What Research is NOT?
The three assertions that follow describe what research is not. Each statement is accompanied with an example that demonstrates a frequent misperception about research.
1. Research is more than just acquiring data. “The teacher sent us to the library today to do research, and I learned a lot about black holes,” a sixth-grader informs her parents. This could be information discovery or gaining reference skills. But it most emphatically is not, as It was labelled as research by the teacher.
2. Research is more than just looking for hard-to-find facts. The house across the street is on the market. You think about buying it and call your agent to see what someone else would offer you for your existing property. “I’ll have to do some research to determine your property’s fair market value,” the realtor says. What the realtor refers to as “doing some research”? He refers to studying information on recent sales of houses comparable to yours; this information will assist the realtor in determining a realistic asking price for your own home. Such an action entails nothing more than searching through numerous files or websites for information. Find out what the realtor didn’t know before. Rummaging through records, whether at one’s own workplace, a library, or on the Internet, is not research. It is more correctly described as a self-enlightenment activity.
3. Research is more than just moving information from one spot to another. A college student reads many articles about William Shakespeare’s sonnets’ mysterious Dark Lady and then produces a “research paper” presenting various scholars’ theories about who the lady might have been. Although the student does engage in certain activities related to Formal research tasks such as collecting material, organising it in a specific way for presentation to others, supporting statements with documentation, and properly referencing statements do not add up to actual research. The student has overlooked the most important aspect of research:
Data interpretation is the process of interpreting data. Nowhere in the article does the student remark, “These facts I’ve gathered seem to indicate such-and-such about the Dark Lady.” This student is embarking on actual research;
However, no matter how polished and appealing the structure, This type of action is more accurately referred to as fact transcription, fact documenting, or fact documentation or fact summary. Going a step further, this learner would have moved from one realm to another: from the world of mere fact conveyance to the world of fact interpretation. The divide between the two worlds is the distinction between information transfer and genuine research—a distinction that beginner researchers must recognise.
Research is a cyclic process
The researcher begins with a problem—a query that remains unresolved.
Everywhere we look and observe things that make us wonder, hypothesize, and ask questions. And by asking questions, we initiate a chain reaction that leads to the research process.
- A curious mind is the driving force behind study; as one popular tabloid put it, “Inquiring minds want to know!”
Take a look around you. Consider unresolved circumstances that raise the following questions: What is the nature of such-and-such a situation? What causes such-and-such an occurrence? What exactly does it all mean? Research begins with queries like these. - The researcher expresses the purpose of the study project clearly and precisely. It is vital to express the situation clearly and unambiguously. The ultimate purpose of the research must be stated in a grammatically full language that properly and precisely answers the question, “What problem do you intend to solve?” When you describe your goal in clear, concrete terms, you will have a good idea of what you need to accomplish and will be able to direct your efforts accordingly.
- The researcher frequently subdivides the main topic into more manageable subproblems.
From the standpoint of design, it is frequently beneficial to divide a main research topic into multiple subproblems that, when solved, can resolve the main problem. In everyday life, we utilise the method of breaking down major difficulties into small, easily solved subproblems. Assume you want to travel from your hometown to a town located many miles or kilometres distant. Your main goal is to go from one spot to another as quickly as possible. However, you quickly realise that the problem has multiple subproblems:
The main issue is getting from Town A to Town B.
Subissues: 1. Which path looks to be the most direct?
2. Is the quickest also the most direct? If not, what is the alternative route? Will it require the least amount of time?
3. Which is more essential to me: reducing my commute time or saving money or reducing my energy consumption?
4. At which important intersections along my chosen route must I turn right or to the left?
What appears to be a single question can be broken down into multiple smaller questions that must be answered before the main question can be resolved.
So it is with the majority of research problems. The researcher frequently discovers relevant subproblems by attentively inspecting the main problem. The researcher can more easily address the major problem by addressing each of the subproblems. The whole study endeavour might become tedious and difficult to manage if a researcher does not take the time or trouble to isolate the minor problems within the big topic.
Identifying and clearly expressing the topic and its subproblems are critical first steps in formal research. - The researcher identifies hypotheses and assumptions that are at the heart of the study effort. Following the formulation of the problem and its associated subproblems, the researcher may make one or more hypotheses regarding what he or she may discover. A hypothesis is a logical assumption, a fair guess, or an educated guess. It offers a preliminary explanation for a phenomenon under examination. It may drive your thoughts to potential sources of knowledge that may aid in the resolution of one or more subproblems and, as a result, may also aid in the resolution of the main research challenge.
Hypotheses are not uncommon in study. When something happens in your daily life, you quickly try to account for it by creating some logical hypotheses. Consider coming home after dark, opening your front door, and reaching inside for the switch that turns on a nearby table lamp. Your fingertips discover the switch. You turn it over. There is no light. At this point, you’ve identified many explanations for the lamp’s failure:
Hypothesis 1: A recent storm has interrupted your power supply.
Hypothesis 2: The light bulb has gone out.
Hypothesis 3: The lamp is not properly inserted into the wall outlet.
Hypothesis 4: The connection connecting the lamp to the wall outlet is faulty.
Hypothesis 5: You failed to pay your utility bill.
Each of these ideas suggests a method for gathering information that could help address the nonfunctioning-lamp problem. To test Hypothesis 1, you could, for example, go outside to see if your neighbours have lights, and to test Hypothesis 2, you could replace the present light bulb with a new one.
A research project’s hypotheses are as speculative as a broken table lamp. A biologist, for example, would hypothesise that some man-made chemical chemicals increase the frequency of birth abnormalities in frogs. A psychologist might hypothesise that some People’s characteristics influence whether they vote liberally or conservatively. A marketing researcher might hypothesise that humour in a television commercial will attract viewers’ attention and so improve the likelihood that viewers will purchase the offered goods. In all of these situations, the term speculate appears. Good researchers always start a study with an open mind about what they might (or might not) find in their data. - The researcher creates a detailed plan for dealing with the problem and its subproblems. Research is not a blind foray into the unknown in the hope that the data required to address the research challenge will appear magically. It is, instead, a meticulously planned itinerary describing the path you aim to travel to achieve your final destination—your study objective. Researchers purposefully prepare their overall study strategy and specific research methodologies in order to collect data relevant to their topic.
Investigate the problem and its subproblems. Different designs and methodologies are more or less appropriate depending on the study subject.
Much can be decided in the early stages of a research project: Is there any existing data that is directly related to the study problem? If so, where are they and how likely are you to have access to them? How might you generate the necessary data if it does not currently exist? And, once you’ve gathered the necessary information, what will you do with it? - The researcher gathers, organizes, and analyses data about the topic and its subproblems. After a researcher has isolated the problem, subdivided it into relevant subproblems, defined hypotheses and assumptions, and selected an appropriate design and methodology, the next stage is to gather any relevant data to the problem and organise and analyse it in useful ways.
Data collected in research investigations can be classified into two types. Quantitative research entails examining quantity of one or more variables of interest. A quantitative researcher often attempts to quantify variables numerically, whether through the use of commonly accepted physical world measures (e.g., rulers, thermometers, oscilloscopes) or by other means carefully crafted measures of psychological traits or behaviours (e.g., tests, questionnaires, rating scales).
In contrast, qualitative research focuses on features or qualities that cannot be completely quantified. A qualitative researcher’s goal is often to investigate the many nuances and intricacies of a certain topic. Qualitative research is most commonly found in studies of complicated human situations (e.g., people’s in-depth viewpoints on a particular subject, the behaviours and values of a particular cultural group) or complex human products. (e.g., television commercials, works of art). However, qualitative research is not confined to human-related research topics. For example, some biologists do research in a unique way.
The complicated social behaviours of various animal species have been studied in a qualitative manner; Dian Fossey’s work with gorillas and Jane Goodall’s study of chimps are two well-known examples. (e.g., see Fossey, 1983; Goodall, 1986). - The significance of the data as it relates to the problem and its subproblems is interpreted by the researcher. Data, both quantitative and qualitative, are nothing more than that. The relevance of the data is determined by how the researcher interprets it. Uninterpreted data are useless in the study since they can never assist us to answer the questions we have set.
However, researchers must recognise and accept the subjective and dynamic nature of interpretation. Consider the several books written about the assassination of U.S. John F. Kennedy was the 35th President of the United States. Different historians have investigated the same events: one may interpret them one way, while another comes to a completely different conclusion. Which is correct? Maybe they’re both, maybe they’re not. Both may have simply created new challenges for subsequent historians to solve. Different minds frequently interpret the same set of data differently.
We used to think that clocks measured time and yardsticks measured space. In some ways, they still do. We also assumed that time and space were distinct things. Then came Einstein’s theory of relativity, which united time and space into a single concept: the time-space continuum. What is the distinction between the old and new perspectives? It’s the way we think about, or interpret, the same information. The realities of time and space have not changed; our interpretation of them has.We must emphasise two critical things concerning the seven-step method previously explained.
To begin, the procedure is iterative, which means that a researcher may need to switch back and forth between two or more steps along the route. For example, while designing a detailed plan for a project (Step 5), a researcher may realise that a genuine resolution of the study topic necessitates tackling a previously unknown subproblem. (Step 3). Second, the process is circular. While interpreting the gathered data (Step 7), a researcher may conclude that new data are required to adequately address the problem (Step 6).
