1. Explain the different types of research.

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Types of Research

Although any typology of research is inevitably arbitrary, Research may be classified crudely according to its major intent or the methods. According to the intent, research may be classified as:

Pure Research -It is undertaken for the sake of knowledge without any intention to apply it in practice, e.g., Einstein's theory of relativity, Newton's contributions, Galileo's contribution, etc. It is also known as basic or fundamental research. It is undertaken out of intellectual curiosity or inquisitiveness. It is not necessarily problem-oriented. It aims at extension of knowledge. It may lead to either discovery of a new theory or refinement of an existing theory. It lays foundation for applied research. It offers solutions to many practical problems. It helps to find the critical factors in a practical problem. It develops many alternative solutions and thus enables us to choose the best solution.

Applied Research -It is carried on to find solution to a real-life problem requiring an action or policy decision. It is thus problem-oriented and action-directed. It seeks an immediate and practical result, e.g., marketing research carried on for developing a news market or for studying the post-purchase experience of customers. Though the immediate purpose of an applied research is to find solutions to a practical problem, it may incidentally contribute to the development of theoretical knowledge by leading to the discovery of new facts or testing of theory or O conceptual clarity. It can put theory to the test. It may aid in conceptual clarification. It may integrate previously existing theories.

Exploratory Research -It is also known as formularizing research. It is preliminary study of an unfamiliar problem about which the researcher has little or no knowledge. It is ill-structured and much less focused on pre-determined objectives. It usually takes the form of a pilot study. The purpose of this research may be to generate new ideas, or to increase the researcher's familiarity with the problem or to make a precise formulation of the problem or to gather information for clarifying concepts or to determine whether it is feasible to attempt the study. Katz conceptualizes two levels of exploratory studies. "At the first level is the discovery of the significant variable in the situations; at the second, the discovery of relationships between variables."

Descriptive Study -It is a fact-finding investigation with adequate interpretation. It is the simplest type of research. It is more specific than an exploratory research. It aims at identifying the various characteristics of a community or institution or problem under study and also aims at a classification of the range of elements comprising the subject matter of study. It contributes to the development of a young science and useful in verifying focal concepts through empirical observation. It can highlight important methodological aspects of data collection and interpretation. The information obtained may be useful for prediction about areas of social life outside the boundaries of the research. They are valuable in providing facts needed for planning social action program.

Diagnostic Study - It is similar to descriptive study but with a different focus. It is directed towards discovering what is happening, why it is happening and what can be done about. It aims at identifying the causes of a problem and the possible solutions for it. It may also be concerned with discovering and testing

whether certain variables are associated. This type of research requires prior knowledge of the problem, its thorough formulation, clear-cut definition of the given population, adequate methods for collecting accurate information, precise measurement of variables, statistical analysis and test of significance.

Evaluation Studies- It is a type of applied research. It is made for assessing the effectiveness of social or economic programmers implemented or for assessing the impact of developmental projects on the development of the project area. It· is thus directed to assess or appraise the quality and quantity of an activity and its performance, and to specify its attributes and conditions required for its success. It is concerned with causal relationships and is more actively guided by hypothesis. It is concerned also with changeover time.

Action Research - It is a type of evaluation study. It is a concurrent evaluation study of an action programmed launched for solving a problem for improving an exiting situation. It includes six major steps: diagnosis, sharing of diagnostic information, planning, developing change programmed, initiation of organizational change, implementation of participation and. communication process, and post experimental evaluation.

According to the methods of study, research may be classified as:

1. Experimental Research: It is designed to asses the effects of particular variables on a phenomenon by keeping the other variables· constant or controlled. It aims at determining whether and in what manner variables are related to each other.

2. Analytical Study: It is a system of procedures and techniques of analysis applied to quantitative data. It may consist of a system of mathematical models or statistical techniques applicable to numerical data. Hence it is also known as the Statistical Method. It aims at testing hypothesis and specifying and interpreting relationships.

3. Historical Research: It is a study of past records and other information sources with a view to reconstructing the origin and development of an institution or a movement or a system and discovering the trends in the past. It is descriptive in nature. It is a difficult task; it must often depend upon inference and logical analysis or recorded data and indirect evidences rather than upon direct observation.

4. Survey: It is a fact-finding study. It is a method of research involving collection of data directly from a population or a sample thereof at particular time. Its purpose is to provide information, explain phenomena, to make comparisons and concerned with cause and effect relationships can be useful for making predications J/.

2. Discuss the criteria of good research problem.

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Criteria of Good research Problem

Horton and Hunt have given following characteristics of scientific research:

1. Verifiable evidence : That is factual observations which other observers can see and check.

2. Accuracy: That is describing what really exists. It means truth or correctness of a statement or describing things exactly as they are and avoiding jumping to unwarranted conclusions either by exaggeration or fantasizing.

3. Precision : That is making it as exact as necessary, or giving exact number or measurement. This avoids colorful literature and vague meanings.

4. Systematization : That is attempting to find all the relevant data, or collecting data in a systematic and organized way so that the conclusions drawn are reliable. Data based on casual recollections are generally incomplete and give unreliable judgments and conclusions.

5. Objectivit y: That is free being from all biases and vested interests. It means observation is unaffected by the observer's values, beliefs and preferences to the extent possible and he is able to see and accept facts as they are, not as he might wish them to be.

6. Recording : That is jotting down complete details as quickly as possible.

3. Since human memory is fallible, all data collected are recorded.

7. Controlling conditions : That is controlling all variables except one and then attempting to examine what happens when that variable is varied. This is the basic technique in all scientific experimentation - allowing one variable to vary while holding all other variables constant.

8. Training investigators : That is imparting necessary knowledge to investigators to make them understand what to look for, how to interpret in and avoid inaccurate data collection.

3. Describe the procedure used to test the hypothesis.

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Testing of Hypothesis

The hypothesis testing determines the validity of the assumption (technically described as null hypothesis) with a view to choose between the conflicting hypotheses about the value of the population hypothesis about the value of the population" of a population parameter. Hypothesis testing helps to secede on the basis of a sample data, whether a hypothesis about the population is likely to be true or false. Statisticians· have developed several tests of hypothesis (also known as tests of significance) for the purpose of testing of hypothesis which can be classified as:

Parametric tests or standard tests of hypothesis Non Parametric test or distribution - free test of the hypothesis.

Parametric tests usually assume certain properties of the parent population from which we draw samples. Assumption like observations come from a normal population, sample size is large, assumptions about the population parameters like mean, variants etc must hold good before parametric test can be used. But there are situation when the researcher cannot or does not want to make assumptions. In such situations we use statistical methods for testing hypothesis which are called non parametric tests because such tests do not depend on any assumption about the parameters of parent population. Besides, most non-parametric test assumes only nominal or original data, where as parametric test require measurement equivalent to at least an interval scale. As a result non-parametric test needs more observation than a parametric test to achieve the same size of Type I & Type II error.

Important Parametric Tests The important parametric tests are: z-test t-test x2-test f-test

All these tests are based on the assumption of normality le. the source of data is considered to be normally distributed. In some cases the population may not be normally distributed, yet the test will be applicable on account of the fact that we mostly deal with samples and the sampling distributions closely approach normal distributions. Z-test is based on the normal probability distribution and is used for judging the significance of several statistical measures, particularly the mean. The relevant test statistic is worked out and compared with its probable value (to be read from the table showing area under normal curve) at a specified level of significance for judging the significance of the measure concerned. This is a most frequently used test in research studies. This test is used even when binomial distribution or t-distribution is applicable on the presumption that such a distribution tends to approximate normal distribution as 'n' becomes larger. Z-test is generally used for comparing the mean of a sample to some hypothesis mean for the population in case of large sample, or when population variance is known as z-test is also used for judging the significance of difference between means to of two independent samples in case of large samples or when population variance is known z-test is generally used for comparing the sample proportion to a theoretical value of population proportion or for judging the difference in

proportions of two independent samples when happens to be large. Besides, this test may be used for judging the significance of median, mode, co-efficient of correlation and several other measures

T-test is based on t-distribution and is considered an appropriate test for judging the significance of sample mean or for judging significance of difference between the two means of the two samples in case of samples when population variance is not known (in which case we use variance of the sample as an estimate the population variance). In case two samples are related, we use paired t-test (difference test) for judging the significance of their mean of difference between the two related samples. It can also be used for judging the significance of co-efficient of simple and partial correlations. The relevant test statistic, t, is calculated from the sample data and then compared with its probable value based on t-distribution at a specified level of significance for concerning degrees of freedom for accepting or rejecting the null hypothesis it may be noted that t-test applies only in case of small sample when population variance is unknown.

X2-test is based on chi-square distribution and as a parametric test is used for comparing a sample variance to a theoretical population variance is unknown. F-test is based on f-distribution and is used to compare the variance of the two-independent samples. This test is also used in the context of variance (AN OVA) for judging the significance of more than two sample means at one and the same time. It is also used for judging the significance of multiple correlation coefficients. Test statistic, f, is calculated and compared with its probable value for accepting or rejecting the Ho.

4. Write a note on experimental design.

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Important Experimental Designs

Experimental design refers to the framework or structure of an experiment and as such there are several experimental designs. We can classify experimental designs into two broad categories, viz., informal experimental designs and formal experimental designs. Informal experimental designs are those designs that normally use a less sophisticated form of analysis based On differences in magnitudes, where as formal experimental designs offer relatively more control and use precise statistical procedures for analysis. Informal experimental designs:

Before and after without control design: In such a design, single test group or area is selected and the dependent variable is measured before then introduction of the treatment. The treatment is then introduced and the dependent variable is measured again after the treatment has been introduced. The effect of the treatment would be equal to the level of the phenomenon after the treatment minus the level of the phenomenon before the treatment.

After only with control design: In this design, two groups or areas (test and control area) are selected and the treatment is introduced into the test area only. The dependent variable is then measured in both the areas at the same time. Treatment impact is assessed by subtracting the value of the dependent variable in the control area from its value in the test area.

Before and after with control design: In this design two areas are selected and the dependent variable is measured in both the areas for an identical time-period before the treatment. The treatment is then introduced into the test area only, and the dependent variable is measured in both for an identical time-period after the introduction of the treatment. The treatment effect is determined by subtracting the change in the dependent variable in the control area from the change in the dependent variable in test area.

Formal Experimental Designs

1. Completely randomized design (CR design): It involves only two principle viz., the principle of replication and randomization. It is generally used when experimental areas happen to be homogenous. Technically, when all the variations due to uncontrolled extraneous factors are included under the heading of chance variation, we refer to the design of experiment as C R Design.

2. Randomized block design (RB design): It is an improvement over the C Research design. In the RB design the principle of local control can be applied along with the other two principles.

3. Latin square design (LS design): It is used in agricultural research. The treatments in a LS design are so allocated among the plots that no treatment occurs more than once in any row or column.

4. Factorial design: It is used in experiments where the effects of varying more than one factor are to be determined. They are especially important in several economic and social phenomena where usually a large number of factors affect a particular problem.

5. Elaborate the ways of making a case study effective.

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Let us discuss the criteria for evaluating the adequacy of the case history or life history which is of central importance for case study. Jonn Dollard has proposed seven criteria for evaluating such adequacy as follows:

i) The subject must be viewed as a specimen in a cultural series. That is, the case drawn out from its total context for the purposes of study must be considered a member of the particular cultural group or community. The scrutiny of the life histories of persons must be done with a view to identify thee community values, standards and their shared way of life.

ii) The organic motto of action must be socially relevant. That is, the action of the individual cases must be viewed as a series of reactions to social stimuli or situation. In other words, the social meaning of behavior must be taken into consideration.

iii) The strategic role of the family group in transmitting the culture must be recognized. That is, in case of an individual being the member of a family, the role of family in shaping his behavior must never be overlooked.

iv) The specific method of elaboration of organic material onto social behaviour must be clearly shown. That is case histories that portray in detail how basically a biological organism, the man, gradually blossoms forth into a social person, are especially fruitful.

v) The continuous related character of experience for childhood through adulthood must be stressed. In other words, the life history must be a configuration depicting the inter-relationships between thee person's various experiences.

vi) Social situation must be carefully and continuously specified as a factor. One of the important criteria for the life history is that a person's life most be shown as unfolding itself in the context of and partly owing

vii) The life history material itself must be organized according to some conceptual framework this in turn would facilitate generalizations at a higher level.

6. What is non probability sampling? Explain its types with examples.

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Non probability sampling- Non probability sampling is not based on the theory of probability. This sampling does not provide a chance of selection to each population element.

Methods of Sampling

Sampling techniques or methods may be classified into two generic types:

Probability or Random Sampling -Probability sampling is based on the theory of probability. It is also known as random sampling. It provides a known nonzero chance of selection for each population element. It is used when generalization is the objective of study, and a greater degree of accuracy of estimation of population parameters is required. The cost and time required is high hence the benefit derived from it should justify the costs.

The following are the types of probability sampling:

i. Simple Random Sampling: This sampling technique gives each element an equal and independent chance of being selected. An equal chance means equal probability of selection. An independent chance means that the draw of one element will not affect the chances of other elements being selected. The procedure of drawing a simple random sample consists of enumeration of all elements in the population.

1. Preparation of a List of all elements, giving them numbers in serial order 1, 2, B, and so on, and

2. Drawing sample numbers by using (a) lottery method, (b) a table of random numbers or (c) a computer.

Suitability: This type of sampling is suited for a small homogeneous population.

Advantages: The advantage of this is that it is one of the easiest methods, all the elements in the population have an equal chance of being selected, simple to understand, does not require prior knowledge of the true composition of the population.

Disadvantages: It is often impractical because of non-availability of population list or of difficulty in enumerating the population, does not ensure proportionate representation and it may be expensive in time and money. The amount of sampling error associated with any sample drawn can easily be computed. But it

is greater than that in other probability samples of the same size, because it is less precise than other methods.

ii. Stratified Random Sampling: This is an improved type of random or probability sampling. In this method, the population is sub-divided into homogenous groups or strata, and from each stratum, random sample is drawn. E.g., university students may be divided on the basis of discipline, and each discipline group may again be divided into juniors and seniors. Stratification is necessary for increasing a sample's statistical efficiency, providing adequate data for analyzing the various sub-populations and applying different methods to different strata. The stratified random sampling is appropriate for a large heterogeneous population. Stratification process involves three major decisions. They are stratification base or bases, number of strata and strata sample sizes.

Stratified random sampling may be classified into:

a) Proportionate stratified sampling: This sampling involves drawing a sample from each stratum in proportion to the latter's share in the total population. It gives proper representation to each stratum and its statistical efficiency is generally higher. This method is therefore very popular. E.g., if the Management Faculty of a University consists of the following specialization groups:

Specialization stream No. of student proportion of each streamProportion 40 0.4Finance 20 0.2Marketing 30 0.3Rural development 10 0.1

100 1.0

Suitability: The application of cluster sampling is extensive in farm management surveys, socio-economic surveys, rural credit surveys, demographic studies, ecological studies, public opinion polls, and large scale surveys of political and social behavior, attitude surveys and so on.

Advantages: The advantages of this method is it is easier and more convenient, cost of this is much less, promotes the convenience of field work as it could be done in compact places, it does not require more time, units of study can be readily substituted for other units and it is more flexible.

Disadvantages: The cluster sizes may vary and this variation could increase the bias of the resulting sample. The sampling error in this method of sampling is greater and the adjacent units of study tend to have more similar characteristics than do units distantly apart.

Area sampling - This is an important form of cluster sampling. In larger field surveys cluster consisting of specific geographical areas like districts, talus, villages or blocks in a city are randomly drawn. As the geographical areas are selected as sampling units in such cases, their sampling is called area sampling. It is not a separate method of sampling, but forms part of cluster sampling.

Multi-stage and sub-sampling - In multi-stage sampling method, sampling is carried out in two or more stages. The population is regarded as being composed of a number of second stage units and so forth. That is, at each stage, a sampling unit is a cluster of the sampling units of the subsequent stage. First, a sample of the first stage sampling units is drawn, then from each of the selected first stage sampling unit, a sample of the second stage sampling units is drawn. The procedure continues down to the final sampling units or population elements. Appropriate random sampling method is adopted at each stage. It is appropriate where the population is scattered over a wider geographical area and no frame or list is available for sampling. It is also useful when a survey has to be made within a limited time and cost budget. The major disadvantage is that the procedure of estimating sampling error and cost advantage is complicated.

Sub-sampling is a part of multi-stage sampling process. In a multi-stage sampling, the sampling in second and subsequent stage frames is called sub-sampling. Sub-sampling balances the two conflicting effects of clustering Le. Cost and sampling errors.

Random Sampling with Probability Proportional to Size - The procedure of selecting clusters with probability Proportional to size (PPS) is widely used. If one primary cluster has twice as large a population as another, it is give twice the chance of being selected. If the same number of persons is then selected from each of the selected clusters, the overall probability of any person will be the same. Thus PPS is a better method for securing a representative sample of population elements in multi-stage cluster sampling.

Advantages: The advantages are clusters of various sizes get proportionate representation, PPS leads to greater precision than would a simple random sample of clusters and a constant sampling fraction at the second stage, equal-sized samples from each selected primary cluster are convenient for field work.

Disadvantages: PPS cannot be used if the sizes of the primary sampling clusters are not known.

Double Sampling and Multiphase Sampling - Double sampling refers to the subsection of the final sample form a reselected larger sample that provided information for improving the final selection. When the procedure is extended to more than two phases of selection, it is then, called multi-phase sampling. This is also known as sequential sampling, as sub-sampling is done from a main sample in phases. Double sampling or multi phase sampling is a compromise solution for a dilemma posed by undesirable extremes. "The statistics based on the sample of "n' can be improved by using ancillary information from a wide base: but this is too costly to obtain from the entire population of N elements. Instead, information is obtained from a larger preliminary sample NL which includes the final sample n.

Replicated or Interpenetrating Sampling- It involves selection of a certain number of Sub-samples rather than one full sample from a population. All the Sub-samples should be drawn using the same sampling technique and each is a self-contained and adequate sample of the population. Replicated sampling can be used with any basic sampling technique: simple or stratified, single or multi-stage or single o. multiphase sampling. It provides a simple means of calculating the sampling error. It is practical. The replicated samples can throw light on variable non sampling errors. But disadvantage is that it limits the amount of stratification that can be employed.

Non-probability or Non Random Sampling - Non-probability sampling or non-random sampling is not based on the theory of probability. This sampling does not provide a chance of selection to each population element.

Advantages: The only merits of this type of sampling are simplicity, convenience and low cost.

Disadvantages: The demerits are it does not ensure a selection chance to each population unit. The selection probability sample may not be a representative one. The selection probability is unknown. It suffers from sampling bias which will distort results.

The reasons for usage of this sampling are when there is no other feasible alternative due to non-availability of a list of population, when the study does not aim at generalizing the findings to the population, when the costs required for probability sampling may be too large, when probability sampling required more time, but the time constraints and the time limit for completing the study do not permit it. It may be classified into:

Convenience or Accidental Sampling - It means selecting sample units in a just 'hit and miss' fashion E.g., interviewing people whom we happen to meet. This sampling also means selecting whatever sampling units are conveniently available, e.g., a teacher may select students in his class. This method is also known as accidental sampling because the respondents whom the researcher meets accidentally are included in the sample.

Suitability: Though this type of sampling has no status, it may be used for simple purposes such as testing ideas or gaining ideas or rough impression about a Subject of interest.

Advantage: It is the cheapest and simplest, it does not require a list of population and it does not require any statistical expertise.

Disadvantage: The disadvantage is that it is highly biased because of researcher's subjectivity, it is the least reliable sampling method and the findings cannot be generalized.

Purposive (or judgment) sampling - This method means deliberate selection of sample units that conform to some pre-determined criteria. This is also known as judgment sampling. This involves selection of cases which we judge as the most appropriate ones for the given study. It is based on the judgment of the researcher or some expert. It does not aim at securing a cross section of a population. The chance that a particular case be selected for the sample depends on the subjective judgment of the researcher.

Suitability: This is used when what is important is the typicality and specific relevance of the sampling units to the study and not their overall representative ness to the population.

Advantage: It is less costly and more convenient and guarantees inclusion of relevant elements in the sample.

Disadvantage: It is less efficient for generalizing, does not ensure the representative ness, requires more prior extensive information and does not lend itself for using inferential statistics.

Quota sampling - This is a form of convenient sampling involving selection of quota groups of accessible sampling units by traits such as sex, age, social class, etc. it is a method of stratified sampling in which the selection within strata is nonrandom. It is this Non-random element that constitutes its greatest weakness.

Suitability: It is used in studies like marketing surveys, opinion polls, and readership surveys which do not aim at precision, but to get quickly some crude results.

Advantage: It is less costly, takes less time, non need for a list of population, and field work can easily be organized.

Disadvantage: It is impossible to estimate sampling error, strict control if field work is difficult, and Subject to a higher degree of classification. Snow-ball sampling - This is the colorful name for a technique of Building up a list or a sample of a special population by using an initial set of its members as informants. This sampling technique may also be used in socio-metric studies.

Suitability: It is very useful in studying social groups, informal groups in a formal organization, and diffusion of information among professional of various kinds.

Advantage: It is useful for smaller populations for which no frames are readily available.

Disadvantage: The disadvantage is that it does not allow the use of probability statistical methods. It is difficult to apply when the population is large. It does not ensure the inclusion of all the elements in the list.