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CHAPTER 4

RESEARCH METHODOLOGY

4.1 INTRODUCTION

This chapter describes the research design adopted in the study to

fulfil the objectives set forth for the study. The research design adopted is a

combination of qualitative and quantitative methodologies in which primary

and secondary sources are very thoughtfully tapped for information and

perspectives. The main focuses of the present study are: Household water

sources, access to water, quality of water, quantity of water, sanitation,

hygiene, health and the willingness to pay. The study examines each of these

in relation to Chennai city, especially in regard to the north and south zones.

Affordability to pay in the two locations, that is, north zone and south zone of

Chennai city. Hence, the two zones with various household options for water

have been selected to conduct interviews with high, middle and low income

groups of people. Data have been collected for two periods, April and June in

2010 in both the zones of study area.

The data collected through interview schedule have been properly

coded, master tabulated and analyzed using the Statistical Package for Social

Sciences (SPSS).

For the purpose of this study water use at the household level has

been obtained from households (primary sources) through an extensive

household survey using a questionnaire designed for the purpose. The

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secondary data needed for the study have been collected from various relevant

government departments. The collected data have been analyzed using

appropriate computer assisted and analytical procedures using SPSS and Arc

GIS.

Primary data collected through questionnaire survey have been

coded, tabulated and analyzed using SPSS software. The analysed data have

been presented with the help of a series of maps, tables and graphs using

digital cartographic techniques. This analysis has included assessment of

households using water from different sources for water uses, assessment of

water, water quality and willingness to pay.

4.2 OPERATIONAL DEFINITIONS

The operational definitions of various terms, particularly water

sources used in the study are presented, which may be trivial but are

necessary for clarity of thought and understanding.

Water Resources: Water resources are sources of water that are useful or

potentially useful to humans. Uses of water include agricultural, industrial,

household, recreational and environmental activities. Virtually all of these

human uses require freshwater. It is estimated that 8 percent of the water use

worldwide is for household purposes. These include drinking water, bathing

water, cooking water, water for sanitation and gardening. Basic household

water requirements have been estimated by Peter Gleick at around 50 litres

per person per day, excluding water for gardens. Drinking water is water that

is of sufficiently high quality so that it can be consumed or used without risk

of immediate or long term harm. Such water is commonly called potable

water. In most developed countries, the water supplied to households,

commerce and industry is all of drinking water standard eventhough only a

very small proportion is actually consumed or used in food preparation.

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Water Supply: Basic needs include access to a safe supply of water for

domestic use, meaning for drinking, food preparation, bathing, laundry,

dishwashing and cleaning. In many cases domestic water may also be used for

watering animals and vegetable plots or gardens.

Water Access: Definitions of ‘access’ (Distance to the nearest water – point

and per capita availability) and ‘safe’ (Water Quality) may vary from country

to country.

WTP: WTP is the maximum amount that an individual status they are willing

to pay for a good or service DfID (1997).

ATP: ATP is the extent to which prices (for example, water supply and

sanitation) are within the financial means of users. An important consideration in

service planning relating to choice of service level and pricing.

Sanitation and Hygiene: The word ‘sanitation’ alone is taken to mean the

safe management of human excreta. It therefore includes both the ‘hardware’

(e.g. Latrines and sewers) and the ‘software’ (regulation, Hygiene promotion)

needed to reduce fecal-oral disease transmission. It encompasses the

re-use of and ultimate disposal of human excreta.

4.3 SELECTION OF STUDY AREA AND SAMPLES

With respect to time and resource constraints, it has been decided to

conduct the field study in Chennai city. The city has a total of 10 Zones and

155 wards. This study has selected two northern and two southern zones for

study. In the northern part, 2 zones, namely, Tondiarpet and Pulianthope have

been selected for interview based questionnaire survey. In the southern part

also two 2 zones, namely, Saidapet and Adyar have been selected for

questionnaire survey (Figure 4.1).

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In the northern part, and in Tondiarpet zone, Kodungaiyur East,

Dr. Radhe Krishnan Nagar North, Jeeva Nagar North and Cheriyan Nagar

South have been included in the study. In Pulianthope zone, Vyasarpet South,

Vyasarpet North, Perambur North, and Elango Nagar have been selected in

4 wards. In the southern part, and in Saidapet zone, G.D Naidu Nagar East,

G.D Naidu Nagar West, Guindy West, Guindy East and in Adyar zone, Adyar

West, Adyar East, Thiruvanmiyur West, and Thiruvanmiyur East have been

selected for questionnaire-based study.

The questionnaire survey has covered 320 samples with each ward

accounting for 21 samples. The survey has been conducted with 3 groups of

people, namely, high, medium and low income. The levels have been

identified using land values in Chennai city. This is thus a stratified sample

survey.

4.3.1 Analytical Framework for the Study

The research framework is integral to any research and indeed

forms the heart of the research work. Devising a research framework requires

a proper and clear understanding of the objectives of research. The framework

for the present study (Figure 4.2) is broadly divided into three

aspects- Theorectical aspects, Data aspects and Analytical aspects. The three

aspects are interlinked and contribute to each other and are meaningless

without the links which connect them. The theoretical aspects refer the

theoretical base in the present research. It consist of theories on which the

research is grounded. In order to gaining a deeper understanding of the

theoretical aspects as discussed earlier and the interplay of the economic,

cultural and environmental factors, the study was carried out in urban

neighbourhoods, in the north and south Chennai. Data was collected from

both primary and secondary data sources. The analytical aspects range from

the formulation of the research questions to using appropriate analytical tools

to answer them.

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Figure 4.2 presents the analytical framework adopted for the study.

It is evident from the schematic that how theoretical, data and analytical

aspects of the study are treated in the context of the study, and the schematic

is rather self-explanatory indicating how primary and secondary data have

been used in the analysis and writing up of the thesis, towards developing

recommendations for resolving problems connected with the water demand

and supply, including WTP and ATP. Figure 4.3 shows elaborately as to how

primary and secondary data have been collected and analysed for

interpretation and inferences.

Figure 4.1 Field Setting: Sample Areas, Sampling and Samples

155 Wards (16 Wards 10 %)

(319 samples)

North: 2 Zones (159 samples) South: 2 Zones (160 samples)

Saidapet Tondiarpet

Pulianthope Adayar

151. Adyar West

152. Adyar East

154. Thiruvanmiyur

West

155. Thiruvanmiyur

East

138. G.D. Naidu Nagar

East

139. G.D. Naidu Nagar

West

140. Guindy West

141. Guindy East

2. Kodungaiyur East

3. Dr. Radha Krishnan Nagar

North

5. Jeeva nagar North

6. Cheriyan Nagar South

32. Vyasarpet South

33. Vyasarpet North

34. Perambur North

36. Elango Nagar

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Figure 4.2 Analytical Framework for the Study

SPPSS analysis

SPSS

Analysis

SYSTHESIS

Conceptual frame work

Water, Hygiene, Sanitation and Health

Two stages of Water Evaluation process

1. Willingness to pay and 2. Affordable to pay

Population

Water Distribution

Water connection, Assesses, Consumer

Water Infrastructure

Demand and supply gap

Review of studies

Socio-Economic Assessment, Water Supply, Sources of water,

Metro water connection, Metro water storages, Cost, Domestic

water sector, Toilets Hygiene, Clinging Sanitary Infrastructures and Diseases and Health, Indoor and outdoor.

GIS

&Excel

Theory

Analysis

nd

Social Surveys

and

Approaches

Factor analysis

Socio-Economic Dimension

Household size and Water Consumption

Dimension

Attitude Dimension

Potable and Bathing water Dimension

Factor Analysis

Socio-Economics of Food and Body Hygiene

Dimension

Hospital Visitation for Health Problems Dimension

Sanitation and Cleanliness Dimension

Social Component Dimension

Implication and Recommendation for Water Supply

and Sanitary Infrastructure, Closed and Well

Maintained Sewer Drains, Sewage and Wastewater

Treatment Plant, Provision of Adequate Health

Services.

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4.3.2 Sources of Data

Since the present study seeks to identify the people’s attitudes

towards paying for Water with WTP and ATP, it has been decided that

obtaining first hand information directly from the respondents would be the

most reliable sources through which the main focus of the study could be

analysed. Even though the study primarily relies upon the primary sources, it

still seeks data for some supportive and supplemental information, which are

secondary in nature.

The secondary data have been collected from the Government

records such as those of the Metro Water Board and maps have been collected

from the Survey and Land Records Department of the Government of Tamil

Nadu, Chennai.

Figure 4.3 Data Collection and Data Analysis in the Study

Result

Water Sources, Supply, Consumer Access,

Demand and Gap

CVM: WTP,

ATP

Sanitation

and Health

Indoors and

Outdoor Risk

Factor

Statistical

Analysis GIS Maps

Frequency and Percentage

Analysis Factor

Analysis

Data Collection

Primary Data Collection Secondary Data Collection

Data Analysis

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4.3.3 Tools of Data Collection (Contingency Valuation Method

(CVM)

A comprehensive literature was carried out during the initial stages

and also on regular intervals, throughout the research process. Initial stage of

review, paved the way for the background theme of the research and enable

the determination of the research problem and the objectives of the research.

Knowledge gained from the earlier studies, carried out in relation to the

research title, enhanced a better understanding of the research problem.

Review also helped the researcher in order to get wider perspective of related

issues and methodologies employed for the research. Updating the literature

during various phases of the research, helped to keep track of the latest

developments in research areas and to develop the research at each stage.

Further, the ongoing review showed the way to describe the research problem

to be examined during the study. After deriving the research problem, precise

research objectives were set down. The research objectives were framed to

devise the research questions. A research framework was developed on the

study to describe in detail of the research theme, so as to satisfy the objectives

of the research. This has just been discussed briefly and the framework is

given in Figures 4.2 and 4.3.

In order to achieve the objectives of the study, combinations of

quantitative method have been used to gather information. The people’s

attitude towards paying for water, in some cases such as water sources,

access, quantity, quality, distributor, willingness to pay, affordability to pay,

sanitation, hygiene and health impact and risk factor, have been considered in

the survey through the interview schedule. The information drawn were

analyzed and presented in the respective sections.

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The quantitative tool selected for study is an interview method

through which the required primary data could be elicited from the

respondents. Since the respondents constitute both literate and illiterate

categories, the methods adopted have been found to be most appropriate.

Interviews have been held with women and men from the households in the 4

zones and 16 wards where interviews have been conducted separately for

respondents and their responses have been immediately noted in the schedule.

There have been some difficulties in the intial stages in getting information

about incomes of the households (respondents,) use of water, sanitation and

health particulars but later when the purpose of the study has been explained

in detail, the respondents felt free to give answers to all the statements

incorporated in the schedule.

The economic concept that Contingent Valuation (CV) surveys

trying to capture is the maximum amount that a respondent would be willing

to pay for the improved water services in the context of the existing

institutional regime within which households are free to allocate their

financial resources (Wedgwood 2003).

The CVM has been increasingly advocated by economists and

water sector specialists as a useful tool for gathering reasonably accurate data

about how much a household can afford and is willing to pay for particular

water and sanitation options presented to them. Early researches in the 1980s

have found that ‘when the CV method is used to estimate the use of goods

and services with which the individuals are familiar, these methods can yield

accurate and useful information on households’ ‘preferences’ when these are

carefully designed and administered. (Wedgwood 2003)

Good quality contingent valuation surveys require both a series of

skills and knowledge, including: survey skills, knowledge of how to develop

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different CV scenarios for population groups with different water supply or

housing/income conditions, and how to train enumerators and analyse results.

A number of steps are advocated in the guidelines for an effective

CVM survey and dissemination of results. The following are the various

stages and steps involved in the estimation of willingness to pay, using the

contingent method (Figure 4.4).

Figure 4.4 Steps in Contingency Value Method in the Study

Step 1: Select interview technique

Step 3: Develop the CVM Scenario

Step 2: Develop a Sampling strategy

Step 5: Costing the options

Step 4: Decide which elicitation method is good to use

Step 6: Write household survey and CVM questionnaire

Step 7: Implement Survey

Step 8: Data Entry and Analysis

Step 9: Using CVM results

Preparation

Implementation

Data Analysis

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Application of CVM in environmental economics includes

estimation of non-use values (for example, Walsh et al 1984; Brookshire

et al 1983), non-market use values (for example, Choe et al 1996; Loomis and

du Vair 1993) or both (for example, Niklitschek and Leon 1996; Desvousges

et al 1993) of environmental resources. Though it is a popular non-market

valuation method, a group of academics criticise the method severely for not

being a proper method of estimating the non-market values ( Hausman 1993).

Summaries of CVM studies by different authors reveal that the major

criticism of results of CVM revolves mainly around two aspects, namely:

(a) their validity and (b) their reliability (Smith 1993; Freeman 1993; NOAA

1993).

The validity of CVM is of three types: content validity, criterion

validity and construct validity (Mitchell and Carson 1989; Bateman et al

2002). The content validity in a CV experiment simply refers to the ability of

the instruments included in the scenario to measure the value in an

appropriate manner. Criterion validity of the CV method may be assessed in

terms of another measure, say a ‘market price’ for the same commodity which

may be considered as a criterion. Construct validity has two forms:

convergent validity and theoretical validity. The convergent validity refers to

the correspondence between two measures of the same theoretical construct.

CV results can be said to be ‘theoretically valid’ if the results conform to the

underlying principles of economic theory. In other words, the theoretical

validity involves assessing the willingness to pay (WTP) values of the CV

method by way of regressing the WTP value against standard economic

variables (Mitchell and Carson 1989). Most important, the CVM has however

been used in several studies in India, particularly in relation to water

resources and water uses. Some authors put emphasis on the need to use

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CVMs in regard to use of water and the willingness to pay for it, in the case of

both irrigation and drinking (for example, Venkatachalam 2004).

4.3.3.1 Contingent valuation method to measure willingness to pay

The WTP value of a good or service may de elicited: (1) directly by

asking consumers, through carefully orchestrated elicitation methods; or

(2) indirectly by examining market prices. The CV method is a survey based

elicitation technique to estimate WTP values of a good that is not traded in the

conventional market.

The CV method is often referred to as stated preference method,

in contrast to revealed preference method, which uses actual revealed

behaviour of consumers in the market. The CV method directly asks

consumers’ WTP for non-marketed goods under a given condition or a

prescribed circumstance. To elicit consumers’ WTP values for non-marketed

goods, a hypothetical market scenario should be formulated and described to

the survey respondents. Thus, the elicited WTP values of a goods are

“contingent upon” the hypothetical market prescribed in the survey

instrument.

Since a CV survey always asks WTP questions, it has been

commonly called a “WTP study.” Subsequently, the key fundamentals of

“contingent” market scenarios are often overlooked by practitioners as the

term “WTP” predominates over “CV method.”

4.3.3.2 Willingness to pay

There are various definitions of willingness to pay, but the most

common one states that: ‘WTP is the maximum amount that an individual

states that he is willing to pay for a good or service’ (DFID 1997).

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In economics, consumer’s WTP means the maximum amount that a

person would be willing to pay for a service rather than do without it. The

demand curve is: (a) based on the idea that the lower the price of a good, the

more consumers will be willing to pay. The area below the demand curve as

shown in Figure 4.5 (a) represents WTP. The total WTP is not simply the

amount plus the “consumers’ surplus” (Figure 4.5(b)). In this case, the supply

curve shows the production cost of various quantities of the good. The price

times the quantity equals the water system revenue. The shaded triangle

represents the consumer’s surplus, which is not revealed (Ghuraiz and

Enshassi 2005).

Method of Determination of WTP

WTP can be estimated indirectly by observing the behavioural

pattern of the people and their stated preferences.

Observe the prices that people pay for goods in various

markets (that is, water vending, buying from neighbours, and

paying local taxes).

Observe individual expenditures of money, time, and labour to

obtain goods or to avoid their loss. This method might involve

an assessment of coping strategies and involves observations,

focus group discussions and even household surveys.

Ask people directly what they are willing to pay for goods or

services in the future.

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(a) (b)

Figure 4.5 Demand and Supply Curve Related to Individual

Consumers’ WTP and Consumer Surplus

4.3.3.3 Affordability to pay

Affordability to pay is the extent to which prices (for example,

water supply and sanitation) are within the financial means of users. An

important consideration in service planning relating to choice of service level

and pricing. Fajita et al. (2005) conducted estimates of willingness to pay and

affordability to pay of beneficiaries for water and sanitation services in

Iquitos city, Japan. The main findings of the study are of two categories: WTP

is approximately twice of the current average payment level, and ATP is

higher than the current payment level.

Hicks (1946) classified the ‘consumer surplus measure’ into two

different categories, namely, the compensating variation and the equivalent

variation. For a ‘proposed welfare gain’ due to provision of public good, the

compensating variation refers to the amount of money income that has to be

given up by the consumer to attain increased level of utility (that is, the WTP

measure). The equivalent variation refers to the amount of compensation

required to be provided to the individual so that he/she could attain an

improved utility level in case the provision of the public good does not take

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place (that is, WTA). For a welfare loss, the compensating variation refers to

the amount of money income that is required to compensate the individual for

the welfare loss experienced (that is, WTA) and equivalent variation refers to

the amount of money income to be sacrificed by the consumer to prevent the

loss from occurring in future (that is, WTP; see Bateman and Turner 1993).

4.3.4 Components of Interview Schedule

The interview starts with questions related to some demographic

characteristics of the households and moves on to family background.

A detailed list of contents in seven parts of the schedule (Appendix 2) is given

below:

I) Profile of Respondents

Name, age, education, community, family size, occupation, type of

family, and ownership of the house.

II) Information on Water

Well details, sources of supply for Metrowater, sufficiency or

otherwise of water sources, water distributor, can water, water problems,

sources of water and uses.

III) Sources of water

Drinking water, cooking water, bathing water, washing clothes in

water sources, distance, quality, quantity, time duration, number of pots,

satisfaction level.

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IV) Willingness to Pay and Affordability to pay

Regular water supply, WTP, ATP and operation and maintaining

cost.

V) Health details

Health problem, diseases, source of water, expenditures of water

buying, boiling and non-boiling, hospital and treatment cost.

VI) Sanitation and Hygiene

Type of sanitation facility, type of toilet, sewage collected and

treated, septic tank to the nearest well, hands wash before and after eating and

preparing food, hand wash after using the toilet or latrine and body hygiene,

indoors and out door risk factors.

4.4 STATISTICAL TECHNIQUES

4.4.1 Frequency and Percentage Analysis

The analysis of data often begins with what is called a “frequency

and percentage analysis.” For the purpose of description of sample and

respondent related characteristics, a frequency and percentage analysis has

been done for all variables extracted from the questionnaire and put into the

dataset. First, a simple frequency of each of the fields with column

percentages has been made and then two-way tables using certain select pairs

of variables have been carried out, in order to measure variations. The analyst

begins to explore the data, by measuring the central tendency of the data, and

more importantly, the dispersion of the data around this central tendency.

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Frequency analysis is particularly useful for describing discrete

categories of data having multiple choice or yes-no response formats. This

analysis involves constructing a frequency distribution. The frequency

distribution is a record of the number of scores that fall within each response

category. The frequency distribution, then, has two elements: (1) the

categories of response, and (2) the frequency with which respondents are

identified with each category.

The only technical requirement of the frequency analysis is that the

categories of response be mutually exclusive and exhaustive. This means that

the same observation cannot be counted as belonging to more than one

response category. The frequency analysis must be exhaustive in the sense

that all respondents must fit into a category. The tables so generated are

numerous, only select tables are therefore included in the text while others are

interpreted so as to show the variations therein.

4.4.2 Factor Analysis

Factor analysis is a statistical technique designed to analyse the

interrelationships within a set of variables by reducing the complex data to an

easily interpretable form (Davis 2002). In multivariate analysis, the

bi-variate techniques are extended so that more than two variables can be

considered, the ‘m’ variable becoming the ‘m’ axes of the test space.

Procedures of multivariate analysis are often concerned with the problem of

reducing the original test space to the minimum number of dimensions needed

to describe the relevant information contained in the original observations.

Multivariate procedures differ in the types of original information they

preserve. Some understanding of matrix algebra is essential to using and

understanding the multivariate analysis.

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A popular multivariate procedure in social science analysis is the

Common Factor Analysis, for which variants are available and are in use in

social sciences as well as in geography. It is a particular psychometric model

that has been in wide use in social sciences. This helps in the study of the

logical implications of systematic inter-correlations within sets of tests.

However, the social sciences follow just one of the many approaches to the

reduction of dimensionality in correlated systems of measurements and the

rotation (varimax, a short form for maximizing variance, for example) of a

reduced number of axes to more meaningful positions.

The Factor Analysis (FA) is also a classification procedure in that it

may be usefully applied to multivariate situations to classifying the N

individuals, on the basis of ‘m’ variables. One particular feature of the FA is

that ‘p’ underlying factors in the multivariate sample space model is always

less than the ‘m’ variables: p < m. The underlying factor dimensions are

drawn from the use of inter-correlations system by generating ‘p’ number of

scores each for the ‘N’ individuals. The scores may however be drawn from

the varimax rotation, which stands for maximizing variance. If we can

measure ‘m’ variables with respect to areal units, the scores may be assigned

to these areal units for constructing one or more maps showing real areal

differences (or regional variations) in respect of ‘p’ reduced dimensions.

The purpose of factor analysis is to interpret the structure within the

variance-covariance matrices of the multivariate data collection made on the

different indicators related to an eco-city related aspects, including

ecofriendliness and sustainability of the city of Gulbarga, in Karnataka, India.

The basic mathematical operations in factor analysis may be stated as follows:

Zj = aj1 P + aj2 P2 + …….+ ajm Pm

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where

Zj = Xj X mean /Oj or standardized variable

Pi =(I = 1,2,…m) are the principal components and

aj = (j= 1,2,…n) are the coefficients or factor loadings of

(I = 1,2,…m) jth

variable relating to the ith

component.

In other words, each factor is nothing but a linear combination of

weighted variables which can also be expressed as:

P1 = aj Xj where

Thus, in factor analysis, a data matrix containing measurements on

‘m’ variables for each of ‘N’ observations is analysed. The technique uses

extraction of the eigenvalues and eigenvectors from the matrices of

correlations or covariance. The basic mathematical operations in factor

analysis are done with many embellishments on the procedures. FA is a deep

and complex methodology. It is one of the most widely used multivariate

procedures. The model is based on several unique assumptions. For one, the

precise number of factor is assumed prior to the analysis. The factors

extracted, or rather the number of factors, are validated by the variance each

of them explain to the total. There is a progressive decline in the value of

variances with the increasing number of factor dimensions. The first or the

main factor dimension has the highest of the total variance explained and the

bipolar the next highest and so on, resulting in progressively declining

variance.

The analysis begins with the standardization of data. In this

procedure, the data is first converted to standardized, or unitless, form by

subtracting from each observation the mean of the data set and dividing by the

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standard deviation. The new or the transformed variables will then have a

mean of 0.0 and a variance of 1.0, which is useful in comparing the

distribution of one variable to that of another when the two variables are

expressed in different units of measurement. It provides, in a manner of

speaking, a way of comparing disparate variables.

Since the variables used in any given application are not

immediately comparable, it is necessary to standardize each individual item of

the data, before computing the variance-covariance matrix. The covariance

matrix of standardized variables is nothing more than the correlation matrix,

which in this analysis is referred to as the inter-correlation (similarity) matrix.

Standardization does not have a tremendous influence on the structure of the

variance-covariance matrix and consequently on the results of the FA. In

social sciences, we have no alternatives but to standardize our data, because

the raw matrices of variances and covariances would contain hodge-podge of

measurement units that logical interpretation would be difficult. Hence, there

is a good reason to standardize.

The FA employs principal components, the eigenvectors of a

variance-covariance matrix, as the starting point for analysis. It belongs to the

category of techniques in which utility is judged by performance and not by

theoretical considerations. It relies on a set of assumptions about the nature of

the parent population from which the samples are drawn. These assumptions

provide the rationale for the operations, which are performed and the manner

in which the results are interpreted.

For the purpose of computation in FA, the relationship within a set

of ‘m’ variables is regarded as reflecting the correlation of each of the

variables with ‘p’ mutually un-correlated underlying factors. The usual

assumption is that p < m. Variance of the ‘m’ variable is therefore derived

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from variance in the ‘p’ factors, but in addition a contribution is made by

unique sources which independently affect the ‘m’ original variables. The FA

refers to the ‘p’ underlying factors as common factors and summarize the

independent contribution as a unique factor.

The FA requires that ‘p’, the number of factors, be known prior to

analysis. This implies that the investigator has some insight into the probable

nature of the factors and can predict a suitable number of factors to be

extracted. The eigenvalue operation in factor analysis is performed on a

standardized variance-covariance or correlation matrix. Hence, the FA used

here is said to be R-mode factor analysis. This assumes not only that all

variables are weighted equally, but also allows us to convert the principal

component vectors into factors. In larger matrices such as ours, the

eigenvalues usually are more uniform for standardized data than for raw data.

And to perform the FA, it is necessary that we convert our unit, or normalize

eigenvalue. The result is a factor, a vector, which is weighted proportionally

to the amount of total variance it represents.

The elements in the factors are referred to as factor loadings. The

eigenvalues represent the proportion of the total variance accounted for by the

eigenvectors. The factor loadings on the other hand are the correlation values

between the old and the new, transformed variables.

If we arrange the factor loadings in a matrix form, we have then a

factor matrix. If we square the elements in the factor matrix and sum within

each variable, the totals are the amount of variance of each variable retained

in the factors. These sums are referred to as the communalities and are

symbolically represented as hj2. The communalities are equal to the original

variances.

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A specific rule that most factor analysts suggest in the extraction of

factor is that of retaining all factors, which have eigenvalues greater than one.

That is, retain all factors, which contain greater variance than the original

standardized variables. But of course in most instances only a few of the

factors will contain most of the variances in the dataset and hence this

recommendation is useful. If factor theory is applicable to any given dataset, a

few factors should account for a very high percentage of the variance and the

communalities of the variables found under each factor dimension is high.

The FA is said to be reducing the dimensionality of a problem to a

manageable size. However, the meaning of the factors may be difficult to

deduce. This problem is overcome by resorting to maximization of the

variance of the loadings on the factors. This in other words is maximizing the

range of the loadings. This is done in the analysis here by a rotation procedure

called Kaiser’s varimax rotation. The rotation of the factor axes is performed,

iteratively. The analysis also results in factor scores, which represent

estimates of the contribution of various factors to each original observation

(residents). In fact, factors themselves are estimated from these same data.

Thus the computation of factor scores is somewhat a circular process and the

results are not unique. Factor analysis explains in a sense the

interrelationships in a large number of variables by the presence of a few

factors (Kaiser 1958; Harman 1960; Lawrence and Upchurch 1982).

The factor extraction is done with a minimum acceptable

eigenvalue of >1.0 (Kaiser 1958; Harman 1960). The factor loading matrix is

rotated to an orthogonal simple structure, according to varimax rotation. It

results in maximization of variance of factor loadings of the variables. This

procedure renders a new rotated factor matrix in which each factor is

described in terms of only those variables and affords greater ease for

interpretation. Factor loading is a measure of the degree of closeness between

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the variables and the factor. The largest loading, either positive or negative,

suggests the meaning of the dimension; positive loading indicates that the

contribution of the variables increases with the increasing loadings in a

dimension; and negative loading indicates a decrease (Lawrence and

Upchurch 1982).

4.5 CONCLUSION

The methodology used in the study is an integrated methodology,

where traditional schedule based data collection and processing is integrated

with the modern, statistical as well as qualitative analysis. The former

complements the latter. The methodology which follows the traditions of

social science research and the latest developments in economic research have

the following components:

1. Field survey (primary data).

2. Collection of documented data (secondary data).

3. Statistical approach (simple frequency and percentage, factor

analysis)

4. Analysis and interpretation of domestic water consumers’ data

from Chennai city.

In selecting the most appropriate tool, the following considerations

were useful: the Uses, and the Resources, Familiarity, Significance and

Industry involved. There are several ways of collecting the appropriate data

which differs considerably in the context of money, cost, time and other

resources at the disposal of the researcher. For the present study, both primary

and secondary data have been collected and used for analysis.