable to testing within a reasonable time 9

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Must explain the facts that gave rise to the need for explanation

Basic Concepts of Hypothesis 1. Null Hypothesis and Alternative Hypothesis 2. The Level of Significance 3. Type I and Type II Errors

1. Null Hypothesis and Alternative Hypothesis In the context of statistical analysis:  If we are to compare Method A with Method B about its superiority and if we proceed on the assumption that both methods are equally good, then this assumption is termed as the Null Hypothesis  As against the above, we may think that the Method A is superior or that the Method B is inferior, we are then stating what is termed as Alternative Hypothesis  Alternative Hypothesis is usually the one which we wish to prove and the Null hypothesis is the one which we wish to disprove  Thus, a null hypothesis represents the hypothesis we are trying to reject, and the alternative hypothesis represents all other possibilities  2. The Level of Significance  In the context of hypothesis-testing, the level of significance is an important concept  It is always some percentage (usually 5%)  This implies that the null hypothesis will be rejected, when the sampling result (observed evidence) has less than 0.05 probability of occurring if the null hypothesis is true  That is, the 5% level of significance means that the researcher is willing to take as much as a 5% risk of rejecting the null hypothesis when it happens to be true 3. Type I and Type II Errors Basically two types of errors are possible: Type I Error – we may reject the null hypothesis when it is true; and Type II Error – we may accept the null hypothesis when in fact the null hypothesis is not true

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That is, Type I error means rejection of the hypothesis which should have been accepted and Type II error means accepting the hypothesis which should have been rejected

Steps in Hypothesis-testing  To test a hypothesis means to state (on the basis of data the researcher has collected) whether or not the hypothesis seems valid  In hypothesis testing the main question is – whether to accept the null hypothesis or not to accept the null hypothesis?  Steps for hypothesis testing refer to all the steps we take for making a choice between rejection and acceptance of the null hypothesis 1. Making a formal statement 2. Selecting a significance level 3. Deciding the distribution to use 4. Selecting a random sample 5. Calculation of the probability 6. Comparing the probability Making a Formal Statement  Consists in making a formal statement of the null hypothesis and also the alternative hypothesis  Ex: The average score in an aptitude test at the national level is 80. To evaluate a state’s education system, the average score of 100 of the state’s students selected on random basis is 75. The state wants to know if there is a significant difference between the state’s scores and the national scores. Hypothesis may be stated as follows: Null hypothesis: population mean = 80 Alternative hypothesis: population mean is not equal to 80 Selecting a Significance Level The hypothesis are tested on predetermined level of significance and should be specified Generally, either 5% level (0.05) or 1% level (0.01) is adopted Deciding the distribution to use The next step is to determine the appropriate sampling distribution Generally, follow the principles of Normal Distribution Selecting the Random Sample Select the random sample and compute an appropriate value The sample should furnish the empirical data Calculation of the Probability The next step is to calculate the probability that the sample result would diverge as it has from expectations, if the null hypothesis were in fact true Comparing the Probability 11

 The next step is to compare the probability thus calculated with the specified value (the significance level)  If the calculated probability is equal to or smaller than the significance level, then reject the null hypothesis (i.e. accept the alternative hypothesis); but if the calculated probability is greater, then accept the null hypothesis

Statistical Tests of Hypothesis Tests of hypothesis are also known as tests of significance They are classified as: 1. Parametric Tests or Standard Tests – ex. are z-test, t-test, F-test etc. and are based on the assumption of normality 2. Non-Parametric Tests or Distribution-free tests of hypothesis

UNIT–II: Research Design – Meaning, Purpose and Principles –Types of Research DesignExploratory – Descriptive – Experimental, Data Collection- Sources of Data-Methods of Data Collection-Questionnaire Design and Pre Testing of Questionnaire.

Research Design • •

A major issue in research is the preparation of the research design of the research project Decisions regarding what, where, when, how much, by what means, concerning an enquiry or a research study constitute a research design

Research Design – Definition “A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure” Is the conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data More explicitly: i. What is the study about? ii. Why is the study being conducted? iii. Where will the study be carried out? iv. What type of data is required? v. Where can the required data be found? 12

vi. vii. viii. ix. x.

What period of time will the study include? What will be the sample design? What techniques of data collection will used? How will the data be analyzed? In what style will the report be prepared?

Research Design has the following parts: i. The Sampling Design – which deals with the method of selecting items to be observed for the given study ii. The Observational Design – which relates to the conditions under which the observations are to be made iii. The Statistical Design – which concerns with the question of how many items are to be observed and how the information and data gathered are to be analyzed iv. The Operational Design – which deals with the techniques by which the procedures specified in the sampling, statistical and observational designs can be carried out In brief, a a. b. c. d.

research design must contain: A clear statement of the research problem Procedures and techniques to be used for gathering information The population to be studied Methods to be used in processing and analyzing data

Research Design – Important Concepts 1. Dependent and Independent Variables 2. Extraneous Variable 3. Control 4. Confounded Relationship 5. Research Hypothesis 6. Experimental and Non-experimental Hypothesis-testing Dependent & Independent Variables A concept which can take on different quantitative values is called a variable. Ex: weight, height, income etc., are examples of a variable Qualitative phenomena (the attribute) are also quantified on the basis of the presence or absence of the concerning attribute Dependent variable (DV) – if one variable depends upon or is a consequence of the other variable, it is termed as a DV And the variable that is antecedent to the DV is termed as the Independent variable IV 1.Dependent & Independent Variables Ex: if we say that height depends upon age, then height is the DV and age is the IV. Further, if height also depends upon the individual’s sex – then, height is the DV and age and sex are the IVs 13

2. Extraneous Variable IVs that are not related to the purpose of the study, but may affect the DV are termed as Extraneous Variable (EV) Ex: suppose the researcher wants to test the hypothesis that there is a relationship between children’s gains in social studies achievement and their self-concept. Here, self-concept is an IV and social studies achievement is a DV. Intelligence may as well affect the social studies achievement, but since it is not related to the purpose of the study, it will be termed as an EV Therefore, a study must be always so designed that the effect upon the DV is attributed entirely to the IVs and not to some EV. 3. Control One important characteristic of a good research design is to minimize the influence or effect of the EV. The term “Control” is used when we design the study minimizing the effects of extraneous variables 4. Confounded Relationship When the DV is not free from the influence of the EVs, the relationship between DV and IV is said to be confounded by the EV 5. Research Hypothesis When a prediction or a hypothesized relationship is to be tested by scientific methods, it is termed as a Research-Hypothesis The Research-Hypothesis is a predictive statement that relates an IV to a DV 6. Experimental and Non-Experimental Hypothesis testing research When the IV is manipulated it is an experimental design Research in which the IV is not manipulated is called Non-experimental hypothesistesting research Ex: a researcher wants to study whether intelligence affects reading ability for a group of students and for this purpose he randomly selects 50 students and tests their intelligence and reading ability by calculating the co-efficient of correlation between the two sets of scores – this is an example of non-experimental hypothesis testing, because the IV, intelligence is not manipulated But now ,suppose that the researcher randomly selects 50 students from a group of students who are to take a course in statistics and then divides them into two groups by randomly assigning 25 to Group A, the common program, and 25 to Group B, the special program. At the end of the course, he administers a test to each group in order to judge the effectiveness of the training program on the students’ performance. This is an example of experimental hypothesis testing because the IV (the type of training program) is manipulated.

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Types of Research Designs 1. Exploratory 2. Descriptive & Diagnostic 3. Experimental

Exploratory Research Design • •

Also known as Formulative Research Design Main purpose – is that of formulating a problem for precise investigation or developing hypotheses from an operational point of view • Major Focus – discovery of new ideas and insights • Exploratory studies must have flexibility in design to provide opportunity for considering different aspects of a problem under study Exploratory Research Design… The following 3 methods are used: •

The survey concerning literature

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The experience survey

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The analysis of “insight-stimulating” examples 1. The Literature Survey • The most simple and useful method of formulating the research problem or developing a hypothesis

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Hypotheses stated by earlier workers may be reviewed and their usefulness evaluated as a basis for further research Use the bibliographical survey of studies already done in one’s area of interest for formulating the problem An attempt must be made to apply concepts and theories

2. Experience Survey • Is the survey of people who have had practical experience with the problem • The object is to obtain insight into relationships between variables and new ideas relating to the research problem 3. Analysis of ‘insight-stimulating’ examples •

The method consists of the intensive study of selected instances of the phenomenon in which one is interested

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For this purpose, existing records may be examined; unstructured interviews with experts may be conducted; etc.

II. Descriptive & Diagnostic Research Design • Descriptive Studies are those which are concerned with describing the characteristics of a particular individual or of a group. • Studies concerned with specific predictions, with narration of facts and characteristics concerning individual, group or situation are ex.'s of descriptive research studies • Diagnostic Studies determine the frequency with which something occurs or its association with something else • Studies about whether certain variables are associated, are ex.’s of diagnostic studies The research design here must focus on the following: 16

1. Formulating the objective of the study 2. Designing the methods of data collection 3. Selecting the sample 4. Collecting the data 5. Processing and analyzing the data 6. Reporting the findings

III. Hypothesis-Testing research design •

Generally known as Experimental Studies – where the researcher tests the hypothesis of causal relationships between variables

• Such studies require procedures that not only reduce bias and increase reliability but will permit drawing of inferences about causality • Prof. R A Fisher’s name is associated with experimental designs. • He developed certain experimental designs for testing hypothesis

Principles of experimental designs 17

The three important principles are: 1. Principle of Replication 2. Principle of Randomization 3. Principle of Local control Principle of Replication The experiment should be repeated more than once to ensure that each treatment is applied in many experimental units instead of one. By doing so the statistical accuracy is increased Principle of Randomization Provides protection against the effect of extraneous factors in an experiment. That is, we design the experiment in such a way that the variations caused by extraneous factors can all be combined under the general heading of “chance”. Principle of Local Control Here the extraneous factor, the known source of variability, is made to vary deliberately over as wide a range as necessary and this needs to be done in such a way that the variability it causes can be measured and hence eliminated from the experimental error Important Experimental Designs There are several designs:  Informal Experimental Designs  Formal Experimental Designs Informal Experimental Designs 1. Before-and-after without control design 18

2. After-only with control design 3. Before-and-after with control design Formal Experimental Designs 1. 2. 3. 4.

Completely Randomized design Randomized block design Latin Square design Factorial design

Methods of Data Collection Essentially two types: 1.

Primary data – are those which are collected for the first time and are original in character

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Secondary data – are those which have already been collected by someone else and which have through some statistical analysis

Collection of Primary Data Primary data may be collected thru: Experiments Surveys (sample surveys or census surveys) Observation Personal Interviews Of the above, the important ones are: 1.Observation Method 2.Interview Method 3.Thru Questionnaires/Schedules 19

I. Observation Method Observation becomes a scientific tool and the method of data collection, when it serves a formulated research purpose, is systematically planned and recorded and is subjected to checks and controls on validity and reliability •

• Under observation – the information is sought by way of investigator’s own direct observation without asking from the respondent Main advantages are: • Subjective bias is eliminated • The information relates to what is currently happening • This method is independent of respondent’s willingness to respond Main Limitations are:  It is expensive  The information provided by this method is very limited  Unforeseen factors may interfere with the observation task

Types of Observation Essentially two types: 1.Structured vs. Unstructured Observation 2.Participant vs. Non-participant Observation

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Structured vs. Unstructured Observation Structured Observation – when the observation is characterized by a careful definition of the units to be observed, the style of recording the observed information, standardized conditions of observation and the selection of pertinent data of observation Unstructured Observation – when it takes place without the above characteristics

Participant vs. Non-participant This distinction depends upon the observer’s sharing or not sharing the life of the group he is observing II.

Interview Method

The Interview Method of collecting data involves presentation of oral-verbal stimuli and reply in terms of oral – verbal responses Personal Interview • PI Method requires the interviewer asking questions in a face-to-face contact with the person. • Collecting information thru PI is structured – the use of a set of predetermined questions and highly standardized techniques of recording. • Thus, the interviewer in a structured interview follows a rigid procedure, asking questions in a form and order prescribed • In unstructured interviews – there is a flexibility of approach to questioning • Unstructured interviews do not follow a system of pre-determined questions and standardized techniques of recording information Focused Interview – to focus attention on the given experience of the respondent and its effects The Interviewer has the freedom to decide the manner and sequence of questions to elicit/explore reasons and motives. The main task is to confine the respondent to a discussion of issues Clinical Interview – is concerned with broad underlying feelings or motivations or with the course of an individual’s life experience. Eliciting information is left to the interviewer’s discretion

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Non-Directive Interview – the interviewer's function is simply to encourage the respondent to talk about the topic with a bare minimum of direct questioning. The interviewer often acts as a catalyst to a comprehensive expression of the respondent’s feelings and beliefs Advantages 1. More information and in greater depth can be obtained 2. Resistance may be overcome by a skilled interviewer 3. Greater flexibility – an opportunity to restructure questions 4. Observation method can also be applied to recording verbal answers 5. Personal information can be obtained 6. Possibility of spontaneous responses and thus more honest responses Disadvantages 1. Expensive method 2. Interviewer bias 3. Respondent bias 4. Time consuming 5. Under the interview method the organization required for selecting, training, and supervising the field staff is complex with formidable problems 6. Establishing rapport to facilitate free and frank responses is very difficult

Data Collection thru Questionnaires • •

Popular in major studies Briefly – a Questionnaire is sent (by post) to the persons concerned with a request to answer the questions and return the Questionnaire. • A Questionnaire consists of a number of questions printed in a definite order on a form. • The Questionnaire is mailed to respondents who are expected to read and understand the questions and write down the reply in the space provided Merits of Questionnaire Method 1. Low cost – even when the universe is large and is widespread 2. Free from interviewer bias 3. Respondents have adequate time to think thru their answers 4. Respondents who are not easily approachable, can also be reached conveniently 5. Large samples can be used Demerits 1. Low rate of return 2. Respondents need to be educated and cooperative 3. Inbuilt inflexibility 4. Possibility of ambiguous replies or omission of items 5. This method is slow Features of a Questionnaire

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Questionnaire is the heart of a survey – needs to be carefully constructed Need to understand the features of the Questionnaire – its general form, question sequence and question formulation and the wording of the questions

1.General Form • May be either structured or unstructured • Structured Questionnaires – are those in which there are definite, concrete, predetermined questions  The questions are presented with exactly the same wording and in the same order to all respondents  The form of the questions may be either closed (yes or no) or open (inviting free responses  Structured Questionnaires may also have fixed alternative questions in which responses are limited to the stated alternatives  Thus, a highly structured Questionnaire is one in which all the questions and answers are specified and comments in the respondents’ own words are held to the minimum • Unstructured Questionnaire – when the above characteristics are absent, it is known as a unstructured Questionnaire • The Interviewer is provided with a general guideline on the type of information to be obtained 2.Question Sequence  Proper sequence is needed to elicit valid responses  Sequence must be clear – that is, the relation of one question to the next  To establish rapport and to gain cooperation from the respondent – difficult questions, personal questions etc should preferably come at the appropriate time rather than at the beginning 3.Question Formulation & Wording  Phrasing the questions must be clear and unambiguous  Questions should be impartial and unbiased  Should be easily understood  Should be simple (one idea at a time)  Should be concrete  Form of questions may be multiple choice or open-ended

Data Collection thru Schedules  Very similar to the Questionnaire method  The main difference is that a schedule is filled by the enumerator who is specially appointed for the purpose  Enumerator goes to the respondents, asks them the questions from the Performa in the order listed, and records the responses in the space provided.  Enumerators must be trained in administering the schedule 23

Other 1. 2. 3. 4. 5. 6. 7. 8.

Methods of Data Collection Warranty Cards Distributor or Store Audits Pantry Audits Consumer Panels Mechanical Devices Depth Interviews Content Analysis Projective Tests

Collection of Secondary Data Published data are available in: 1. Publications of State/Central govt.’s 2. Publications of International Bodies 3. Technical and Trade Journals 4. Books, Magazines and Newspapers 5. Reports/Publications of various organizations (banks, stock exchanges, business houses, etc) 6. Reports – by scholars, Universities, etc 7. Public records, Historical Documents, etc Secondary Data must possess the following characteristics:  Reliability of data – may be tested by checking:  Who collected the data?  What were the sources of the data?  Was the data collected properly?  Suitability of data – data that are suitable for one enquiry may not be necessarily suitable in another enquiry.  Therefore, the researcher must scrutinize the definition of various terms and units of collection. Also, the objectives, scope and nature of the original enquiry must be studied.  Adequacy of data – the data will be considered inadequate, if they are related to an area which may be either narrower or wider than the area of the present enquiry

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UNIT–III: Sampling & Sampling Designs- Determination of Sample Size-Census Survey Vs Sample Survey-Advantages of Sampling – Sampling Methods-Probability Sampling-Non Probability Sampling.

UNIT III - Sampling Design Census & Sample Survey All the items in any field of inquiry constitute a “Universe” or “Population” A complete enumeration of all items in the population is known as Census Inquiry Most times census inquiry is not practically possible Sample Survey –of a few items of the population • The respondents selected should be representative of the total population • The sampling process is called the sampling technique • The survey so conducted is known as the sample survey • The researcher must prepare a sample design for his study – that is, how a sample should be selected and what size such a sample would be Steps in Sample Design The following are crucial: 1. Type of Universe – define the set of objects, technically called the Universe, to be studied 2. Sampling Unit – sampling unit may be a geographical one (district, city, village) or it may be a social unit (family, club, school) or it may an individual 3. Source List – also known as ‘sampling frame’ from which the sample is to be drawn. It contains all items of a universe 4. Size of Sample – refers to the number of items to be selected from the universe to constitute a sample; a major issue here is – the size should neither be excessively large nor too small. An optimum sample is one which fulfills the requirements of efficiency, representativeness, reliability and flexibility 5. Budgetary Constraint – cost considerations have a major impact upon decisions relating to the size of the sample 6. Sampling Procedure – finally, the type of sample to be used, that is, the technique to be used in selecting the items for the sample. There are several sample designs, from which the researcher can choose.

25

Criteria of Selecting a sampling Procedure There are two costs involved in a sampling analysis – the cost of collecting the data and the cost of an incorrect inference resulting from the data The researcher, therefore, must be aware of the two causes of incorrect inferences: a) Systematic bias b) Sampling error • A systematic bias results from errors in the sampling procedures and it cannot be reduced or eliminated by increasing the sample size • Sampling Errors are the random variations in the sample estimates around the true population. Generally, sampling errors decreases with the increase in the size of the sample Types of Sample Designs All the sample designs are based on two factors – the representation basis and the element selection technique Representation Basis – the sample may be probability sampling or nonprobability sampling. Probability sampling is based on the concept of random selection; non-probability sampling is “non – random” sampling. Element Selection Basis – the sample may be either restricted or unrestricted. Unrestricted sampling is when each element is drawn individually from the population at large. Restricted sampling is when all other forms of sampling are used. Thus, sample designs are basically of two types: 1. Probability Sampling 2. Non-Probability Sampling

Non-Probability Sampling 26

Is that sampling procedure which does not afford any basis for estimating the probability that each item in the population has of being in included in the sample? Also known as deliberate sampling, purposive sampling and judgment sampling Here, items for the sample are selected deliberately by the researcher, that is, purposively choose the particular units of the universe for constituting a sample on the basis that the small mass that they select out of a huge one will be representative of the whole. Ex.’s – if the economic condition of people living in a state are to be studied, a few towns and villages may be purposively selected for intensive study on the principle that they can be representative of the entire state. Here, personal element (bias) has a great chance of entering into the selection of the sample However, if the investigators are impartial, work without bias and have the necessary experience – the results obtained may be tolerably reliable. Sampling Error cannot be estimated and the element of bias is always This is why; this design is rarely adopted in large inquiries of importance Quota Sampling is also an example of non probability sampling. Under quota sampling the interviewers are simply given quotas to be filled from the different strata Very convenient and inexpensive

Probability Sampling 27

Also know as Random sampling or Chance sampling Under this design, every item of the universe has an equal chance of inclusion in the sample It is a lottery method in which individual units are picked up from the whole group not deliberately but by some mechanical process It is blind chance alone that determines whether one item is selected or not The results obtained from probability or random sampling can be assured in terms of probability, that is, we can measure the errors of estimation or the significance Random sampling ensures the law of statistical regularity (which states that if on an average the sample chosen is a random one, the sample will have the same composition and characteristics as the universe) This is why it is considered as the best technique of selecting a very representative sample In sum, Random sampling: 1. Gives each element in the population an equal probability of getting into the sample; and all choices are independent of one another 2. Gives each possible sample combination an equal probability of being chosen

How to select a Random Sample 28

In actual/ideal practice – the Random Sample is taken by the following process: write each of the possible samples on a slip of paper, mix these slips in a box/container and then draw as a lottery. In complex and large universes this is practically possible. An easier method is – without taking the trouble of enlisting all possible samples on paper slips, we can write the name of each element of a finite population on a slip of paper, put the slip into a box and mix them thoroughly and then draw (without looking) the required number of slips. In doing so we must make sure that in successive drawings each of the remaining elements of the population has the same chance of being drawn This procedure will also result in the same probability for each possible sample Thus, to draw a sample from a finite population is easy, with the aid of random number tables, only when lists are available and items are readily numbered Complex Random Sampling Designs Systematic Sampling: the most practical way of sampling is to select every ith item on a list; an element of randomness is introduced into this kind of a sampling by using random numbers to pick up the unit with which to start. Ex.’s – if a 4% sample is desired, the first item would be selected randomly from the first 25 and thereafter every 25th item automatically be included in the sample. Thus, in systematic sampling, only the first unit is selected randomly and the remaining units of the sample are selected at fixed intervals. The merits of systematic sampling are:  It is an improvement over simple random sampling – because the sample is spread more evenly over the entire population  It is easier and less expensive  Can be used in large population The demerits are:  If there is a hidden periodicity in the population Stratified Sampling: if a population from which a sample is to be drawn does not constitute a homogeneous group, stratified sampling is applied in order to obtain a representative sample; here, the population is divided into several sub populations that are individually more homogeneous (these sub populations are called “strata”). Cluster Sampling: if the total area of interest happens to be a big one, a convenient way to take a sample is to divide the area into a number of 29

smaller non overlapping areas and then to randomly select a number of these smaller areas (called “clusters”), with the ultimate sample consisting of all units in these clusters. Area Sampling: if clusters happen to be some geographic subdivisions, then it is better known as area sampling Multi-stage Sampling: is a further development of the principle of cluster sampling. Ex. – suppose we want to investigate the working efficiency of nationalized banks in India and we want to take a sample of few banks for this purpose – the first stage is to select large primary sampling units such as States – then we select certain districts and interview all the banks in the chosen districts. This is two-stage sampling design Conclusion Normally one should resort to simple random sampling because under it, bias is generally eliminated and the sampling error can be estimated. But purposive sampling is more appropriate when the universe happens to be small. At times several methods of sampling may be used in the same study.

UNIT–IV: Data Tabulation-Analysis and Interpretation: Editing, Decoding and Classification of Data-Preparation of Tables-Analysis of Data - Scaling Techniques - Graphic and Diagrammatic Representation of Data.

30

Data Tabulation • The data after collection has to be processed – processing means editing, coding, classification and tabulation of the collected data, so that they are amenable to analysis. • Analysis refers to the computation of certain measures, along with searching for patterns of relationships among data groups. • In other words, analysis of data is performed with the purpose of summarizing the collected data and organizing these in such a manner that they answer the research question (s)

Processing Operations The following are the processing operations: 1. Editing 2. Coding 3. Classification 4. Tabulation

Editing • • • • • •

Editing is the process of examining the collected data (especially in surveys) to detect errors and correct these where possible Involves a careful scrutiny of the completed questionnaires/schedules Editing can take place at two stages: i) field editing and ii) central editing Field editing consists of reviewing of the questionnaire forms by the investigator for completing (translating or rewriting) what the respondent has written in abbreviated and/or illegible form; should not correct the errors of omission. Central editing should take place when all forms/schedules have been completed and returned Editors may correct the obvious errors such as an entry in the wrong place, etc

Coding • • • • •

Refers to the process of assigning numerals or other symbols to answers so that responses can be put in to a limited number of categories or classes. Such classes should be appropriate and have the characteristic of exhaustiveness, that is, there must be a class for every data item; and also mutual exclusivity, meaning that a specific answer can be placed in one and only one cell in a given category set. Coding is necessary for efficient analysis Coding decisions should usually be taken at the designing stage Coding is usually done by hand and the usual method is to code in the margin with a color pencil or to transcribe the data taken from the questionnaire to a coding sheet.

31

Classification •

Classification of data is the process of arranging data in groups or classes on the basis of common characteristics • Data having a common characteristic are placed in one class and in this way the entire data gets divided into a number of groups or classes Classification is of two types: 1. Classification according to Attributes 2. Classification according to Class Intervals 1. Classification according to attributes – • It is classified on basis of common characteristics which can be either descriptive (such as literacy, sex, honesty) or numerical (like weight, height, income) • Descriptive characteristics refer to qualitative phenomenon, which can’t be measured quantitatively – only their presence or absence in an individual item can be observed 2. Class Intervals – • The numerical characteristics refer to quantitative phenomenon which can be measured thru some statistical unit – data relating to income, production, age, weight are examples • Such data are classified on the basis of class intervals – each group of class interval, thus has an upper and a lower limit, known as class limits • The difference between two classes is known as the class magnitude • The number of items which fall in a given class is known as the frequency All the classes/groups, with their respective frequencies taken together and put in the form of a table are described as group frequency distribution or simply frequency distribution Important points to consider: i. How many classes should be there? (typically 5 – 15 classes are usual) ii. How to choose class limits? (normally, class limits should be located at multiples of 2, 5, 10, 20, 100, etc) Class intervals may be stated thus: Exclusive Type Class interval: 10 – 20 20 – 30 30 – 40 40 – 50 These are known as exclusive because, the upper limit of a class interval is excluded and items with values less than the upper limit (but not less than the lower limit) are put in the given interval Inclusive Type Class interval 11 – 20 21 – 30 31 – 40 41 – 50 In the inclusive type class interval the upper interval is also included in the class interval 32

iii. How to determine the frequency of each class? Can be done by tally sheets or mechanical devices

Tabulation • • •

When a mass of data has been collected, it becomes necessary to arrange it in a concise and logical order Thus, tabulation is the process of summarizing raw data and displaying it in a compact form, for further analysis Tabulation is essential for:  It conserves space and reduces explanatory and descriptive statement to a minimum  It facilitates the process of comparisons  It facilitates the summation of items and the detection of errors and omissions  It provides a basis for various statistical computations

Principles of Tabulation 1. Should have a clear, concise title, which is self explanatory 2. Should be distinctly numbered, for easy reference 3. The column headings (captions) and row headings (stubs) of the table should be clear and brief 4. Explanatory footnotes, if any, concerning the table should be placed directly beneath the table 5. Sources of the table must be indicated just below the table 6. Usually the columns are separated by lines, which make the table more readable and attractive 7. Those columns whose data has to be compared should be kept side by side. So also the percentages and/or averages – close to the data 8. It is important that all column figures be properly aligned. Decimal points and + or minus signs should be in perfect alignment 9. Abbreviations should not be used 10. Table should be made as logical, clear, accurate as possible 11. Total of rows should normally be placed in the extreme right column and that of the columns should be placed at the bottom 12. The arrangement of the categories in a table may be chronological, geographical, alphabetical, etc., to facilitate computation

33

UNIT-V: Research Analysis and Report Writing: Multiple Regression(General Linear Model), Principals of Component Analysis, Discriminate Analysis –Factor Analysis- Types of ReportsContents of Report-Formats of Reports-Presentation of Reports.

MEASURES

OF

CENTRAL TENDENCY

• Mean • Median • Mode Measures of Central Tendency • These statistics provide a measure of what values lie at the center of the distribution. • The most common is called the MEAN or sometimes the AVERAGE (or the EXPECTED VALUE) • The formula for the sample mean is the sum of all values divided by the number of observations. Mean Mean is used for interval (ratio) data such as income, age, wage rate and n test score etc. xi ∑ i =1 x= n Response

Percent of respondents Town -I

Town - II

Very important

10

5

Important

30

20

Neither Imp nor unimportant

20

10

Unimportant

25

40

Very unimportant

15

25

Mean 34

Median Median is used for ordinal or interval level of data, but not for nominal level of Data as it requires ordering of items from Highest to lowest or vice versa.

Age in Group (In years)

No of respondents

0-20

15

20-40

32

40-60

54

60-80

30

80-100

19

Mode Mode is used to measure the qualitative data. Since mode requires only frequency, it can be applied to any set of data at the nominal, level of data Discipline

Frequency( No of applicants)

Engineering

210

Commerce

180

Arts

110

Science

90

Others

50

Total

640

35

Life in Hours

No of Bulbs

0-20

9

20-40

17

40-60

45

60-80

46

80-100

32

100-120

18

120-140

14

140-160

10

160-180

9

Measures of dispersion or variability • The formula for the sample variance is the sum of squared deviations from the mean divided by the number of observations minus 1: 1 n 2 s = ( xi − x ) 2 ∑ n − 1 i =1 • •

•

The sample standard deviation (= s) is simply the square root of the sample variance. The book ignores another common measure of dispersion, the COEFFICIENT OF VARIATION, which is, simply a mean-standardized standard deviation. The coefficient of variation is the standard deviation divided by the mean, and is thus a measure of “relative” dispersion rather than “absolute” dispersion. C.ofV . =

s x

Skewness • Skewness tells us about whether the data is symmetric or not. Shape of the Distribution 36

• •

The shape of the distribution provides information about the central tendency and variability of measurements. Three common shapes of distributions are: – Normal: bell-shaped curve; symmetrical – Skewed: non-normal; non-symmetrical; can be positively or negatively skewed – Multimodal: has more than one peak (mode)

Relative Locations for Measures of Central Tendency

Karl Pearson Coefficient of Skewness is given by Mean − Mode

Skew=

σ

Bowley’s Coefficient of Skewness (Q + Q1 − 2 Median ) Skew= 3 Q3 − Q1

• Karl Pearson Coefficient of skewness Lies between -3 and +3. • Bowleys Coefficient of skewness lies between -1 and +1 Kurtosis • Kurtosis refers to the degree of flatness or peakedness of a curve. • Measures of Kurtosis – Kurtosis is a measure of the flatness or peakedness of a Distribution • Normal Kurtosis - Mesokurtic • Flat Kurtosis - Platokurtic • Peaked Kurtosis - Leptokurtic – A Measure of Kurtosis based on the 4th moment about

µ kurt = µ

2 4 2

−3

2

37

Correlation Correlation tells us the degree of association between two or more variables. The limits for correlation coefficient are -1 to +1 r=

[n∑ x

n∑ xy − ( ∑ x )( ∑ y )

2

][

− ( ∑ x ) * n∑ y 2 − ( ∑ y ) 2

2

]

1. The annual advertising expenditure (in lakhs of rupees) and the corresponding annual sales ( in crores of rupees) for the past ten years of a company are presented in the table. Find the correlation coefficient between annual advertising expenditure and annual sales. Also test the significance of correlation coefficient at 5% L.O. S Year

1

2

3

4

5

6

7

8

9

10

Annual advertising expenditure

10

12

14

16

18

20

22

24

26

28

Annual Sales

20

30

37

50

56

78

89

100

120

110

Test for significance of correlation coefficient: H0 : r = 0

H1 : r ≠ 0 t=

Test statistic:

r

( n − 2) 1− r2

Fallows t-distribution with n-2 degrees of freedom Probable error of correlation coefficient is used to in determining the reliability of the value of coefficient of correlation. P.E = 0.6745

1 −r 2 n

38

If r< P.E then there is no significant relation between the variables. If r>6( P.E) then there is significant relation between the variables. Using probable error we can find the limits for the population correlation coefficient by using the relation r ± P.E

Regression Line of regression of y on x is Line of regression of y on x is y = a +b x

x = a +bxy y

yx

y−y =r

byx =

σy x−x σx

(

)

x−x =r

n( ∑ xy ) − ( ∑ x )( ∑ y )

(

)

n ∑ x − (∑ x) 2

bxy =

2

(

σx y−y σy

)

n( ∑ xy ) − ( ∑ x )( ∑ y ) n

(∑ y ) − (∑ y ) 2

2

Find the lines of regression and estimate the value of y when x= 16 and value of x when y=17

P rice (Rs) S upply (Tonnes)

4 8

6 9 10 12

10 8 12 14 10 15 11 13 15 10

15 12 16 10

Find the line of regression of y on x for the following data Annual sales :20 23 25 27 21 29 22 24 27 35 Sales force :8 13 8 18 23 16 10 12 14 20 Annual advert Expenditure :28 23 38 16 20 28 23 30 26 32

Preparation of Report/Thesis Has to prepare the report The layout of the report is as follows:  The prefatory part  The Main Body/Text  The Supplemental Part

    

  

The Prefatory Part Title page Certification Acknowledgments Preface Contents page The Main Body Introduction Summary of Findings Main Report 39

conclusion



   

The Supplemental Part References, or Bibliography Appendices Index

ANALYSIS AND PRESENTATION OF DATA PRESENTING RESULTS: Written and Oral Reports Written Research Report • Short report – For well-defined, limited-scope problems with straight-forward methodology – Usually 5 or fewer pages 40

Formats • Findings summary (graphical or tabular) attached to letter of transmittal • Business letter • Internal memorandum Short Report (Memorandum) – Reason for writing – Answer direct inquiry with specific answer and supporting detail – Expository style with brevity and directness – Attach detailed materials as appendices when needed Long report – Technical report – Management report –

•

•

LONG REPORT COMPONENTS PREFATORY ITEMS – LETTER OF TRANSMITTAL – TITLE PAGE – AUTHORIZATION LETTER – EXECUTIVE SUMMARY – TABLE OF CONTENTS • INTRODUCTION – PROBLEM STATEMENT – RESEARCH OBJECTIVES – BACKGROUND • METHODOLOGY – SAMPLING DESIGN – RESEARCH DESIGN – DATA COLLECTION – DATA ANALYSIS – LIMITATIONS • CONCLUSIONS – SUMMARY CONCLUSIONS – RECOMMENDATIONS • APPENDICES • BIBLIOGRAPHY WRITTEN REPORT CONSIDERATIONS • ORDER OF REPORT – SENTENCE OUTLINE – TOPIC OUTLINE COMPREHENSIBILITY – READABILITY INDICES – PACE – TONE •

PRESENTATION

OF

STATISTICS 41

• • • •

TEXT PARAGRAPH FORMAT SEMI-TABULAR FORMAT TABULAR FORMAT GRAPHICAL FORMAT

GRAPHICAL FORMATS • LINE GRAPHS • AREA CHARTS • PIE CHARTS • BAR CHARTS • PICTOGRAPHS/GEO-GRAPHICS • 3-D GRAPHICS ORAL PRESENTATIONS • PREPARATION – LENGTH – CONTENT – STYLE • SCRIPTED • EXTEMPORANEOUS – AUDIOVISUALS • TYPE • ROLE • CONTENT – OPENING – FINDINGS AND CONCLUSIONS – RECOMMENDATIONS • DELIVERY – VOCAL CHARACTERISTICS – PHYSICAL CHARACTERISTICS

AUDIOVISUAL AIDS • • • • • • • •

CHALKBOARDS WHITEBOARDS HANDOUT MATERIALS FLIP CHARTS SLIDES OVERHEAD TRANSPARENCIES COMPUTER-DRAWN VISUALS COMPUTER ANIMATION

WRITING BUSINESS REPORTS

AND

PROPOSALS 42

Objectives  Discuss the structure of informational reports.  Explain the structure of analytical reports.  List the most popular types of visuals and discuss when to use them.  Clarify five principles of graphic design to remember when preparing visuals.  Identify and briefly describe five tools that writers can use in long reports to help readers stay on track. Deciding on Length and Format When selecting a format, you have four options:  Preprinted form  Memo  Letter  Manuscript Organizing Informational Reports To arrange your material, use a topical organization such as  Importance  Spatial orientation  Sequence  Geography  Chronology  Category Analytical Reports  What are some drawbacks of using the direct approach for a research and analysis report?  Under what circumstances would you write a justification report?  Under what circumstances would you write a report based on a logical argument?

Structural Approaches for Logical Argument  2 + 2 = 4 Approach  Scientific method  Yardstick approach Preparing the Final Outline  What is the purpose of a final outline?  What does a final outline force you to reevaluate?  Are outline headings important?  The final outline gives you a visual diagram of the report.  You reevaluate the information you have collected.  Yes, they affect the tone of the report. Visual Aids 43

    

Clarify and simplify the text Depict relationships between points Emphasize and summarize points Attract and build credibility Reinforce understanding

“Visualizing” Information  Decide on the message.  Identify points requiring visual support.  Maintain a balance between illustrations and words.  Consider your production schedule. Which Graphic Should You Use? What is the best use of each of the following types of graphics?  Tables  Bar charts  Pie charts  Line charts    

Flow charts Maps Drawings Organization charts

Using Graphic Design Principles  Continuity  Emphasis  Contrast  Simplicity  Experience

Fitting Graphics Into Text  Introduce graphics in the text.  Place them near the text they illustrate.  Choose titles and legends that convey a message and explain the graphic clearly.  Match title and legend style to heading style (informative or descriptive).  Phrase all legends and titles consistently throughout the report. Composing Reports -- True or False?  All reports are written formally.  To achieve a formal tone, use personal pronouns. 44

   

By using verb tense consistently, you add to the clarity of your report. False, many can be informal. False, use impersonal words. True

Guiding Readers through Reports  Start with an opening that indicates the report’s subject and importance.  Use headings, subheadings, and lists effectively.  Use transitions to bind the report.  Use preview and review sections.  Create an ending that leaves a strong, lasting impression. Test          

Your Knowledge What are your options for structuring an informational report? What are your options for structuring an analytical report? How does topical organization differ from logical organization? When is it appropriate to use tables, line charts, surface charts, and pie charts in a report? What five principles apply to effective visuals for business reports? How does a flowchart differ from an organization chart? What tools can you use to help readers follow the structure and flow of information in a long report? What ethical issue is raised by the use of technology to alter photographs in reports? What is the purpose of adding titles and legends to visual aids in reports? How do writers use transitions in reports?

COMPLETING FORMAL REPORTS

AND

PROPOSALS

Objectives  List the three tasks involved in completing reports and proposals, and briefly explain what is involved in revising them.  Explain the prefatory parts of a formal report  Describe four important functions of a formal report’s introduction, and identify the possible topics it might include. 45

 Discuss the four areas of specific information that must be covered in a proposal.  List four questions to ask when proofing visual aids. Revising Formal Reports and Proposals  Revise by evaluating both content and organization.  Review for style and readability.  Edit and rewrite your message clearly and concisely.  Refer to Chapter 6 for more tips on revising and proofreading. Deciding on Report Format  Formal reports  Can be short or long  Can be direct or indirect  Can be informational or analytical  Can be external or internal  The parts you use depend upon what type of report it is.  For long reports, prefatory parts should have their own pages. Components of a Formal Report  Prefatory parts  Text parts  Supplementary parts

Prefatory Parts for a Report What is contained in each of the following? Cover Transmittal letter Titles fly Table of contents Title page List of illustrations Authorization letter Executive summary Acceptance letter Synopsis

Body of a Formal Report  What three sections are contained in the body?  What factors should you consider when deciding on amount of detail to include? 46

 Information, analysis, and interpretation.  Nature of the information, the purpose of your report, and preferences of your audience. Final Sections of the Report Text  Summary  Key findings of your report  Conclusions  An analysis of what the findings mean  Recommendations  Opinions about the desired course of action

Supplementary Parts Explain what is contained in each of the following:  Appendix  Bibliography  Index

Prefatory Parts of a Proposal  Formal proposals may have a copy of a request for proposal (RFP).  The letter of transmittal should persuade the reader you have something to offer.  A synopsis or an executive summary provides a preview of your proposal. A Proposal’s Introduction  Background or statement of the problem  Overview of approach  Scope  Report organization Body of a Formal Proposal The body must cover some specific information:  Proposed approach  Work plan  Statement of qualifications  Costs  You may want to include a summary or conclusion if you need another opportunity for persuasion. Proofreading Formal Reports and Proposals  Check over textual materials.  Check visual aids:  Is each visual necessary? 47

  

Are the visuals accurate? Are the visuals documented? Are the visuals honest?

Getting Feedback  You’ll want to get feedback from readers, even if you have to nudge them to do so.  Be ready for less-than-glowing reactions to your recommendations.  Be prepared to get no response at all.  Accept criticism graciously. Test          

Your Knowledge What are the tasks involved in revising a report or proposal? What are the ten prefatory parts of a formal report? How do writers use an introduction in a formal report? What four questions do writers need to ask when checking visual aids for a report? What information is included on the title page of a report? What is a letter of transmittal, and where is it positioned within a report? How does a synopsis differ from an executive summary? How does the summary section of a report differ from the conclusions section? What are three supplementary parts often included in formal reports? Why is the work plan a key component of a proposal?

48