Research methodology

Module -2Module -2

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Steps in searchSteps in search PlanningPlanning1.1. Formulating research problemFormulating research problem2.2. Review of LiteratureReview of Literature3.3. Developing hypothesisDeveloping hypothesis4.4. Preparing the research designPreparing the research design5.5. Determining the sample designDetermining the sample design OperationOperation6.6. Collection of dataCollection of data7.7. Execution of projectExecution of project8.8. Analysis of dataAnalysis of data9.9. Hypothesis testingHypothesis testing10.10. Generalisation and InterpretationGeneralisation and Interpretation ReportingReporting11.11. Preparation and presentation of ReportPreparation and presentation of Report


HypothesisHypothesis

1.1. Imaginable verifiable conclusion is called a Imaginable verifiable conclusion is called a HypothesisHypothesis

2.2. The hypothesis starts from a proposition which The hypothesis starts from a proposition which is defined as a statement about a concept that is defined as a statement about a concept that may turn out to be true or false when referred may turn out to be true or false when referred to observable phenomena.to observable phenomena.When the proposition is suitably formulated for When the proposition is suitably formulated for empirical verification, we name it as a empirical verification, we name it as a hypothesis.hypothesis.

3.3. The hypothesis is a declarative tentative The hypothesis is a declarative tentative statement and is conjectural in nature.statement and is conjectural in nature.


The suggestion formulated in the The suggestion formulated in the hypothesis may ultimately lead to the hypothesis may ultimately lead to the solution of the problem.solution of the problem.

Hypothesis relates theory to observation Hypothesis relates theory to observation and observation to theory.and observation to theory.

Hypothesis is a clear statement of what is Hypothesis is a clear statement of what is intended to be investigated. It should be intended to be investigated. It should be specified before research is conducted specified before research is conducted and openly stated in reporting the results.and openly stated in reporting the results.


It is neither too specific nor too general.It is neither too specific nor too general. It is considered valuable even if proven It is considered valuable even if proven

falsefalse It is a prediction of consequences.It is a prediction of consequences.A hypothesis can be directional or non-A hypothesis can be directional or non-

directionaldirectional


HypothesisHypothesis Definit ionDefinit ion

A hypothesis can be defined as a A hypothesis can be defined as a tentative explanation for certain tentative explanation for certain behaviors, phenomena or events that behaviors, phenomena or events that have occurred or wil l occur, that is a have occurred or wil l occur, that is a possible outcome of the research or an possible outcome of the research or an educated guess about the research educated guess about the research outcome which can be tested for outcome which can be tested for possible acceptance.possible acceptance.


Types of HypothesisTypes of Hypothesis

With reference to function:With reference to function:1.1. Descriptive hypothesisDescriptive hypothesis2.2. Relational hypothesisRelational hypothesis


Descriptive hypothesis are propositions Descriptive hypothesis are propositions that typically state the existence, size, that typically state the existence, size, form or distribution of some variable.form or distribution of some variable.Examples: Examples:

Executives stay longer time in the officeExecutives stay longer time in the office Public enterprises are more amenable Public enterprises are more amenable

for centralised planningfor centralised planning The educational system is not oriented to The educational system is not oriented to

human resources needs of a country.human resources needs of a country.www.StudsPlanet.comwww.StudsPlanet.com

Relational hypothesis is used to describe Relational hypothesis is used to describe a relationship between two variables.a relationship between two variables.

The relation ships may be either an The relation ships may be either an unspecified relationship or an unspecified relationship or an explanatory/casual relationshipexplanatory/casual relationship

Cause-effect relationshipCause-effect relationship


Relational hypothesisRelational hypothesis1.1. Casual Hypotheses state that the existence of, Casual Hypotheses state that the existence of,

or a change in, one variable causes or leads or a change in, one variable causes or leads to an effect on another variable: First variable - to an effect on another variable: First variable - Independent Second variable – DependentIndependent Second variable – Dependent

Examples:Examples:i.i. Families with higher incomes spend more for Families with higher incomes spend more for

recreationrecreationii.ii. Participative learning promotes motivation Participative learning promotes motivation

among studentsamong students


2.2. Correlational hypothesisCorrelational hypothesisThe unspecified relationship gives rise to The unspecified relationship gives rise to Correlational hypotheses where the Correlational hypotheses where the variables occur together in a specified variables occur together in a specified manner without implying that one causes manner without implying that one causes the other.the other.Example: Younger machinists are less Example: Younger machinists are less productive than those who are older. productive than those who are older.


Working HypothesesWorking HypothesesWhile planning the study of a problem While planning the study of a problem hypotheses are formed temporarily hypotheses are formed temporarily which are referred as Working which are referred as Working Hypotheses. These subject to Hypotheses. These subject to modifications while research proceeds.modifications while research proceeds.

1.1. Statistical HypothesesStatistical Hypotheses2.2. Null HypothesesNull Hypotheses


A research hypothesis is a statement about the A research hypothesis is a statement about the relationship one expects to find the analysis of relationship one expects to find the analysis of research results.research results.

A null hypothesis is opposite of Research A null hypothesis is opposite of Research hypothesishypothesis

In case it is not possible to test Research In case it is not possible to test Research hypothesis with the help of statistical techniques hypothesis with the help of statistical techniques it can be transformed into another type of it can be transformed into another type of hypothesis called Null Hypothesishypothesis called Null Hypothesis


Statistical HypothesesStatistical Hypotheses

These are statements about statistical These are statements about statistical population. These are derived from a population. These are derived from a sample. Quantitative in nature.sample. Quantitative in nature.Ex. Group A is older than Group BEx. Group A is older than Group B


Null HypothesisNull Hypothesis A Null Hypothesis is the opposite of what the A Null Hypothesis is the opposite of what the

researcher expectsresearcher expects The reason for using Null Hypothesis is that it The reason for using Null Hypothesis is that it

enables to distinguish the real difference from enables to distinguish the real difference from the observed difference due to chance only to the observed difference due to chance only to through statistical tests.through statistical tests.

If the Null Hypothesis (Ho)is rejected, the If the Null Hypothesis (Ho)is rejected, the research hypothesis, stated as alternative research hypothesis, stated as alternative Hypothesis (HHypothesis (HA or A or HH1)1) is expected to be true. is expected to be true.


Level of AbstractionLevel of Abstraction1.1. Common-sense HypothesesCommon-sense Hypotheses2.2. Complex HypothesesComplex Hypotheses3.3. Analytical HypothesesAnalytical Hypotheses


1.1. Common-sense Hypotheses : These Common-sense Hypotheses : These represent the common sense ideas. represent the common sense ideas. They state the existence of empirical They state the existence of empirical uniformities perceived through day to uniformities perceived through day to day observationsday observationsEx: Labour in un-organised sector lack Ex: Labour in un-organised sector lack motivationmotivation


2.2. Complex HypothesesComplex Hypotheses These aim at testing the existence of These aim at testing the existence of

logically derived relationships between logically derived relationships between empirical uniformities.empirical uniformities.

The function of such hypothesis is to create The function of such hypothesis is to create tools and problems for further research in tools and problems for further research in otherwise very complex areas of otherwise very complex areas of investigations.investigations.

Ex: 1.Concentric growth charectarize a cityEx: 1.Concentric growth charectarize a city2. Members of minority group suffer from 2. Members of minority group suffer from

oppression psychosisoppression psychosis


3.3. Analytical HypothesesAnalytical Hypotheses These are concerned with relation hip of analytical These are concerned with relation hip of analytical

variables. These hypothesis occur at the highest level variables. These hypothesis occur at the highest level of abstraction.of abstraction.

These specify relationship between changes in one These specify relationship between changes in one property and changes in another.property and changes in another.

This level of hypothesis is the most sophisticated mode This level of hypothesis is the most sophisticated mode of formulation and contributes to the development of of formulation and contributes to the development of ‘brilliant’ abstract theories.‘brilliant’ abstract theories.

Ex: There are two segments in India. One with higher Ex: There are two segments in India. One with higher income and the other with lower income. The higher income and the other with lower income. The higher income group have less children than the lower income income group have less children than the lower income group of peoplegroup of people


Difference between Hypothesis and Difference between Hypothesis and ProblemProblem

1.1. Problem is a question Problem is a question and is not testable.and is not testable.

2.2. Relation between Relation between variable in problem variable in problem statementstatement

3.3. Is A related to BIs A related to B4.4. How e A and B related to How e A and B related to

C?C?5.5. How is A related to B How is A related to B

under conditions C and under conditions C and DD

1.1. Hypothesis is a Hypothesis is a statement and can be statement and can be testedtested

2.2. Relation between Relation between variables in hypothesisvariables in hypothesis

3.3. If A, then BIf A, then B4.4. If A and B, then CIf A and B, then C

5.5. If A, then B under If A, then B under conditions C and Dconditions C and D


Criteria for good HypothesisCriteria for good Hypothesis

1.1. Must be conceptually clear, unabiguous and Must be conceptually clear, unabiguous and have explanatory powerhave explanatory power

2.2. Must have empirical referentsMust have empirical referents3.3. Hypothesis must be exact and specific and Hypothesis must be exact and specific and

exact to enable its verificationexact to enable its verification4.4. Should give insight to research questionShould give insight to research question5.5. A good hypothesis states clearly and concisely A good hypothesis states clearly and concisely

as possible, the expected relationship or as possible, the expected relationship or difference between two variables and defines difference between two variables and defines these variables. these variables.


Generation of HypothesisGeneration of HypothesisInitial Ideas (Often vogue & general)

Initial observations

Search for existing literature

Statement of the problem

Operational definition of constructs

Research Hypothesiswww.StudsPlanet.comwww.StudsPlanet.com

Generation of Hypothesis

Initial Ideas (Often vogue & general)

Initial observations

Search for existing literature

Statement of the problem

Operational definition of constructs

Research Hypothesiswww.StudsPlanet.com

Sample vs. CensusTable 11.1

Condit ions Favor ing the Use of Type of Study

Sample Census

1. Budget

Small

Large

2. Time available

Short Long

3. Populat ion size

Large Small

4. Var iance in t he character ist ic

Small Large

5. Cost of sampling errors

Low High

6. Cost of nonsampling errors

High Low

7. Nature of measurement

Dest ruct ive Nondest ruct ive

8. At tent ion t o individual cases Yes No www.StudsPlanet.com

The Sampling Design ProcessFig. 11.1

Define the Population

Determine the Sampling Frame

Select Sampling Technique(s)

Determine the Sample Size

Execute the Sampling Process

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Define the Target Population

The target population is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made. The target population should be defined in terms of elements, sampling units, extent, and time.

– An element is the object about which or from which the information is desired, e.g., the respondent.

– A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process.

– Extent refers to the geographical boundaries.– Time is the time period under consideration.

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Define the Target Population

Important qualitative factors in determining the sample size

– the importance of the decision– the nature of the research– the number of variables– the nature of the analysis– sample sizes used in similar studies– incidence rates– completion rates– resource constraints

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Sample Sizes Used in Marketing Research Studies

Table 11.2

Type of Study

Minimum Size Typical Range

Problem ident if icat ion research (e.g. market potent ial)

500

1,000-2,500

Problem-solving research (e.g. pricing)

200 300-500

Product test s

200 300-500

Test market ing studies

200 300-500

TV, radio, or pr int advert ising (per commercial or ad t ested)

150 200-300

Test -market audit s

10 stores 10-20 stores

Focus groups

2 groups 4-12 groups www.StudsPlanet.com

Classification of Sampling TechniquesFig. 11.2

Sampling Techniques

NonprobabilitySampling Techniques

ProbabilitySampling Techniques

ConvenienceSampling

JudgmentalSampling

QuotaSampling

SnowballSampling

SystematicSampling

StratifiedSampling

ClusterSampling

Other SamplingTechniques

Simple RandomSampling www.StudsPlanet.com

Convenience Sampling

Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected because they happen to be in the right place at the right time.

– use of students, and members of social organizations– mall intercept interviews without qualifying the

respondents– department stores using charge account lists– “people on the street” interviews

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Judgmental Sampling

Judgmental sampling is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher.

– test markets– purchase engineers selected in industrial marketing

research – bellwether precincts selected in voting behavior research– expert witnesses used in court

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Quota SamplingQuota sampling may be viewed as two-stage restricted judgmental sampling. – The first stage consists of developing control categories, or quotas, of

population elements. – In the second stage, sample elements are selected based on convenience

or judgment.

Population Samplecomposition composition

ControlCharacteristic Percentage Percentage NumberSex Male 48 48 480 Female 52 52 520

____ ____ ____100 100 1000

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Snowball Sampling

In snowball sampling, an initial group of respondents is selected, usually at random.

– After being interviewed, these respondents are asked to identify others who belong to the target population of interest.

– Subsequent respondents are selected based on the referrals.

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Simple Random Sampling

• Each element in the population has a known and equal probability of selection.

• Each possible sample of a given size (n) has a known and equal probability of being the sample actually selected.

• This implies that every element is selected independently of every other element.

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Systematic Sampling• The sample is chosen by selecting a random starting point and then

picking every ith element in succession from the sampling frame. • The sampling interval, i, is determined by dividing the population size N by

the sample size n and rounding to the nearest integer. • When the ordering of the elements is related to the characteristic of

interest, systematic sampling increases the representativeness of the sample.

• If the ordering of the elements produces a cyclical pattern, systematic sampling may decrease the representativeness of the sample. For example, there are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, i, is 100. A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.

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Stratified Sampling

• A two-step process in which the population is partitioned into subpopulations, or strata.

• The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted.

• Next, elements are selected from each stratum by a random procedure, usually SRS.

• A major objective of stratified sampling is to increase precision without increasing cost.

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Stratified Sampling

• The elements within a stratum should be as homogeneous as possible, but the elements in different strata should be as heterogeneous as possible.

• The stratification variables should also be closely related to the characteristic of interest.

• Finally, the variables should decrease the cost of the stratification process by being easy to measure and apply.

• In proportionate stratified sampling, the size of the sample drawn from each stratum is proportionate to the relative size of that stratum in the total population.

• In disproportionate stratified sampling, the size of the sample from each stratum is proportionate to the relative size of that stratum and to the standard deviation of the distribution of the characteristic of interest among all the elements in that stratum.

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Cluster Sampling• The target population is first divided into mutually exclusive and

collectively exhaustive subpopulations, or clusters. • Then a random sample of clusters is selected, based on a probability

sampling technique such as SRS. • For each selected cluster, either all the elements are included in the

sample (one-stage) or a sample of elements is drawn probabilistically (two-stage).

• Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as homogeneous as possible. Ideally, each cluster should be a small-scale representation of the population.

• In probability proportionate to size sampling, the clusters are sampled with probability proportional to size. In the second stage, the probability of selecting a sampling unit in a selected cluster varies inversely with the size of the cluster.

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Types of Cluster SamplingFig. 11.3 Cluster Sampling

One-StageSampling

MultistageSampling

Two-StageSampling

Simple ClusterSampling

ProbabilityProportionate

to Size Sampling

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Technique Strengths WeaknessesNon probability Sampling Convenience sampling

Least expensive, leasttime-consuming, mostconvenient

Selection bias, sample notrepresentative, not recommended fordescriptive or causal research

Judgmental sampling Low cost, convenient,not time-consuming

Does not allow generalization,subjective

Quota sampling Sample can be controlledfor certain characteristics

Selection bias, no assurance ofrepresentativeness

Snowball sampling Can estimate rarecharacteristics

Time-consuming

Probability sampling Simple random sampling(SRS)

Easily understood,results projectable

Difficult to construct samplingframe, expensive, lower precision,no assurance of representativeness.

Systematic sampling Can increaserepresentativeness,easier to implement thanSRS, sampling frame notnecessary

Can decrease representativeness

Stratified sampling Include all importantsubpopulations,precision

Difficult to select relevantstratification variables, not feasible tostratify on many variables, expensive

Cluster sampling Easy to implement, costeffective

Imprecise, difficult to compute andinterpret results

Table 11.3

Strengths and Weaknesses of Basic Sampling Techniques

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Procedures for Drawing Probability SamplesFig. 11.4

Simple Random Sampling

1. Select a suitable sampling frame

2. Each element is assigned a number from 1 to N (pop. size)

3. Generate n (sample size) different random numbers between 1 and N

4. The numbers generated denote the elements that should be included in the sample

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Procedures for DrawingProbability SamplesFig. 11.4 cont. Systematic

Sampling

1. Select a suitable sampling frame

2. Each element is assigned a number from 1 to N (pop. size)

3. Determine the sampling interval i:i=N/n. If i is a fraction, round to the nearest integer

4. Select a random number, r, between 1 and i, as explained in simple random sampling

5. The elements with the following numbers will comprise the systematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i

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Choosing Nonprobability vs. Probability Sampling

Condit ions Favor ing the Use of Factors

Nonprobabilit y sampling

Probabil it y sampling

Nature of research

Explorat ory

Conclusive

Relat ive magnitude of sampling and nonsampling errors

Nonsampling errors are larger

Sampling errors are larger

Var iabili t y in t he populat ion

Homogeneous ( low )

Heterogeneous (high)

Stat ist ical considerat ions

Unfavorable Favorable

Operat ional considerat ions Favorable Unfavorable

Table 11.4 cont.

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Preparing the Research Design

• After the formulation of the problem and hypothesis the next task is to build up a Research design to streamline the research

• It determines ‘what and ‘how’ the researcher hopes to find the best solution to the problem.

• Research design is about organising research activity, including collection of data in ways that are most likely to achieve the research goals and objectives.

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• A research design is a string of logic or blue print that ultimately links the data to be collected and the conclusions to be drawn to the initial questions of study.

• It is a plan/blue print for conducting the proposed research work.

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Need for a Research Design

1. Research design systematises the research operations.2. Advanced planning helps organising data collection which

involves the availability of field staff, time and money and devising solutions for possible field problems.

3. A proper design of research study enhances the reliability of research results and minimises any error that may upset the project

4. An appropriate design helps the researcher to systematically organise his ideas in a form which enable him to locate any flaws and inadequacies and thereby prompting him to revise the research design.

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Classification of Research Design:

• Common classification is according to purpose known as Basic research designs:

1. Exploratory Research Design

2. Conclusive Research Design

i. Descriptive research design

ii. Casual research design

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Other types of classification are:

• Data Collection techniques1. Personal observation 2. Interviewing• Period of study1. Cross sectional2. Longitudinal• Scope of Study1. Case studies2. Statistical studies• Types of research questions1. Explorative2. Formal

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• Study enviroment:1. Field condition2. Laboratory condition• Simulation1. Control of independent variables2. Exprimental quasi-experimental3. Non-experimental• Participants perceptions1. Unbiased2. Biased

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Basic Research Designs

Research Designs

Exploratory Research Conclusive Research

Descriptive Research Casual Research

Cross-sectional Research Longitudinal Researchwww.StudsPlanet.com

Exploratory research Design

1. Ex: Our sales are declining and we do not know why?

2. Discovery of ideas and insights

3. Exploratory research are useful when the researcher does not have a clear idea about the problem or may have a vague idea

4. Tend to rely more on secondary data

5. Uses both qualitative and quantitative but relies more on qualitative techniques.

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Conclusive Research Design

• This is meant to provide information that is useful in reaching conclusions or decision making

• Relies both on Primary and Secondary data

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Descriptive Research design:

• Ex: What kind of people buy our product ?

Who buy’s our competitors products ?

Describe market charecteristics or functions• This is a statistical research providing data about the

population.• It is useful for researches including population

census, Industrial census, employment survey, etc.• This is a factual data as accurate as possible.

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• Cross-sectional research: Cross sectional study calls for data for a single time.

• Longitudanal study are studies which observe the state of the world without manipulating, at several points of time.

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Casual Relationship

• Ex: Would buyers prefer this new package design.• Cause and effect between two variables or more• Manipulation of one or more independent variable

i. Symmetrical

ii. Asymmetrical

iii. Reciprocal

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Structure of a Research Design

1. Problem Identification2. Problem Formulation3. Determination of research designi. Designing measurementsii. Instrumentsiii. Sampling; caswetudies;etc4. Detrmination of data collection procedures5. Determination of analytical proceduresi. Data preparationii. Data analysis6. Research reporting and evaluation

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Research methodology

Technology

hypothesisthe hypothesis

comdescriptive hypothesis

comhypothesisd hypothesis

research outcome

variable causes

research designdetermining

variable dependentexamples

variable independent