Module 2: Research Problem andResearch Design

Defining and Identifying the Problem

Formulation of Hypothesis Techniques involved in defining

the Problem Meaning and features of

Research Design Types of Research: Qualitative

and Quantitative Research Developing a Research Plan:

Industry Specific Research Proposals

Identify the Problem

The first step for any researchproject, whether it is adissertation, a grant proposal orresearch to find the bestpractice intervention is formulatea question you want to answer.The research problem may besomething the agency identifiesas a problem, some knowledgeor information that is needed bythe agency, or the desire toidentify a recreation trendnationally Being clear on thequestion makes it easier toformulate a research strategy forfinding the best informationabout this question.

Statement of the problem shouldcome without any unnecessaryintroduction. It presents an overviewof the problem that researcher has inhis/her mind.Topic of a research study should haveadequacy, relevancy and simplicity.Normally a research topic shouldsatisfy the following criteria: (i) statethe key variables included in thestudy, (ii) state relationship betweenvariables, (iii) state population towhich results would be applicable, (iv)avoid redundant words, and (v) useonly acceptable scientific terms.Besides, a topic should neither be toolong to be over explicit nor too shortto be over implicit. It should beconcise and to the point.

HYPOTHESES In our day to day activities we

are often faced with problems.We undertake a number ofactivities to solve them. First wetry to identity possible reasonsfor the problem. Then we thinkof possible interventionstrategies that would solve theproblem. We try to find asolution to the problem throughlogical reasoning. Theseintelligent and logicalguesses about possibledifferences, relationships,causes and solutions arecalled HYPOTHESES.

Definition

A Hypothesis is a hunch or a shrewdguess or a tentative solution or aninference or sub-position to be testedby empirical evidences.

Once the investigator diagnoses thecauses of the pinpointed/specificproblems, he/she starts thinkingabout what concrete action, if taken,would bring about the desiredchange/solution.

Then he/she formulates hypothesisspecifying the immediate actions thatcould be taken to solve the problems.

Hypothesis

A hypothesis (plural hypotheses) is aproposed explanation for aphenomenon. For a hypothesis to bea scientific hypothesis, the scientificmethod requires that one can test it.Scientists generally base scientifichypotheses on previous observationsthat cannot satisfactorily be explainedwith the available scientific theories.Even though the words "hypothesis"and "theory" are often usedsynonymously, a scientific hypothesisis not the same as a scientific theory.A working hypothesis is aprovisionally accepted hypothesisproposed for further research.

In its ancient usage, hypothesis referred to asummary of the plot of a classical drama. TheEnglish word hypothesis comes from theancient Greek (hupothesis), meaning "to putunder" or "to suppose".

In Plato's Meno (86e87b), Socrates dissectsvirtue with a method used bymathematicians, that of "investigating from ahypothesis." In this sense, 'hypothesis' refersto a clever idea or to a convenientmathematical approach that simplifiescumbersome calculations. CardinalBellarmine gave a famous example of thisusage in the warning issued to Galileo in theearly 17th century: that he must not treat themotion of the Earth as a reality, but merely asa hypothesis.

HypothesisFormulation

Once you have identifiedyou research question, it istime to formulate yourhypothesis. While theresearch question is broadand includes all thevariables you want yourstudy to consider, thehypothesis is a statementthat specific relationship youexpect to find from yourexamination of thesevariables. When formulatingthe hypothesis(es) for yourstudy, there are a few thingsyou need to keep in mind

Good hypotheses meetthe following criteria

Identify the independent and dependentvariables to be studied.

Specify the nature of the relationship thatexists between these variables.

Simple (often referred to as parsimonious). Itis better to be concise than to be long-winded. It is also better to have severalsimple hypotheses than one complicatedhypothesis.

Does not include reference to specificmeasures.

Does not refer to specific statisticalprocedures that will be used in analysis.

Implies the population that you are going tostudy.

Is falsifiable and testable.

As indicated , it is better to haveseveral simple hypotheses than onecomplex one. However, it is also agood idea to limit the number ofhypotheses you use in a study to sixor fewer. Studies that address morehypotheses than six will often be tootime consuming to keep participantsinterested, and uninterestedparticipants do not take theimportance of their responses asseriously. Another advantage tolimiting the number of formalhypotheses you formulate is that toomany can make the discussionsection of your paper very hard towrite.

It is important to remember that youdo not have to have a formalhypothesis to justify all comparisonsand statistical procedures you mightuse. For instance, it is only when youstart doing exploratory analysis ofyour data that you realize that genderis an influencing factor. You do nothave to back up and write ahypothesis that addresses thisfinding. In fact, it is better in mostcases to not do this. You can reportany statistical findings you feel arerelevant, whether or not you have ahypothesis that addressed them.

CHARACTERISTICS OFA GOOD ACTION

HYPOTHESIS

The hypotheses formulated in action research are called ACTION HYPOTHESES

A good action hypothesis should be Logically related to the problem Testable in classrooms situations Clearly stated without ambiguity Directly stated in terms of the

expected outcome (should not be a generalized statement)

Testable within a considerably short time (maximum of three months)

DIFFERENT FORMS FORSTATING ACTION

HYPOTHESIS

a) Declarative form: An action hypothesis may be formulated as a statement with a positive relationship between the two factors identified, one being the cause and the other being the effect. This is also calleda directional hypothesis.

b) Predictive form: An actionhypothesis clearly predictingthe expected out come which would emerge after the action plan is implemented. This can be stated using if and then statement.

c) Question form: Questions can beraised as action hypotheses as whatwould be the result of the intendedaction plan.

d) Null form: A null hypothesis statesthat no relationship exists betweenthe factors considered in theproblems. This form is mostly usedwhen rigorous statistical techniquesare to be used.(A thoroughly workedout example for all these forms isgiven in the next unit.) Thus, anaction hypothesis provides clarity anddirection to solve a problem. Hence itis considered an important stage inaction research.

FORMULATION OF ANACTION HYPOTHESIS

To form a hypothesis the investigator should

Have a thorough knowledge about the problem

Be clear about the desired goal (solution) Make a real effort to look at the problem in

new ways other than the regular practices (come out form conventional thinking)

Give importance for imagination and speculation

Think of many alternative solutions. Thoroughly examine the conditions/contexts

in which the problem exists and then

State the hypothesis

Statistical hypothesis testing- In statistical hypothesis testing, two

hypotheses are compared. These are calledthe null hypothesis and the alternativehypothesis. The null hypothesis is thehypothesis that states that there is no relationbetween the phenomena whose relation isunder investigation, or at least not of the formgiven by the alternative hypothesis. Thealternative hypothesis, as the namesuggests, is the alternative to the nullhypothesis: it states that there is some kind ofrelation. The alternative hypothesis may takeseveral forms, depending on the nature of thehypothesized relation; in particular, it can betwo-sided (for example: there is some effect,in a yet unknown direction) or one-sided (thedirection of the hypothesized relation, positiveor negative, is fixed in advance).

Conventional significance levelsfor testing hypotheses(acceptable probabilities ofwrongly rejecting a true nullhypothesis) are .10, .05, and .01. Whether the null hypothesisis rejected and the alternativehypothesis is accepted, must bedetermined in advance, beforethe observations are collected orinspected. If these criteria aredetermined later, when the datato be tested are already known,the test is invalid.

Research Design:Meaning and Importance

A research design is aframework or blueprint forconducting the marketingresearch project. It detailsthe procedures necessaryfor obtaining the informationneeded to structure or solvemarketing researchproblems. In simple words itis the general plan of howyou will go about yourresearch.

Definitions ofResearch Design

According to Kerlinger

Research design is the plan,structure and strategy of investigationconceived so as to obtain answers toresearch questions and to controlvariance.

According to Green and Tull A research is the specification of

methods and procedures foracquiring the information needed. It isthe overall operational pattern orframework of the project thatstipulates what information is to becollected from which sources by whatprocedures.

The function of a researchdesign is to ensure that requisitedata in accordance with theproblem at hand is collectedaccurately and economically.Simply stated, it is theframework, a blueprint for theresearch study which guides thecollection and analysis of data.The research design, dependingupon the needs of theresearcher may be a verydetailed statement or onlyfurnish the minimum informationrequired for planning theresearch project.

To be effective, a research design should furnish at least the following details.

A statement of objectives of the study or the research output.

A statement of the data inputs required on the basis of which the research problem is to be solved.

The methods of analysis which shall be used to treat and analyze the data inputs.

More explicitly, the design decisions happen to be in respect of:

What is the study about? Why is the study being made? Where will the study be carried out? What type of data is required? Where can the required data be

found?

What periods of time will the study include?

What will be the sample design? What techniques of data collection

will be used?

How will the data be analyzed? In what style will the report be

prepared?

Advantages ofresearch design

Consumes less time. Ensures project time schedule. Helps researcher to prepare himself

to carry out research in a proper and a systematic way.

Better documentation of the various activities while the project work is going on.

Helps in proper planning of the resources and their procurement in right time.

Provides satisfaction and confidence, accompanied with a sense of successfrom the beginning of the work of the research project.

Need for Research Design

Research design is needed because itfacilitates the smooth sailing of the variousresearch operations, thereby makingresearch as efficient as possible yieldingmaximal information with minimal expenditureof effort, time and money. Research designhas a significant impact on the reliability ofthe results obtained. It thus acts as a firmfoundation for the entire research.

For example, economical and attractiveconstruction of house we need a blueprint (orwhat is commonly called the map of thehouse) well thought out and prepared by anexpert architect, similarly we need a researchdesign or a plan in advance of data collectionand analysis for our research project.

Research design stands for advanceplanning of the methods to be adopted forcollecting the relevant data and thetechniques to be used in their analysis.

The need for researchdesign

The need for research design is as follows:

It reduces inaccuracy; Helps to get maximum efficiency and

reliability;

Eliminates bias and marginal errors; Minimizes wastage of time;

Helpful for collecting research materials;

Helpful for testing of hypothesis; Gives an idea regarding the type of

resources required in terms of money,manpower, time, and efforts;

Provides an overview to other experts;

Guides the research in the right direction

What is ResearchProposal?

Research proposal is a specific kindof document written for a specificpurpose. Research involves a seriesof actions and therefore it presents allactions in a systematic and scientificway. In this way, Research proposal isa blue print of the study which simplyoutlines the steps that researcher willundertake during the conduct ofhis/her study. Proposal is a tentativeplan so the researcher has every rightto modify his proposal on the basis ofhis reading, discussion andexperiences gathered in the processof research. Even with this relaxationavailable to the researcher, writing ofresearch proposal is a must for theresearcher.

Importance of a proposalbefore conducting a

research

Writing the research proposal is veryimportant before actual conducting ofany research. Because research is ateam work and you have opinion ofothers if it is in written form. ResearchProposal is used for finalization of aresearch plan after presentation anddiscussion before research committeeor board. It is also necessary tosubmit for applying grants to anyagency. Once developed, it serves asa plan for conducting the research.In reality, as Best (1983) puts it, noworthwhile research can result in theabsence of a well designed proposal.

By formulating a research proposal,researcher wants to show that theproblem propose to investigate issignificant enough, the method planto use is suitable and feasible, andthe results are likely to prove fruitfuland will make an original contribution.In short, through research proposalresearcher wants to convince theother peoples (reader or audience)regarding selected problem.

Main components of a researchproposal

There are no hard and fast rulesgoverning the structure orcomponents of a proposal. It mostlydepends on the nature of a researchor format approved by a particularuniversity or sponsoring agency.Generally, in a typical format maincomponents of a research proposalare as below :

Topic of a research Background for the problem Relevant literature & researches Problem and its key terms Objectives

Questions of the study/ hypothesis(es)

Research design Population and sample Research tools Procedures for data collection Statistical techniques for data

analysis

Time schedule Cost estimate and budgeting References/ bibliography

Sampling

In statistics, quality assurance, &survey methodology, sampling isconcerned with the selection of asubset of individuals from within astatistical population to estimatecharacteristics of the wholepopulation. Each observationmeasures one or more properties(such as weight, location, color) ofobservable bodies distinguished asindependent objects or individuals. Insurvey sampling, weights can beapplied to the data to adjust for thesample design, particularly stratifiedsampling. Results from probabilitytheory and statistical theory areemployed to guide practice. Inbusiness and medical research,sampling is widely used for gatheringinformation about a population.

The sampling process comprises several stages:

Defining the population of concern Specifying a sampling frame, a set of

items or events possible to measure

Specifying a sampling method for selecting items or events from the frame

Determining the sample size Implementing the sampling plan Sampling and data collecting Data which can be selected

Populationdefinition

Successful statistical practice isbased on focused problem definition.In sampling, this includes defining thepopulation from which our sample isdrawn. A population can be definedas including all people or items withthe characteristic one wishes tounderstand. Because there is veryrarely enough time or money togather information from everyone oreverything in a population, the goalbecomes finding a representativesample (or subset) of that population.

Sometimes what defines apopulation is obvious. Forexample, a manufacturer needsto decide whether a batch ofmaterial from production is ofhigh enough quality to bereleased to the customer, orshould be sentenced for scrapor rework due to poor quality. Inthis case, the batch is thepopulation.

Sampling frame

A sampling frame which has theproperty that we can identifyevery single element andinclude any in our sample.Themost straightforward type offrame is a list of elements of thepopulation (preferably the entirepopulation) with appropriatecontact information. Forexample, in an opinion poll,possible sampling framesinclude an electoral register anda telephone directory.

Probabilitysampling

A probability sample is a sample in whichevery unit in the population has a chance(greater than zero) of being selected in thesample, and this probability can be accuratelydetermined. The combination of these traitsmakes it possible to produce unbiasedestimates of population totals, by weightingsampled units according to their probability ofselection.

Example: We want to estimate the totalincome of adults living in a given street. Wevisit each household in that street, identify alladults living there, and randomly select oneadult from each household. (For example, wecan allocate each person a random number,generated from a uniform distributionbetween 0 and 1, and select the person withthe highest number in each household). Wethen interview the selected person and findtheir income.

Nonprobability Sampling

Nonprobability sampling is any samplingmethod where some elements of thepopulation have no chance of selection(these are sometimes referred to as 'out ofcoverage'/'undercovered'), or where theprobability of selection can't be accuratelydetermined. It involves the selection ofelements based on assumptions regardingthe population of interest, which forms thecriteria for selection. Hence, because theselection of elements is nonrandom,nonprobability sampling does not allow theestimation of sampling errors. Theseconditions give rise to exclusion bias, placinglimits on how much information a sample canprovide about the population. Informationabout the relationship between sample andpopulation is limited, making it difficult toextrapolate from the sample to thepopulation.

Example: We visit everyhousehold in a given street, andinterview the first person toanswer the door. In anyhousehold with more than oneoccupant, this is anonprobability sample, becausesome people are more likely toanswer the door (e.g. anunemployed person whospends most of their time athome is more likely to answerthan an employed housematewho might be at work when theinterviewer calls) and it's notpractical to calculate theseprobabilities.

Probability sampling includes:Simple Random Sampling,Systematic Sampling, StratifiedSampling, ProbabilityProportional to Size Sampling,and Cluster or MultistageSampling. These various waysof probability sampling have twothings in common:

Every element has a knownnonzero probability of beingsampled and

involves random selection atsome point.

Simple random sampling

In a simple random sample (SRS)of a given size, all such subsets ofthe frame are given an equalprobability. Furthermore, any givenpair of elements has the samechance of selection as any othersuch pair (and similarly for triples,and so on). This minimises biasand simplifies analysis of results. Inparticular, the variance betweenindividual results within the sampleis a good indicator of variance inthe overall population, whichmakes it relatively easy to estimatethe accuracy of results.

Systematicsampling

Systematic sampling relies onarranging the study populationaccording to some ordering schemeand then selecting elements atregular intervals through that orderedlist. Systematic sampling involves arandom start and then proceeds withthe selection of every kth elementfrom then onwards. In this case,k=(population size/sample size). It isimportant that the starting point is notautomatically the first in the list, but isinstead randomly chosen from withinthe first to the kth element in the list. Asimple example would be to selectevery 10th name from the telephonedirectory (an 'every 10th' sample, alsoreferred to as 'sampling with a skip of10').

Stratified sampling

Where the populationembraces a number ofdistinct categories, theframe can be organized bythese categories intoseparate "strata." Eachstratum is then sampled asan independent sub-population, out of whichindividual elements can berandomly selected. Thereare several potentialbenefits to stratifiedsampling.

The researcher collectsthese data at the firstsession and at the lastsession of the program.These two sets of data arenecessary to determine theeffect of the walkingprogram on weight, bodyfat, and cholesterol level.Once the data are collectedon the variables, theresearcher is ready to moveto the final step of theprocess, which is the dataanalysis.

First, dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample.

Second, utilizing a stratified sampling method can lead to more efficient statistical estimates (provided that strata are selected based upon relevance to the criterion in question, instead of availability of the samples)

Third, it is sometimes the case thatdata are more readily available forindividual, pre-existing strata within apopulation than for the overallpopulation; in such cases, using astratified sampling approach may bemore convenient than aggregatingdata across groups

Finally, since each stratum is treatedas an independent population,different sampling approaches can beapplied to different strata, potentiallyenabling researchers to use theapproach best suited (or most cost-effective) for each identified subgroupwithin the population.

A stratified sampling approachis most effective when three conditions are met

Variability within strata are minimized

Variability between strata are maximized

The variables upon which the population is stratified are strongly correlated with the desired dependent variable.

Cluster sampling

Sometimes it is more cost-effective toselect respondents in groups('clusters'). Sampling is oftenclustered by geography, or by timeperiods. (Nearly all samples are insome sense 'clustered' in time -although this is rarely taken intoaccount in the analysis.) For instance,if surveying households within a city,we might choose to select 100 cityblocks and then interview everyhousehold within the selected blocks.

Clustering can reduce traveland administrative costs. Inthe example , an interviewercan make a single trip tovisit several households inone block, rather thanhaving to drive to a differentblock for each household.

Quota sampling

In quota sampling, thepopulation is firstsegmented into mutuallyexclusive sub-groups, justas in stratified sampling.Then judgement is used toselect the subjects or unitsfrom each segment basedon a specified proportion.For example, an interviewermay be told to sample 200females and 300 malesbetween the age of 45 and60.

Errors in sample surveys

Survey results are typicallysubject to some error. Totalerrors can be classified intosampling errors and non-sampling errors. The term"error" here includes systematicbiases as well as random errors.

Sampling errors and biases Sampling errors and biases are

induced by the sample design.They include:

Selection bias: When the trueselection probabilities differ fromthose assumed in calculatingthe results.

Random sampling error:Random variation in the resultsdue to the elements in thesample being selected atrandom.

Third, many of the ethical normshelp to ensure that researcherscan be held accountable to thepublic. For instance, federalpolicies on researchmisconduct, conflicts of interest,the human subjects protections,and animal care and use arenecessary in order to make surethat researchers who are fundedby public money can be heldaccountable to the public.

Fourth, ethical norms in research also help to build public support for research. People more likelyto fund research project if they can trust the quality and integrity of research

Non-sampling error Non-sampling errors are other errors which

can impact the final survey estimates, causedby problems in data collection, processing, orsample design. They include:

Over-coverage: Inclusion of data fromoutside of the population.

Under-coverage: Sampling frame does notinclude elements in the population.

Measurement error: e.g. when respondentsmisunderstand a question, or find it difficult toanswer.

Processing error: Mistakes in data coding. Non-response: Failure to obtain complete

data from all selected individuals.

Levels Of Measurement AndScaling

A common feature of marketingresearch is the attempt to haverespondents communicate theirfeelings, attitudes, opinions, andevaluations in some measurableform. To this end, marketingresearchers have developed a rangeof scales. Each of these has uniqueproperties. What is important for themarketing analyst to realise is thatthey have wildely differingmeasurement properties. Somescales are at very best, limited in theirmathematical properties to the extentthat they can only establish anassociation between variables. Otherscales have more extensivemathematical properties and some,hold out the possibility of establishingcause and effect relationshipsbetween variables.

Most texts on marketingresearch explain the four levelsof measurement: nominal,ordinal, interval and ratio and sothe treatment given to them herewill be brief. However, it is animportant topic since the type ofscale used in takingmeasurements directly impingeson the statistical techniqueswhich can legitimately be usedin the analysis.

Nominal scales

This, the crudest of measurementscales, classifies individuals,companies, products, brands or otherentities into categories where noorder is implied. Indeed it is oftenreferred to as a categorical scale. It isa system of classification and doesnot place the entity along acontinuum. It involves a simply countof the frequency of the casesassigned to the various categories,and if desired numbers can benominally assigned to label eachcategory as in the example below:

Which of the following food items do you tend to buy at least once per month? (Please tick)

Okra Palm Oil

Milled Rice Peppers Prawns Pasteurised milk

The numbers have no arithmeticproperties and act only as labels. Theonly measure of average which canbe used is the mode because this issimply a set of frequency counts.Hypothesis tests can be carried outon data collected in the nominal form.The most likely would be the Chi-square test. However, it should benoted that the Chi-square is a test todetermine whether two or morevariables are associated and thestrength of that relationship. It can tellnothing about the form of thatrelationship, where it exists, i.e. it isnot capable of establishing cause andeffect.

Ordinal scales

Ordinal scales involve theranking of individuals,attitudes or items along thecontinuum of thecharacteristic being scaled.For example, if a researcherasked farmers to rank 5brands of pesticide in orderof preference he/she mightobtain responses like thosein table 3.2 below.

An example of an ordinal scale used to determine farmers' preferences among 5 brands of pesticide.

Order of preference

Brand 1 Rambo 2 R.I.P. 3 Killalot 4 D.O.A. 5 Bugdeath

From such a table the researcherknows the order of preference butnothing about how much more onebrand is preferred to another, that isthere is no information about theinterval between any two brands. Allof the information a nominal scalewould have given is available from anordinal scale. In addition, positionalstatistics such as the median, quartileand percentile can be determined.

Interval scales

It is only with an interval scaled datathat researchers can justify the use ofthe arithmetic mean as the measureof average. The interval or cardinalscale has equal units ofmeasurement, thus making it possibleto interpret not only the order of scalescores but also the distance betweenthem. However, it must be recognisedthat the zero point on an intervalscale is arbitrary and is not a truezero. This of course has implicationsfor the type of data manipulation andanalysis we can carry out on datacollected in this form.

It is possible to add or subtract a constant toall of the scale values without affecting theform of the scale but one cannot multiply ordivide the values. It can be said that tworespondents with scale positions 1 and 2 areas far apart as two respondents with scalepositions 4 and 5, but not that a person withscore 10 feels twice as strongly as one withscore 5. Temperature is interval scaled, beingmeasured either in Centigrade or Fahrenheit.We cannot speak of 50F being twice as hotas 25F since the correspondingtemperatures on the centigrade scale, 10Cand -3.9C, are not in the ratio 2:1.

Interval scales may be either numeric orsemantic

Examples of interval scales in numeric and semantic formats

Please indicate your views on Balkan Olives by scoring them on a scale of 5 down to 1 (i.e. 5 = Excellent; = Poor) on each of the criteria listed

Balkan Olives are:

Circle the appropriate score on each line Succulence 5 4 3 2 1 Fresh tasting 5 4 3 2 1 Free of skin blemish 5 4 3 2 1 Good value 5 4 3 2 1 Attractively packaged 5 4 3 2 1

Most of the common statisticalmethods of analysis require onlyinterval scales in order that theymight be used. These are notrecounted here because theyare so common and can befound in virtually all basic textson statistics.

Ratio scales

The highest level of measurement isa ratio scale. This has the propertiesof an interval scale together with afixed origin or zero point. Examples ofvariables which are ratio scaledinclude weights, lengths and times.Ratio scales permit the researcher tocompare both differences in scoresand the relative magnitude of scores.For instance the difference between 5and 10 minutes is the same as thatbetween 10 and 15 minutes, and 10minutes is twice as long as 5 minutes.

Ratio scales

The highest level of measurement isa ratio scale. This has the propertiesof an interval scale together with afixed origin or zero point. Examples ofvariables which are ratio scaledinclude weights, lengths and times.Ratio scales permit the researcher tocompare both differences in scoresand the relative magnitude of scores.For instance the difference between 5and 10 minutes is the same as thatbetween 10 and 15 minutes, and 10minutes is twice as long as 5 minutes.

MCQQ1A sampling frame is:

a) A summary of the various stages involved in designing a survey

b) An outline view of all the main clusters of units in a sample

c) A list of all the units in the population from which a sample will be selected

d) A wooden frame used to display tables of random numbers

Q2 A simple random sample is one in which: a) From a random starting point, every nth unit from the sampling frame is selected b) A non-probability strategy is used, making the results difficult to generalize c) The researcher has a certain quota of respondents to fill for various social groups d)Every unit of the population has an equal chance of being selected

Q3 It is helpful to use a multi-stage cluster sample when:

a) The population is widely dispersed geographically

b) You have limited time and money available for travelling

c) You want to use a probability sample in order to generalise the results

d) All of the aboveQ4 The standard error is a statistical measure of:

a) The normal distribution of scores around the sample mean

b) The extent to which a sample mean is likely to differ from the population mean

c) The clustering of scores at each end of a surveyscale

d) The degree to which a sample has been accurately stratified

Q5 What effect does increasing the sample size have upon the sampling error? a) It reduces the sampling error

b) It increases the sampling error

c) It has no effect on the sampling error d) None of the aboveQ6 Which of the following is not a type of non-

probability sampling?

a) Snowball sampling b) Stratified random sampling c) Quota sampling d) Convenience sampling

Q 7 Which of the following is not a characteristic of quota sampling?

a) The researcher chooses who to approach and so might bias the sample

b) Those who are available to be surveyed in public places are unlikely to constitute a representative sample

c) The random selection of units makes it possibleto calculate the standard error

d) It is a relatively fast and cheap way of finding out about public

Q 8 The findings from a study of young single mothersat a university can be generalised to the population of:

a) All young single mothers at that university b) All young single mothers in that society c) All single mothers in all universities d) All young women in that university

Q 9 The term 'data processing error' refers to:

a) Activities or events related to the sampling process, e.g. non-response

b) Faulty techniques of coding and managingdata

c) Problems with the implementation of the research process

d) The unavoidable discrepancy between the sample and the population

Q10 What is the difference between interval/ratio and ordinal variables?

The distance between categories is equal across the range of interval/ratio data

b) Ordinal data can be rank ordered, but interval/ratio data cannot

c) Interval/ratio variables contain only two categories

d) Ordinal variables have a fixed zero point, whereas interval/ratio variables do not

Key

1 c 2-d 3-d 4-b 5-a 6-b 7-c 8-a 9-b 10-a

Thank YouPlease forward your query

To: psinghal@amity.eduCC:

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