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Research Method And Statistics

Research Method And Statistics

We are proud to present you this section on Research Method And Statistics. We hope this section on Research Method And Statistics will be as useful to you as it is meant to be.

    * Statistics
          o Applications Of Statistics
          o Methods Of Central Tendency Of Averages
          o Measures Of Dispersion
          o Coefficient Of Dspersion
          o Association Of Attributes
          o Regression
    * Scientific Study of Social Phenomena
          o Elements of Scientific Methods
          o Theory and Facts
          o Gathering information and constructing Explanations
          o Hypothesis
          o Research Design
          o Content Analysis
          o Problems of Objectivity
          o Sociology as a Value-free Science
    * Techniques of Data Collection
          o Social survey
          o Interviewing
          o Observation: Participant and non participant
          o Sampling
          o Measurement of Attitude


The word 'Statistics' refers to some numerical facts relating to any phenomena in social sciences or exact sciences. Facts and figures pertaining to population, production, national income, profits, sales, bank rates, family patterns, dowry system, animal kingdom, plant life, bacteria; will all constitute statistics. The word 'statistics' seems to have derived from either the Latin word 'status' or the Italian word 'Statitsta' both meaning 'a political state'.
The word 'statistics' is presently referred to in two distinct senses. In its first reference as a plural noun, it means an aggregate or collection of numerical or quantitative expressions of facts i.e. 'numerical data' or simply 'data'. In its second reference as a singular noun, it means a body of principles and methods used in the collection, presentation, analysis and interpretation of numerical data.
Bowley defines statistics as the science of counting in one context. The emphasis made here is only on the collection of data. At another place he says: statistics may rightly be called the science of averages.Boddington defines statistics as the science of estimates and probabilities. According to Lovitt, the science of statistics deals with the collection. Classification and tabulation of numerical facts as the basis for explanation, description and comparison of phenomena. Seligman defines statistics as the science which deals with the methods of collecting classifying, presenting, comparing and interpreting numerical data collected to throw some light on any sphere of enquiry. Croxton and Cowden define statistics as the collection, presentation analysis and interpretation of numerical data.

Characteristics of Statistics

1. Statistics should be numerically expressed. For example the statement Rajan is of height 6' 1" makes the fact clear and easily understandable.
2. Statistics are aggregates of facts. Statistics means the facts pertaining to a group of individuals or individual item.
3. Statistics are affected to a market extent by a multiplicity of causes. There are a variety of forces or factors operating on the facts and figures in an aggregate. The influence of any particular factor cannot be isolated.
4. Statistics must be collected in a systematic manner for a predetermined purpose. Determination of the main purpose or objectives of any scientific study is the first and the most important step which in turn paves way for other operations to follow.
5. Statistics are enumerated or estimated according to reasonable standards of accuracy.
6. Statistics should be placed in relation to each other.

Applications of Statistics

Statistics and Sociology

Sociology is one of the social sciences aiming to discover the basic structure of human society, to identify the main forces that hold groups together or weaken them and to learn the conditions that transform social life. It highlights and illuminates aspects of social life that otherwise might be only obscurely recognized and understood. The sociologist may be called upon for help with a special problem such as social conflict, urban plight or the war on poverty or crimes. His practical contribution lies in the ability to clarify the underlaying nature of social problems to estimate more exactly their dimensions and to identify aspects that seem most amenable to remedy with the knowledge and skills at hand. He naturally lands in sociological research which is the purposeful effort to learn more about society than one can in the ordinary course of living. Keeping in view of the problem he sets forth his objectives collects materials or data and uses statistical techniques and the knowledge and theory already established on similar topics to achieve his objectives. So statistical data and statistical methods are quite indispensable for sociological research studies. There is a growing emphasis recently on social survey methods or research methodology in all faculties of arts.

Sociologists seek the help of statistical tools to study cultural change in the society, family pattern,prostitution,crime,marriage system etc.They also study statistically the relation between prostitution and poverty, crime and poverty,drunkness and crime, illiteracy and crime etc.Thus statistics is of immense use in various sociological studies.

Statistics and Government

The functions of a government are more varied and complex. Various depts in the state are required to collect and record statistical data in a systematic manner for an effective administration. Data pertaining to various fields namely population, natural resources, production both agricultural and industrial,finance,trade,exports and imports, prices, labor, transport and communication, health, education,defence ,crimes etc are the most fundamental requirements of the state for its administration. It is only on this basis of such data; the government decides on the priority areas, gives more attention to them through target oriented programmes and studies the impact of the programmes for its future guidelines.

Statistics and Planning

Modern age is an age of planning and statistics are indispensable for planning. According to Tippett planning greater or lesser degree according to the government in power is the order of the day and without statistics, planning is inconceivable. Based only on a correct assessment of various resources both human and material of the country proper planning can be made. A study of data relating to population, agriculture, industry, prices, employment, health, education enables the planners to fix up time-bound targets on the social and economic fronts evaluation of such economic and social programmes at different stages by means of related data gathered continuously and systematically is also done to decide whether the programmes are on towards the goal or targets set.

Statistics and Economics

In the fields of economics it is almost impossible to think of a problem which does not require an extensive use of statistical data. Most of the laws in economics are based on a study of a large number of units and their analysis is enabled by statistical data and the statistical methods. The important economic aspects like production, consumption, exchange and distribution are described, compared and correlated with the aid of statistical tools. By a statistical study of time series on prices, sales, production one can study their trends, fluctuations and the underlaying causes. Thus statistics is indispensable in economic analysis.

Methods of Central Tendency of Averages

Condensation of data is necessary for a proper statistical analysis. A large number of big numbers are not only confusing to mind but also difficult to analyse.After a thorough scrutiny of collected data, classification which is a process of arranging data into different homogenous classes according to resemblances and similarities is carried out first.Then of course tabulation of data is resorted to. The classification and tabulation of the collected data besides removing the complexity render condensation and comparison.

An average is defined as a value which should represent the whole mass of data. It is a typical or central value summarizing the whole data. It is also called a measure of central tendency for the reason that the individual values in the data show some tendency to centre about this average. It will be located in between the minimum and the maximum of the values in the data.
There are five types of average which are
1.Arithmatic Mean
4.Geometric Mean and
5. Harmonic Mean

Arithmetic Mean

The Arithmetic mean or simply the mean is the best known easily understood and most frequently used average in any statistical analysis. It is defined as the sum of all the values in the data.


Median is another widely known and frequently used average.It is defined as the most central or the middle most value of the data given in the form of an array. By an array, we mean an arrangement of the data either in ascending order or descending order of magnitude. In the case of ungrouped data one has to form an array first and then locate the middle most value which is the median. For ungrouped data the median is fixed by using,
Median = [n+1/2] the value in the array.


The word mode seems to have been derived French 'a la mode' which means 'that which is in fashion'. It is defined as the value in the data which occurs most frequently. In other words, it is the most frequently occurring value in the data. For ungrouped data we form the array and then fix the mode as the value which occurs most frequently. If all the values are distinct from each other, mode cannot be fixed. For a frequency distribution with just one highest frequency such data are called unimodal or two highest frequencies [such data are called bimodal],mode is found by using the formula,
Mode = l + cf2/f1+f2
Where l is the lower limit of the model class, c is its class interval f1 is the frequency preceding the highest frequency and f2 is the frequency succeeding the highest frequency.

Relative merits and demerits of Mean, Median and Mode


The mean is the most commonly and frequently used average. It is a simple average, understandable even to a layman. It is based on all the values in a given data. It is easy to calculate and is basic to the calculation of further statistical measures of dispersion, correlation etc. Of all the averages, it is the most stable one. However it has some demerits. It gives undue weightages to extreme value. In other words it is greatly influenced by extreme values.Moreover; it cannot be calculated for data with open - ended classes at the extreme. It cannot be fixed graphically unlike the median or the mode. It is the most useful average of analysis when the analysis is made with full reference to the nature of individual values of the data.Inspite of a few shortcomings; it is the most satisfactory average.


The median is another well-known and widely used average. It is well-defined formula and is easily understood. It is advantageously used as a representative value of such factors or qualities which cannot be measured. Unlike the mean, median can be located graphically. It is also possible to find the median for data with open ended classes at the extreme. It is amenable for further algebraic processes.However,it is an average, not based on all the values of the given data. It is not as stable as the mean. It has only a limited use in practice.


It is a useful measure of central tendency, as a representative of the majority of values in the data. It is a practical average, easily understood by even laymen. Its calculations are not difficult. It can be ascertained even for data with open-ended classes at the extreme. It can be located by graphical means using a frequency curve. The mode is not based on all the values in the data. It become less useful when the data distribution is not uni-model.Of all the averages, it is the most unstable average.

Measures Of Dispersion

When a mass of quantitative data is collected for a statistical purpose, it is tabulated to a form with in view that its characteristics as a whole may be readily determined. A single significant and representative expression or a measure called average is then computed to summarize or explain as a whole the entire data. This form of a comparing two or more groups or series but an average along is not a satisfactory criterion for such a purpose. Dispersion means the extent of values around some average. There are four measures of dispersion, namely
  • Range
  • Quartile Deviation or semi interquartile range
  • Mean Deviation
  • Standard Deviation


Range is the simplest measure of dispersion. It is defined as the positive difference between the largest and the smallest values in the given data. It is easily understood and computed but depends exclusively on the two extreme values while it is desirable to have a measure dependent on all the values. The range is a very useful measure in statistical quality control of products in industries wherein the interest lies in getting a quick rather than an accurate measure of variability.

Quartile Deviation or Semi-interquartile range

This measure is based on two measures called the lower or first quartile and the upper or third quartile. The lower quartile denoted as Q1 is defined as the value which leaves ¼ of the values below it when the data forms an array and the third quartile denoted as Q3 is the value which leaves 3/4s of values below it when the data forms an array. Once Q1 and Q3 are known, the quartile deviation Q-D is given by
Q-D= Q3 - Q1/2 and
Q3- Q1 is called the interquartile range

Mean Deviation

The mean deviation of a data is defined as the means of the absolute deviation of values from some average especially the arithmetic mean or median. It is a better measure of dispersion than range and Q D as it takes into account, all the values in the given data for ungrouped data.
M.D about median M can also be defined by considering median instead of mean.

Standard Deviation

The standard deviation abbreviated as S.D and symbolically represents as sigma is the most important and wide used measure of dispersion. It is defined as the root mean square deviation of values from their mean i.e. it is the square root of the means of square deviation of values from their mean. The square of the s.d. is called the variance.

Coefficient of Dispersion

For any data, it is always desirable that the measure of dispersion is less. A small value for the measure means that the values in the data are more or less consistent, centering on their average. Suppose it is required to compare the dispersion between two or more data. For this purpose any measure of dispersion cannot be used as such for two reasons 1.the two data under study may certain to different variables such as heights and weights of the individual. Number of marriages and ages of persons who have committed suicides etc and 2.even if the variables in the two data are the same, their averages may be different. Hence one is in need of a relative measure of dispersion which will be free from the two differences cited above. Such a measure is the coefficient of dispersion which is defined as the ratio between measure dispersion and an average. This coefficient is a constant, rendering ready comparison of dispersion between data.

There are four coefficients which are commonly referred, namely
  1. Quartile coefficient of dispersion = Quartile Deviation/ Mean
  2. Coefficient of variation (C.V) = Standard Deviation /Mean x 100
  3. Mean Deviation/Mean
  4. Mean Deviation/Median of which the first two are the most frequently used coefficients.

Association of attributes

In social sciences, we come across certain phenomena which are incapable of quantitative measurement.Blindness, deafness, religion; juvenile delinquency, marital status etc are some phenomena which are not measurable. Such characteristics are called attributes. In these cases, one can make only counting of individuals who possess or do not possess these attributes. In other words what can do is to state so many individuals are blind or so many non-blind. While dealing with one attribute the classification of data is done on the basis of presence or absence of the attribute. It is also absolutely essential that a clear-cut definition of the attribute under study is made because only such a definition paves way for the counting of the individuals possessing or not possessing the attribute. Two attributes are said to be associated only if they appear together in a great number of cades than is to be expected if they are independent. On the other hand, if the number of observed cases is less than the expected, under assumption of independence, attributes are associated. In order to ascertain whether the attributes are associated or not the following methods can be used.

  1. Comparison of observed and expected frequencies.
  2. Proportion method
  3. Yule's coefficient of Association
  4. Coefficient of colligation
  5. Coefficient of contingency


The dictionary meaning of the term 'regression' is the act of returning or going back. The term was first used by Sir Francis Galton in 1877 when he analyzed the relationship between the height of fathers and sons. His study of the heights of fathers and sons revealed a very interesting relationship-tall fathers tend to have tall sons and short fathers, short sons, but the average height of the son of a group of tall fathers is less than that of the fathers and the average height of the sons of short fathers is greater than that of the fathers. The line describing this tendency to regress or step back was called by Galton a Regression Line. The term continues to be in use to represent the trend present but no longer has it necessarily carried the original implication of stepping back that Galton intended.

The statistical device that enables the estimation or prediction of the unknown values of one variable based on the known values of another variable is termed regression. Regression is the measure of average relationship between variables.

Linear Regression

In case there exists relationship between two variables x and y the plotted points in the scatter diagram lie concentrated around a curve and the relationship is said to be expressed by means of curvilinear regression. In the particular case, when the curve is a straight line, is called the line of regression and the regression is said to be linear. Once it is known that the relationship is expressible by means of straight line, a technique known as the Principles of Least Squares is adopted to fit the line on the basis of the given bivariate data. A line of regression gives the most probable values of one variable for the given value of the other variable. For two series x and y there are two regression lines, one on assuming x as dependent and y dependent variable and the two on assuming x as dependent and independent variable.

Scientific Study of Social Phenomena

The need to have sociology as a new branch of knowledge was realized quite late and many branches of knowledge had already taken shape and gained respectability before sociology was conceived. These branches of knowledge have been termed as Sciences. Sociology grew under the shadow of illustrious predecessors like Physics and Biology tended to emulate their patterns. The basic assumption that is central to these sciences that distinguish them from medieval learning is that: Truth about the world can be known through sensory observation. Thus the scientist seeks his truth by observing the world rather than by waiting for revelations. Sociology also inherited this premise. An illustration of knowledge based upon sensory observation is if one sees a bird that is called by the people a crow and finds its black, then one arrives at the conclusions that crow is black. The veracity of this knowledge lies in the fact that it is supported by sensory observation. But our senses can sometimes deceive us .Scientists adopt certain procedural steps that seek to reduce such a possibility. These acts of procedural steps constitute the Scientific Method.

Theory and Facts

There is an intricate relation between theory and fact. The popular understanding of this relationship obscures more than it illuminates. They are generally conceived as direct opposites. Theory is confused with speculation and theory remains speculation until it is proved. When this proof is made, theory becomes fact. Facts are thought to be definite, certain, without question and their meaning to be self-evident. Science is thought to be concerned with facts alone. Theory is supposed to be realm of philosophers. Scientific theory is therefore thought to be merely summation of facts that have been accumulated upon a given subject. However if we observe the way scientists actually do research, it becomes clear 1. Theory and fact are not diametrically opposed but inextricably intertwined.2. Theory is not speculation.3.Scientists are very much concerned with both theory and facts.

A fact is regarded as an empirically verifiable observation. A theory refers to the relationship between facts or to the ordering of them in some meaningful way. Facts of science are the product of observations that are not random but meaningful, i.e., theoretically relevant. Therefore we cannot think of facts and theory as being opposed rather they are interrelated in many complex ways. The development of science can be considered as a constant interplay between theory and fact.
Theory is a tool of science in these ways defines the major orientation of a science, by defining the kinds of data that are to be abstracted. offers a conceptual scheme by which the relevant phenomena are systematized, classified and interrelated. summarizes facts into empirical generalizations and systems of generalizations.
4. It predicts facts and
5. It points to gaps in our knowledge.
On the other hand facts are also productive of theory in these ways :
( 1) Facts help to initiate theories.
(2) They lead to the reformulation of existing theory.
(3) They cause rejection of theories that do not fit the facts.
(4) They change the focus and orientation of theory and
(5) they clarify and redefine theory.
There is interplay between theory and fact. Although popular opinion thinks of theory as being opposed to fact since theory is mere speculation, observation of what scientists actually do suggests that fact and theory stimulate each other. The growth of science is seen is seen in new facts and new theory. Facts take their ultimate meaning from the theories which summarize them, classify them, predict them, point them out and define them. However theory may direct the scientific process, facts in turn play a significant role in the development of theory. New and anomalous facts may initiate new theories. New observations lead to the rejection and reformulation of existing theory or may demand that we redefine our theories. Concepts which had seemed definite in meaning are clarified by the specific facts relating to them. The sociologist must accept the responsibilities of the scientists who must see fact in theory and theory in fact. This is more difficult than philosophic speculation about reality or the collection of superficial certainties but it leads more surely to the achievement of scientific truth about social behavior.


Facts are dependent upon a theoretical framework for their meaning. They are also statements of relationships between concepts. Theory can give direction to the search for facts. A hypothesis states what we are looking for. When facts are assembled, ordered and seen in a relationship they constitute a theory. The theory is not speculation but is built upon fact. Now the various facts in a theory may be logically analyzed and relationships other than those stated in the theory can be deduced. At this point there is no knowledge as to whether such deductions are correct. The formulation of the deduction however constitutes a hypothesis; if verified it becomes part of a future theoretical construction. The relation between the hypothesis and theory is very close indeed. A theory states a logical relationship between facts. From this theory other propositions can be deduced that should be true, if the first relationship holds. These deduced propositions are hypotheses.

A hypothesis looks forward. It is a proposition which can be put to a test to determine its validity. It may seem contrary to or in accord with common sense. It may prove to be correct or incorrect. In any event however, it leads to an empirical test. Whatever the outcome, the hypothesis is a question put in such a way that an answer of some kind can be forthcoming. It is an example of the organized skepticism of science. The refusal to accept any statement without empirical verification. Every worthwhile theory then permits the formulation of additional hypotheses. These when tested are either proved or disapproved and in turn constitute further tests of the original theory.

Design of Proof: Testing the Hypothesis

The function of the hypothesis is to state a specific relationship between phenomena in such a way that this relationship can be empirically tested. The basic method of this demonstration is to design the research so that logic will require the acceptance or rejection of the hypothesis on the basis of resulting data. The basic designs of logical proof were formulated by John Stuart Mill and still remain the foundation of experimental procedure although many changes have been made. His analysis provides two methods. The first of these is called the method of agreement. When stated positively this holds that when two or more cases of a given phenomenon have one and only one condition in common then that condition may be regarded as the cause or effect of the phenomenon. The classical experimental design is a development from both the positive and negative canons and attempts to avoid the weaknesses of both of them. In the simplified form Mill called it the method of difference. To develop the classical design of proof by the method of difference it is necessary only to make two series of observations and situations.

Design of Sociological Research

"Design of Sociological Research" or Research Design is a broad plan of a piece of empirical research specifying the manner in which data are to be collected and analyzed in order to test Research Design derived from theory, or to develop insights into the problem being investigated. It combines relevance of the problem with economy in procedure. The design stage is most crucial phase of the research process. A particular design may specify whether experiment, social survey, participant observation, other methods, or a combination of more than one method will be used.
Nowadays it has became imperative to chart out the research design before starting any work, Modern research in sociology thus specifies the probable method to be used for date collection analysis, etc keeping in view, time money and, of course, the topic of research. Generally, a research design includes the following steps:

a). Universe of Study (whether a tribe, or a village, or an urban areas, or a particular group, etc.)
b). Subject of Study (whether it focuses on the whole society, or any specific institution or a part of it).
c). Tentative relationship between certain variables (Formulating a Research Design but it is not obligatory to start with a Research Design; certain research designs lack Research Design).
d). Sets of selected methods (whether participant observation, Interview, Questionnaire, or some other methods of data collection would be used).
e). Analytical categories (by which the empirical data is subjected to analysis and interpretation).
Although the steps for formulating a research design remain common the designs differ, depending on the research purpose. The latter may be to report an unknown tribe, or to investigate the intricacies of an institution, or to test a specific Research Design in field situation, or to test a well-designed Research Design in controlled situations. Depending on the research purpose, one delineates an appropriate research design. However, validity of the steps for forming the design will always have to be there. Every study has its own purpose, but all the research purposes can be conceptualized as falling in one of the following categories. Each category refers to a type of research design. Thus, generally, social scientists identify three types of research design on the basis of different research purposes.
These are:

(a)Explanatory research Design

When the purpose of the study is to explore a new universe, one that has not been studied earlier, the research design, is called explanatory. The research purpose in this case is to gain familiarity in unknown areas. Often explanatory research design is used to formulate a problem for precise investigation, or aims at formulating Research Design. Thus, often when the universe of study is an unknown community, explanatory design forms the first step of research, after which other types of research designs can be used.
Two very good examples of explanatory designs are:
(i) Malinowski's study of Trobriand society; and
(ii) Whyte's study of the Street Corner Society.

Both these studies for the collection of data have relied on the special method of participant observation. Both researchers had an explanatory objective. Rather than aiming to test a limited set of specific Research Design, Malinowski and Whyte present in advance only the out line a conceptual model and provide a wide range of detail from which a number of other Research Design can be derived. Instead of concentrating on just unspecific areas and selecting a few aspects for consideration (as may be the case in descriptive research design), researchers gather such a great variety of data that they are able to see the actors in their total life situation. Explanatory studies are not to be confused with raw empiricism, with fact gathering that is unrelated to sociological theory. The explanatory study always carries with it a set of concepts that guide the researcher to look for the facts.

(b) Descriptive research Design

Generally, if a researcher is studying a community which is familiar and his research purpose is to depict accurately and in detail the characteristics of a particular institution, group or an event in the community, the appropriate research design is called Descriptive research Design. Sometimes, descriptive design forms a second step of research, the first step being explanatory design. Thus some times, research Research Design is formulated through explanatory design and to test the Research Design, descriptive design is formulated.

(c) Experimental research Design

The research design that is used to test a Research Design of causal relationship under controlled situation is called experimental design. The essence of the experimental design (in sociology) lies in its testing Research Design derived from a theory.
The experimentation in sociology observes the following aspects:
a. In an experimental design, the investigator controls or manipulates an independent variable or stimulus (X),
b. And observes the effects on the dependent variable (Y), and
c. The effect of the independent variable on the dependent variable is observed by minimizing the effects of extraneous variables that might confound the result.
e. These propositions are tested off on the sample, generally called the experimental sample (E).

Experimentation in sociology raises certain important questions, viz. ethical question, difficulties in forming a control sample and retaining it over time; the difficulties encountered in controlling the extraneous environment, etc. Realizing these problems, in some of the 'experiments' carried out by sociologists, the experimental sample is used as the control sample. It is debatable whether the absence of a control means a non -experimental study. This actually is a modification of the classic experimental design.
The theoretical propositions followed here are the following.
i. Experimental sample is also the control sample.
ii. The experimental sample is measured in the given respect before introducing the independent variable,
iii. After it has been measured, the stimulus for independent variable is introduced.
iv. The experimental sample is measured after stimulus and the change is calculated.
This modification of the experimental design in generally accepted in sociology and is called before and after research. The best example of this type of research design is the Hawthorne study carried out by E. Mayo, F. Roethlisberger, W. Disckson and G. Homans.In this study, the relationship between physical conditions of world (independent variable) and the productivity of the worker (dependent variable) is examined.

Content Analysis

Content analysis is a research technique for the systematic, objective and quantitative description of the content of research data procured through interviews, questionnaires, schedules and other linguistic expressions, written or oral. This definition is a slight modification of the one formulated by Bernard Berelson in his famed communications researches.Familarity with social science concepts and theory greatly aids in categorizing research data. Frequently certain categories seem to flow out of the data at hand. On the whole however the use of concepts and categories requires deliberate thought.

Psychologist D.C McClelland who regards a written research record as a piece of frozen behavior calls attention to various forms of content-analysis to which such records can be subjected; interaction process analysis; value analysis in which attempts are made to classify and conceptualize the content according to various values referred to in the behavior units, need -sequence analysis that attempts to score the changes which occur in the data when the subjects are under the influence of induced need-states; symbolic analysis which is a technique for analyzing latent meaning behind manifest content especially in psycho-analytical materials. Other social scientists suggest other forms of social analysis. Whatever form of analysis to which qualitative data are subjected an explicit breakdown is required of some totality into the smallest possible units if the data will be quantified. In short individual cases of human behavior can become of scientific significance since it is possible to classify and categorize behavior patterns, social processes, and personal traits to isolate their similarities and differences and conceptualize them appropriately. But as George Lundberg has stressed unless the varied data are gathered according to scientific principles are systematically classified and generalized into specific types of behavior individual cases are useless for scientific purposes.

Problems of Objectivity

Objectivity is a goal of scientific investigation. Sociology also being a science aspires for the goal objectivity. Objectivity is a frame of mind so that personal prejudices, preferences or predilections of the social scientists do not contaminate the collection of analysis of data. Thus scientific investigations should be free from prejudices of race, color, religion, sex or ideological biases.
The need of objectivity in sociological research has been emphasized by all important sociologists. For example Durkheim in the Rules of the Sociological Method stated that social facts must be treated as things and all preconceived notions about social facts must be abandoned. Even Max Weber emphasized the need of objectivity when he said that sociology must be value free. According to Radcliff Brown the social scientist must abandon or transcend his ethnocentric and egocentric biases while carrying out researches. Similarly Malinowski advocated cultural relativism while anthropological field work in order to ensure objectivity.

However objectivity continues to be an elusive goal at the practical level. In fact one school of thought represented by Gunnar Myrdal states that total objectivity is an illusion which can never be achieved. Because all research is guided by certain viewpoints and view points involve subjectivity.Myrdal suggested that the basic viewpoints should be made clear. Further he felt that subjectivity creeps in at various stages in the course of sociological research. Merton believes that the very choice of topic is influenced by personal preferences and ideological biases of the researcher.
Besides personal preferences the ideological biases acquired in the course of education and training has a bearing on the choice of the topic of research. The impact of ideological biases on social-research can be very far-reaching as seen from the study of Tepostalan village in Mexico. Robert Redfield studied it with functionalist perspective and concluded that there exists total harmony between various groups in the village while Oscar Lewis studied this village at almost the same time from Marxist perspective and found that the society was conflict ridden. Subjectivity can also creep in at the time of formulation of hypotheses. Normally hypotheses are deduced from existing body of theory. All sociological theories are produced by and limited to particular groups whose viewpoints and interests they represent. Thus formulation of hypotheses will automatically introduce a bias in the sociological research. The third stage at which subjectivity creeps in the course of research is that of collection of empirical data. No technique of data collection is perfect. Each technique may lead to subjectivity in one way or the other. In case of participant observation the observer as a result of nativisation acquires a bias in favour of the group he is studying. While in non-participant observation of the sociologist belongs to a different group than that under study he is likely to impose his values and prejudices.
In all societies there are certain prejudices which affect the research studies. In case of interview as a technique the data may be influenced by context of the interview, the interaction of the participants, and participant's definition of the situation and if adequate rapport does not extend between them there might be communication barriers. Thus according to P.V Young interview sometimes carries a subjectivity. Finally it can also affect the field limitations as reported by Andre Beteille study of Sripuram village in Tanjore where the Brahmins did not allow him to visit the untouchable locality and ask their point of view.
Thus complete objectivity continues to be an elusive goal. The researcher should make his value preference clear in research monograph. Highly trained and skilled research workers should be employed. Various methods of data collection research should be used and the result obtained from one should be cross-checked with those from the other. Field limitations must be clearly stated in the research monograph.

Sociology as a value-free science

The subject matter of sociology is human behavior in society. All social behavior is guided by values. Thus the study of social behavior can never be value-free if value freedom is interpreted in the sense of absence of values because values of the society under investigation form a part of the social facts to be studied by sociology. Moreover social research is in itself a type of social behavior and is guided by the value of search for true knowledge. Then what is meant as clarified by Max Weber value-free sociology means that the sociologist while carrying social research must confine called value relevance. Thus the values can operate at three levels:

  • At the level of philological interpretation.
  • At the level of ethical interpretation in assigning value to an object of enquiry.
  • At the level of rational interpretation in which the sociologists seeks the meaningful relationship between phenomena in terms of causal analysis. The point of value interpretation is to establish the value towards which an activity is directed.
Sociologists should observe value neutrality while conducting social research. It means that he should exclude ideological or non -scientific assumption from research. He should not make evaluative judgment about empirical evidence. Value judgment should be restricted to sociologists' area of technical competence. He should make his own values open and clear and refrain from advocating particular values. Value neutrality enables the social scientists to fulfill the basic value of scientific enquiry that is search for true knowledge. Thus sociology being a science cherishes the goal of value neutrality. According to Alvin Gouldner value-free principle did enhance the autonomy of sociology where it could steadily pursue basic problems rather than journalistically react to passing events and allowed it more freedom to pursue questions uninteresting either to the respectable or to the rebellious. It made sociology freer as Comte had wanted it to be -to pursue all its own theoretical implications. Value free principle did contribute to the intellectual growth and emancipation of the enterprise.Value-free doctrine enhanced freedom from moral compulsiveness; it permitted a partial escape from the parochial prescriptions of the sociologists' local or native culture. Effective internalization of the value-free principle has always encouraged at least a temporary suspension of the moralizing reflexes built into the sociologist by his own society. The value-free doctrine has a paradoxical potentiality; it might enable men to make better value judgments rather than none. It could encourage a habit of mind that might help men in discriminating between their punitive drives and their ethical sentiments. However in practice it has been extremely difficult to fulfill this goal of value neutrality. Values creep in various stages in sociological research. According to Gunnar Myrdal total value neutrality is impossible. 'Chaos does not organize itself into cosmos. We need view points.' Thus in order to carry out social research viewpoints are needed which form the basis of hypothesis which enables the social scientists to collect empirical data. These view-points involve valuations and also while formulating the hypothesis. Thus a sociologist has to be value frank and should make the values which have got incorporated in the choice of the topic of the research of the formulation of hypothesis clear and explicit at the very outset in the research. The value-free doctrine is useful both to those who want to escape from the world and to those who want to escape into it. They think of sociology as a way of getting ahead in the world by providing them with neutral techniques that may be sold on the open market to any buyer. The belief that it is not the business of sociologist to make value judgments is taken by some to mean that the market on which they can vend their skills is unlimited. Some sociologists have had no hesitation about doing market research designed to sell more cigarettes although well aware of the implications of recent cancer research. According to Gouldner the value-free doctrine from Weber's standpoint is an effort to compromise two of the deepest traditions of the western thought, reason and faith but that his arbitration seeks to safeguard the romantic residue in modern man. Like Freud, Weber never really believed in an enduring peace or in a final resolution of this conflict. What he did was to seek a truce through the segregation of the contenders by allowing each to dominate in different spheres of life.

Techniques Of Data Collection

Basic requirements for scientific data are that it should be reliable and impartial. In Sociology these conditions are hard to meet. Yet numerous methods are used to minimize errors in data. Some of the commonly used sources in collecting data are:
  • Existing materials including the official statistical record and historical and contemporary documents.
  • Social surveys through questionnaire and schedules
  • Interviewing
  • Observation- Participants and non-participant

Existing Material

Statistical Sources

Government statistics particularly census or statistics produced by large industrial or commercial firms, trade unions or other organizations provide one important account of data which sociologist can use in their analysis. An outstanding example of the imaginative use of official statistics in the positivist tradition is the study of suicide made by the famous French sociologist Emile Durkheim in the 19th century. However official statistics are the kind of data that are not collected by sociologists themselves and so there problems while analyzing the data.

Historical documents

Records and accounts of qualitative kind for example relating to belief, values, social relationship or social behavior may also be contemporary or may refer to earlier periods. There are several difficulties immediately present themselves in the use of records from the past. Few chroniclers of social relation and social action record observations in the systematic way in which the sociologists are interested. There are often intriguing and sympathetic records but the information that is vital to the sociologist is often missing.

Contemporary Records

Contemporary records relating to social relationship and social behavior are seldom used as the sole source of information and sociological research. They are usually one source of a particular account or achievement.

Techniques Of Data Collection

Social Survey

The basic procedure in survey is that people are asked a number of questions on that aspect of behavior which the sociologist is interested in. A number of people carefully selected so that their representation of their population being studied are asked to answer exactly the same question so that the replies to different categories of respondents may be examined for differences. One type of survey relies on contacting the respondents by letter and asking them to complete the questionnaire themselves before returning it. These are called Mail questionnaires. Sometimes questionnaires are not completed by individuals separately but by people in a group under the direct supervision of the research worker. A variation of the procedure can be that a trained interviewer asks the questions and records the responses on a schedule from each respondent.

These alternate procedures have different advantages and disadvantages. Mail questionnaires are relatively cheap and can be used to contact respondents who are scattered over a wide area. But at the same time the proportion of people who return questionnaires sent through post is usually rather small. The questions asked in main questionnaires have also to be very carefully worded in order to avoid ambiguity since the respondents cannot ask to have questions clarified for them. Using groups to complete questionnaires means that the return rate is good and that information is assembled quickly and fairly. Administrating the interview schedules to the respondents individually is probably the most reliable method. Several trained interviewers may be employed to contact specific individuals. The questionnaires and schedules can consist of both close-ended and open-ended questions. Also a special attention needs to be paid to ensure that the questionnaires are filled in logical order.
Where aptitude questions are included great care must be exercised to ensure the proper words are used. In case of schedules emphasis and interactions may also be standardized between different individuals and from respondents to respondents. Finally proper sampling techniques must be used to ensure that the sample under study represents the universe of study. In order to enhance the reliability of data collected through questionnaires and schedules, these questionnaires and schedules must be pretested through pilot studies.

Techniques Of Data Collection


Social surveys may depend either on questionnaires that are self-administered or on schedules completed by trained research workers personally interviewing then is not a method of data collection distinct from social surveying but rather a technique which may vary from the brief formal contact as when the interviewer is working for the firms public opinion consultants or a market research organization and simply asks a housewife a few highly specific questions on limited range of topics to a long interview in which the research worker allows the respondents to develop points at leisure and take up others as he chooses.
The brief formal interview in which the working of the questions and the order in which they are asked is fixed is called structured interview while the freer discursive interview is called unstructured interview. The object of using structured interview is to standardize the interview as much as possible and thus to reduce the effect that the interviewer's personal approach or biases may have upon the result and even when structured interviews are used, proper training can do a lot to ensure further the reliability and validity of research. The personality of the interviewer and the social characteristics that the respondents attribute

to him can be having influence on the result. The effort of interviewer's bias can be estimated by comparing one interviewer's result with other. The problem of interviewer's bias in an unstructured interview is much greater. Here the interviewer is left to his common devices as far as the way he approaches a respondent is concerned. There is no fixed list of questions to work through. Instead the interviewer may work from a guide that will remind him of the topics he wishes to cover.
The training of the interviewer is crucial here not simply training in the social skills of keeping the conversation going on a topic that the respondent may not be very interested in but also in acquiring sensitivity to those things his respondents tells him which are specially relevant to the theoretical topics he is pursuing. This means that unstructured interviews can be carried out by people trained in sociological theory. They are then able to size upon stray comments made by the respondents which can be developed and lead on to important theoretical insight.

Techniques Of Data Collection

Observation: Participant and non participant

The rationale behind the use of observation in sociological research is that the sociologist should become party to a set of social actions sufficiently able to be able to assess directly the social relationship involved. The degree of involvement may vary considerable from being merely a watcher on the sidelines to be deeply involved in and being a part of what is going on. The former type of observation techniques are called non-participant while the latter is called participant observation. Sometimes one way observations screen have been used to watch groups in actions that they are unaware that they are being watched and the observer cannot affect their actions by his presence. The sociologist is visibly present and is a part of the situation either as a sociologist or in another guise. Where the sociologist is merely an observer it is usually assumed that he knows enough about what the actors are doing to be able to understand their behaviour.

Any sociological observer has then to some extent be a participant observer he must at least share sufficient cultural background with the actors to be able to construe their behavior meaningfully but the degree of participation and of sharing of meaning may vary considerably. Examples of such studies are Nel Anderson's study of Hobo-Indians and William White study of Street Corner Society.

Techniques Of Data Collection


For practical and cost reasons, it is often impossible to collect information about the entire population of people or things in which social researchers are interested. In these cases, a sample of the total is selected for study. Most statistical studies are based on samples and not on complete enumerations of all the relevant data. The main criteria when sampling are to ensure that a sample provides a faithful representation of the totality from which it is selected, and to know as precisely as possible the probability that a sample is reliable in this way. Randomization meets these criteria, because it protects against bias in the selection process and also provides a basis on which to apply statistical distribution theory that allows an estimate to be made of the probability that conclusions drawn from the sample are correct. A statistical sample is a miniature picture or cross-section of the entire group or aggregate from which the sample is taken. The entire group from which a sample is chosen is known as the population, universe or supply.

Simple random sampling

The basic type of random sample is known as a simple random sample, one in which each person or item has an equal chance of being chosen. Often a population contains various distinct groups or strata that differ on the attribute that is being researched.

Stratified random sampling

Stratified random sampling involves sampling of each stratum separately. This increases precision, or reduces time, effort and cost of allowing smaller sample sizes for a given level of precision. For example, poverty is known to be most common among the elderly, the unemployed and single parent families, so research on the effect of poverty might will sample separately each of these three strata as part of a survey of poverty in the population as a whole which would permit the total sample size to be reduced because the investigator would know that the groups most affected by poverty were guaranteed inclusion.

Cluster sampling

Cluster sampling is sometimes used when the population naturally congregates into clusters. For example, managers are clustered in organizations, so a sample of managers could be obtained by taking a random sample of organizations and investigating the managers in each of these. Interviewing or observing managers on this basis would be cheaper and easier than using a simple random sample of managers scattered across all organizations in the country. This is usually less precise than a simple random sample of the same size, but in practice the reduction in cost per element more than compensated for the decrease in precision.

Multi-stage sampling

Sampling may be done as one process or in stages, known as multi-stage sampling .Multi-stage designs are common when populations are widely dispersed. Thus a survey of business managers might proceed by selecting a sample of corporations as first stage units, perhaps choosing these corporations with a probability proportionate to their size, and then selecting a sample of managers within these corporations at the second stage. Alternatively, a sample of individual factories or office buildings within each corporation could be chosen as the second stage units, followed by sample of managers in each of these as a third stage. Stratification can also be used in the design, if for example occupational sub-groups are known to differ from each other, by selecting state such as personnel, production, and finance management and sampling within each of these. For sampling to be representative, one needs a complete and accurate list of the first stage units that make up the relevant population, a basic requirement that is not always easily met. This forms the sampling frame. Selection from the frame is best done by numbering the items and using a table of random numbers to identify which items form the sample, though a quasi-random method of simply taking every item from the list is often appropriate. The reliability of a sample taken from a population can be assessed by the spread of the sampling distribution, measured by the standard deviation of this distribution, called the standard error. As a general rule, the larger is the size of the sample the smaller the standard error.

Area sampling

In sampling of this kind small areas are designated as sampling units and the households interviewed include all or a specified fraction of those found in a canvass of these designated small areas. The basic sampling units or segments chosen may be relatively large or relatively small depending on such factors as the type of area being studied, population distribution, the availability of suitable maps and other information and the nature and desired accuracy of the data being collected.

Measurement of Attitude

Attitudinal behavior is a certain set of observable behavior which is preparatory to and indicative of the subsequent actual behavior. For the purpose of measuring attitudes only the overt symbolic type of acts are taken into account because such acts alone can be observed. Examples of such acts are speaking; writing and gesturing etc.Attitude indicate a tendency which can be helpful in predicting the subsequent behaviour.Herein lies the importance of measuring attitudes. Measurement of attitudes is useful in various aspects of day to day life. For example it helps in predicting consumer behavior in making demand forecasts in providing an insight into the public response to various welfare measured indicated by the Government in maintaining peace and social order and in social research.

The sources of information regarding the attitude of a person are:
  • Life history documents including biographies, autobiographies, diaries, letters and memories.
  • Oral interviews: opinions of the respondent may be elicited by personally asking them various questions.
  • Questionnaires and polls: Sometimes in place of persons contact mailed questionnaire is also used for the purpose of getting opinions. Similarly public opinion polls are conducted to know peoples opinion on various issue.
In order to measure the degree of intensity of the attitude various kinds of scales have been devised.
These scales may be divided into the following categories:
  • Point scales
  • Ranking scales
  • Rating of intensity scales etc
Other scales for the measurement of attitudes are social distance, scale of Bogardus Thurston Scale, Likert scale and socio-metric scale by Moreno. However standard scales with universal application are yet to be devised.

Likert Scale

The Likert technique presents a set of attitude statements. Subjects are asked to express agreement or disagreement of a five-point scale. Each degree of agreement is given a numerical value from one to five. Thus a total numerical value can be calculated from all the responses.

Analysis and Interpretation of Data

The purpose of assembling data is to present some theoretical analysis or interpretation of it. But the processes of observation and analysis are rarely independent of one another. The problems become redefined as the research proceeds and this means changing accounts of observations made. In the social survey the pilot stage is very important since the sociologist derives preliminary information from it which he then uses to test existing hypotheses in a crude way. He may then have to modify both the hypothesis and in consequence the techniques for example he may change the schedule that he is using. Unstructured interview techniques and observations are particularly suitable where the questions must be changed when an analysis begins to throw up new problems which demand new information in order to answer them. Analysis of data involves seeking through observations with object of determination in what circumstances they do not or to check that if sociologist can support one interpretation rather then another. At this stage it is necessary to point out two difficulties in the use of sociological information for analytical or interpretative purposes. The first of these is called the reliability of data. This refers to the extent to which investigation are repeatable that is if the same procedures of data collection the same object categories and the same rules for establishing the veracity are used on the same subject by different observers or by the same observers on different occasions, no relevant changes have taken place on the main attempt results comparable with earlier studies can be obtained. If different answer emerged from the enquiries which should yield the same response then the date may not be used to represent and establish underlying regularity. The measures that sociologist can take to overcome unreliability in response will depend upon what procedures are used to collect the information and what type of analysis is to be made. The second difficulty is that of the validity of data. Validity refers to the extent to which sociologist interpretation of underlying characteristics he wishes to reflect is in fact the faithful representation of the characteristics. The sociologists working with a positivistic framework may wish to represent some abstract notion such as Alienation by a set of relatively easily identified indicators. He may attempt to combine these into a single indicator of characteristics he wants to represent. Having done this however how can he be sure that his indicator reflects the characteristics of alienation effectively. The usual way to ascertain the suitability of indicators is to test them empirically on samples of subjects which are known from other evidence to be alienated or not alienated. Given however that the sociologist is reasonable satisfied with both the reliability and validity of data how does the analysis or interpretation proceed? This depends upon the framework within which the sociologist is working. Within a positivistic framework the sociologist will be interested in some hypothesis which he has derived from theory by examining the connection in his data between some specified dependent variable which he suspects have some causal influence. This implies that the initial stages of analysis which may be going on while the data are being assembled must be concerned with identifying the variables and in deciding what criteria may be reasonably used to represent these variables. Only after the positivist sociologist has satisfactorily defined and operationalised the variables he wants to test the casual proposition he is postulating can be proceed to test this.

Attached: Research Method And Statistics
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