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SMU ASSIGNMENTS ON MB0050 “Research Methodolog. ALL THE BEST TO YOU....

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1. 1. Master of Business Administration Semester III MB0050- Research Methodology Assignment Set- 1Q.1 a. Distinguish between Doubles sampling and multiphase sampling.b. What is replicated or interpenetrating sampling?Answer: a. Distinguish between Doubles sampling and multiphase sampling.A standard form of sample design for industrial inspection purposes. In accordance with thecharacteristics of a particular plan, two samples are drawn, n1 and n2, and the first sample inspected.The batch can then be accepted or rejected upon the results of this inspection or the second samplebe inspected and the decision made upon the combined result.Double sampling plans were invented to give a questionable lot another chance.For example, if in double sampling the results of the first sample are not conclusive with regard toaccepting or rejecting, a second sample is taken. Application of double sampling requires that a firstsample of size n1 is taken at random from the (large) lot. The number of defectives is then countedand compared to the first samples acceptance number a1 and rejection number r1. Denote the number of defectives in sample 1 by d1 and in sample 2 by d2, then:If d1 a1, the lot is accepted.If d1 r1, the lot is rejected.If a1 < d1 < r1, a second sample is taken.
2. 2. If a second sample of size n2 is taken, the number of defectives, d2, is counted. The total number ofdefectives is D2 = d1 + d2. Now this is compared to the acceptance number a2 and the rejectionnumber r2 of sample 2. In double sampling, r2 = a2 + 1 to ensure a decision on the sample. If D2 a2, the lot is accepted. If D2 r2, the lot is rejected. MULTI-PHASE SAMPLINGDefinition:It is sometimes convenient and economical to collect certain items of information from the wholeof the units of a sample and other items of usually more detailed information from a sub-sampleof the units constituting the original sample. This may be termed two-phase sampling, e.g. if thecollection of information concerning variate, y, is relatively expensive, and there exists someother variate, x, correlated with it, which is relatively cheap to investigate, it may be profitable tocarry out sampling in two phases.At the first phase, x is investigated, and the information thus obtained is used either (a) to stratifythe population at the second phase, when y is investigated, or (b) as supplementary informationat the second phase, a ratio or regression estimate being used.Two-phase sampling is sometimes called "double sampling".Multistage sampling is a complex form of cluster sampling.Advantages cost and speed that the survey can be done in convenience of finding the survey sample normally more accurate than cluster sampling for the same size sampleDisadvantages Is not as accurate as SRS if the sample is the same size More testing is difficult to do
3. 3. Using all the sample elements in all the selected clusters may be prohibitively expensive or notnecessary. Under these circumstances, multistage cluster sampling becomes useful. Instead ofusing all the elements contained in the selected clusters, the researcher randomly selects elementsfrom each cluster. Constructing the clusters is the first stage. Deciding what elements within thecluster to use is the second stage. The technique is used frequently when a complete list of allmembers of the population does not exist and is inappropriate. b. What is replicated or interpenetrating sampling?Replication is not the same as repeated measurements of the same item: they are dealt withdifferently in statistical experimental design and data analysis.For proper sampling, a process or batch of products should be in reasonable statistical control;inherent random variation is present but variation due to assignable (special) causes is not.Evaluation or testing of a single item does not allow for item-to-item variation and may notrepresent the batch or process. Replication is needed to account for this variation among itemsand treatments.Example: As an example, consider a continuous process which produces items. Batches of itemsare then processed or treated. Finally, tests or measurements are conducted. Several optionsmight be available to obtain ten test values. Some possibilities are: One finished and treated item might be measured repeatedly to obtain ten test results. Only one item was measured so there is no replication. The repeated measurements help identify observational error. Ten finished and treated items might be taken from a batch and each measured once. This is not full replication because the ten samples neither are not random and not representative of the continuous nor batch processing. Five items are taken from the continuous process based on sound statistical sampling. These are processed in a batch and tested twice each. This includes replication of initialsamples but does not allow for batch-to-batch variation in processing. The repeated tests on each provide some measure and control of testing error. Five items are taken from the continuous process based on sound statistical sampling. These are processed in five different batches and tested twice each. This plan includes proper replication of initial samples and also includes batch-to-batch variation. The repeated tests on each provide some measure and control of testing error.Each option would call for different data analysis methods and yield different conclusions.
4. 4. 2. What are the differences between observation and interviewing as methods of datacollection?Give two specific examples of situations where either observation or interviewing would bemore.Answer: Observation means viewing or seeing.Observation may be defined as a systematic viewing of a specific phenomenon in its propersetting for the specific purpose of gathering data for a particular study. Observation is classicalmethod of scientific study. Observation as a method of data collection has certain characteristics.1. It is both a physical and a mental activity: The observing eye catches many things that are present. But attention is focused on data that arepertinent to the given study.2. Observation is selective: A researcher does not observe anything and everything, but selects the range of things to beobserved on the basis of the nature, scope and objectives of his study. For example, suppose aresearcher desires to study the causes of city road accidents and also formulated a tentativehypothesis that accidents are caused by violation of traffic rules and over speeding. When heobserved the movements of vehicles on the road, many things are before his eyes; the type,make, size and colour of the vehicles, the persons sitting in them, their hair style, etc. All suchthings which are not relevant to his study are ignored and only over speeding and trafficviolations are keenly observed by him.3. Observation is purposive and not casual: It is made for the specific purpose of noting things relevant to the study. It captures the naturalsocial context in which persons behaviour occur. It grasps the significant events and occurrencesthat affect social relations of the participants.4. Observation should be exact and be based on standardized tools of research and such asobservation schedule, social metric scale etc., and precision instruments, if any.
5. 5. Interviewing is one of the prominent methods of data collection.It may be defined as a two way systematic conversation between an investigator and aninformant, initiated for obtaining information relevant to a specific study. It involves not onlyconversation, but also learning from the respondent‟s gesture, facial expressions and pauses, andhis environment. Interviewing requires face to face contact or contact over telephone and callsfor interviewing skills. It is done by using a structured schedule or an unstructured guide.Interviewing may be used either as a main method or as a supplementary one in studies ofpersons. Interviewing is the only suitable method for gathering information from illiterate or lesseducated respondents. It is useful for collecting a wide range of data from factual demographicdata to highly personal and intimate information relating to aperson‟s opinions, attitudes, values,beliefs past experience and future intentions. When qualitative information is required or probingis necessary to draw out fully, and then interviewing is required. Where the area covered for thesurvey is a compact, or when a sufficient number of qualified interviewers are available, personalinterview is feasible.Interview is often superior to other data-gathering methods. People are usually more willing totalk than to write. Once report is established, even confidential information may be obtained. Itpermits probing into the context and reasons for answers to questions.Interview can add flesh to statistical information. It enables the investigator to grasp thebehavioural context of the data furnished by the respondents.Observation is suitable for a variety of research purposes. It may be used for studying(a) The behavior of human beings in purchasing goods and services: life style, customs, andmanner, interpersonal relations, group dynamics, crowd behavior, leadership styles, managerialstyle, other behaviours and actions;(b) The behaviour of other living creatures like birds, animals etc.(c) Physical characteristics of inanimate things like stores, factories, residences etc.(d) Flow of traffic and parking problems.(e) Movement of materials and products through a plant.
6. 6. 3. How is the Case Study method useful in Business Research?Answer: Case Study as a Method of Business ResearchIn-depth analysis of selected cases is of particular value to business research when a complex setof variables may be at work in generating observed results and intensive study is needed tounravel the complexities. For instance, an in-depth study of a firm‟s top sales people andcomparison with the worst sales people might reveal characteristics common to stellarperformers. The exploratory investigator is best served by the active curiosity and willingness todeviate from the initial plan, when the finding suggests new courses of enquiry, might provemore productiveCase study of particular value when a complex set of variables may be at work in generatingobserved results and intensive study is needed to unravel the complexities. For example, an in-depth study of a firm‟s top sales people and comparison with worst salespeople might revealcharacteristics common to stellar performers. Here again, the exploratory investigation is bestserved by an active curiosity and willingness to deviate from the initial plan when findingssuggest new courses of inquiry might prove more productive. It is easy to see how theexploratory research objectives of generating insights and hypothesis would be well served byuse of this technique. Making Case Study Effective John Dollard has proposed seven criteria for evaluating such adequacy as follows:i) The subject must be viewed as a specimen in a cultural series. That is, the case drawn out fromits total context for the purposes of study must be considered a member of the particular culturalgroup or community. The scrutiny of the life histories of persons must be done with a view toidentify the community values, standards and their shared way of life.ii) The organic motto of action must be socially relevant. That is, the action of the individualcases must be viewed as a series of reactions to social stimuli or situation. In other words, thesocial meaning of behaviour must be taken into consideration.iii) The strategic role of the family group in transmitting the culture must be recognized. That is,in case of an individual being the member of a family, the role of family in shaping hisbehaviourmust never be overlooked.
7. 7. iv) The specific method of elaboration of organic material onto social behaviour must beclearlyshown. That is case histories that portray in detail how basically a biological organism, theman,gradually blossoms forth into a social person, are especially fruitful.v) The continuous related character of experience for childhood through adulthood must bestressed. In other words, the life history must be a configuration depicting the inter-relationshipsbetween thepeople‟s various experiences.vi) Social situation must be carefully and continuously specified as a factor. One of the importantcriteria for the life history is that a person‟s life must be shown as unfolding itself in the contextof and partly owing to specific social situations.vii) The life history material itself must be organized according to some conceptual framework;this in turn would facilitate generalizations at a higher level
9. 9. Independent variable:The treatment, factor, or presumed cause that will produce a change in the dependent variable.This is what the experimenter tries to manipulate. It is denoted as "X" on the horizontal axis of agraph. Dependent variable:The presumed effect or consequence resulting from changes in the independent variable. This isthe observation made and is denoted by "Y" on the vertical axis of a graph. The score of "Y"depends on the score of "X." Population:The complete set of subjects that can be studied: people, objects, animals, plants, etc. Sample:A subset of subjects that can be studied to make the research project more manageable.There arevarieties of ways samples can be taken. If a large enough random samples are taken, theresults can be statistically similar to taking a census of an entire population--with reduced effortand cost. Case Study:A case study is conducted for similar purpose as the above but is usually done with a smallersample size for more in-depth study. A case study often involves direct observation or interviewswith single subjects or single small social units such as a family, club, school classroom, etc.This is typically considered qualitative research.Purpose: Explain or PredictType of Research to Use:Relational Study In a relational study you start with a research hypothesis, that is, is what youre trying to"prove."Examples of research hypotheses for a relational study: The older the person, the more healthproblems he or she encounters.
10. 10. 5. What are the contents of research reports?Answer: The outline of a research report is given below:I. Prefatory Items Title page Declaration Certificates Preface/acknowledgements Table of contents· List of tables List of graphs/figures/charts Abstract or synopsisII.Body of the Report Introduction Theoretical background of the topic Statement of the problem Review of literature· The scope of the study The objectives of the study Hypothesis to be tested Definition of the concepts Models if any Design of the study
11. 11. Methodology Method of data collection Sources of data Sampling plan Data collection instruments Field work Data processing and analysis plan Overview of the report Limitation of the study Results: findings and discussions Summary, conclusions and recommendationsIII.Reference Material Bibliography Appendix Copies of data collection instruments Technical details on sampling plan Complex tables Glossary of new terms used.
12. 12. 6. Write short notes on the following:a. Medianb. Standard DeviationAnswer: a. MedianOne problem with using the mean is that it often does not depict the typical outcome. If there isone outcome that is very far from the rest of the data, then the mean will be strongly affected bythis outcome. Such an outcome is called andoutlier. An alternative measure is the median. Themedian is the middle score. If we have an even number of events we take the average of the twomiddles. The median is better for describing the typical value. It is often used for income andhome prices.Example:Suppose you randomly selected 10 house prices in the South Lake Tahoe area. You areinterested in the typical house price. In \$100,000 the prices were 2.7, 2.9, 3.1, 3.4, 3.7, 4.1, 4.3, 4.7, 4.7, 40.8If we computed the mean, we would say that the average house price is 744,000. Although thisnumber is true, it does not reflect the price for available housing in South Lake Tahoe. A closerlook at the data shows that the house valued at 40.8 x \$100,000 = \$4.08 million skews the data.Instead, we use the median. Since there is an even number of outcomes, we take the average ofthe middle two 3.7 + 4.1 = 3.9 2The median house price is \$390,000. This better reflects what house shoppers should expect tospend.There is an alternative value that also is resistant to outliers. This is called the trimmedmean which is the mean after getting rid of the outliers or 5% on the top and5% on the bottom.We can also use the trimmed mean if we are concerned with outliers skewing the data; howeverthe median is used more often since more people understand it.
13. 13. Example:At a ski rental shop data was collected on the number of rentals on each of ten consecutiveSaturdays: 44, 50, 38, 96, 42, 47, 40, 39, 46, 50.To find the sample mean, add them and divide by 10: 44 + 50 + 38 + 96 + 42 + 47 + 40 + 39 + 46 + 50 = 49.2 10Notice that the mean value is not a value of the sample.To find the median, first sort the data: 38, 39, 40, 42, 44, 46, 47, 50, 50, 96Notice that there are two middle numbers 44 and 46. To find the median we take the average ofthe two. 44 + 46 Median = = 45 2Notice also that the mean is larger than all but three of the data points. The mean is influencedby outliers while the median is robust.
14. 14. b. Standard DeviationStandard DeviationThe mean, mode, median, and trimmed mean do a nice job in telling where the center of the dataset is, but often we are interested in more.For example, a pharmaceutical engineer develops a new drug that regulates iron in the blood.Suppose she finds out that the average sugar content after taking the medication is the optimallevel. This does not mean that the drug is effective. There is a possibility that half of thepatients have dangerously low sugar content while the other half has dangerously high content.Instead of the drug being an effective regulator, it is a deadly poison. What the pharmacist needsis a measure of how far the data is spread apart. This is what the variance and standard deviationdo. First we show the formulas for these measurements. Then we will go through the steps onhow to use the formulas.We define the variance to beand the standard deviation to beVariance and Standard Deviation:Step by Step 1. Calculate the mean, x. 2. Write a table that subtracts the mean from each observed value. 3. Square each of the differences. 4. Add this column. 5. Divide by n -1 where n is the number of items in the sample. This is the variance. 6. To get the standard deviation we take the square root of the variance.
15. 15. Example:The owner of the Ches Tahoe restaurant is interested in how much people spend at therestaurant. He examines 10 randomly selected receipts for parties of four and writes down thefollowing data. 44, 50, 38, 96, 42, 47, 40, 39, 46, 50He calculated the mean by adding and dividing by 10 to get x = 49.2Below is the table for getting the standard deviation: x x - 49.2 (x - 49.2 )2 44 -5.2 27.04 50 0.8 0.64 38 11.2 125.44 96 46.8 2190.24 42 -7.2 51.84 47 -2.2 4.84 40 -9.2 84.64 39 -10.2 104.04 46 -3.2 10.24 50 0.8 0.64Total 2600.4Now 2600.4 = 288.7 10 - 1Hence the variance is 289 and the standard deviation is the square root of 289 = 17.Since the standard deviation can be thought of measuring how far the data values lie from themean, we take the mean and move one standard deviation in either direction. The mean for thisexample was about 49.2 and the standard deviation was 17.
16. 16. We have:49.2 - 17 = 32.2and49.2 + 17 = 66.2What this means is that most of the patrons probably spent between \$32.20 and \$66.20.The sample standard deviation will be denoted by s and the population standard deviation will bedenoted by the Greek letter.The sample variance will be denoted by s2 and the population variance will be denoted by  . 2The variance and standard deviation describe how spread out the data is.If the data all lies closeto the mean, then the standard deviation will be small, while if the data is spread out over a largerange of values, s will be large. Having outliers will increase the standard deviation.One of the flaws involved with the standard deviation, is that it depends on the units that areused. One way of handling this difficulty, is called the coefficient of variation which is thestandard deviation divided by the mean times 100%  CV = 100% In the above example, it is 17 100% = 34.6% 49.2This tells us that the standard deviation of the restaurant bills is 34.6% of the mean.
17. 17. Set -21. What is the significance of research in social and business sciences?Answer: Significance of Research in Social and Business SciencesAccording to a famous Hudson Maxim, “All progress is born of inquiry. Doubt is often betterthan overconfidence, for it leads to inquiry, and inquiry leads to invention”. It brings out thesignificance of research, increased amounts of which makes progress possible. Researchencourages scientific and inductive thinking, besides promoting the development of logicalhabits of thinking and organization.The role of research in applied economics in the context of an economy or business is greatlyincreasing in modern times. The increasingly complex nature of government and business hasraised the use of research in solving operational problems. Research assumes significant role informulation of economic policy, for both the government and business. It provides the basis foralmost all government policies of an economic system. Government budget formulation, forexample, depends particularly on the analysis of needs and desires of the people, and theavailability of revenues, which requires research. Research helps to formulate alternativepolicies, in addition to examining the consequences of these alternatives. Thus, research alsofacilitates the decision making of policy-makers, although in itself it is not a part of research. Inthe process, research also helps in the proper allocation of a country‟s scare resources. Researchis also necessary for collecting information on the social and economic structure of an economyto understand the process of change occurring in the country. Collection of statistical informationthough not a routine task, involves various research problems.Therefore, large staff of research technicians or experts is engaged by the government these daysto undertake this work.Thus, research as a tool of government economic policy formulation involves three distinctstages of operation which are as follows: Investigation of economic structure through continual compilation of facts Diagnoses of events that are taking place and the analysis of the forces underlying them; and The prognosis, i.e., the prediction of future developments
18. 18. Research also assumes a significant role in solving various operational and planning problemsassociated with business and industry. In several ways, operations research, market research, andmotivational research are vital and their results assist in taking business decisions. Marketresearch is refers to the investigation of the structure and development of a market for theformulation of efficient policies relating to purchases, production and sales. Operational researchrelates to the application of logical, mathematical, and analytical techniques to find solution tobusiness problems such as cost minimization or profit maximization, or the optimizationproblems. Motivational research helps to determine why people behave in the manner they dowith respect to market characteristics.More specifically, it is concerned with the analyzing the motivations underlying consumerbehaviour. All these researches are very useful for business and industry, which are responsiblefor business decision making.Research is equally important to social scientist for analyzing social relationships and seekingexplanations to various social problems. It gives intellectual satisfaction of knowing things forthe sake of knowledge. It also possesses practical utility for the social scientist to gain knowledgeso as to be able to do something better or in a more efficient manner. This, research in socialsciences is concerned with both knowledge for its own sake, and knowledge for what it cancontribute to solve practical problems.
19. 19. 2. What is the meaning of hypothesis? Discuss the types of hypothesis.Answer: Meaning of hypothesis:  The relationship between/ among variables,  The research hypothesis is a predictive statement, capable for beingtested by scientific methods, that relates an independent variable to some dependent variable. The level of influence of independent variables on the dependent variables.  E.g.: “ students who receive counseling will show a greater increase in creativity than students not receiving counseling.”  A proposal intended to explain certain facts or observations.  A hypothesis is a precise testable statement prediction of what the researcher expects to find or prove.  It is a tentative answer to a research question.  A hypothesis is a tentative proposition formulated for empirical testing. It is a declarative statement combining concept. Definition of hypothesis:Goode and Hatt have defined a hypothesis, “a proposition which can be put to test to determinevalidity.” Various types of Hypothesis:1. Descriptive hypothesis:These are propositions that describe the characteristics (such as size, form, or distribution) of avariable. The variable may be an object, person, organization, situation or event.
20. 20. Some examples are:“The rate of unemployment among arts graduates is higher than that of commercegraduates.”“Public enterprises are more amenable for centralized planning.”2. Relational hypothesis:These are propositions, which describe the relationship between two variables. T h e relationshipsuggested may be positive or negative correlation or causal relationship.Some examples:“Families with higher incomes spend more for recreation.”“The lower the rate of job turnover in a work group, the higher the work productivity.”3. Casual hypothesis:State that the existence of, or a change in, one variable causes or leads to an effecton another variables. The first variable is called the independent variable, and thelatter the dependent variables the researcher must consider the direction in which suchrelationships flow.ie. Which are cause and which effect is4. Working hypothesis:While planning the study of a problem, hypotheses are formed. Initially they are not be veryspecific. In such cases, they are referred to as “Working hypothesis “which are subjectto modification as the investigation proceeds.5. Null hypothesis:These are hypothetical statements denying what are explicitly indicated in working hypothesis.They are formed in the negative statement.For example:” There is no relationship between families‟ income level a n d expenditure on recreation”.Null hypothesis are formulated for testing statistical significance. Since, this form isa convenient approach to statistical analysis. As the test would nullify the null hypothesis, theyare so called. There is some justification for using null hypotheses. They conform to the qualities ofdetachment and objectivity to be possessed by are searcher. If the attempts to test hypotheses which heassumes to be true, it would appear as if he is not behaving objectively.
21. 21. The problem does not arise when he uses null hypotheses. Moreover, null hypotheses aremore exact. It is easier to reject the contrary of hypotheses than to confirm it with completecertainty. Hence the concept of null hypothesis is found to be very useful.6. Alternate Hypothesis {Ha}It is a statement, which is accepted, after a null hypothesis is rejected based on the test result.Ex: If the null hypothesis is that “there is no relationship between the eye colour of husbands andwives”, it is rejected then automatically the alternative hypothesis is that “there is relationshipbetween the eye colour of husbands and wives is accepted.”7. Statistical hypothesis:There are statements about a statistical population. These are derived from a sample.These are quantitative in nature in that they are numerically measurable, e.g., “Group A is olderthan Group B.”8. Common sense Hypothesis:These represent the common sense ideas. They state the existence of empirical uniformitiesperceived through day-to-day observations.“Soldiers from upper-class are less adjusted in thearmy than lower class men”“Fresh students conform to the conventions set up by seniors”9. Complex Hypothesis:These aim at testing the existence of logically derived relationships betweenempirical uniformities.For example, “The concentric growth circles characterize a city”.10. Analytical Hypothesis:These are concerned with the relationship of analytic variables. These hypotheses occur at the highestlevel of abstraction. These specify relationship between changes in one property and changes inanother.
22. 22. 3) What is frequency distribution? What are the types and general rules for graphicalrepresentation of data?Answer: Frequency DistributionFrequency Distribution:Variables that are classified according to magnitude or size are oftenarranged in the form of a frequency table. In constructing this table, it is necessary to determinethe number of class intervals to be used and the size of the class intervals.A distinction is usually made between continuous and discrete variables. A continuous variablehas an unlimited number of possible values between the lowest and highest with no gaps orbreaks. Examples of continuous variable are age, weight, temperature etc. A discrete variable canhave a series of specified values with no possibility of values between these points. Each valueof a discrete variable is distinct and separate. Types of Graphs and General RulesThe most commonly used graphic forms may be grouped into the following categories:a) Line Graphs or Chartsb) Bar Chartsc) Segmental presentations.d) Scatter plotse) Bubble chartsf) Stock plotsg) Pictographsh)Chesnokov Faces
23. 23. The general rules to be followed in graphic representations are:1. The chart should have a title placed directly above the chart.2. The title should be clear, concise and simple and should describe the nature of the datapresented.3. Numerical data upon which the chart is based should be presented in an accompanying table.4. The horizontal line measures time or independent variable and the vertical line the measuredvariable.5. Measurements proceed from left to right on the horizontal line and from bottom to top on thevertical.6. Each curve or bar on the chart should be labelled.7. If there are more than one curves or bar, they should be clearly differentiated from one anotherby distinct patterns or colours.8. The zero point should always be represented and the scale intervals should be equal.9. Graphic forms should be used sparingly. Too many forms detract rather than illuminating thepresentation.10. Graphic forms should follow and not precede the related textual discussion.
24. 24. 4. List down various measures of central tendency and explain the difference betweenthem?Answer: Central tendencyCentral tendency: central tendency relates to the way in which quantitative data is clusteredaround some value. Ameasure of central tendency is a way of specifying - central value. Inpractical statistical analysis, the terms are often used before one has chosen even a preliminaryform of analysis: thus an initial objective might be to "choose an appropriate measure of centraltendency".In the simplest cases, the measure of central tendency is an average of a set of measurements, theword average being variously construed as mean, median, or other measure of location,depending on the context. However, the term is applied to multidimensional data as well as tounivariate data and in situations where a transformation of the data values for some or alldimensions would usually be considered necessary: in the latter cases, the notion of a "centrallocation" is retained in converting an "average" computed for the transformed data back to theoriginal units. In addition, there are several different kinds of calculations for central tendency,where the kind of calculation depends on the type of data (level of measurement).Both "central tendency" and "measure of central tendency" apply to either statistical populationsor to samples from a population.Basic measures of central tendencyThe following may be applied to individual dimensions of multidimensional data, aftertransformation, although some of these involve their own implicit transformation of the data. Arithmetic mean - the sum of all measurements divided by the number of observations in the data set Median - the middle value that separates the higher half from the lower half of the data set Mode - the most frequent value in the data set Geometric mean - the nth root of the product of the data values Harmonic mean - the reciprocal of the arithmetic mean of the reciprocals of the data values Weighted mean - an arithmetic mean that incorporates weighting to certain data elements Truncated mean - the arithmetic mean of data values after a certain number or proportion of the highest and lowers data values have been discarded. Midrange - the arithmetic mean of the maximum and minimum values of a data set.
25. 25. Arithmetic mean: In mathematics and statistics, the arithmetic mean, often referred to as simply the mean oraverage when the context is clear, is a method to derive the central tendency of a sample space.The term "arithmetic mean" is preferred in mathematics and statistics because it helps distinguishit from other means such as the geometric and harmonic mean.In addition to mathematics and statistics, the arithmetic mean is used frequently in fields such aseconomics, sociology, and history, though it is used in almost every academic field to someextent. For example, per capita GDP gives an approximation of the arithmetic average income ofa nations population.While the arithmetic mean is often used to report central tendencies, it is not a robust statistic,meaning that it is greatly influenced by outliers. Notably, for skewed distributions, the arithmeticmean may not accord with ones notion of "middle", and robust statistics such as the median maybe a better description of central tendency.Median:Amedian is described as the numeric value separating the higher half of a sample, a population,or a probability distribution, from the lower half. The median of a finite list of numbers can befound by arranging all the observations from lowest value to highest value and picking themiddle one. If there is an even number of observations, then there is no single middle value; themedian is then usually defined to be the mean of the two middle values.In a sample of data, or a finite population, there may be no member of the sample whose value isidentical to the median (in the case of an even sample size), and, if there is such a member, theremay be more than one so that the median may not uniquely identify a sample member.Nonetheless, the value of the median is uniquely determined with the usual definition. A relatedconcept, in which the outcome is forced to correspond to a member of the sample, is the medoid.At most, half the population have values less than the median, and, at most, half have valuesgreater than the median. If both groups contain less than half the population, then some of thepopulation is exactly equal to the median. For example, if a<b<c, then the median of the list {a,b, c} is b, and, if a<b<c<d, then the median of the list {a, b, c, d} is the mean of b and c; i.e., it is(b + c)/2.The median can be used as a measure of location when a distribution is skewed, when end-valuesare not known, or when one requires reduced importance to be attached to outliers, e.g., becausethey may be measurement errors. A disadvantage of the median is the difficulty of handling ittheoretically.
26. 26. Mode (statistics):In statistics, the mode is the value that occurs most frequently in a data set or a probabilitydistribution. In some fields, notably education, sample data are often called scores, and thesample mode is known as the modal score.Like the statistical mean and the median, the mode is a way of capturing important informationabout a random variable or a population in a single quantity. The mode is in general differentfrom the mean and median, and may be very different for strongly skewed distributions.The mode is not necessarily unique, since the same maximum frequency may be attained atdifferent values. The most ambiguous case occurs in uniform distributions, wherein all values areequally likely.Geometric mean:The geometric mean, in mathematics, is a type of mean or average, which indicates the centraltendency or typical value of a set of numbers. It is similar to the arithmetic mean, except that thenumbers are multiplied and then the nth root (where n is the count of numbers in the set) of theresulting product is taken.For instance, the geometric mean of two numbers, say 2 and 8, is just the square root of theirproduct; that is 2√ 2 × 8 = 4. As another example, the geometric mean of the three numbers 4, 1,and 1/32 is the cube root of their product (1/8), which is 1/2; that is 3√ 4 × 1 × 1/32 = ½ .The geometric mean can also be understood in terms of geometry. The geometric mean of twonumbers, a and b, is the length of one side of a square whose area is equal to the area of arectangle with sides of lengths a and b. Similarly, the geometric mean of three numbers, a, b, andc, is the length of one side of a cube whose volume is the same as that of a right cuboid withsides whose lengths are equal to the three given numbers.The geometric mean only applies to positive numbers.[1] It is also often used for a set of numberswhose values are meant to be multiplied together or are exponential in nature, such as data on thegrowth of the human population or interest rates of a financial investment.The geometric mean is also one of the three classic Pythagorean means, together with theaforementioned arithmetic mean and the harmonic mean. For all positive data sets containing atleast one pair of unequal values, the harmonic mean is always the least of the three means, whilethe arithmetic mean is always the greatest of the three and the geometric mean is always inbetween (see Inequality of arithmetic and geometric means.)
27. 27. Harmonic mean:Theharmonic mean (sometimes called the sub contrary mean) is one of several kinds ofaverage. Typically, it is appropriate for situations when the average of rates is desired.The harmonic mean H of the positive real numbersx1, x2, ..., xn> 0 is defined to beFrom the third formula in the above equation it is more apparent that the harmonic mean isrelated to the arithmetic and geometric means.Equivalently, the harmonic mean is the reciprocal of the arithmetic mean of the reciprocals. As asimple example, the harmonic mean of 1, 2, and 4 isWeighted mean:The weighted mean is similar to an arithmetic mean (the most common type of average), whereinstead of each of the data points contributing equally to the final average, some data pointscontribute more than others. The notion of weighted mean plays a role in descriptive statisticsand also occurs in a more general form in several other areas of mathematics.If all the weights are equal, then the weighted mean is the same as the arithmetic mean. Whileweighted means generally behave in a similar fashion to arithmetic means, they do have a fewcounter-intuitive properties, as captured for instance in Simpsons paradox.The term weighted average usually refers to a weighted arithmetic mean, but weighted versionsof other means can also be calculated, such as the weighted geometric mean and the weightedTruncated mean:A truncated mean or trimmed mean is a statisticalmeasure of central tendency, much like themean and median. It involves the calculation of the mean after discarding given parts of aprobability distribution or sample at the high and low end, and typically discarding an equalamount of both.For most statistical applications, 5 to 25 percent of the ends are discarded. In some regions ofCentral Europe it is also known as a Windsor mean, but this name should not be confused with
28. 28. the Winsorized mean: in the latter, the observations that the trimmed mean would discard areinstead replaced by the largest/smallest of the remaining values.Mid-range:The mid-range or mid-extreme of a set of statistical data values is the arithmetic mean of themaximum and minimum values in a data set, or:As such it is a measure of central tendency.The midrange is highly sensitive to outliers and ignores all but two data points. It is therefore avery non-robust statistic (having a breakdown point of 0, meaning that a single observation canchange it arbitrarily), and it is rarely used in statistical analysis.The midhinge is the 25% trimmed mid-range, and is more robust, having a breakdown point of25%.
29. 29. 5. Select any topic for research and explain how you will use both secondary and primarysources to gather the required information.Answer: For performing research on the literacy levels among families, the primary and secondarysources of data can be used very effectively.More specifically the primary sources of data collection is suggested in this regard. Becausepersonal data or data related to human beings consist of: 1. Demographic and socio-economic characteristics of individuals: Age, sex, race, social class, religion, marital status, education, occupation income, family size, location of the household life style etc. 2. Behavioral variables: Attitudes, opinions, awareness, knowledge, practice, intentions, etc. 3. Organizational data consist of data relating to an organizations origin, ownership, objectives, resources, functions, performance and growth. 4. Territorial data are related to geo-physical characteristics, resource endowment, population, occupational pattern infrastructure degree of development, etc. of spatial divisions like villages, cities, talluks, districts, state and the nation.The data serve as the bases or raw materials for analysis. Without an analysis of factual data, nospecific inferences can be drawn on the questions under study. Inferences based on imaginationor guess work cannot provide correct answers to research questions. The relevance, adequacyand reliability of data determine the quality of the findings of a study.Data form the basis for testing the hypothesis formulated in a study. Data also provide the factsand figures required for constructing measurement scales and tables, which are analyzed withstatistical techniques. Inferences on the results of statistical analysis and tests of significanceprovide the answers to research questions. Thus, the scientific process of measurements,analysis, testing and inferences depends on the availability of relevant data and their accuracy.Hence, the importance of data for any research studies.
30. 30. The sources of data may be classified into (a) primary sources and (b) secondary sources.Primary Sources of DataPrimary sources are original sources from which the researcher directly collects data that havenot been previously collected e.g.., collection of data directly by the researcher on brandawareness, brand preference, brand loyalty and other aspects of consumer behavior from asample of consumers by interviewing them,. Primary data are first hand information collectedthrough various methods such as observation, interviewing, mailing etc.Advantage of Primary Data It is original source of data It is possible to capture the changes occurring in the course of time. It flexible to the advantage of researcher. Extensive research study is based of primary dataDisadvantage of Primary Data Primary data is expensive to obtain It is time consuming It requires extensive research personnel who are skilled. It is difficult to administer.Methods of Collecting Primary DataPrimary data are directly collected by the researcher from their original sources. In this case, theresearcher can collect the required date precisely according to his research needs, he can collectthem when he wants them and in the form he needs them. But the collection of primary data iscostly and time consuming. Yet, for several types of social science research required data are notavailable from secondary sources and they have to be directly gathered from the primary sources.In such cases where the available data are inappropriate, inadequate or obsolete, primary datahave to be gathered. They include: socio economic surveys, social anthropological studies ofrural communities and tribal communities, sociological studies of social problems and socialinstitutions. Marketing research, leadership studies, opinion polls, attitudinal surveys, readership,