Research Methodology Final Ppt

Research Design

• Research design follows the task of defining research problem

• A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.

Research Design

• The design includes an outline of what the researcher will do from writing the hypothesis and its operational implications to the final analysis of data.

i. What is the study about?ii. Why is study being made ?iii.Where will the study be carried out?iv.What type of data is required?v. Where can the required data be found?vi.What periods of time will the study include?vii.What will be the sample design?viii.What techniques of data collection will be used?ix.How will the data be analysed?x. In what style will the report be prepared?

Features of a Good Design

• Flexible, appropriate, efficient, economical.

• Data with minimum bias & maximum reliability of data.

• Design with smallest experimental error.

• Yields maximal information & should consider different aspects of a problem.

Concepts of Research Design

• Dependent & Independent variables:

A concept which can take on different quantitative values is called a Variable.– Continuous & Discrete variables.

– If one variable depends upon or is a consequence of the other variable.

– The variable that is antecedent to the dependent variable is Independent variable.

• Extraneous Variable : Independent variables that are not related to the purpose of the study but may affect the dependent variables.

• Control: Designing the study to minimize the effects of extraneous independent variables.

• Confounded relationship: When the dependent variable is not free from the influence of extraneous variable the relationship between the dependent and independent variables is said to be confounded by an extraneous variable.


• Research hypothesis: A predictive statement that relates an independent variable to a dependent variable

• Experimental hypothesis testing research where the independent variable is manipulated .

• Non-experimental hypothesis testing research where the independent variable is not manipulated.

• Experimental & control groups: when a group is exposed to usual conditions it is termed as ‘control group’, while the group exposed to special novel conditions is referred to as ‘experimental group’.


Concepts of Research Design• Treatment: The different conditions under

which experimental & control groups are put are usually referred to as treatments.

• Experiment: examining the truth of a statistical hypothesis related to some research problem.

• Experimental units: The pre- determined plots or the blocks, where different treatments are used.

Research Designs

• Different Research Designs can be categorized as:

1. Exploratory research studies

2. Descriptive & Diagnostic research studies

3. Research design in case of hypothesis testing research studies

Research design in case of exploratory research studies

• Formulative research studies

• Purpose: Formulating a problem for more precise investigation Of developing working hypotheses from an operational view.

• Emphasis on discovery of ideas & insights.


• Flexible & provide an opportunity for different aspects of problem under study.

• Three methods are generally followed in exploratory research studies.– Survey of concerning literature

– Experience survey

– Analysis of insight-stimulating

Survey of concerning literature• Simple & fruitful method.• The researcher should– Review the earlier stated hypothesis &evaluate the

usefulness for further research.– Also consider whether the already stated hypothesis

suggest new hypothesis.– Review the available material for deriving relevant

hypothesis.– Also attempt to apply concepts & theories developed

in different research contexts.– Works of creative writers may provide fertile ground

for hypothesis formulation

Experience Survey• Survey of people who have had practical experience

with the problem to be stated.• Objective- Insight into relationship between

variables & new ideas relating to the problem.• The researcher– Should carefully select the competent respondents & can

contribute to new ideas– Prepares interview schedule – systematic, flexible– Sends a copy of questions to be discussed & interviewed.

• Thus enables to define the problem more concisely & present practical possibilities for doing different types of research

Analysis of ‘insight-stimulating’ examples

• Intensive study of selected instances of the phenomenon.

• This requires– Examination of any existing records. – Unstructured interviewing.

• Main features for evoking insights are – Attitude of investigator– Intensity of the study– Ability of researcher to draw together diverse information

into a unified integration


• Thus an exploratory research study merely leads to insights or hypotheses whatever research method is adopted.

• It should remain flexible so that many different facets of a problem are considered.

Research design in case of Descriptive & Diagnostic Research

Studies• The design must be rigid & not flexible and

must focus on:– Formulating the objective of the study

– Designing the methods of data collection

– Selecting the sample

– Collecting the data

– Processing & analyzing the data

– Reporting the findings

Research design in case of Descriptive & Diagnostic Research

Studies• Thus the research design in case of

descriptive/diagnostic studies is a comparative design and must be prepared keeping in view of the objective of the study and resources available.

• It must ensure minimum bias &maximum reliability

• It can be referred as survey design.

Research design in case of hypothesis- testing research Studies• The researcher tests the hypotheses of causal

relationship between variables.• These studies require procedure that will

permit drawing inferences about causality.• Experiments meet this requirement hence the

hypothesis testing research study is generally referred to as experimental studies often meaning the design of experiments

Research design in case of hypothesis- testing research Studies• Prof.R.A.Fischer at Rothamsted Experimental

Station made a beginning of experimental designs.

• Division of agricultural fields into different blocks and then by conducting experiments, information collected & inferences were found to more reliable.

SAMPLE DESIGN

Need for Sampling Design

• All items in any field of enquiry constitute a universe or population.

• When field studies are undertaken in practical life, considerations of time and cost almost invariably lead to selection of respondents which is representative of total population in order to produce a miniature cross-section. The selected respondents constitute what is technically called a ‘sample‘ and the selection process is called a ‘sampling technique’ the survey so conducted is known as ‘sample survey’.

Steps in Sample Design

• Type of Universe:– Clearly define the set of objects to be studied.

– Universe may either be finite or infinite.

• Sampling Unit:– Sampling unit may be a geographical,

constructional, social unit or an individual.

– Decide one or more such units that has to be studied

Steps in Sample Design

• Source list : – It is a sampling frame from which sample is to be drawn . It

contains names of all items of a finite universe.– If not available the researcher should prepare it.– The source list should be comprehensive, correct, reliable,

appropriate and representative of the population.

• Size of sample: – An optimum sample fulfills the requirements of efficiency,

representativeness, reliability & flexibility. – The size of population, parameters of interest & budgetary

constraint also dictate the size of sample.

Steps in Sample Design• Parameters of interest:– Consider the specific population parameters such as

estimation the proportion of persons with some characteristic in the population ,average or some other measure.

• Budgetary constraint:– Cost considerations throw impact upon the size of sample as

well type of sample which may lead to non-probability sample

• Sampling procedure:– Select the sample design which, for a given sample size and

for a given cost, has a smaller sampling error.

Criteria of Selecting a Sampling Procedure

• Systematic bias & sampling error cause incorrect inferences from the data . – The errors in the sampling procedures is due to the

following factors which results in systematic bias.

• Inappropriate sampling frame: A biased representation of the universe results in systematic bias.

• Defective measuring device: Biased questionnaires or the interviewer in case of survey work results in constant error.

Criteria of Selecting a Sampling Procedure

• Non-respondents: Unable to sample all the individuals initially included in the sample.

• Indeterminacy principle: Individuals act indifferently when kept under observation than what they do when kept in non observed conditions.

• Natural bias in reporting data: People tend to give what they think is the correct answer rather than revealing their true situation.

Characteristics of a Good Sample Design

• Sample design must– Result in truly representative sample.– Result in a small sampling error.– Be viable in context of funds available for the research

study.– Be such that systematic bias is controlled in a better

way.– Be such that the results of the sample study can be

applied to the universe in general with a reasonable level of confidence

Types of sample designsElement selection technique

Representation basis

Unrestricted sampling

Simple random sampling Haphazard sampling orConvenience sampling

Restricted sampling

Complex random sampling Purposive sampling

Probability sampling Non-probability sampling

Non-probability sampling• This procedure does not afford any basis for estimating the probability

that each item in the population has of being included in the sample.

• Deliberate sampling/Purposive sampling/ Judgment sampling.

• Adopted in small inquiries and researches by individuals.

• Sampling error cannot be estimated and bias is always present

• Quota sampling – The interviewer are given the quotas to be filled, the interviewer is free to

select the items of the sample.– Size of quota for each stratum is generally proportionate to the size of that

stratum in the population.– Convenient & relatively inexpensive.– Inferences drawn are not amenable to statistical treatment in a formal way.

Probability sampling

• Random sampling/chance sampling.

• Every item of the universe has an equal chance of inclusion in the sample.

• Errors of estimation or significance of results obtained can be assured.

• Random sampling ensures the law of statistical regularity.

Random samplingRandom sample from a finite universe• Sample without replacement• Each element in the

population has an equal probability of getting into the sample.

• All the choices are independent of one another

• Each possible sample combination has an equal probability of being chosen.

Random sample from an infinite universe

• Sample with replacement from an infinite population.

• Selection of each item in a random sample from an infinite population is controlled by the same probabilities.

• Successive selections are independent of one another

Complex Random sampling designs

• Mixed sampling designs-combination of probability and non- probability sampling designs.– Systematic sampling design

– Stratified sampling design

– Cluster sampling

– Sampling with probability proportional to size

– Sequential sampling

Systematic Sampling

• An element of randomness is introduced by using random numbers to pick up the unit with which to start.

• Selecting every ith item from the sampling frame until the desired number is secured.– Sample is spread evenly over entire population.– Easier & less costly method.– Conveniently used in large populations.– lists of population are available and are of

considerable length.

Stratified sampling

• Applied to heterogeneous group to obtain a representative sample. The population is divided into several strata and the items are selected from each stratum to constitute a sample.

• Formation of strata:– On the basis of common characteristics of items.– Strata are purposively formed & based on experience.

personal judgment of the researcher.– consideration of the relationship between the

characteristics of the population & the characteristics to be estimated are used to define the strata.

Stratified sampling

• Selection of items from each stratum– Simple random sampling/Systematic sampling.

• Allocation of sample size of each stratum– Method of proportional allocation(size of samples from different

strata are kept proportional to the sizes of strata).– Pi - proportion of population included in stratum i,

n - total sample size to be drawn

Then, the number of elements selected from stratum i is n. Pi

Cluster sampling

• To divide the area into a number of smaller non-overlapping areas and then to randomly select a number of these smaller areas with the ultimate sample consisting of all units in these samples units in these small areas of or clusters.

• Although cluster sampling reduces cost it is less precise than random sampling.

Area sampling

• Cluster sampling incase of geographic subdivisions is known as area sampling.

Multi-stage sampling

• Multi-stage sampling is applied in big enquiries extending to a considerable large geographical area.

• Advantages – It is easier to administer than most single stage designs

mainly because of the fact that sampling frame under multi-stage sampling is developed in partial units.

– A large number of units can be sampled for a given cost under multi-stage sampling because of sequential clustering, whereas this not possible in most of the designs.

Multi-stage sampling

• Consider, to investigate the working efficiency of nationalized banks in India.

– Select large primary sampling unit such as states in a country.– Select certain districts – Select towns and interview all banks in chosen towns.– This represents three stage sampling design.

• This would represent a two stage sampling design with ultimate sampling units being clusters of districts.

• If banks are randomly selected at all the stages it is ‘Multi-stage sampling design’

Sampling with Probability Proportional to Size

• In this design the probability of each cluster being included in the sample is proportional to size of the cluster.

• The following are the number of departmental stores in 5 cities: 35,17,10,32,70,28,26,19,26,66,37,44,33,29 and 28. If we want to select a sample of 10 stores, using cities as clusters and selecting within clusters proportional to size, how many stores from each city should be chosen?

City number

No. of depart-mental stores

Cumulative total

Sample

1 35 35 10

2 17 52

3 10 62 60

4 32 94

5 70 164 110 160

6 28 192

7 26 218 210

8 19 237

9 26 263 260

10 66 329 310

11 37 366 360

12 44 410 410

13 33 443

14 29 472 460

15 28 500

We have 500 departmental stores from which we have to select a sample of 10 stores, the appropriates sampling interval is 50.As we have to use the starting pont of 10,so we add successively increments of 50 till 10 numbers have been selected. The numbers, thus obtained are 10,60,110,160,210,260,310,410 and 460 presented in the column of cumulative totals. From this it is to be concluded that two stores should be selected randomly from city number five and one each from city number 1,3,7,9,10,11,12 and 14. this sample of 10 stores is the sample with probability proportional to size.

ILLUSTRATION

Sequential Sampling

• This is somewhat a complex sample design where the ultimate size of the sample is not fixed in advance but is determined according to mathematical decisions on the basis of information yielded as survey progresses. This design usually adopted under the acceptance sampling plan in the context of statistical quality control.

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Research Methodology Final Ppt