How to Start Research
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OBJECTIVES OF RESEARCH
The research aims to discover answers to questions by applying scientific procedures. The research aims to find the hidden truth that has yet to be discovered. Though each research study has its own specific purpose, we may think of research objectives as falling into many following broad groupings:
1. To gain familiarity with a phenomenon or to achieve new insights into it (studies with this object in view are termed exploratory research studies);
2. To portray accurately the characteristics of a particular individual, situation, or group (studies with this object in view are known as descriptive research studies);
3. To determine the frequency with which something occurs or with which it is associated with something else (studies with this object in view are known as diagnostic research studies);
4. To test a hypothesis of a causal relationship between variables (such studies are known as hypothesis-testing research studies).
MOTIVATION IN RESEARCH
What makes people undertake research?
This is a question of fundamental importance. The possible motives for doing research may be either one or more of the following:
1. The desire to get a research degree along with its consequential benefits;
2. The desire to face the challenge of solving unsolved problems, i.e., concern over practical problems initiates research;
3. The desire to get the intellectual joy of doing some creative work;
4. The desire to be of service to society;
5. The desire to get respectability.
However, this is not an exhaustive list of factors motivating people to undertake research studies. Many more elements, such as government directives, employment conditions, curiosity about new things, desire to understand causal relationships, social thinking and awakening, and the like, may motivate people to perform research.
1. To gain familiarity with a phenomenon or to achieve new insights into it (studies with this object in view are termed exploratory research studies);
2. To portray accurately the characteristics of a particular individual, situation, or group (studies with this object in view are known as descriptive research studies);
3. To determine the frequency with which something occurs or with which it is associated with something else (studies with this object in view are known as diagnostic research studies);
4. To test a hypothesis of a causal relationship between variables (such studies are known as hypothesis-testing research studies).
MOTIVATION IN RESEARCH
What makes people undertake research?
This is a question of fundamental importance. The possible motives for doing research may be either one or more of the following:
1. The desire to get a research degree along with its consequential benefits;
2. The desire to face the challenge of solving unsolved problems, i.e., concern over practical problems initiates research;
3. The desire to get the intellectual joy of doing some creative work;
4. The desire to be of service to society;
5. The desire to get respectability.
However, this is not an exhaustive list of factors motivating people to undertake research studies. Many more elements, such as government directives, employment conditions, curiosity about new things, desire to understand causal relationships, social thinking and awakening, and the like, may motivate people to perform research.
TYPES OF RESEARCH
The basic types of research are as follows:
(i) Descriptive vs. Analytical
Descriptive research includes surveys and fact-finding inquiries of different kinds. The major purpose of descriptive research is to describe the state of affairs as it currently exists. In social science and business research, we often use Ex post facto research for descriptive research studies. The main characteristic of this method is that the researcher has no control over the variables; the researcher can only report what has happened or is happening. Most ex-post facto research projects are used for descriptive studies in which the researcher seeks to measure such items as, for example, the frequency of shopping, preferences of people, or similar data. Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The research methods utilized in descriptive research are survey methods of all kinds, including comparative and correlational methods.
In analytical research, on the other hand, the researcher has to use facts or information already available and analyze these to make a critical evaluation of the material.
(ii) Applied vs. Fundamental
Research can be applied (or action) or fundamental (to basic or pure) research.
Applied research aims to solve an immediate problem facing a society or an industrial/business organization. In contrast, fundamental research mainly concerns generalizations and theory formulation. “Gathering knowledge for knowledge’s sake" is termed ‘pure’ or ‘basic’ research.”
Research concerning some natural phenomenon or relating to pure mathematics are examples of fundamental research. Similarly, research studies concerning human behavior carried on to make generalizations about human behavior are also examples of basic research. Still, research aimed at certain conclusions (say, a solution) facing a concrete social or business problem is an example of applied research. Research to identify social, economic, or political trends that may affect a particular institution or copy research (research to find out whether certain communications will be read and understood) or marketing or evaluation research are examples of applied research. Thus, the central aim of applied research is to discover a solution for some pressing practical problem. In contrast, fundamental research is directed towards finding information with a broad application base and, thus, adds to the already existing organized body of scientific knowledge.
(iii) Quantitative vs. Qualitative
Quantitative research is based on the measurement of quantity or amount. It applies to phenomena that can be expressed in terms of quantity.
Qualitative research, on the other hand, is concerned with the qualitative phenomenon, i.e., phenomena relating to or involving quality or kind. For instance, when we are interested in investigating the reasons for human behavior (i.e., why people think or do certain things), we often talk of ‘Motivation Research’, an essential type of qualitative research. This type of research aims to discover the underlying motives and desires using in-depth interviews. Other techniques of such research are word association tests, sentence completion tests, story completion tests, and similar projective techniques. Attitude or opinion research, i.e., research designed to determine how people feel or think about a particular subject or institution, is also qualitative research. Qualitative research is significant in the behavioral sciences, where the aim is to discover the underlying motives of human behavior. Through such research, we can analyze the factors that motivate people to behave in a particular manner or make people like or dislike a specific thing. It may be stated, however, that applying qualitative research in practice is a relatively difficult job; therefore, while doing such research, one should seek guidance from experimental psychologists.
(iv) Conceptual vs. Empirical
Conceptual research is related to some abstract idea(s) or theory. It is generally used by philosophers and thinkers to develop new concepts or to reinterpret existing ones.
On the other hand, empirical research relies on experience or observation alone, often without due regard for system and theory. It is data-based research, coming up with conclusions that can be verified by observation or experiment. We can also call it an experimental type of research. In such research, it is necessary to get at facts firsthand, at their source, and actively do certain things to stimulate the production of desired information. The researcher must first provide a working hypothesis or guess the probable results of such research. He then works to get enough facts (data) to prove or disprove his hypothesis. He then sets up experimental designs which he thinks will manipulate the persons or the materials concerned to bring forth the desired information. Such research is thus characterized by the experimenter’s control over the variables under study and his deliberate manipulation of one of them to study its effects. Empirical research is appropriate when the proof is sought that certain variables affect other variables somehow. Today, evidence gathered through experiments or empirical studies is considered the most powerful support possible for a given hypothesis.
(v) Some Other Types of Research
All other types of research are variations of one or more of the above-stated approaches based on the purpose of research, the time required to accomplish research, the environment in which research is done, or based on some other similar factor. From the point of view of time, research is either one-time or longitudinal. In the former case, the research is confined to a single time period, whereas in the latter case, the research is carried on over several periods.
Research can be field-setting, laboratory, or simulation research, depending upon the environment in which it is to be carried out.
Research can as well be understood as clinical or diagnostic research. Such research follows case-study methods or in-depth approaches to reach the fundamental causal relations. Such studies usually go deep into the causes of things or events that interest us, using very small samples and very deep probing data-gathering devices.
The research may be exploratory or it may be formalized. The objective of exploratory research is the development of hypotheses rather than their testing, whereas formalized research studies are those with substantial structure and with specific hypotheses to be tested.
Historical research is that which utilizes historical sources like documents, remains, etc., to study events or ideas of the past, including the philosophy of persons and groups at any remote point in time.
Research can also be classified as conclusion-oriented and decision-oriented. While doing conclusion-oriented research, a researcher is free to pick up a problem, redesign the inquiry as he proceeds, and is prepared to conceptualize as he wishes. Decision-oriented research is always for the need of a decision-maker, and the researcher, in this case, is not free to embark upon research according to his own inclination. Operations research is an example of decision-oriented research since it is a scientific method of providing executive departments with a quantitative basis for decisions regarding operations under their control.
(i) Descriptive vs. Analytical
Descriptive research includes surveys and fact-finding inquiries of different kinds. The major purpose of descriptive research is to describe the state of affairs as it currently exists. In social science and business research, we often use Ex post facto research for descriptive research studies. The main characteristic of this method is that the researcher has no control over the variables; the researcher can only report what has happened or is happening. Most ex-post facto research projects are used for descriptive studies in which the researcher seeks to measure such items as, for example, the frequency of shopping, preferences of people, or similar data. Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The research methods utilized in descriptive research are survey methods of all kinds, including comparative and correlational methods.
In analytical research, on the other hand, the researcher has to use facts or information already available and analyze these to make a critical evaluation of the material.
(ii) Applied vs. Fundamental
Research can be applied (or action) or fundamental (to basic or pure) research.
Applied research aims to solve an immediate problem facing a society or an industrial/business organization. In contrast, fundamental research mainly concerns generalizations and theory formulation. “Gathering knowledge for knowledge’s sake" is termed ‘pure’ or ‘basic’ research.”
Research concerning some natural phenomenon or relating to pure mathematics are examples of fundamental research. Similarly, research studies concerning human behavior carried on to make generalizations about human behavior are also examples of basic research. Still, research aimed at certain conclusions (say, a solution) facing a concrete social or business problem is an example of applied research. Research to identify social, economic, or political trends that may affect a particular institution or copy research (research to find out whether certain communications will be read and understood) or marketing or evaluation research are examples of applied research. Thus, the central aim of applied research is to discover a solution for some pressing practical problem. In contrast, fundamental research is directed towards finding information with a broad application base and, thus, adds to the already existing organized body of scientific knowledge.
(iii) Quantitative vs. Qualitative
Quantitative research is based on the measurement of quantity or amount. It applies to phenomena that can be expressed in terms of quantity.
Qualitative research, on the other hand, is concerned with the qualitative phenomenon, i.e., phenomena relating to or involving quality or kind. For instance, when we are interested in investigating the reasons for human behavior (i.e., why people think or do certain things), we often talk of ‘Motivation Research’, an essential type of qualitative research. This type of research aims to discover the underlying motives and desires using in-depth interviews. Other techniques of such research are word association tests, sentence completion tests, story completion tests, and similar projective techniques. Attitude or opinion research, i.e., research designed to determine how people feel or think about a particular subject or institution, is also qualitative research. Qualitative research is significant in the behavioral sciences, where the aim is to discover the underlying motives of human behavior. Through such research, we can analyze the factors that motivate people to behave in a particular manner or make people like or dislike a specific thing. It may be stated, however, that applying qualitative research in practice is a relatively difficult job; therefore, while doing such research, one should seek guidance from experimental psychologists.
(iv) Conceptual vs. Empirical
Conceptual research is related to some abstract idea(s) or theory. It is generally used by philosophers and thinkers to develop new concepts or to reinterpret existing ones.
On the other hand, empirical research relies on experience or observation alone, often without due regard for system and theory. It is data-based research, coming up with conclusions that can be verified by observation or experiment. We can also call it an experimental type of research. In such research, it is necessary to get at facts firsthand, at their source, and actively do certain things to stimulate the production of desired information. The researcher must first provide a working hypothesis or guess the probable results of such research. He then works to get enough facts (data) to prove or disprove his hypothesis. He then sets up experimental designs which he thinks will manipulate the persons or the materials concerned to bring forth the desired information. Such research is thus characterized by the experimenter’s control over the variables under study and his deliberate manipulation of one of them to study its effects. Empirical research is appropriate when the proof is sought that certain variables affect other variables somehow. Today, evidence gathered through experiments or empirical studies is considered the most powerful support possible for a given hypothesis.
(v) Some Other Types of Research
All other types of research are variations of one or more of the above-stated approaches based on the purpose of research, the time required to accomplish research, the environment in which research is done, or based on some other similar factor. From the point of view of time, research is either one-time or longitudinal. In the former case, the research is confined to a single time period, whereas in the latter case, the research is carried on over several periods.
Research can be field-setting, laboratory, or simulation research, depending upon the environment in which it is to be carried out.
Research can as well be understood as clinical or diagnostic research. Such research follows case-study methods or in-depth approaches to reach the fundamental causal relations. Such studies usually go deep into the causes of things or events that interest us, using very small samples and very deep probing data-gathering devices.
The research may be exploratory or it may be formalized. The objective of exploratory research is the development of hypotheses rather than their testing, whereas formalized research studies are those with substantial structure and with specific hypotheses to be tested.
Historical research is that which utilizes historical sources like documents, remains, etc., to study events or ideas of the past, including the philosophy of persons and groups at any remote point in time.
Research can also be classified as conclusion-oriented and decision-oriented. While doing conclusion-oriented research, a researcher is free to pick up a problem, redesign the inquiry as he proceeds, and is prepared to conceptualize as he wishes. Decision-oriented research is always for the need of a decision-maker, and the researcher, in this case, is not free to embark upon research according to his own inclination. Operations research is an example of decision-oriented research since it is a scientific method of providing executive departments with a quantitative basis for decisions regarding operations under their control.
RESEARCH APPROACHES
The above description of the types of research brings to light the fact that there are two basic approaches to research, viz., the quantitative approach and the qualitative approach.
The former involves the generation of data in the quantitative form, which can be subjected to rigorous quantitative analysis in a formal and rigid fashion. This approach can be further sub-classified into inferential, experimental, and simulation approaches to research.
The purpose of the inferential approach to research is to form a database from which to infer characteristics or relationships of the population. This usually means survey research where a sample of the population is studied (questioned or observed) to determine its characteristics, and it is then inferred that the population has the same characteristics.
The experimental approach is characterized by much greater control over the research environment, and in this case, some variables are manipulated to observe their effect on other variables.
The simulation approach involves the construction of an artificial environment within which relevant information and data can be generated. This permits an observation of the dynamic behavior of a system (or its sub-system) under controlled conditions. The term ‘simulation’ in the context of business and social sciences applications refers to the operation of a numerical model that represents the structure of a dynamic process. Given the values of initial conditions, parameters, and exogenous variables, a simulation is run to represent the behavior of the process over time. The simulation approach can also be useful in building models for understanding future conditions.
The qualitative approach to research is concerned with the subjective assessment of attitudes, opinions, and behavior. Research in such a situation is a function of the researcher’s insights and impressions. Such an approach to research generates results either in a non-quantitative form or in a form which are not subjected to rigorous quantitative analysis. Generally, the techniques of focus group interviews, projective techniques, and depth interviews are used. All these are explained at length in the chapters that follow.
The former involves the generation of data in the quantitative form, which can be subjected to rigorous quantitative analysis in a formal and rigid fashion. This approach can be further sub-classified into inferential, experimental, and simulation approaches to research.
The purpose of the inferential approach to research is to form a database from which to infer characteristics or relationships of the population. This usually means survey research where a sample of the population is studied (questioned or observed) to determine its characteristics, and it is then inferred that the population has the same characteristics.
The experimental approach is characterized by much greater control over the research environment, and in this case, some variables are manipulated to observe their effect on other variables.
The simulation approach involves the construction of an artificial environment within which relevant information and data can be generated. This permits an observation of the dynamic behavior of a system (or its sub-system) under controlled conditions. The term ‘simulation’ in the context of business and social sciences applications refers to the operation of a numerical model that represents the structure of a dynamic process. Given the values of initial conditions, parameters, and exogenous variables, a simulation is run to represent the behavior of the process over time. The simulation approach can also be useful in building models for understanding future conditions.
The qualitative approach to research is concerned with the subjective assessment of attitudes, opinions, and behavior. Research in such a situation is a function of the researcher’s insights and impressions. Such an approach to research generates results either in a non-quantitative form or in a form which are not subjected to rigorous quantitative analysis. Generally, the techniques of focus group interviews, projective techniques, and depth interviews are used. All these are explained at length in the chapters that follow.
RESEARCH METHODS VERSUS METHODOLOGY
Research methods are all those methods/techniques that are used for the conduction of research. Research methods or techniques*, thus, refer to the methods the researchers use in performing research operations. In other words, all those methods used by the researcher while studying his research problem are termed research methods. Since the object of research, particularly applied research is to arrive at a solution for a given problem, the available data and the unknown aspects of the problem have to be related to each other to make a solution possible. Keeping this in view, research methods can be put into the following three groups:
1. In the first group, we include those methods which are concerned with the collection of data. These methods will be used where the data already available are not sufficient to arrive at the required solution;
2. The second group consists of those statistical techniques which are used for establishing relationships between the data and the unknowns;
3. The third group consists of methods used to evaluate the accuracy of the results obtained.
Research methods falling in the above-stated last two groups are generally taken as the analytical tools of research.
Research methodology is a way to systematically solve the research problem. It is the science of studying how research is done scientifically. In it, we study the various steps generally adopted by a researcher in studying his research problem and the logic behind them.
The researcher must know the research methods/techniques and the methodology. Researchers not only need to understand how to develop specific indices or tests, how to calculate the mean, the mode, the median, or the standard deviation, and how to apply particular research techniques, but they also need to know which of these methods or techniques, are relevant and which are not, and what would they mean and indicate and why.
Researchers also need to understand the assumptions underlying various techniques and know the criteria by which they can decide that specific techniques and procedures will be applied to certain problems and others will not.
All this means that the researcher must design his methodology for this problem as the same may differ from problem to problem. For example, an architect, who designs a building, has to consciously evaluate the basis of his decisions, i.e., he has to determine why and on what basis he selects a particular size, number, and location of doors, windows, and ventilators, uses specific materials and not others and the like. Similarly, in research, the scientist must expose the research decisions to evaluation before implementation. He has to specify very clearly and precisely what decisions he selects and why he selects them so that they can be evaluated by others also.
From what has been stated above, we can say that research methodology has many dimensions, and research methods do constitute a part of the research methodology. The scope of research methodology is wider than that of research methods. Thus, when we talk about research methodology, we not only talk of the research methods but also consider the logic behind the methods we use in the context of our research study and explain why we are using a particular method or technique and why we are not using others so that research results are capable of being evaluated either by the researcher himself or by others. Why a research study has been undertaken, how the research problem has been defined, in what way and why the hypothesis has been formulated, what data have been collected and what particular method has been adopted, why a particular technique of analyzing data has been used and a host of similar other questions are usually answered when we talk about research methodology concerning a research problem or study.
1. In the first group, we include those methods which are concerned with the collection of data. These methods will be used where the data already available are not sufficient to arrive at the required solution;
2. The second group consists of those statistical techniques which are used for establishing relationships between the data and the unknowns;
3. The third group consists of methods used to evaluate the accuracy of the results obtained.
Research methods falling in the above-stated last two groups are generally taken as the analytical tools of research.
Research methodology is a way to systematically solve the research problem. It is the science of studying how research is done scientifically. In it, we study the various steps generally adopted by a researcher in studying his research problem and the logic behind them.
The researcher must know the research methods/techniques and the methodology. Researchers not only need to understand how to develop specific indices or tests, how to calculate the mean, the mode, the median, or the standard deviation, and how to apply particular research techniques, but they also need to know which of these methods or techniques, are relevant and which are not, and what would they mean and indicate and why.
Researchers also need to understand the assumptions underlying various techniques and know the criteria by which they can decide that specific techniques and procedures will be applied to certain problems and others will not.
All this means that the researcher must design his methodology for this problem as the same may differ from problem to problem. For example, an architect, who designs a building, has to consciously evaluate the basis of his decisions, i.e., he has to determine why and on what basis he selects a particular size, number, and location of doors, windows, and ventilators, uses specific materials and not others and the like. Similarly, in research, the scientist must expose the research decisions to evaluation before implementation. He has to specify very clearly and precisely what decisions he selects and why he selects them so that they can be evaluated by others also.
From what has been stated above, we can say that research methodology has many dimensions, and research methods do constitute a part of the research methodology. The scope of research methodology is wider than that of research methods. Thus, when we talk about research methodology, we not only talk of the research methods but also consider the logic behind the methods we use in the context of our research study and explain why we are using a particular method or technique and why we are not using others so that research results are capable of being evaluated either by the researcher himself or by others. Why a research study has been undertaken, how the research problem has been defined, in what way and why the hypothesis has been formulated, what data have been collected and what particular method has been adopted, why a particular technique of analyzing data has been used and a host of similar other questions are usually answered when we talk about research methodology concerning a research problem or study.
RESEARCH AND SCIENTIFIC METHOD
For a clear perception of the term research, one should know the meaning of the scientific method. Research and the scientific method are closely related.
Research can be termed as an inquiry into the nature of, the reasons for, and the consequences of any particular circumstances, whether experimentally controlled or recorded just as they occur. Further, research implies the researcher is interested in more than specific results; he is interested in the repeatability of the results and their extension to more complicated and general situations. On the other hand, the philosophy common to all research methods and techniques, although they may vary considerably from one science to another, is usually named the scientific method.
The scientific method is the pursuit of truth as determined by logical considerations. The aim of science is to achieve a systematic interrelation of facts. The scientific method attempts to achieve this by experimentation, observation, logical arguments from accepted postulates, and a combination of these three in varying proportions.
In the scientific method, logic aids in formulating propositions explicitly and accurately so that their possible alternatives become clear. Further, logic develops the consequences of such alternatives, and when these are compared with observable phenomena, it becomes possible for the researcher or the scientist to state which alternative is most in harmony with the observed facts. All this is done through experimentation and survey investigations which constitute integral parts of the scientific method. Experimentation is done to test hypotheses and to discover new relationships.
Research can be termed as an inquiry into the nature of, the reasons for, and the consequences of any particular circumstances, whether experimentally controlled or recorded just as they occur. Further, research implies the researcher is interested in more than specific results; he is interested in the repeatability of the results and their extension to more complicated and general situations. On the other hand, the philosophy common to all research methods and techniques, although they may vary considerably from one science to another, is usually named the scientific method.
The scientific method is the pursuit of truth as determined by logical considerations. The aim of science is to achieve a systematic interrelation of facts. The scientific method attempts to achieve this by experimentation, observation, logical arguments from accepted postulates, and a combination of these three in varying proportions.
In the scientific method, logic aids in formulating propositions explicitly and accurately so that their possible alternatives become clear. Further, logic develops the consequences of such alternatives, and when these are compared with observable phenomena, it becomes possible for the researcher or the scientist to state which alternative is most in harmony with the observed facts. All this is done through experimentation and survey investigations which constitute integral parts of the scientific method. Experimentation is done to test hypotheses and to discover new relationships.
RESEARCH PROCESS
The research process consists of a series of actions or steps necessary to effectively carry out research and the desired sequencing of these steps.
Formulating the Research Problem
There are two types of research problems, those which relate to states of nature and those which relate to relationships between variables. At the very outset, the researcher must single out the problem he wants to study, i.e., decide the general area of interest or aspect of a subject matter he would like to inquire into. Initially, the problem may be stated broadly, and then the ambiguities, if any, relating to the issue be resolved. Then, the feasibility of a particular solution has to be considered before a working formulation of the problem can be set up. The formulation of a general topic into a specific research problem, thus, constitutes the first step in a scientific inquiry.
Two steps are involved in formulating the research problem, understanding the problem thoroughly, and rephrasing the same into meaningful terms from an analytical point of view.
The best way to understand the problem is to discuss it with one’s colleagues or those with expertise in the matter. In an academic institution, the researcher can seek the help of a guide who is usually experienced and has several research problems in mind. Often, the guide puts forth the problem in general terms, and it is up to the researcher to narrow it down and phrase it in operational terms. In private business units or governmental organizations, the problem is usually earmarked by the administrative agencies with whom the researcher can discuss how the problem initially came about and what considerations are involved in its possible solutions.
At the same time, the researcher must examine all available literature to get acquainted with the selected problem. He may review two types of literature—the conceptual literature concerning the concepts and theories and the empirical literature consisting of earlier studies similar to the one proposed. The basic outcome of this review will be the knowledge of what data and other materials are available for operational purposes, enabling the researcher to specify his research problem in a meaningful context.
After this, the researcher rephrases the problem into analytical or operational terms, i.e., to put the problem in as specific terms as possible. Formulating or defining a research problem is a step of greatest importance in the entire research process. The issue to be investigated must be defined unambiguously, which will help to discriminate relevant data from irrelevant ones. Care must, however, be taken to verify the objectivity and validity of the background facts concerning the problem.
Professor W.A. Neiswanger states that the statement of the objective is of basic importance because it determines the data which are to be collected, the characteristics of the data which are relevant, the relations which are to be explored, the choice of techniques to be used in these explorations and the form of the final report. If there are specific pertinent terms, the same should be clearly defined along with the task of formulating the problem. In fact, the formulation of the problem often follows a sequential pattern where a number of formulations are set up, each formulation more specific than the preceding one, each phrased in more analytical terms, and each more realistic regarding the available data and resources.
Extensive Literature Survey
Once the problem is formulated, a brief summary of it should be written down. It is compulsory for a research worker writing a thesis for a Ph.D. degree to write a synopsis of the topic and submit it to the necessary Committee or the Research Board for approval. At this juncture, the researcher should conduct an extensive literature survey on the problem. For this purpose, abstracting and indexing journals and published or unpublished bibliographies are the first places to go. Academic journals, conference proceedings, government reports, books, etc., must be tapped depending on the nature of the problem. In this process, it should be remembered that one source will lead to another. The earlier studies, if any, which are similar to the study in hand, should be carefully studied. A sound library will greatly help the researcher at this stage.
Development of Working Hypotheses
After an extensive literature survey, the researcher should clearly state the working hypothesis or hypotheses. The working hypothesis is a tentative assumption made to draw out and test its logical or empirical consequences. As such, how research hypotheses are developed is particularly important since they provide the focal point for research. They also affect how tests must be conducted in the analysis of data and, indirectly, the quality of data required for the analysis. In most types of research, developing a working hypothesis plays an important role. The hypothesis should be very specific and limited to the research because it must be tested. The role of the hypothesis is to guide the researcher by delimiting the area of research and keep him on the right track. It sharpens his thinking and focuses attention on the more critical facets of the problem. It also indicates the type of data required and the type of data analysis methods to be used.
Two steps are involved in formulating the research problem, understanding the problem thoroughly, and rephrasing the same into meaningful terms from an analytical point of view.
The best way to understand the problem is to discuss it with one’s colleagues or those with expertise in the matter. In an academic institution, the researcher can seek the help of a guide who is usually experienced and has several research problems in mind. Often, the guide puts forth the problem in general terms, and it is up to the researcher to narrow it down and phrase it in operational terms. In private business units or governmental organizations, the problem is usually earmarked by the administrative agencies with whom the researcher can discuss how the problem initially came about and what considerations are involved in its possible solutions.
At the same time, the researcher must examine all available literature to get acquainted with the selected problem. He may review two types of literature—the conceptual literature concerning the concepts and theories and the empirical literature consisting of earlier studies similar to the one proposed. The basic outcome of this review will be the knowledge of what data and other materials are available for operational purposes, enabling the researcher to specify his research problem in a meaningful context.
After this, the researcher rephrases the problem into analytical or operational terms, i.e., to put the problem in as specific terms as possible. Formulating or defining a research problem is a step of greatest importance in the entire research process. The issue to be investigated must be defined unambiguously, which will help to discriminate relevant data from irrelevant ones. Care must, however, be taken to verify the objectivity and validity of the background facts concerning the problem.
Professor W.A. Neiswanger states that the statement of the objective is of basic importance because it determines the data which are to be collected, the characteristics of the data which are relevant, the relations which are to be explored, the choice of techniques to be used in these explorations and the form of the final report. If there are specific pertinent terms, the same should be clearly defined along with the task of formulating the problem. In fact, the formulation of the problem often follows a sequential pattern where a number of formulations are set up, each formulation more specific than the preceding one, each phrased in more analytical terms, and each more realistic regarding the available data and resources.
Extensive Literature Survey
Once the problem is formulated, a brief summary of it should be written down. It is compulsory for a research worker writing a thesis for a Ph.D. degree to write a synopsis of the topic and submit it to the necessary Committee or the Research Board for approval. At this juncture, the researcher should conduct an extensive literature survey on the problem. For this purpose, abstracting and indexing journals and published or unpublished bibliographies are the first places to go. Academic journals, conference proceedings, government reports, books, etc., must be tapped depending on the nature of the problem. In this process, it should be remembered that one source will lead to another. The earlier studies, if any, which are similar to the study in hand, should be carefully studied. A sound library will greatly help the researcher at this stage.
Development of Working Hypotheses
After an extensive literature survey, the researcher should clearly state the working hypothesis or hypotheses. The working hypothesis is a tentative assumption made to draw out and test its logical or empirical consequences. As such, how research hypotheses are developed is particularly important since they provide the focal point for research. They also affect how tests must be conducted in the analysis of data and, indirectly, the quality of data required for the analysis. In most types of research, developing a working hypothesis plays an important role. The hypothesis should be very specific and limited to the research because it must be tested. The role of the hypothesis is to guide the researcher by delimiting the area of research and keep him on the right track. It sharpens his thinking and focuses attention on the more critical facets of the problem. It also indicates the type of data required and the type of data analysis methods to be used.
How does one go about developing working hypotheses? The answer is by using the following approaches:
(a) Discussions with colleagues and experts about the problem, its origin, and the objectives in seeking a solution;
(b) Examination of data and records, if available, concerning the problem of possible trends, peculiarities, and other clues;
(c) Review of similar studies in the area or of the studies on similar problems; and
(d) The exploratory personal investigation involves original field interviews on a limited scale with interested parties and individuals with a view to secure greater insight into the practical aspects of the problem. Thus, working hypotheses arise as a result of prior thinking about the subject, examination of the available data and material, including related studies, and the counsel of experts and interested parties. Working hypotheses are more useful when stated in precise and clearly defined terms. It may be remembered that occasionally we encounter a problem where we do not need working hypotheses, especially in the case of exploratory research, which does not aim at testing the hypothesis. But as a general rule, the specification of working hypotheses is the next primary step of the research process in solving most research problems.
Preparing the Research Design
The research problem having been formulated in clear-cut terms, the researcher will be required to prepare a research design, i.e., he will have to state the conceptual structure within which research would be conducted. The preparation of such a design facilitates research to be as efficient as possible, yielding maximal information. In other words, the function of research design is to provide for the collection of relevant evidence with minimal expenditure of effort, time, and money. But how all these can be achieved depends mainly on the research purpose. Research purposes may be grouped into four categories, viz., (i) Exploration, (ii) Description, (iii) Diagnosis, and (iv) Experimentation. A flexible research design that provides an opportunity for considering many different aspects of a problem is appropriate if the purpose of the research study is that of exploration. But when the purpose is an accurate description of a situation or of an association between variables, the suitable design will be one that minimizes bias and maximizes the reliability of the data collected and analyzed.
There are several research designs, such as experimental and non-experimental hypothesis testing. Experimental designs can be either informal designs (such as before-and-after without control, after-only with control, or before-and-after with control) or formal designs (such as completely randomized design, randomized block design, Latin square design, simple and complex factorial designs), out of which the researcher must select one for his own project.
The preparation of the research design appropriate for a particular research problem usually involves the consideration of the following: (i) the means of obtaining the information; (ii) the availability and skills of the researcher and his staff (if any); (iii) explanation of the way in which selected means of obtaining information will be organized, and the reasoning leading to the selection; (iv) the time available for research; and (v) the cost factor relating to research, i.e., the finance available for the purpose.
Determining Sample Design
All the items under consideration in any field of inquiry constitute a ‘universe’ or ‘population.’ A complete enumeration of all the items in the ‘population’ is known as a census inquiry. It can be presumed that in such an inquiry, no element of chance is left when all the items are covered, and the highest accuracy is obtained. But in practice, this may not be true. Even the slightest element of bias in such an inquiry will get larger and larger as the number of observations increases. Moreover, there is no way of checking the element of bias or its extent except through a resurvey or the use of sample checks. Besides, this inquiry involves much time, money, and energy. Moreover, census inquiry is not possible in practice under many circumstances. For instance, blood testing is done only on a sample basis. Hence, we often select only a few items from the universe for our study purposes. The items so selected constitute what is technically called a sample. The researcher must decide how to establish a sample or what is popularly known as the sample design. In other words, a sample design is a definite plan determined before any data are collected to obtain a sample from a given population. Thus, the plan to select 12 of a city’s 200 drugstores in a certain way constitutes a sample design. Samples can be either probability samples or non-probability samples. With probability samples, each element has a known probability of being included in the sample, but the non-probability samples do not allow the researcher to determine this probability. Probability samples are based on simple random, systematic, stratified, and cluster/area sampling. In contrast, non-probability samples are based on convenience, judgment, and quota sampling techniques.
A brief mention of the important sample designs is as follows:
(i) Deliberate sampling:
Deliberate sampling is also known as purposive or non-probability sampling. This sampling method involves the purposive or intentional selection of particular units of the universe for constituting a sample that represents the universe. When population elements are selected for inclusion in the sample based on ease of access, it can be called convenience sampling. If a researcher wishes to secure data from gasoline buyers, he may select a fixed number of petrol stations and conduct interviews at these stations. This would be an example of a convenience sample of gasoline buyers. Such a procedure may sometimes give very biased results, particularly when the population is not homogeneous. On the other hand, in judgment sampling, the researcher’s judgment is used for selecting items that he considers representative of the population. For example, a judgment sample of college students might be taken to secure reactions to a new teaching method. Judgment sampling is used quite frequently in qualitative research where the desire happens to be to develop hypotheses rather than to generalize to larger populations.
(ii) Simple random sampling:
This type of sampling is also known as chance sampling or probability sampling, where each and every item in the population has an equal chance of inclusion in the sample, and each one of the possible samples, in the case of a finite universe, has the same probability of being selected. For example, if we have to select a sample of 300 items from a universe of 15,000 items, we can put the names or numbers of all the 15,000 items on slips of paper and conduct a lottery. Using the random number tables is another method of random sampling. Each item is assigned a number from 1 to 15,000 to select the sample. Then, 300 five-digit random numbers are selected from the table. To do this, we select some random starting point, and then a systematic pattern is used in proceeding through the table. We might start in the 4th row, second column, proceed down the column to the bottom of the table and then move to the top of the next column to the right. When a number exceeds the limit of the numbers in the frame, in our case over 15,000, it is simply passed over, and the following number selected does fall within the relevant range. Since the numbers were placed in the table completely randomly, the resulting sample is random. This procedure gives each item an equal probability of being selected. In the case of an infinite population, the selection of each item in a random sample is controlled by the same probability, and successive selections are independent of one another.
(iii) Systematic sampling:
Sometimes, the most practical way of sampling is to select every 15th name on a list, every 10th house on one side of a street, and so on. Sampling of this type is known as systematic sampling. Randomness is usually introduced into this kind of sampling by using random numbers to pick up the unit with which to start. This procedure is applicable when a sampling frame is available as a list. In such a design, the selection process begins by picking some random point in the list, and then every nth element is selected until the desired number is secured.
(iv) Stratified sampling:
If the population from which a sample is drawn does not constitute a homogeneous group, then the stratified sampling technique is applied to obtain a representative sample. This technique stratifies the population into many non-overlapping subpopulations or strata, and sample items are selected from each stratum. If the items selected from each stratum are based on simple random sampling, the entire procedure, first stratification and then simple random sampling, is known as stratified random sampling.
(v) Quota sampling:
In stratified sampling, the cost of taking random samples from individual strata is often so expensive that interviewers are simply given a quota to be filled from different strata, the actual selection of items being left to the interviewer’s judgment. This is called quota sampling. The quota size for each stratum is generally proportionate to the size of that stratum in the population. Quota sampling is thus an essential form of non-probability sampling. Quota samples generally happen to be judgment samples rather than random samples.
(vi) Cluster sampling and area sampling:
Cluster sampling involves grouping the population and selecting the groups or the clusters rather than individual elements for inclusion in the sample. Suppose some departmental store wishes to sample its credit card holders. It has issued its cards to 15,000 customers. The sample size is to be kept, say, 450. For cluster sampling, this list of 15,000 cardholders could be formed into 100 clusters of 150 cardholders each. Three clusters might then be selected for the sample randomly. The sample size must often be larger than the simple random sample to ensure the same level of accuracy because cluster sampling procedural potential for order bias and other sources of error is usually accentuated. The clustering approach can, however, make the sampling procedure relatively easier and increase fieldwork efficiency, especially in personal interviews.
Area sampling is quite close to cluster sampling and is often talked about when the total geographical area of interest is big. Under area sampling, we first divide the total area into smaller, non-overlapping areas, generally called geographical clusters. A number of these smaller areas are randomly selected, and all units in these small areas are included in the sample. Area sampling is beneficial when we do not have the list of the population concerned. It also makes field interviewing more efficient since the interviewer can do many interviews at each location.
(vii) Multi-stage sampling:
This is a further development of the idea of cluster sampling. This technique is meant for big inquiries extending to a considerably large geographical area like an entire country. Under multi-stage sampling, the first stage may be to select large primary sampling units such as states, districts, towns, and certain families within cities. If the random sampling technique is applied at all stages, the sampling procedure is described as multi-stage random sampling.
(viii) Sequential sampling:
This is a complex sample design where the ultimate size of the sample is not fixed in advance but is determined according to mathematical decisions based on information yielded as the survey progresses. This design is usually adopted under the acceptance sampling plan in the context of statistical quality control.
In practice, several of the methods of sampling described above may well be used in the same study, in which case it can be called mixed sampling. One should usually resort to random sampling to eliminate bias and estimate sampling error. But purposive sampling is considered desirable when the universe happens to be small and a known characteristic of it is to be studied intensively. Also, there are conditions under which sample designs other than random sampling may be considered better for convenience and low costs. The researcher must decide the sample design to be used, taking into consideration the nature of the inquiry and other related factors.
COLLECTING DATA
In dealing with any real-life problem, it is often found that the data at hand need to be revised; hence, it becomes necessary to collect appropriate data. Several ways of managing the appropriate data differ considerably regarding money costs, time, and other resources at the researcher's disposal. Primary data can be collected either through experiments or through surveys. If the researcher conducts an experiment, he observes some quantitative measurements, or the data, with the help of which he examines the truth in his hypothesis. But in the case of a survey, data can be collected in any one or more of the following ways:
(i) By observation: This method implies collecting information through the investigator’s observation without interviewing the respondents. The information obtained relates to what is currently happening and is not complicated by either the past behavior or future intentions or attitudes of respondents. This method is no doubt expensive, and the information provided by this method is also minimal. As such, this method is not suitable for inquiries where large samples are concerned.
(ii) Through personal interview: The investigator follows a rigid procedure and seeks answers to a set of pre-conceived questions through personal interviews. This method of collecting data is usually carried out in a structured way where output depends upon the interviewer's ability to a large extent.
(iii) Through telephone interviews: This method of collecting information involves contacting the respondents on the telephone. This is not a very widely used method. Still, it plays a vital role in industrial surveys in developed regions, particularly when the survey has to be completed minimally.
(iv) By mailing questionnaires: The researcher and the respondents do come in contact with each other if this survey method is adopted. Questionnaires are mailed to the respondents with a request to return after completing the same. It is the most extensively used method in various economic and business surveys. Before applying this method, a Pilot Study for testing the questionnaire is usually conducted, which reveals the weaknesses, if any, of the questionnaire. The questionnaire to be used must be prepared very carefully so that it may prove to be effective in collecting the relevant information.
(v) Through schedules: Under this method, the enumerators are appointed and given training. They are provided with schedules containing relevant questions. These enumerators go to respondents with these schedules. Data are collected by filling up the schedules by enumerators based on replies given by respondents. Much depends upon the capability of enumerators so far as this method is concerned. Some occasional field checks on the enumerators' work may ensure sincere work. The researcher should select one of these methods of collecting the data considering the nature of the investigation, the objective and scope of the inquiry, financial resources, available time, and the desired degree of accuracy. Though he should pay attention to all these factors, much depends upon the ability and experience of the researcher.
EXECUTION OF THE PROJECT
Execution of the project is a significant step in the research process. If the execution of the project proceeds on the correct lines, the data to be collected will be adequate and dependable. The researcher should see that the project is executed systematically and on time. Data can be readily machine-processed if the survey is to be conducted using structured questionnaires. In such a situation, questions, as well as possible answers, may be coded. If the data are to be collected through interviewers, arrangements should be made for proper selection and training of the interviewers.
(a) Discussions with colleagues and experts about the problem, its origin, and the objectives in seeking a solution;
(b) Examination of data and records, if available, concerning the problem of possible trends, peculiarities, and other clues;
(c) Review of similar studies in the area or of the studies on similar problems; and
(d) The exploratory personal investigation involves original field interviews on a limited scale with interested parties and individuals with a view to secure greater insight into the practical aspects of the problem. Thus, working hypotheses arise as a result of prior thinking about the subject, examination of the available data and material, including related studies, and the counsel of experts and interested parties. Working hypotheses are more useful when stated in precise and clearly defined terms. It may be remembered that occasionally we encounter a problem where we do not need working hypotheses, especially in the case of exploratory research, which does not aim at testing the hypothesis. But as a general rule, the specification of working hypotheses is the next primary step of the research process in solving most research problems.
Preparing the Research Design
The research problem having been formulated in clear-cut terms, the researcher will be required to prepare a research design, i.e., he will have to state the conceptual structure within which research would be conducted. The preparation of such a design facilitates research to be as efficient as possible, yielding maximal information. In other words, the function of research design is to provide for the collection of relevant evidence with minimal expenditure of effort, time, and money. But how all these can be achieved depends mainly on the research purpose. Research purposes may be grouped into four categories, viz., (i) Exploration, (ii) Description, (iii) Diagnosis, and (iv) Experimentation. A flexible research design that provides an opportunity for considering many different aspects of a problem is appropriate if the purpose of the research study is that of exploration. But when the purpose is an accurate description of a situation or of an association between variables, the suitable design will be one that minimizes bias and maximizes the reliability of the data collected and analyzed.
There are several research designs, such as experimental and non-experimental hypothesis testing. Experimental designs can be either informal designs (such as before-and-after without control, after-only with control, or before-and-after with control) or formal designs (such as completely randomized design, randomized block design, Latin square design, simple and complex factorial designs), out of which the researcher must select one for his own project.
The preparation of the research design appropriate for a particular research problem usually involves the consideration of the following: (i) the means of obtaining the information; (ii) the availability and skills of the researcher and his staff (if any); (iii) explanation of the way in which selected means of obtaining information will be organized, and the reasoning leading to the selection; (iv) the time available for research; and (v) the cost factor relating to research, i.e., the finance available for the purpose.
Determining Sample Design
All the items under consideration in any field of inquiry constitute a ‘universe’ or ‘population.’ A complete enumeration of all the items in the ‘population’ is known as a census inquiry. It can be presumed that in such an inquiry, no element of chance is left when all the items are covered, and the highest accuracy is obtained. But in practice, this may not be true. Even the slightest element of bias in such an inquiry will get larger and larger as the number of observations increases. Moreover, there is no way of checking the element of bias or its extent except through a resurvey or the use of sample checks. Besides, this inquiry involves much time, money, and energy. Moreover, census inquiry is not possible in practice under many circumstances. For instance, blood testing is done only on a sample basis. Hence, we often select only a few items from the universe for our study purposes. The items so selected constitute what is technically called a sample. The researcher must decide how to establish a sample or what is popularly known as the sample design. In other words, a sample design is a definite plan determined before any data are collected to obtain a sample from a given population. Thus, the plan to select 12 of a city’s 200 drugstores in a certain way constitutes a sample design. Samples can be either probability samples or non-probability samples. With probability samples, each element has a known probability of being included in the sample, but the non-probability samples do not allow the researcher to determine this probability. Probability samples are based on simple random, systematic, stratified, and cluster/area sampling. In contrast, non-probability samples are based on convenience, judgment, and quota sampling techniques.
A brief mention of the important sample designs is as follows:
(i) Deliberate sampling:
Deliberate sampling is also known as purposive or non-probability sampling. This sampling method involves the purposive or intentional selection of particular units of the universe for constituting a sample that represents the universe. When population elements are selected for inclusion in the sample based on ease of access, it can be called convenience sampling. If a researcher wishes to secure data from gasoline buyers, he may select a fixed number of petrol stations and conduct interviews at these stations. This would be an example of a convenience sample of gasoline buyers. Such a procedure may sometimes give very biased results, particularly when the population is not homogeneous. On the other hand, in judgment sampling, the researcher’s judgment is used for selecting items that he considers representative of the population. For example, a judgment sample of college students might be taken to secure reactions to a new teaching method. Judgment sampling is used quite frequently in qualitative research where the desire happens to be to develop hypotheses rather than to generalize to larger populations.
(ii) Simple random sampling:
This type of sampling is also known as chance sampling or probability sampling, where each and every item in the population has an equal chance of inclusion in the sample, and each one of the possible samples, in the case of a finite universe, has the same probability of being selected. For example, if we have to select a sample of 300 items from a universe of 15,000 items, we can put the names or numbers of all the 15,000 items on slips of paper and conduct a lottery. Using the random number tables is another method of random sampling. Each item is assigned a number from 1 to 15,000 to select the sample. Then, 300 five-digit random numbers are selected from the table. To do this, we select some random starting point, and then a systematic pattern is used in proceeding through the table. We might start in the 4th row, second column, proceed down the column to the bottom of the table and then move to the top of the next column to the right. When a number exceeds the limit of the numbers in the frame, in our case over 15,000, it is simply passed over, and the following number selected does fall within the relevant range. Since the numbers were placed in the table completely randomly, the resulting sample is random. This procedure gives each item an equal probability of being selected. In the case of an infinite population, the selection of each item in a random sample is controlled by the same probability, and successive selections are independent of one another.
(iii) Systematic sampling:
Sometimes, the most practical way of sampling is to select every 15th name on a list, every 10th house on one side of a street, and so on. Sampling of this type is known as systematic sampling. Randomness is usually introduced into this kind of sampling by using random numbers to pick up the unit with which to start. This procedure is applicable when a sampling frame is available as a list. In such a design, the selection process begins by picking some random point in the list, and then every nth element is selected until the desired number is secured.
(iv) Stratified sampling:
If the population from which a sample is drawn does not constitute a homogeneous group, then the stratified sampling technique is applied to obtain a representative sample. This technique stratifies the population into many non-overlapping subpopulations or strata, and sample items are selected from each stratum. If the items selected from each stratum are based on simple random sampling, the entire procedure, first stratification and then simple random sampling, is known as stratified random sampling.
(v) Quota sampling:
In stratified sampling, the cost of taking random samples from individual strata is often so expensive that interviewers are simply given a quota to be filled from different strata, the actual selection of items being left to the interviewer’s judgment. This is called quota sampling. The quota size for each stratum is generally proportionate to the size of that stratum in the population. Quota sampling is thus an essential form of non-probability sampling. Quota samples generally happen to be judgment samples rather than random samples.
(vi) Cluster sampling and area sampling:
Cluster sampling involves grouping the population and selecting the groups or the clusters rather than individual elements for inclusion in the sample. Suppose some departmental store wishes to sample its credit card holders. It has issued its cards to 15,000 customers. The sample size is to be kept, say, 450. For cluster sampling, this list of 15,000 cardholders could be formed into 100 clusters of 150 cardholders each. Three clusters might then be selected for the sample randomly. The sample size must often be larger than the simple random sample to ensure the same level of accuracy because cluster sampling procedural potential for order bias and other sources of error is usually accentuated. The clustering approach can, however, make the sampling procedure relatively easier and increase fieldwork efficiency, especially in personal interviews.
Area sampling is quite close to cluster sampling and is often talked about when the total geographical area of interest is big. Under area sampling, we first divide the total area into smaller, non-overlapping areas, generally called geographical clusters. A number of these smaller areas are randomly selected, and all units in these small areas are included in the sample. Area sampling is beneficial when we do not have the list of the population concerned. It also makes field interviewing more efficient since the interviewer can do many interviews at each location.
(vii) Multi-stage sampling:
This is a further development of the idea of cluster sampling. This technique is meant for big inquiries extending to a considerably large geographical area like an entire country. Under multi-stage sampling, the first stage may be to select large primary sampling units such as states, districts, towns, and certain families within cities. If the random sampling technique is applied at all stages, the sampling procedure is described as multi-stage random sampling.
(viii) Sequential sampling:
This is a complex sample design where the ultimate size of the sample is not fixed in advance but is determined according to mathematical decisions based on information yielded as the survey progresses. This design is usually adopted under the acceptance sampling plan in the context of statistical quality control.
In practice, several of the methods of sampling described above may well be used in the same study, in which case it can be called mixed sampling. One should usually resort to random sampling to eliminate bias and estimate sampling error. But purposive sampling is considered desirable when the universe happens to be small and a known characteristic of it is to be studied intensively. Also, there are conditions under which sample designs other than random sampling may be considered better for convenience and low costs. The researcher must decide the sample design to be used, taking into consideration the nature of the inquiry and other related factors.
COLLECTING DATA
In dealing with any real-life problem, it is often found that the data at hand need to be revised; hence, it becomes necessary to collect appropriate data. Several ways of managing the appropriate data differ considerably regarding money costs, time, and other resources at the researcher's disposal. Primary data can be collected either through experiments or through surveys. If the researcher conducts an experiment, he observes some quantitative measurements, or the data, with the help of which he examines the truth in his hypothesis. But in the case of a survey, data can be collected in any one or more of the following ways:
(i) By observation: This method implies collecting information through the investigator’s observation without interviewing the respondents. The information obtained relates to what is currently happening and is not complicated by either the past behavior or future intentions or attitudes of respondents. This method is no doubt expensive, and the information provided by this method is also minimal. As such, this method is not suitable for inquiries where large samples are concerned.
(ii) Through personal interview: The investigator follows a rigid procedure and seeks answers to a set of pre-conceived questions through personal interviews. This method of collecting data is usually carried out in a structured way where output depends upon the interviewer's ability to a large extent.
(iii) Through telephone interviews: This method of collecting information involves contacting the respondents on the telephone. This is not a very widely used method. Still, it plays a vital role in industrial surveys in developed regions, particularly when the survey has to be completed minimally.
(iv) By mailing questionnaires: The researcher and the respondents do come in contact with each other if this survey method is adopted. Questionnaires are mailed to the respondents with a request to return after completing the same. It is the most extensively used method in various economic and business surveys. Before applying this method, a Pilot Study for testing the questionnaire is usually conducted, which reveals the weaknesses, if any, of the questionnaire. The questionnaire to be used must be prepared very carefully so that it may prove to be effective in collecting the relevant information.
(v) Through schedules: Under this method, the enumerators are appointed and given training. They are provided with schedules containing relevant questions. These enumerators go to respondents with these schedules. Data are collected by filling up the schedules by enumerators based on replies given by respondents. Much depends upon the capability of enumerators so far as this method is concerned. Some occasional field checks on the enumerators' work may ensure sincere work. The researcher should select one of these methods of collecting the data considering the nature of the investigation, the objective and scope of the inquiry, financial resources, available time, and the desired degree of accuracy. Though he should pay attention to all these factors, much depends upon the ability and experience of the researcher.
EXECUTION OF THE PROJECT
Execution of the project is a significant step in the research process. If the execution of the project proceeds on the correct lines, the data to be collected will be adequate and dependable. The researcher should see that the project is executed systematically and on time. Data can be readily machine-processed if the survey is to be conducted using structured questionnaires. In such a situation, questions, as well as possible answers, may be coded. If the data are to be collected through interviewers, arrangements should be made for proper selection and training of the interviewers.
The training may be given with the help of instruction manuals which clearly explain the interviewers' job at each step. Occasional field checks should ensure that the interviewers are doing their assigned job sincerely and efficiently. Careful watch should be kept for unanticipated factors to make the survey as realistic as possible. This means that steps should be taken to ensure that the study is under statistical control so that the collected information is per the pre-defined standard of accuracy. If some respondents do not cooperate, suitable methods should be designed to tackle this problem. One way of dealing with the non-response problem is to make a list of the non-respondents and take a small sub-sample of them, and then with the help of experts, vigorous efforts can be made to secure the response.
ANALYSIS OF DATA
After the data have been collected, the researcher turns to analyzing them. Data analysis requires many closely related operations, such as the establishment of categories and the application of these categories to raw data through coding, tabulation, and then drawing statistical inferences. The unwieldy data should be condensed into manageable groups and tables for further analysis. Thus, the researcher should classify the raw data into purposeful and usable categories. A coding operation is usually done at this stage, through which the data types are transformed into symbols that may be tabulated and counted. Editing is the procedure that improves the quality of the data for coding. With coding, the stage is ready for tabulation. Tabulation is a part of the technical procedure wherein the classified data are put in tables. The mechanical devices can be made use of at this juncture. A great deal of data, especially in significant inquiries, is tabulated by computers. Computers save time and make it possible to study many variables affecting a problem simultaneously. Analysis work after tabulation is generally based on the computation of various percentages, coefficients, etc., by applying multiple well-defined statistical formulae. In the analysis process, relationships or differences supporting or conflicting with original or new hypotheses should be subjected to tests of significance to determine with what validity data can indicate any conclusion(s). For instance, if there are two samples of weekly wages, each piece drawn from factories in different parts of the same city, giving two different mean values, our problem may be whether the two mean values are significantly different or the difference is just a matter of chance. Through the use of statistical tests, we can establish whether such a difference is a real one or is the result of random fluctuations. If the difference is accurate, the inference will be that the two samples come from different universes. If the difference is due to chance, the conclusion would be that the two samples belong to the same universe. Similarly, the variance analysis technique can help us analyze whether three or more varieties of seeds grown on specific fields yield significantly different results. In brief, the researcher can analyze the collected data with the help of various statistical measures.
Hypothesis-testing
After analyzing the data as stated above, the researcher can test the hypotheses, if any, he had formulated earlier. Do the facts support the hypotheses, or are they contrary? This is the usual question that should be answered while testing hypotheses. For this purpose, various tests, such as the Chi-square test, t-test, and F-test, have been developed by statisticians. The hypotheses may be tested using one or more such tests, depending on the nature and object of the research inquiry. Hypothesis testing will result in either accepting the hypothesis or rejecting it. If the researcher had no hypotheses, generalizations based on data may be stated as hypotheses to be tested.
Generalizations and Interpretation
If a hypothesis is tested and upheld several times, it may be possible for the researcher to arrive at a generalization, i.e., to build a theory. As a matter of fact, the real value of research lies in its ability to reach certain generalizations. If the researcher had no hypothesis, he might seek to explain his findings based on some theory. It is known as interpretation. The process of interpretation often triggers off new questions, which in turn may lead to further research.
PREPARATION OF THE REPORT
Writing of report must be done with great care keeping in view the following:
1. The layout of the report should be as follows:
(i) the preliminary pages;
(ii) the main text, and
(iii) the end matter.
The report should carry the title and date in its preliminary pages, followed by acknowledgment and forward. Then there should be a table of contents followed by a list of tables, graphs, and charts, if any, given in the report.
The main text of the report should have the following parts:
(a) Introduction: It should clearly state the research objective and an explanation of the methodology adopted in accomplishing the research. The study's scope and various limitations should be stated in this part.
(b) Summary of findings: After the introduction, a statement of findings and recommendations appear in non-technical language. If the results are extensive, they should be summarised.
(c) Main report: The report's main body should be presented logically and broken down into readily identifiable sections.
(d) Conclusion: Towards the end of the main text, the researcher should again put down the results of his research clearly and precisely. In fact, it is the final summing up. At the end of the report, appendices should be enlisted for all technical data.
(e) Bibliography, i.e., list of books, journals, reports, etc., consulted, should also be given at the end. An index should also be provided, especially in a published research report.
ANALYSIS OF DATA
After the data have been collected, the researcher turns to analyzing them. Data analysis requires many closely related operations, such as the establishment of categories and the application of these categories to raw data through coding, tabulation, and then drawing statistical inferences. The unwieldy data should be condensed into manageable groups and tables for further analysis. Thus, the researcher should classify the raw data into purposeful and usable categories. A coding operation is usually done at this stage, through which the data types are transformed into symbols that may be tabulated and counted. Editing is the procedure that improves the quality of the data for coding. With coding, the stage is ready for tabulation. Tabulation is a part of the technical procedure wherein the classified data are put in tables. The mechanical devices can be made use of at this juncture. A great deal of data, especially in significant inquiries, is tabulated by computers. Computers save time and make it possible to study many variables affecting a problem simultaneously. Analysis work after tabulation is generally based on the computation of various percentages, coefficients, etc., by applying multiple well-defined statistical formulae. In the analysis process, relationships or differences supporting or conflicting with original or new hypotheses should be subjected to tests of significance to determine with what validity data can indicate any conclusion(s). For instance, if there are two samples of weekly wages, each piece drawn from factories in different parts of the same city, giving two different mean values, our problem may be whether the two mean values are significantly different or the difference is just a matter of chance. Through the use of statistical tests, we can establish whether such a difference is a real one or is the result of random fluctuations. If the difference is accurate, the inference will be that the two samples come from different universes. If the difference is due to chance, the conclusion would be that the two samples belong to the same universe. Similarly, the variance analysis technique can help us analyze whether three or more varieties of seeds grown on specific fields yield significantly different results. In brief, the researcher can analyze the collected data with the help of various statistical measures.
Hypothesis-testing
After analyzing the data as stated above, the researcher can test the hypotheses, if any, he had formulated earlier. Do the facts support the hypotheses, or are they contrary? This is the usual question that should be answered while testing hypotheses. For this purpose, various tests, such as the Chi-square test, t-test, and F-test, have been developed by statisticians. The hypotheses may be tested using one or more such tests, depending on the nature and object of the research inquiry. Hypothesis testing will result in either accepting the hypothesis or rejecting it. If the researcher had no hypotheses, generalizations based on data may be stated as hypotheses to be tested.
Generalizations and Interpretation
If a hypothesis is tested and upheld several times, it may be possible for the researcher to arrive at a generalization, i.e., to build a theory. As a matter of fact, the real value of research lies in its ability to reach certain generalizations. If the researcher had no hypothesis, he might seek to explain his findings based on some theory. It is known as interpretation. The process of interpretation often triggers off new questions, which in turn may lead to further research.
PREPARATION OF THE REPORT
Writing of report must be done with great care keeping in view the following:
1. The layout of the report should be as follows:
(i) the preliminary pages;
(ii) the main text, and
(iii) the end matter.
The report should carry the title and date in its preliminary pages, followed by acknowledgment and forward. Then there should be a table of contents followed by a list of tables, graphs, and charts, if any, given in the report.
The main text of the report should have the following parts:
(a) Introduction: It should clearly state the research objective and an explanation of the methodology adopted in accomplishing the research. The study's scope and various limitations should be stated in this part.
(b) Summary of findings: After the introduction, a statement of findings and recommendations appear in non-technical language. If the results are extensive, they should be summarised.
(c) Main report: The report's main body should be presented logically and broken down into readily identifiable sections.
(d) Conclusion: Towards the end of the main text, the researcher should again put down the results of his research clearly and precisely. In fact, it is the final summing up. At the end of the report, appendices should be enlisted for all technical data.
(e) Bibliography, i.e., list of books, journals, reports, etc., consulted, should also be given at the end. An index should also be provided, especially in a published research report.
2. The report should be written concisely and objectively in simple language, avoiding vague expressions such as ‘it seems,’ ‘there may be,’ and the like. Charts and illustrations in the main report should be used only if they present the information more clearly and forcibly. Calculated ‘confidence limits’ must be mentioned, and the various constraints experienced in conducting research operations may also be stated.
QUALITIES OF GOOD RESEARCH
1. Good research is systematic: It means that research is structured with specified steps to be taken in a specified sequence following a well-defined set of rules. The systematic characterization of the study does not rule out creative thinking, but it certainly does reject the use of guessing and intuition in arriving at conclusions.
2. Good research is logical: This implies that research is guided by the rules of logical reasoning, and the logical process of induction and deduction is of great value in carrying out research. Induction is the process of reasoning from a part to the whole, whereas deduction is the process of reasoning from some premise to a conclusion that follows from that premise. In fact, logical reasoning makes research more meaningful in decision-making.
3. Good research is empirical: Research is related to one or more aspects of an actual situation and deals with concrete data that provides a basis for external validity to research results.
4. Good research is replicable: This characteristic allows research results to be verified by replicating the study, thereby building a sound basis for decisions.
4. Good research is replicable: This characteristic allows research results to be verified by replicating the study, thereby building a sound basis for decisions.
