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Approved Study Center

ALLAMA IQAL OPEN UNIVERSITY (AIOU)

ASSIGNMENT # 01

COURSE: BUSINESS RESEARCH METHODS(5599)

LevelExecutive MBA/MPA (3rd) Semester: SPRING 2010

Submitted to

MR.MAZHAR IQBAL

Submitted ByMuhammad Idrees

Roll # AD514761

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Q.1 Explain:

a) Problem definition

b) Research design

c) Data collection

d) Data analysis

e) Interpretation of results

A. Problem

A problem is an obstacle which makes it difficult to achieve a desired goal,objective or purpose. It refers to a situation, condition, or issue that is yetunresolved. In a broad sense, a problem exists when an individual becomesaware of a significant difference between what actually is and what is desiredbetween one or more individual.

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Problem solving

Every theoretical problem asks for an answer or solution. Trying to find asolution to a problem is known as problem solving. That is, a problem is a gapbetween an actual and desired situation. The time it takes to solve a problemis a way of measuring complexity. Many problems have no discovered solution

and are therefore classified as an open problem.

From the mid 20th century, the field of theoretical computer science hasexplored the use of computers to solve problems.

Examples

Mathematical problem is a question about mathematical objects andstructures that may require a distinct answer or explanation or proof.Examples include word problems at school level or deeper problems suchas shading a map with only four colors.

In society, a problem can refer to particular social issues, which if solvedwould yield social benefits, such as increased harmony or productivity,and conversely diminished hostility and disruption.

In business and engineering, a problem is a difference between actualconditions and those that are required or desired. Often, the causes of aproblem are not known, in which case root cause analysis is employed tofind the causes and identify corrective actions.

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In chess, a problem is a puzzle set by somebody using chess pieces on achess board, for others to get instruction or intellectual satisfaction fromdetermining the solution.

In theology, there is what is referred to as the Synoptic Problem, whichincludes in its discourse a concern for assumptions of historical accuracythat are challenged by apparent contradictions in the Gospels' accounts

of allegedly historical events. In academic discourse a problem is a challenge to an assumption, an

apparent conflict that requires synthesis and reconciliation. It is a normalpart of systematic thinking, the address of which adds to or detractsfrom the veracity of a conclusion or idea.

An optimization problem is finding the best solution from all feasiblesolutions. A good example of this type of problem is the travellingsalesperson problem which is based on calculating the most efficientroute between many places

In computability theory a decision problem requires a simple yes-or-noanswer.

In rock climbing a problem is a series ofrocks that forces the climber toclimb.

In reading, a problem is a combination of a series of words with theoverall plotline, which the reader must attempt to decipher.

In walking, a mobility problem is presented. Motion is achieved viamechanical interaction of the legs and a surface.

B. Design researchDesign research investigates the process ofdesigning in all its many fields. Itis thus related to Design methods in general or for particular disciplines. Aprimary interpretation of design research is that it is concerned withundertaking research into the design process. Secondary interpretations wouldrefer to undertaking research within the process of design. The overallintention is to better understand and to improve the design process.

Origins

Design Research emerged as a recognisable field of study in the 1960s, initially

marked by a conference on Design methods at Imperial College London, in1962. It led to the founding of the Design Research Society (DRS) in 1966.John Christopher Jones (who initiated the 1962 conference) founded apostgraduate Design Research Laboratory at the University of ManchesterInstitute of Science and Technology, and L. Bruce Archer founded thepostgraduate Department of Design Research at the Royal College of Art,London, becoming the first Professor of Design Research.

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The Design Research Society has always stated its aim as: to promote the

study of and research into the process ofdesigning in all its many fields. Itspurpose therefore is to act as a form of learned society, taking a scholarly anddomain independent view of the process of designing.

Some of the origins of design methods and design research lay in the

emergence after the 2nd World War of operational research methods andmanagement decision-making techniques, the development of creativitytechniques in the 1950s, and the beginnings of computer programs forproblem solving in the 1960s. A statement by Bruce Archer encapsulated whatwas going on: The most fundamental challenge to conventional ideas ondesign has been the growing advocacy of systematic methods of problemsolving, borrowed from computer techniques and management theory, for theassessment of design problems and the development of design solutions.

Herbert Simon established the foundations for a science of design, whichwould be a body of intellectually tough, analytic, partly formalizable, partly

empirical, teachable doctrine about the design process churba!

Early work

Early work was mainly within the domains ofarchitecture and industrial design,but research in engineering design developed strongly in the 1980s; forexample, through ICEDthe series of International Conferences onEngineering Design. These developments were especially strong in Germanyand Japan. In the USA there were also some important developments in designtheory and methodology, including the publications of the Design MethodsGroup and the series of conferences of the Environmental Design Research

Association. The National Science Foundation initiative on design theory andmethods led to substantial growth in engineering design research in the late-1980s. A particularly significant development was the emergence of the first journals of design research. DRS initiated Design Studies in 1979, DesignIssuesappeared in 1984, and Research in Engineering Design in 1989.

Development

The development of design research has led to the establishment of design as

a coherent discipline of study in its own right, based on the view that designhas its own things to know and its own ways of knowing them. Bruce Archeragain encapsulated the view in stating his new belief that there exists adesignerly way of thinking and communicating that is both different fromscientific and scholarly ways of thinking and communicating, and as powerfulas scientific and scholarly methods of enquiry when applied to its own kinds ofproblems. This view was developed further in a series of papers by Nigel

Cross, collected as a book on 'Designerly Ways of Knowing' Significantly,

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C. Data collection

Data collection is a term used to describe a process of preparing andcollecting data - for example as part of a process improvement or similarproject. The purpose of data collection is to obtain information to keep onrecord, to make decisions about important issues, to pass information on to

others. Primarily, data is collected to provide information regarding a specifictopic.

Data collection usually takes place early on in an improvement project, and isoften formalised through a data collection plan which often contains thefollowing activity.

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

presentation.

Prior to any data collection, pre-collection activity is one of the most crucialsteps in the process. It is often discovered too late that the value of theirinterview information is discounted as a consequence of poor sampling of bothquestions and informants and poor elicitation techniques. After pre-collectionactivity is fully completed, data collection in the field, whether by interviewingor other methods, can be carried out in a structured, systematic and scientificway.

A formal data collection process is necessary as it ensures that data gathered

is both defined and accurate and that subsequent decisions based onarguments embodied in the findings are valid. The process provides both abaseline from which to measure from and in certain cases a target on what toimprove.

Types of data collection

1-By mail questionnaires 2-By personal interview.

Other main types of collection include census, sample survey, andadministrative by-product and each with their respective advantages anddisadvantages. A census refers to data collection about everyone or everythingin a group or population and has advantages, such as accuracy and detail anddisadvantages, such as cost and time. A sample survey is a data collectionmethod that includes only part of the total population and has advantages,such as cost and time and disadvantages, such as accuracy and detail.Administrative by-product data is collected as a byproduct of an organizations

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day-to-day operations and has advantages, such as accuracy, time simplicityand disadvantages, such as no flexibility and lack of control.

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D. Data analysis

Analysis of data is a process of inspecting, cleaning, transforming, andmodeling data with the goal of highlighting useful information, suggestingconclusions, and supporting decision making. Data analysis has multiple facetsand approaches, encompassing diverse techniques under a variety of names,

in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modelingand knowledge discovery for predictive rather than purely descriptivepurposes. Business intelligence covers data analysis that relies heavily onaggregation, focusing on business information. In statistical applications, somepeople divide data analysis into descriptive statistics, exploratory data analysis,and confirmatory data analysis. EDA focuses on discovering new features in thedata and CDA on confirming or falsifying existing hypotheses. Predictiveanalytics focuses on application of statistical or structural models for predictive

forecasting or classification, while text analytics applies statistical, linguistic,and structural techniques to extract and classify information from textualsources, a species ofunstructured data. All are varieties of data analysis.

Data integration is a precursor to data analysis, and data analysis is closelylinked to data visualization and data dissemination. The term data analysis issometimes used as a synonym for data modeling, which is unrelated to thesubject of this article.

The process of data analysis

Data analysis is a process, within which several phases can be distinguished:

Data cleaning Initial data analysis (assessment of data quality) Main data analysis (answer the original research question) Final data analysis (necessary additional analyses and report)

Data cleaning

Data cleaning is an important procedure during which the data are inspected,

and erroneous data are -if necessary, preferable, and possible- corrected. Datacleaning can be done during the stage of data entry. If this is done, it isimportant that no subjective decisions are made. The guiding principleprovided by Adr (ref) is: during subsequent manipulations of the data,information should always be cumulatively retrievable. In other words, itshould always be possible to undo any data set alterations. Therefore, it isimportant not to throw information away at any stage in the data cleaningphase. All information should be saved (i.e., when altering variables, both the

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original values and the new values should be kept, either in a duplicate datasetor under a different variable name), and all alterations to the data set shouldcarefully and clearly documented, for instance in a syntax or a log.

Initial data analysis

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

Quality of data

The quality of the data should be checked as early as possible. Data quality canbe assessed in several ways, using different types of analyses: frequencycounts, descriptive statistics (mean, standard deviation, median), normality

(skewness, kurtosis, frequency histograms, normal probability plots),associations (correlations, scatter plots).Other initial data quality checks are:

Checks on data cleaning: have decisions influenced the distribution of thevariables? The distribution of the variables before data cleaning iscompared to the distribution of the variables after data cleaning to seewhether data cleaning has had unwanted effects on the data.

Analysis ofmissing observations: are there many missing values, and arethe values missing at random? The missing observations in the data areanalyzed to see whether more than 25% of the values are missing,

whether they are missing at random (MAR), and whether some form ofimputation (statistics) is needed.

Analysis ofextreme observations: outlying observations in the data areanalyzed to see if they seem to disturb the distribution.

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

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

phase. by philip kotler

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Quality of measurements

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

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

of the reliability of a measurement instrument, i.e., whether all items fitinto a unidimensional scale. During this analysis, one inspects thevariances of the items and the scales, theCronbach's of the scales, and

the change in the Cronbach's alpha when an item would be deleted froma scale.

Initial transformations

After assessing the quality of the data and of the measurements, one mightdecide to impute missing data, or to perform initial transformations of one ormore variables, although this can also be done during the main analysis phase.

Possible transformations of variables are:

Square root transformation (if the distribution differs moderately from normal)

Log-transformation (if the distribution differs substantially from normal) Inverse transformation (if the distribution differs severely from normal) Make categorical (ordinal / dichotomous) (if the distribution differs

severely from normal, and no transformations help)

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E. Interpretation of Results and Recommendations

The previous section presented our summarization and analysis of the plannerruns. In this section, we reflect on what those results mean for empiricalcomparison of planners; we summarize the results and recommend somepartial solutions. It is not possible to guarantee fairness and we propose nomagic formula for performing evaluations, but the state of the practice ingeneral can certainly be improved. We propose three generalrecommendations and12 recommendations targeted to specific assumptions.

Many of the targeted recommendations amount to requesting problem andplanner developers to be more precise about the requirements for andexpectations of their contributions. Because the planners are extremelycomplex and time consuming to build, the documentation may be inadequateto determine how a subsequent version differs from the previous or under whatconditions (e.g., parameter settings, problem types) the planner can be fairly

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compared. With the current positive trend in making planners available, itbehooves the developer to include such information in the distribution of thesystem.

The most sweeping recommendation is to shift the research focus away fromdeveloping the bestgeneral-purpose planner. Even in the competitions, some

of the planners identified as superior have been ones designed for specificclasses of problems, e.g., FF and IPP. The competitions have done a great jobof exciting interest and encouraging the development and public availability ofplanners that incorporate the same representation.

However, to advance the research, the most informative comparativeevaluations are those designed for a specific purpose - to test some hypothesisor prediction about the performance of a planner. An experimental hypothesisfocuses the analysis and often leads naturally to justified design decisionsabout the experiment itself. For example, Hoffmann and Nebel, the authors ofthe Fast-Forward (FF) system, state in the introduction to their JAIR paper thatFF's development was motivated by a specific set of the benchmark domains;because the system is heuristic, they designed the heuristics to fit theexpectations/needs of those domains [Hoffmann Nebel 2001]. Additionally, inpart of their evaluation, they compare to a specific system on which their ownsystem had commonalities and point out the various advantages ordisadvantages of their design decisions on specific problems. Follow-up work orresearchers comparing their own systems to FF now have a well-definedstarting point for any comparison.

Recommendation 1: Experiments should be driven by hypotheses.

Researchers should precisely articulate in advance of the experiments theirexpectations about how their new planner or augmentations to an existingplanner add to the state of the art. These expectations should in turn justifythe selection of problems, other planners and metrics that form the core of thecomparative evaluation.A general issue is whether the results are accurate. We reported the results asthey are output by the planners. If a planner stated in its output that it hadbeen successful, we took it at face value. However, by examining some of theoutput, we determined that some claims of successful solution were erroneous- the proposed solution would not work. The only way to ensure that theoutput is correct is with a solution checker. Drew McDermott used a solutionchecker in the AIPS98 competition. However, the planners do not all provideoutput in a compatible format with his checker. Thus, another concern with anycomparative evaluation is that the output needs to be cross-checked. Becausewe are not declaring a winner (i.e., that some planner exhibited superiorperformance), we do not think that the lack of a solution checker casts seriousdoubt on our results. For the most part, we have only been concerned withfactors that cause the observed success rates to change.

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Recommendation 2: Just as input has been standardized with PDDL, outputshould be standardized, at least in the format of returned plans.Another general issue is whether the benchmark sets are representative of thespace of interesting planning problems. We did not test this directly (in fact,we are not sure how one could do so), but the clustering of results andobservations by others in the planning community suggest that the set is

biased toward logistics problems. Additionally, many of the problems aregetting dated and no longer distinguish performance. Some researchers havebegun to more formally analyze the problem set, either in service of buildingimproved planners (e.g., [Hoffmann Nebel 2001]) or to better understandplanning problems. For example, in the related area of scheduling, our grouphas identified distinctive patterns in the topology of search spaces for differenttypes of classical scheduling problems and has related the topology toperformance of algorithms [Watson et al. 2001]. Within planning, Hoffman hasexamined the topology of local search spaces in some of the small problems inthe benchmark collection and found a simple structure with respect to somewell-known relaxations [Hoffmann 2001]. Additionally, he has worked out apartial taxonomy, based on three characteristics, for the analyzed domains.Helmert has analyzed the computational complexity of a subclass of thebenchmarks, transportation problems, and has identified key features thataffect the difficulty of such problems [Helmert 2001].Recommendation 3: The benchmark problem sets should themselves beevaluated and over-hauled. Problems that can be easily solved should beremoved. Researchers should study the benchmark problems/domains toclassify them into problem types and key characteristics. Developers shouldcontribute application problems and realistic versions of them to the evolvingset.

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Q.2 Differentiate between Exploratory and Conclusive research.

Exploratory Research

The objectives of exploratory research are:

1. To establish, a classification of marketing research projects into two maincategories: exploratory research and conclusive research.2. To define and describe exploratory research.3. To explain two main uses of exploratory research.4. To describe the three different exploratory research designs.5. To describe the proper use of focus groups in exploratory research and toclarify their limitations.6. To describe the case study method, its uses and its limitations.

Despite the difficulty of establishing an entirely satisfactory classificationsystem, it is helpful to classify marketing research projects on the basis of thefundamental objectives of the research. Consideration of the different types,their applicability, their strengths and their weaknesses will help the student toselect the type best suited to a specific problem The two general types ofresearch are: (1) exploratory and (2) conclusive.

These terms are not generally used by marketing practitioners, who tend touse the terms qualitative and quantitative instead of exploratory andconclusive. But the terms qualitative and quantitative suggest the character ofthe data and the process by which they are gathered rather than thefundamental objectives of the research. We believe the terminology used hereis more useful in guiding research planning. Exploratory research seeks todiscover new relationships, while conclusive research is designed to helpexecutives choose among various possible courses of action that is, makedecisions.

Each of these two general types of research can be subdivided as follows:

Exploratory research:

1. Search of secondary data2. Survey of knowledgeable persons3. Case study

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Conclusive Research:

1. Descriptive research2. Case study3. Statistical study4. Experimentation

The following discuss is organized according to the above classification.

Exploratory Research Looks for Hypotheses: In well established fields of studyhypotheses usually are drawn from ideas developed or glimpsed in perviousresearch studies or are derived from theory. Hypotheses are tentative answersto questions that serve as guides for most research projects. For example, acandy manufacturer, on the basis of experience, might state a hypothesis thatconsumers will prefer crushed peanuts instead of whole peanuts in a particularcandy bar. Research could then be used to determine if the hypothesis wascorrect.

Too little is known however about consumer reaction to marketing stimuli topermit the formulation of sound hypotheses in many specific situations. As aresult much marketing research is of an exploratory nature; emphasis is placedof finding hypotheses relative to new products or, marketing practices that canbe changed profitably.

Use of exploratory Research:

It was clear during the summer of 1985 that significant changes wereoccurring in the home entertainment marketplace and that pay TV was nolonger the hottest game in town (said the research director of the cableTelevision Administration and Marketing Society Inc).

The committee felt that a better understanding of the key factors affecting payTV, as well as an examination of programmers and ad operators marketingstrategies, would contribute to improved marketing of cable TV.

Executives in the cable television industry were uneasy with developments in

the industry, but they had no specific ideas as to what they should do.Exploratory research was undertaken to develop some specific hypothesesrelative to possible actions.

Exploratory research usually results when a researcher is called in by amanager or client who says were not getting the sales volume we think weshould. Whats wrong? Or as in the cable television example, a manager may

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sense that changes are taking place in the market that may open anopportunity for a new product or create a problem for an established product.He wants help in deciding what actions to take. Exploratory research is anatural step.

Under such circumstances the researchers may guess at a number of factors

the product may be inferior in quality or style, the wrong channels ofdistribution may be used, the number of sales representatives in the field maybe too few the advertising appeals may not be the best and so on. As in the TVexample, executives may be prepared to look anywhere for new ideas.

Conclusive Research

As the term suggests, conclusive research is meant to provide information thatis useful in reaching conclusions or decision-making. It tends to be quantitativein nature that is to say in the form of numbers that can be quantified and

summarized. It relies on both secondarydata, particularly existing databasesthat are reanalyzed to shed light on a different problem than the original onefor which they were constituted, and primary research , or data specificallygathered for the current study.

The purpose of conclusive research is to provide a reliable or representativepicture of thepopulation through the use of a validresearch instrument. In thecase of formal research, it will also test hypothesis.

Conclusive research can be sub-divided into two major categories:

1. Descriptive or statistical research, and

2. Causal research

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Q.3 (a) Elaborate the concept of independent variable, dependent variable,

demographic variable, dummy variables and hypothesis in detail.

(b) What are the elements of research problem? Keeping in view all these

elements, formulate any management related research problem and develop a

hypothesis for that problem.

*Dependent and independent variables

The terms "dependent variable" and "independent variable" are used insimilar but subtly different ways in mathematics and statistics as part of thestandard terminology in those subjects. They are used to distinguish betweentwo types of quantities being considered, separating them into those availableat the start of a process and those being created by it, where the latter(dependent variables) are dependent on the former (independent variables).

Simplified example

The independent variable is typically the variable representing the value beingmanipulated or changed and the dependent variable is the observed result ofthe independent variable being manipulated. For example concerning nutrition,the independent variable of daily vitamin C intake (how much vitamin C oneconsumes) can influence the dependent variable of life expectancy (theaverage age one attains). Over some period of time, scientists will control thevitamin C intake in a substantial group of people. One part of the group will begiven a daily high dose of vitamin C, and the remainder will be given a placebo

pill (so that they are unaware of not belonging to the first group) withoutvitamin C. The scientists will investigate if there is any statistically significantdifference in the life span of the people who took the high dose and those whotook the placebo (no dose). The goal is to see if the independent variable ofhigh vitamin C dosage has a correlation with the dependent variable ofpeople's life span. The designation independent/dependent is clear in this case,because if a correlation is found, it cannot be that life span has influencedvitamin C intake, but an influence in the other direction is possible.

Use in mathematics

In traditional calculus, a function is defined as a relation between two termscalled variables because their values vary. Call the terms, for example, xandy. If every value ofxis associated with exactly one value ofy, then yis said tobe a function of x. It is customary to use x for what is called the"independent variable," and yfor what is called the "dependent variable"because its value depends on the value ofx. Therefore, y=x2 means that y,the dependent variable, is the square ofx, the independent variable.

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The most common way to denote a "function" is to replace y, the dependentvariable, by f(x), where f is the first letter of the word "function." Thus, y=f(x) = x2 means that y, a dependent variable, a function ofx, is the square ofx. Also, in this form, the expression is called an "explicit" function of x,contrasted withx2y= 0, which is called an "implicit" function.

Use in statistics

Controlled experiments

In a statistics experiment, the dependent variable is the event studied andexpected to change whenever the independent variable is altered.

In the design of experiments, an independent variable's values are controlledor selected by the experimenter to determine its relationship to an observedphenomenon (i.e., the dependent variable). In such an experiment, an attemptis made to find evidence that the values of the independent variable determine

the values of the dependent variable. The independent variable can bechanged as required, and its values do not represent a problem requiringexplanation in an analysis, but are taken simply as given. The dependentvariable, on the other hand, usually cannot be directly controlled.

Controlled variables are also important to identify in experiments. They are thevariables that are kept constant to prevent their influence on the effect of theindependent variable on the dependent. Every experiment has a controllingvariable, and it is necessary to not change it, or the results of the experimentwon't be valid.

"Extraneous variables" are those that might affect the relationship betweenthe independent and dependent variables. Extraneous variables are usually nottheoretically interesting. They are measured in order for the experimenter tocompensate for them. For example, an experimenter who wishes to measurethe degree to which caffeine intake (the independent variable) influencesexplicit recall for a word list (the dependent variable) might also measure theparticipant's age (extraneous variable). She can then use these age data tocontrol for the uninteresting effect of age, clarifying the relationship betweencaffeine and memory.

*Demographic variable

Demographics or demographic data are the characteristics of a humanpopulation as used in government, marketing or opinion research, or thedemographic profiles used in such research. Note the distinction from the term"demography" (see below.) Commonly used demographics include gender,race, age, income, disabilities, mobility (in terms of travel time to work or

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number of vehicles available), educational attainment, home ownership,employment status, and even location. Distributions of values within ademographic variable, and across households, are both of interest, as well astrends over time. Demographics are frequently used in economic andmarketing research. It is important to distinguish between demographics andpsychographics.

Demographic trends describe the changes in demographics in a populationover time. For example, the average age of a population may increase overtime. It may decrease as well. Certain restrictions may be set in placechanging those numbers. For instance in China with the one child policy.

The term demographics as a noun is often used erroneously in place ofdemography, the study of human population, its structure and change.Although there is no absolute delineation, demography focuses on populationstructure, processes and dynamics, whereas demographics is most often usedin the fields of media studies, advertising, marketing, and polling, and shouldnot be used interchangeably with the term "demography" or (more broadly)"population studies".

Demographic profiles in marketing

Marketers typically combine several variables to define a demographic profile.A demographic profile (often shortened to "a demographic") provides enoughinformation about the typical member of this group to create a mental pictureof this hypothetical aggregate. For example, a marketer might speak of thesingle, female, middle-class,age 18 to 24, college educated demographic.

Marketing researchers typically have two objectives in this regard: first todetermine what segments or subgroups exist in the overall population; andsecondly to create a clear and complete picture of the characteristics of atypical member of each of these segments. Once these profiles areconstructed, they can be used to develop a marketing strategy and marketingplan. The five types of demographics in marketing are age, gender, incomelevel, race and ethnicity.

Generational cohorts

A generational cohort has been defined as "the group of individuals (withinsome population definition) who experience the same event within the sametime interval".The notion of a group of people bound together by the sharing ofthe experience of common historical events developed in the early 1920s.Today the concept has found its way into popular culture through well knownphrases like "baby boomer" and "Generation X".

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The United Kingdom has a series of four national birth cohort studies, the firstthree spaced apart by 12 years: the 1946 National Survey of Health andDevelopment, the 1958 National Child Development Study, the 1970 BritishCohort Study, and the Millennium Cohort Study, begun much more recently in2000. These have followed the lives of samples of people (typically beginningwith around 17,000 in each study) for many years, and are still continuing. As

the samples have been drawn in a nationally representative way, inferencescan be drawn from these studies about the differences between four distinctgenerations of British people in terms of their health, education, attitudes,childbearing and employment patterns. The last three are run by the Centre forLongitudinal Studies.

*Dummy Variables

A dummy variable is a numerical variable used in regression analysis torepresent subgroups of the sample in your study. In research design, a dummy

variable is often used to distinguish different treatment groups. In the simplestcase, we would use a 0,1 dummy variable where a person is given a value of 0if they are in the control group or a 1 if they are in the treated group. Dummyvariables are useful because they enable us to use a single regression equationto represent multiple groups. This means that we don't need to write outseparate equation models for each subgroup. The dummy variables act like'switches' that turn various parameters on and off in an equation. Anotheradvantage of a 0,1 dummy-coded variable is that even though it is a nominal-level variable you can treat it statistically like an interval-level variable (if thismade no sense to you, you probably should refresh your memory on levels ofmeasurement). For instance, if you take an average of a 0,1 variable, the

result is the proportion of1s in the distribution.

To illustrate dummy variables, consider the simple regression model for aposttest-only two-group randomized experiment. This model is essentially thesame as conducting a t-test on the posttest means for two groups or

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conducting a one-way Analysis of Variance (ANOVA). The key term in themodel is b1, the estimate of the difference between the groups. To see howdummy variables work, we'll use this simple model to show you how to usethem to pull out the separate sub-equations for each subgroup. Then we'llshow how you estimate the difference between the subgroups by subtractingtheir respective equations. You'll see that we can pack an enormous amount of

information into a single equation using dummy variables. All I want to showyou here is that b1 is the difference between the treatment and control groups.

To see this, the first step is to compute what the equation would be for each ofour two groups separately. For the control group, Z = 0. When we substitutethat into the equation, and recognize that by assumption the error termaverages to 0, we find that the predicted value for the control group is b0, theintercept. Now, to figure out the treatment group line, we substitute the valueof 1 for Z, again recognizing that by assumption the error term averages to 0.The equation for the treatment group indicates that the treatment group valueis the sum of the two beta values.

Now, we're ready to move on to the second step -- computing the differencebetween the groups. How do we determine that? Well, the difference must be

the difference between the equations for the two groups that we worked outabove. In other word, to find the difference between the groups we just findthe difference between the equations for the two groups! It should be obviousfrom the figure that the difference is b1. Think about what this means. Thedifference between the groups is b1. OK, one more time just for the sheer heckof it. The difference between the groups in this model is b1!

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Whenever you have a regression model with dummy variables, you can alwayssee how the variables are being used to represent multiple subgroup equationsby following the two steps described above:

create separate equations for each subgroup by substituting the dummyvalues

find the difference between groups by finding the difference betweentheir equations

* Hypothesis

A hypothesis (from Greek ; plural hypotheses) is a proposedexplanation for an observable phenomenon. The term derives from the Greek, hypotithenai meaning "to put under" or "to suppose." For ahypothesis to be put forward as a scientific hypothesis, the scientific methodrequires that one can test it. Scientists generally base scientific hypotheses onprevious observations that cannot satisfactorily be explained with the availablescientific theories. Even though the words "hypothesis" and "theory" are oftenused synonymously in common and informal usage, a scientific hypothesis isnot the same as a scientific theory. A working hypothesis is a provisionallyaccepted hypothesis.

In a related but distinguishable usage, the term hypothesis is used for theantecedent of a proposition; thus in proposition "IfP, then Q", Pdenotes thehypothesis (or antecedent); Q can be called a consequent.Pis the assumptionin a (possibly counterfactual)What Ifquestion.

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The adjective hypothetical, meaning "having the nature of a hypothesis," or"being assumed to exist as an immediate consequence of a hypothesis," canrefer to any of these meanings of the term "hypothesis."

Uses

In Plato's Meno (86e87b), Socrates dissects virtue with a method used bymathematicians, that of "investigating from a hypothesis." In this sense,'hypothesis' refers to a clever idea or to a convenient mathematical approachthat simplifies cumbersome calculations. Cardinal Bellarmine gave a famousexample of this usage in the warning issued to Galileo in the early 17thcentury: that he must not treat the motion of the Earth as a reality, but merelyas a hypothesis.

In common usage in the 21st century, a hypothesis refers to a provisional ideawhose merit requires evaluation. For proper evaluation, the framer of ahypothesis needs to define specifics in operational terms. A hypothesis requiresmore work by the researcher in order to either confirm or disprove it. In duecourse, a confirmed hypothesis may become part of a theory or occasionallymay grow to become a theory itself. Normally, scientific hypotheses have theform of a mathematical model. Sometimes, but not always, one can alsoformulate them as existential statements, stating that some particular instanceof the phenomenon under examination has some characteristic and causalexplanations, which have the general form of universal statements, statingthat every instance of the phenomenon has a particular characteristic.

Any useful hypothesis will enable predictions by reasoning (including deductive

reasoning). It might predict the outcome of an experiment in a laboratorysetting or the observation of a phenomenon in nature. The prediction may alsoinvoke statistics and only talk about probabilities. Karl Popper, followingothers, has argued that a hypothesis must be falsifiable, and that one cannotregard a proposition or theory as scientific if it does not admit the possibility ofbeing shown false. Other philosophers of science have rejected the criterion offalsifiability or supplemented it with other criteria, such as verifiability (e.g.,verificationism) or coherence (e.g., confirmation holism). The scientific methodinvolves experimentation on the basis of hypotheses in order to answerquestions and explore observations.

In framing a hypothesis, the investigator must not currently know the outcomeof a test or that it remains reasonably under continuing investigation. Only insuch cases does the experiment, test or study potentially increase theprobability of showing the truth of a hypothesis. If the researcher alreadyknows the outcome, it counts as a "consequence" and the researcher shouldhave already considered this while formulating the hypothesis. If one cannotassess the predictions by observation or by experience, the hypothesis classes

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as not yet useful, and must wait for others who might come afterward to makepossible the needed observations. For example, a new technology or theorymight make the necessary experiments feasible.

Scientific hypothesis

People refer to a trial solution to a problem as a hypothesis often called an"educated guess"[5] because it provides a suggested solution based on theevidence. Experimenters may test and reject several hypotheses before solvingthe problem.

According to Schick and Vaughn, researchers weighing up alternativehypotheses may take into consideration:

Testability (compare falsifiability as discussed above) Simplicity (as in the application of "Occam's razor", discouraging the

postulation of excessive numbers ofentities) Scope the apparent application of the hypothesis to multiple cases of

phenomena Fruitfulness the prospect that a hypothesis may explain further

phenomena in the future Conservatism the degree of "fit" with existing recognized knowledge-

systems

Evaluating hypotheses

Karl Popper's formulation ofhypothetico-deductive method, which he called themethod of "conjectures and refutations", demands falsifiable hypotheses,framed in such a manner that the scientific community can prove them false(usually by observation). According to this view, a hypothesis cannot be"confirmed", because there is always the possibility that a future experimentwill show that it is false. Hence, failing to falsify a hypothesis does not provethat hypothesis: it remains provisional. However, a hypothesis that has beenrigorously tested and not falsified can form a reasonable basis for action, i.e.,we can act as if it were true, until such time as it is falsified. Just becausewe've never observed rain falling upward, doesn't mean that we never willhowever improbable, our theory of gravity may be falsified some day.

Popper's view is not the only view on evaluating hypotheses. For example,some forms of empiricism hold that under a well-crafted, well-controlledexperiment, a lack of falsification does count as verification, since such an

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experiment ranges over the full scope of possibilities in the problem domain.Should we ever discover some place where gravity did not function, and rainfell upward, this would not falsify our current theory of gravity (which, on thisview, has been verified by innumerable well-formed experiments in the past) it would rather suggest an expansion of our theory to encompass some newforce or previously undiscovered interaction of forces. In other words, our

initial theory as it stands is verified but incomplete. This situation illustratesthe importance of having well-crafted, well-controlled experiments that rangeover the full scope of possibilities for applying the theory.

In recent years philosophers of science have tried to integrate the variousapproaches to evaluating hypotheses, and the scientific method in general, toform a more complete system that integrates the individual concerns of eachapproach. Notably, Imre Lakatos and Paul Feyerabend, both former students ofPopper, have produced novel attempts at such a synthesis.

Hypotheses, Concepts and Measurement

Concepts, as abstract units of meaning, play a key role in the development andtesting of hypotheses. Concepts are the basic components of hypotheses. Mostformal hypotheses connect concepts by specifying the expected relationshipsbetween concepts. For example, a simple relational hypothesis such as education increases income specifies a positive relationship between the

concepts education and income. This abstract or conceptual hypothesis

cannot be tested. First, it must be operationalized or situated in the real worldby rules of interpretation. Consider again the simple hypothesis Educationincreases Income. To test the hypothesis the abstract meaning of education

and income must be derived or operationalized. The concepts should bemeasured. Education could be measured by years of school completed or

highest degree completed etc. Income could be measured by hourly rate of

pay or yearly salary etc.

When a set of hypotheses are grouped together they become a type ofconceptual framework. When a conceptual framework is complex andincorporates causality or explanation it is generally referred to as a theory.According to noted philosopher of science Carl Gustav Hempel An adequateempirical interpretation turns a theoretical system into a testable theory: Thehypothesis whose constituent terms have been interpreted become capable oftest by reference to observable phenomena. Frequently the interpretedhypothesis will be derivative hypotheses of the theory; but their confirmationor disconfirmation by empirical data will then immediately strengthen orweaken also the primitive hypotheses from which they were derived.

Hempel provides a useful metaphor that describes the relationship between aconceptual framework and the framework as it is observed and perhaps tested

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(interpreted framework). The whole system floats, as it were, above the plane

of observation and is anchored to it by rules of interpretation. These might beviewed as strings which are not part of the network but link certain points ofthe latter with specific places in the plane of observation. By virtue of thoseinterpretative connections, the network can function as a scientific theory

Hypotheses with concepts anchored in the plane of observation are ready to be

tested. In actual scientific practice the process of framing a theoreticalstructure and of interpreting it are not always sharply separated, since theintended interpretation usually guides the construction of the theoretician.

It is, however, possible and indeed desirable, for the purposes of logical

clarification, to separate the two steps conceptually.

Statistical hypothesis testing

When a possible correlation or similar relation between phenomena isinvestigated, such as, for example, whether a proposed remedy is effective intreating a disease, that is, at least to some extent and for some patients, thehypothesis that a relation exists cannot be examined the same way one mightexamine a proposed new law of nature: in such an investigation a few cases inwhich the tested remedy shows no effect do not falsify the hypothesis. Instead,statistical tests are used to determine how likely it is that the overall effectwould be observed if no real relation as hypothesized exists. If that likelihoodis sufficiently small (e.g., less than 1%), the existence of a relation may beassumed. Otherwise, any observed effect may as well be due to pure chance.

In statistical hypothesis testing two hypotheses are compared, which are calledthe null hypothesis and the alternative hypothesis. The null hypothesis is thehypothesis that states that there is no relation between the phenomena whoserelation is under investigation, or at least not of the form given by thealternative hypothesis. The alternative hypothesis, as the name suggests, isthe alternative to the null hypothesis: it states that there is some kind ofrelation. The alternative hypothesis may take several forms, depending on thenature of the hypothesized relation; in particular, it can be two-sided (forexample: there is some effect, in a yet unknown direction) or one-sided (thedirection of the hypothesized relation, positive or negative, is fixed inadvance).

Proper use of statistical testing requires that these hypotheses, and thethreshold (such as 1%) at which the null hypothesis is rejected and thealternative hypothesis is accepted, all be determined in advance, before theobservations are collected or inspected. If these criteria are determined later,when the data to be tested is already known, the test is invalid.

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Methods

Give enough information so that others can follow your procedure,and can replicate it (and hopefully come up with the same findings andconclusions as you did!)

Describe your procedure as completely as possible so that someone canduplicate it completely

Define your sample and its characteristicsThese should be consistent throughout the test

List the variables usedThese are what change, or that you manipulate, throughout the test

Try to anticipated criticism that affects either your internal or externalvalidityThese might be considered "flaws"

Findings

This is descriptive and numeric data

Discussion

Develop your argument based upon your findings.While the data may read for itself, you will need to interpret

how it validates your hypothesis what falls outside of validity how it impacts the literature you cited where further research is needed

Conclusion

Restate and summarize your findings and discussion either in order to simplycomplexity or to provide a summary for those who skip to it!

References

Verify with your teacher the proper format

Recommendations:

A research paper is not an essay, an editorial, or a story.All assertions of fact must be documented.Be careful of any generalizations that you make.Strive to be value-free in your inquiry.Review ourGuide on the Scientific Method

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it's worth stressing that the evaluation of your paper will never be determinedby whether or not your hypotheses are verified. It is important to rememberthat a hypothesis supported by the data does not mean that it is true as thereconceivably is an infinite number of other theories that lead to the sameprediction. Similarly, failure of support does not necessarily mean that yourhypothesis is wrong: it may be hold true in some populations, you may have

incorrectly measured your theory's concepts, your sampling may be flawed,etc. Philosopher Karl Popper, in fact, argues that science is not a method forverifying hypotheses. Instead, all that science can logically lead to is thefalsification of hypotheses. In sum, negative results can be every bit asimportant as positive ones.

Formulate any management related research

A research problem is the first step and the most important requirement in theresearch process. It serves as the foundation of a research studyf ie. if well

formulated, you expect a good study to follow.

According the Kerlinger; in order for one to solve a problem, one must knowwhat the problem is. The large part of the problem is knowing what one istrying to do.

A research problem and the way you formulate it determines almost every stepthat follows in the research study.

Formulation of the problem is like the input into the study and the output is thequality of the contents of the research report.

Steps involved in formulating a Research Problem are as below:

1. Identify a broad area of interest in your academic /professional field.

2. Dissect the broad area into sub-areas by having a brain storming sessionwith your colleages

3. Select the sub-area in which you would like to conduct your researchthrough the process of elimination.

4. Reverse the research questions that you would like to answer through yourstudy. This can be after formulation of the objectives or can lead you to theformulation of the objective

5. Assess these objectives to ascertain the feasibility of attaining them in thelight of time and other issues like finances and human resource expertise.

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Formatting HypothesesSome years ago, I found a nice short lesson on hypotheses that really allowsthe student to get a handle on this process skill. The book was Patterns andProcesses by the BSCS group who wrote this edition for middle school'ers.

Amazi ngly, for as clear as they present hypotheses in this book, they neverseemed to have utilized the technique in any other BSCS publications. This hasalways seemed strange to me since BSCS labs often call on the student towrite some sort of hypothesis based on the lab procedure.

Most commonly, hypotheses take three formats:

1. a question, "Does temperature affect fermentation?"2. a conditional statement, "Temperature may affect fermentation."3. an If, then statement, "If fermentation rate is related to temperature,

then increasing the temperature will increase gas production.

The third type is more structured and I'll refer to it as a "formalized"hypothesis. A caution is necessary at this point. Beware! Not all "if-then"statements are hypotheses. For example, "If you warm yeast, then more gaswill be produced." This is a simple prediction, not a hypothesis! The problemwith this statement is that there is no proposition to test. What is related towhat? Is temperature a variable? Is yeast a variable? I s gas production avariable?

Research models limit variables to two. The structure of a formalized

hypothesis is useful because it makes the student focus on two variables thatmay be related. Furthermore, it forces the student to make a prediction of howmanipulating one variable independent will affect the other variable dependent.Let's take another example from biology.

Ifthe diffusion rate(dependent variable) through a membrane is relatedtomolecular size, (independent variable)then the smaller the molecule the faster it will pass through a membrane.

Notice that in the formalized hypotheses the "if" clause proposes a relationship

between two things, the variables. The variables here are diffusion rate andmolecular size. In the experimental design, we can manipulate molecular sizeby simply selecting soluble substances of different molecular size, e.g.., iodine,glucose, starch. The student will know what the dependent variable is becauseit is the thing she/he watches for results, i.e. movement of iodine (color),glucose (indicator change), and starch (indicator change).

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If this hypothesis is stated as a question (Does molecular size affect diffusionthrough a membrane?) the student must infer what is to be manipulated andwhat is to be observed. Furthermore, the student doesn't have to make aprediction. Experience has taught me that few students infer what relationshipis implied by a hypothesis in the question format. More likely, studentsinterpret these hypothetical questions literally and try to answer them yes or

no! Such as "does molecular size effect molecular movement through amembrane?" Answer:

"yes!" No inference is made, only a deduction is concluded which is a lowerlevel of thinking.

Another value of a formalized hypothesis is that when a student is given such ahypothesis, he can be asked to design an experiment that will test thehypothesis. For example, describe an experiment that would test the followinghypothesis.

If the rate of photosynthesisis related to wave lengths of light, then exposing aplant to different colors of light will produce different amounts of oxygen.

The use of the phrase "is related" is intentional in these examples. Otherphrases such as "is affected" will work here, but I prefer "is related" because itreminds students that we are investigating relationships, not just cause andeffect events. Students get into less trouble if they stick to this phrase. Todownload a student lesson on how to write hypotheses, click WritingHypotheses: a student lesson.

Teaching Strategies for HypothesesHaving students design every experiment would consume more time than isusually available. How can real science be imbedded on a regular basis? Analternative strategy to student design is to give out a lab paper the day beforea lab. Students are to read over the procedure the night before, identify thevariables, and write an appropriate hypothesis. Still some opportunities forstudent design are highly desirable and should be included periodically. Forexamples of student lessons where students write the hypothesis based on theprocedure, click Labs With Hypotheses.

Students need to be reminded frequently, that a hypothesis is still valid evenwhen results are the opposite of what is predicted because it will still shed lighton the true nature of the relationship being tested. This lowers the risks ofbeing wrong. For example, "If the period of a pendulum is related to its length,then the longer the pendulum the shorter the period." Actually the result sshow just the opposite is true.

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Q.4 Compare primary and secondary data? Explain collection and security of

secondary data.

Primary & Secondary Data Whats The Difference?

Primary research entails the use of immediate data in determining the survivalof the market. The popular ways to collect primary data consist of surveys,interviews and focus groups, which shows that direct relationship betweenpotential customers and the companies. Whereas secondary research is ameans to reprocess and reuse collected information as an indication forbetterments of the service or product. Both primary and secondary data areuseful for businesses but both may differ from each other in various aspects.

In secondary data, information relates to a past period. Hence, it lacks aptnessand therefore, it has unsatisfactory value. Primary data is moreaccommodating as it shows latest information.

Secondary data is obtained from some other organization than the oneinstantaneously interested with current research project. Secondary data wascollected and analyzed by the organization to convene the requirements ofvarious research objectives. Primary data is accumulated by the researcherparticularly to meet up the research objective of the subsisting project.

Secondary data though old may be the only possible source of the desired dataon the subjects, which cannot have primary data at all. For example, surveyreports or secret records already collected by a business group can offerinformation that cannot be obtained from original sources.

Firm in which secondary data are accumulated and delivered may notaccommodate the exact needs and particular requirements of the currentresearch study. Many a time, alteration or modifications to the exact needs ofthe investigator may not be sufficient. To that amount usefulness of secondarydata will be lost. Primary data is completely tailor-made and there is noproblem of adjustments.

Secondary data is available effortlessly, rapidly and inexpensively. Primarydata takes a lot of time and the unit cost of such data is relatively high.

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Introduction for data collection methods:

Data collection is the one of the important process of statistics chapterin mathematics subject. A data collection is defined as group of informationfrom some person or some incidents. Datas are collecting from variousmethods. Online study is one of the learning methods. We are going to explain

brief about the types of data collection online study.

Explanation Data Collection, Types & Online Study:

Data collection:

Data collection is defined as, the information or data collects from

way of first investigation process, inquiry and hear news with some others. Itssimply states that how the information is gathered? It is known as datacollection. The getting data is may be give us to particular information related

to that data.

There are two types of datas are solved in data collection methods.

1. Primary data, and

2. Secondary data.

Online study:

Online study is the learning process from online with the help of internetand online tutor (real person).

Online study is the students studied from the person (online tutor). It isstudy method for one person to one person, one person to manypersons, and many persons to many persons. In this type of online studyis very popular on now days.

Online study helps more for the students, because here tutor andstudents interact with each other with the help of online so, the studentsdont have fear to ask questions. And also searching related question

answers in educational websites. These online study methods, tutors and students contact through chat

and also video mode. It is more helpful for them.

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Explain about Data Collection Methods:

The both primary and secondary data types are having different datacollection solve methods. There are given by following explanation,

1. Primary data methods:

The primary data collection is having three data collection types.These datas are mostly pure and original datas. There are explaining with

given below:

A). Personal investigation methods:

The researchers are data collectors have conduct the survey andcollecting datas. This method we have to collect more accurate data andoriginal data. But in this method is very useful for small data collections onlynot big projects.

B). Data collection through investigation:

In this method trained investigators are working as employee forcollecting the data. This method, the researcher will collect the informationfrom asking required questions from the individual persons.

C). Data collection through telephones:

The data researches collect the information or data through the

telephones and mobiles. It is accurate and very quick process for datacollection.

2. Secondary data methods:

The secondary data is the data, which is collection of data from thesecond hand information. It means the given data is already collected fromany one person for any other purpose, and it is available for the presentissues. And mostly these secondary datas are not relevant and pure datas.

The secondary data collection methods also have two variousimportant methods and it is explaining given below:

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A). Official:

Datas collecting from the ministry of finance, Agriculture, Industry

and etc These data collection methods are official.

B). Semi official:

This is the method of data collection from, Railway boards, banks,population committee and etc

These all are the different types of data collection methods online study.

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Q.5 a) Differentiate between qualitative and quantitative analysis.

b) What are the methods of collecting primary data?

Quantitative research focuses on numbers or quantities. Quantitative studieshave results that are based on numeric analysis and statistics. Often, thesestudies have many participants. It is not unusual for there to be over athousand people in a quantitative research study. It is ideal to have a largenumber of participants because this gives analysis more statistical power.

Qualitative research studies are focused on differences in quality, rather thandifferences in quantity. Results are in words or pictures rather than numbers.Qualitative studies usually have fewer participants than quantitative studiesbecause the depth of the data collection does not allow for large numbers ofparticipants.

Quantitative and qualitative studies both have strengths and weaknesses. A

particular strength of quantitative research is that statistical analysis allows forgeneralization (to some extent) to others. A goal of quantitative research is tochoose a sample that closely resembles the population. Qualitative researchdoes not seek to choose samples that are representative of populations.

However, qualitative data does provide a depth and richness of data notpossible with quantitative data. Although there are fewer participants, theresearchers generally know more details about each participant. Quantitativeresearchers collect data on more participants, so it is not possible to have thedepth and breadth of knowledge about each.

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Primary Data Collection Methods

In prima