This article throws light upon the ten main steps involved in the research process. The steps are: 1. Formulating the Research Problem 2. Extensive Literature Survey 3. Development of Working Hypotheses 4. Preparing the Research Design 5. Determining Sample Design 6. Collecting the Data 7. Execution of the Project 8. Analysis of Data 9. Hypothesis-Testing 10. Generalisations and Interpretation.
Step # 1. Formulating the Research Problem:
These are two types of research problems, viz., 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., he must decide the general area of interest or aspect of a subject-matter that he would like to inquire into. Initially the problem may be stated in a broad general way and then the ambiguities, if any, relating to the problem be resolved.
The formulation of a general topic into a specific research problem, thus, constitutes the first step in a scientific enquiry. Essentially two steps are involved in formulating the research problem, viz., understanding the problem thoroughly, and rephrasing the same into meaningful terms from an analytical point of view.
The best way of understanding the problem is to discuss it with one’s own colleagues or with those having some expertise in the matter. In an academic institution the researcher can seek the help from a guide who is usually an experienced man 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 the problem in operational terms.
In private business units or in governmental organisations, the problem is usually remarked by the administrative agencies with which the researcher can discuss how the problem originally came about and what considerations are involved in it, possible solutions.
Step # 2. 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.
For this purpose, the abstracting and indexing journals and published or unpublished bibliographies are the first place to go to. 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 good library will be a great help to the researcher at this stage.
Step # 3. Development of Working Hypotheses:
After extensive literature survey, researcher should state in clear terms the working hypothesis or hypotheses. Working hypothesis- is tentative assumption made in order to draw out and test its logical or empirical sequences. As such the manner which research hypotheses are developed is particularly important since they provide the focal point for research.
They also affect the manner in which contest must be conducted in the analysis of data and indirectly the quality of data which is required for the analysis. In most types of research, the development of working hypothesis plays an important role. Hypothesis should be very specific and limited to the piece of research in hand because it has to be tested.
The role of the hypothesis is to guide the research by delimiting the area of research and to keep him on the right track. It sharpens his thinking and focuses attention on the more important facets of the problem. It also-indicates the type of data required and the type of methods of data analysis to be used.
How does one go about Developing Working Hypotheses?
The answer is by using the following approach:
I. Discussions with colleagues and experts about the problem, its origin and the objectives in seeking a solution;
II. Examination of data and records, if available, concerning the problem for possible trends, peculiarities and other clues;
III. Review of similar studies in the area or of the studies on similar problems; and
IV. Exploratory personal investigation which 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 a priori 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 as well be remembered that occasionally we may encounter a problem where we do not need working hypotheses, especially in the case of exploratory or formulative researches which do not aim at testing the hypothesis. But as a general rule, specification of working hypotheses in another basic step of the research process in most research problems.
Step # 4. Preparing the Research Design:
The research problem have 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.:
(iii) Diagnosis, and
A flexible research design which provides opportunity for considering many different aspects of a problem is considered appropriate if the purpose of the research study is that of exploration. But when the purpose happens to be an accurate description of a situation or of an association between variables, the suitable design will be one that minimises bias and maximises the reliability of the data collected and analysed.
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, 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 research design, appropriate for a particular research problem, involves usually 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 organised 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.
Step # 5. 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 when all the items are covered no element of chance is left and 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 any of checking the element of bias or its extent except through a resurvey or use of sample checks.
Besides, this type of inquiry involves a great deal of time, money and energy. Not only this; the census inquiry is not possible to practice under many circumstances. For instance, blood testing is done only on sample basis. Hence, quite often we 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 the way of selecting a sample or what is popularly known as the sample design. In other words, it sample design is a definite plan determined before any data are actually collected for obtaining 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 those based on simple random sampling, systematic sampling, stratified sampling, cluster/area sampling whereas non- probability samples are those based on convenience sampling, judgement sampling and quota sampling techniques.
A brief mention of the important, it sample designs is as follows:
(i) Deliberate Sampling:
Deliberate sampling is also known as purposive or non-probability sampling. This sampling method involves purposive or deliberate selection of particular units of the universe for constituting a sample which represents the universe. When population elements are selected for inclusion in the sample based on the ease of access, it can be called convenience sampling.
If a researcher wishes to secure data from, say, gasoline buyers, he may select a fixed number of petrol stations and may conduct interviews at these stations. This would be an example of convenience sample of gasoline buyers. At times such a procedure may give very biased results particularly when the population is also homogeneous.
On the other hand, in judgement sampling the researcher’s judgement is used for selecting items which he considers as representative of the population. For example, a judgement sample of college students might be taken to secure reactions to a new method of teaching. Judgement sampling is used quite frequently in qualitative research where the desire happens to be to develop hypotheses rather than to generalise 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 case of 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, then 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. To select the sample, each item is assigned a number from 1 to 15,000.
Then, 300 five digit’ random numbers are selected from the table. To do this we select random starting point and then a systematic pattern is used proceeding through the table. We might start in the 4th row, second column and 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 next number selected that does fall within the relevant range.
Since the numbers were placed .in the table in a completely random fashion, the resulting sample is random. This procedure gives each item an equal probability of being selected. In case of infinite population, the selection of each item in a random sample is controlled by the same probability’ and that successive selections are independent of one another.
(iii) Systematic Sampling:
In some instances 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. An element of 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 useful when sampling frame is available in the form of a list. In such a design the selection process starts 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 to be drawn does not constitute a homogeneous group, then stratified sampling technique is applied so as to obtain a representative sample.
In this technique, the population is stratified in to a number of non- overlapping subpopulations or strata and sample items are selected from each stratum. If the items selected from each stratum is 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 quota to be filled from different strata; the actual selection of items for sample being left to the interviewer’s judgement. This is called quota sampling.
The size of the quota for each stratum is generally proportionate to the size of that stratum in the population. Quota sampling is thus an important form of non- probability sampling. Quota samples generally happen to be judgement samples rather than random samples.
(vi) Cluster Sampling and Area Sampling:
Cluster sampling involves grouping the population and then 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 to be kept says 450. For cluster sampling this list of 15,000 card holders could be formed into 100 clusters of 150 card holders each. Three clusters might then be selected for the sample randomly.
The sampling size must often be larger than the simple random sample to ensure the same level of accuracy because is cluster sampling procedural potential for order bias and other sources of error are usually accentuated. The clustering approach can, however, make the sampling procedure relatively easier and increase the efficiency of field work, especially in the case of personal interviews.
(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, then districts, then towns and finally certain families within towns. If the technique of random-sampling is applied at all stages, the sampling procedure is described as multi-stage random sampling.
(viii) Sequential Sampling:
This is somewhat a complex sample design where the ultimate size of the sample is not fixed in advance but is determined in for according to mathematical decisions on the basis of information yielded as survey progresses. This design is usually adopted under acceptance sampling plan in the context of statistical quality control.
Step # 6. Collecting the Data:
In dealing with any real life problem it is often found that data at hand are inadequate, and hence, it becomes necessary to collect data that are appropriate. There are several ways of collecting the appropriate data which differ considerably in context of money costs, time and other resources at the disposal of the researcher.
Primary data can be collected either through experiment or through survey. If the researcher conducts an experiment, he observes some quantitative measurements, or the data, with the help of which he examines the truth contained in his hypothesis.
But in the case of survey, data can be collected by any one or more of the following ways:
(i) By Observation:
This method implies the collection of information by way of investigator’s own observation without interviewing the respondents. The information obtained relates to what is currently-happening and is not complicated by either the past behaviour or future intentions or attitudes of respondents. This method is no doubt an expensive method and the information provided by this method is also very limited. As such this method is not suitable in inquiries where large samples are concerned.
(ii) Through Personal Interviews:
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 structured way where output depends upon the ability of the interviewer to a large extent.
(iii) Through Telephone Interviews:
This method of collecting information involves contacting the respondents on telephone itself. This is not a very widely used method but it plays an important role in industrial surveys in developed regions, particularly, when the survey has to be accomplished in a very limited time.
(iv) By Mailing of Questionnaires:
The researcher and the respondents do come in contact with each other if this method of survey 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, usually a Pilot Study for testing the questionnaire is conducted this reveals the weaknesses, if any, of the questionnaire. 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 on the basis of replies given by respondents. Much depends upon the capability of enumerators so far as this method is concerned. Some occasional field checks on the work of the enumerators may ensure sincere work.
Step # 7. Execution of the Project:
Execution of the project is a very important step in the research process. If the execution of the project proceeds on correct lines, the data to be collected would be adequate and dependable. The researcher should see that the project is executed in a systematic manner and in time.
If the survey is to be conducted by means of structured questionnaires, data can be readily machine-processed. In such a situation, questions as well as the 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 explain clearly the job of the interviewers at each step. Occasional field checks should be made to ensure that the interviewers are doing their assigned job sincerely and efficiently.
Step # 8. Analysis of Data:
After the data have been collected, the researcher turns to the task of analysing them. The analysis of data requires a number of closely related operations such as establishment of categories, the application of these categories to raw data through coding, tabulation and then drawing statistical inferences.
The unwidely data-should necessarily be, condensed into a few manageable groups and tables for further analysis. Thus, researcher should classify the raw data into some purposeful and usable categories. Coding operation is usually done at this stage through which the categories of data 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 the form of tables. The mechanical devices can be made use of at this juncture. A great deal of data, especially in large inquiries, is tabulated by computers. Computers not only save time but also make it possible study large number of variables affecting a problem simultaneously.
Step # 9. Hypothesis-Testing:
After analysing the data as sated above, the researcher is in a position to test the hypotheses, or they happen to be contrary? This is the usual question which should be answered while testing hypotheses, various tests, such as Chi square test, test F-test have been developed by statisticians for the purpose.
The hypotheses may be tested through these of one or more of such tests, depending upon the nature and object of research inquiry. Hypothesis-testing will result in either accepting the hypothesis or in rejecting it. If the researcher had no hypotheses to start with, generalisations established on the basis of data may be stated as hypotheses to be tested by subsequent researches in times to come.
Step # 10. Generalisations and Interpretation:
If a hypothesis is tested and upheld several times, it may be possible for the researcher to arrive generalisation i.e., build a theory, As a matter of fact, the real value of research lies in its ability to arrive at certain generalisations.
If the researcher had no hypothesis to start with, he might seek to explain his findings on the basis of some theory. It is known as interpretation. The process of interpretation may quite often trigger off new questions which in turn may lead to further researches.