Research Method And Statistics
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o Applications Of Statistics
o Methods Of Central Tendency Of Averages
o Measures Of Dispersion
o Coefficient Of Dspersion
o Association Of Attributes
* Scientific Study of Social Phenomena
o Elements of Scientific Methods
o Theory and Facts
o Gathering information and constructing Explanations
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 Observation: Participant and non participant
o Measurement of Attitude
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 Statistics1. 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 SociologySociology 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.
Statistics and GovernmentThe 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 PlanningModern 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 EconomicsIn 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
There are five types of average which are
4.Geometric Mean and
5. Harmonic Mean
Arithmetic MeanThe 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.
MedianMedian 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.
ModeThe 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
Mean: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.
Median: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.
Mode: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
- Quartile Deviation or semi interquartile range
- Mean Deviation
- Standard Deviation
RangeRange 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 rangeThis 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 Â¼ 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 DeviationThe 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 DeviationThe 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
- Quartile coefficient of dispersion = Quartile Deviation/ Mean
- Coefficient of variation (C.V) = Standard Deviation /Mean x 100
- Mean Deviation/Mean
- Mean Deviation/Median of which the first two are the most frequently used coefficients.
Association of attributes
- Comparison of observed and expected frequencies.
- Proportion method
- Yule's coefficient of Association
- Coefficient of colligation
- Coefficient of contingency
Linear RegressionIn 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
Theory and Facts
Theory is a tool of science in these ways
1.it defines the major orientation of a science, by defining the kinds of data that are to be abstracted.
2.it offers a conceptual scheme by which the relevant phenomena are systematized, classified and interrelated.
3.it 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.
Design of Proof: Testing the HypothesisThe 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
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:
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.
(a)Explanatory research DesignWhen 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 DesignGenerally, 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 DesignThe 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.
Problems of Objectivity
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.
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
- 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.
Techniques Of Data Collection
- Existing materials including the official statistical record and historical and contemporary documents.
- Social surveys through questionnaire and schedules
- Observation- Participants and non-participant
Statistical SourcesGovernment 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 documentsRecords 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 RecordsContemporary 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 SurveyThe 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.
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
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
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
Techniques Of Data Collection
SamplingFor 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 samplingThe 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 samplingStratified 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 samplingCluster 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 samplingSampling 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 samplingIn 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
- 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.
These scales may be divided into the following categories:
- Point scales
- Ranking scales
- Rating of intensity scales etc
Likert ScaleThe 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 DataThe 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.
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