eChapters

t is the method of gathering the data by seeing, hearing and then recording the observation on the phenomenon/behavior of the respondent rather than the subjects’ self-reporting of the response.

Interview method: It is a two way interactive (either face-to-face or through multimedia technology), method of data collection wherein the researchers elicit required response from the respondent by presenting stimulus in the form of series of questions pertinent to it.

Interview schedule: It is a data collection tool consisting of properly worded, sequentially arranged questions, tests relevant to objective of research.

Scientific research is always based on observable and measurable facts. This applies to physical as well as humanitarian fields of science. In order to investigate social or psychological phenomenon, most of the times we need quantified information related to the phenomenon under investigation. In order to secure this data, we need one or more method/ means of collecting observable/measurable data.

Quantitative studies are preferred over qualitative studies for their relatively higher objectivity. But many of the times, complexities involved in the occurrence of phenomenon, its causes, and consequences demand for in-depth analysis of a single unit of study. Whenever we want to study a single unit in depth both qualitatively and quantitatively then case study is the best method of research. Case study is a best tool to use, when we are interested in exploring a new phenomenon on which there is very less scientific information available and also when we are interested in formulating new hypothesis for causal study.

Every practical problem need not have a documented solution. Most of the times to solve a practical problem, we need to apply our experience, creativity and experiment with the novel solutions in order to address the problem which have no readymade solution. Under such circumstance, we will apply our knowledge and experience in selecting, planning, experimenting and evaluation of the possible solutions. This type of problem solving approach involves action research.  Action research is a type of social research that aims at gaining an insight into the problem by learning from experiences gained in an effort to solve the problem. Here, action itself is a source of knowledge.

Not all the scientific problems demand the detection and establishment of causal relationship among natural phenomenon. Many of the social research problems seek to study the status quo rather than the relation between two or more natural phenomenon. Few of the impact assessment studies (which are kind of causal relationship studies to an extent) also demand for knowing the status quo before and after implementation of interventions. Many other studies demand for benchmark assessment to make policy decisions. Under such circumstances, survey research is a useful research method.

Sampling is the process of drawing a part of population supposed to be the representative of that population.

Types: Sampling process can be either random or non-random. Most frequently we use random sampling method in our studies. But sometimes the purpose of the study demands for non-random sampling in one or other phase of sampling (Example: When we want to study the impact of life skills development programme on the disabled female beneficiaries)

In previous section we discussed about the ability of the research design to precisely detect the relationship between cause and effect under controlled conditions. But in social research it becomes difficult to conduct the research under highly controlled conditions and moreover many of the researches in social science aim at prediction rather than understanding.

As we discussed earlier, aim of science is development theory, understanding, prediction and explanation of natural phenomenon. In many of the cases we are more interested in predicting the occurrence of social phenomenon rather than explaining it or understanding its relationship with other phenomenon. Under such conditions internal validity is of less use because a relationship that is established in artificially controlled conditions (with high internal validity) may fail to express itself in natural setup due to effect of extraneous variables. When the relationship between cause and effect is weak enough to be expressed in natural setup then it is of less use in predicting the phenomenon. Under such conditions we require experiments that are conducted in nearly natural setup, so that we can generalize the findings of the research to the wider domain.

In previous sections we discussed about the validity and estimation of validity. Now the question is whether the reliable and valid measuring instrument is sufficient condition to ensure the reliable and valid results in actual experiments? What if the measuring instruments are perfectly reliable and valid but the experiment is conducted in less controlled conditions? Can we expect accurate predictions under such conditions? The answer is No.

Reliability aims at assessing the accuracy and consistency of whatsoever the measuring instrument measures irrespective of whether we are measuring what we are supposed to measure or not. Whereas the validity emphasises on the question “are we measuring what we were supposed to measure?”

To clarify the difference between reliability and validity, let us take an example of instruments that measure colour intensity. Assume, an expert develops two separate instruments X and Y to measure the intensity of green and red colour, respectively. But, the commercial manufacturer wrongly tags the red colour measuring instrument as X. When someone uses this wrongly tagged instrument for measuring the green colour intensity, whatever intensity values the instrument yields are reliable but not valid. It is so because the instrument is measuring red colour intensity accurately (hence data is reliable) but it is not measuring what it was supposed to measure –the green colour intensity (hence data is not valid).

In previous section, we discussed about the concept of the reliability and its importance. In this section, we will discuss about different types of reliability and procedures for estimation of the reliability. We already discussed that one cannot measure the reliability but of course it can be estimated.

Reliability deals with the dependency, consistency, stability, predictability and accuracy of the research data. In our day to day life, when we say that a person is reliable then it means his behaviour is predictable, his behaviour (the way he behaved in past, he is behaving now and the way he will behave in future) will be consistent and he is dependable. On other hand an unreliable person’s behaviour is unpredictably variable and he cannot be depended upon.

There can be no scientific studies which do not involve the process of measuring. Measurement process is the integral part of research activity. Measurement can be defined as the assignment of numerals to the objects or events according to set of rules.  The emphasis in this definition is on phrase ‘set of rules’. If the rules can be set up based on some rational or empirical basis, then theoretically anything can be measured. It is very much easy to understand the concept of measurement in case of physical sciences (Example weight, length, mass etc.) but it is harder understand the concept of measurement in case of psychological objects/phenomenon (intelligence, aggressiveness, attitude etc.) without understanding the predetermined rules of measurement. A rule is a guide, command that tells you what to do/how to do.

Variable is any character or attribute of the given object/set of objects which varies from object to object or from one point of time to another point of time. For example height of individuals is a variable; it varies from person to person.  Kerlinger (1973) defined variable as a symbol to which numerals or values are assigned.

Importance of variables: In previous sections, we discussed about formulating the research problem and development of research hypothesis. Variables are the building blocks of any scientific research problem and research hypothesis. When we consider a hypothesis to be relational hypothesis, we mean that it make claims about relationship between two or more variables. When we test hypothesis, as such we do not test the hypothesis, rather we test the significance of hypothesised relationship between two or more variables. Since variables play central role in observation, measurement and testing of relationship, it is of prime importance to understand the meaning of variables, different types and the way we define these variables.

Most of the scientific research studies –except few descriptive survey research studies–will have one or more hypotheses to be tested as a part of study. Hypothesis is a prediction intended to be tested in a research study (Aron, Coups, and Aron, 2012). Hypothesis is the guide which directs the purpose and process of research by indicating the dependent and independent variables to be observed, extraneous variables to be controlled and the active variables to be manipulated.

Whenever we design a scientific study it is designed in a specific contextual background to solve one or another scientific problem. Subsequently it also involves a set of variables/phenomenon operating in a particular context, set of relationships among these variables and the conditions under which they operate. Certainly, to address an unsolved scientific problem we need to carry out research by employing well defined research methodology and have an idea about possible outcomes.  For example when a extension scientist is asked to study the impact of a novel programme, he needs to understand the background and the context under which the programme was introduced, what are the developmental inputs given under the programme, to whom, when (variables), with what purpose (context), what kind of impact he/she is supposed to investigate (presumed relationship), what methods he is going to use to investigate the impact of developmental programme (research methodology) and what are the possible outcomes of the investigation and how programme interventions are related to the outcome (relationship).

Rationally selected and contextually defined research problem is the foundation to the successful research project. If the selected research problem is not in line with the interests of the researcher, available time, resources, infrastructure & knowledge base then achieving success in research process remains highly uncertain. Consequently, in addition to hindering of professional growth of the researcher, it also results in wastage of time, money and human effort. Most importantly, it may sometimes add up incorrect knowledge into scientific base, hindering the process of scientific progress. Hence the identification of the research problem and designing the problem statement is the most important step in the research process.

Term ‘Scientific research’ is too broad to be explained by using one or two definitions. One should go through the different expert views about scientific research in order to internalize the concept of scientific research. In essence a scientific research is characterized by its rigor, testability, replicability, precision, confidence, objectivity, generalizability and parsimony.

Research: Uma Sekaran (2006) defined research as an organized, systematic, data-based, critical, objective, scientific inquiry or investigation into a specific problem, undertaken with the purpose of finding answers or solutions to it.

Abstract

Agricultural produce, such as vegetables, flowers and some fruits capture higher market price if they can be produced ahead or later than the main crop season. The demand of such unseasonal products is particularly high in the fashionable markets of big cities. Good quality of the product is, however, an essential requirement to realise this price advantage. Modern technologies can pave the way to take the full advantage of high price realisation. Growing crops under protected covers is being popularised and the application of various approaches of greenhouse technology can further extend this advantage because it offers greater control over the production environment and timing of crops. There has been a consistent increase in area under greenhouse cultivation nationally and is turning out to be a useful technology for resource efficient agriculture. The types of greenhouse structures range from hi-tech climate controlled systems to very elementary trenches or solar greenhouses used in high altitude cold desert regions of the Himalayas. Greenhouse vegetable production on a commercial scale is a capital and labour intensive production system and requires proper planning for sustainable production. Higher yield, efficient resource use, earliness, off-season availability and superior quality is the hallmark of greenhouse grown vegetables. Greenhouse vegetable production can be done from very basic soil based planting to fully automated hydroponic or soilless systems. This production system requires specialised crops and cultivars which should meet the demand of high value markets.

Abstract

Vegetable crops are inherently rich sources of various health-promoting nutrients and thus they play an important role in balancing our daily diet. Being excellent source of vitamins and minerals, vegetables have a relatively variable nutrient content per unit of energy. These nutrients can be increased by proper selection of vegetables in our diet. Whereas their intrinsic quality maintains human health, the extrinsic quality has significance in determining acceptability by the consumer. Vegetables also have medicinal properties. These diversified and highly nutritive crops are of great importance in alleviating hunger and malnutrition. The present production of vegetable crops is, however, not enough to meet per capita daily dietary requirement as per dietitians’ recommendations. The vegetable production, therefore, need to be almost doubled by 2025 to meet requirement of ever increasing population from diminishing land, water and other natural resources. Although, there has been a trend of increase in production and productivity of vegetable crops since liberalization of seed trade in 1988 by Government of India, yet it has not been commensurate with the requirements. Compared globally, we are far behind in productivity of vegetable crops in spite of being second largest producer after China.