Introduction to Research K P Mohanan and Tara Mohanan

4 March 2021

www.thinq.education

CHAPTER 5

OBSERVATIONAL RESEARCH

5.1 Looking Back and Looking Forward

5.2 Bitter Gourd and Diabetes 5.2.1 How do we Proceed? 5.2.2 Observational Generalisations and Interpretations 5.2.3 The Concept of the Bitter Gourd 5.2.4 The Correlation between Bitter Gourd and Diabetes 5.2.5 The Issue of Representativeness

5.3 Mindfulness Meditation and Clinical Depression 5.3.1 The Research Question 5.3.2 The Research Design

5.4 Phonics vs. Whole Language Pedagogy

5.5 Causal Interpretations in Research

5.6 Wrapping up

The Research Gym

5.1 Looking Back and Looking Forward

In Chapter 2, we defined ‘research methodology’ as ways of looking for answers to research questions.

To look for an answer to a question, we need to first be clear about what the question means, which requires conceptual clarification. This was the subject of Chapter 3.

Chapter 4 went on to discuss ‘justification’ as another component of research.

We have also been expanding our understanding of the different components of research and the relations among them, as in Fig. 5.1.

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Figure 5.1

We clarify concepts by figuring out definitions for them, and critically evaluating the definitions. In exploring conceptual clarification, we discovered that it is needed not only when we look for answers to questions, but also when we critically evaluate our answers and conclusions. Like critical thinking, the processes of conceptual clarification and defining run through all aspects of research.

We refine the diagram to capture these relations, as in Fig. 5.2:

Figure 5.2

[At this point, you may wish to consider Fig. 5.2 carefully, and figure out how our modifications and discussion have enhanced our original concept of research. We will return to this diagram at the end of the chapter.]

We now move on to look at how these components of research operate in the two modes of research that we will henceforth refer to as observational research and theoretical research. While theoretical research is relevant for all domains of knowledge, observational research is relevant only in those domains where conclusions are justified on the basis of observation (data). Thus, observational research is characteristic of inquiry in the physical, biological, and human sciences (psychology, sociology, history, and so on), but not in mathematics and the humanities (philosophy, literary studies, and some aspects of history).

In this chapter, we will explore observational research in greater detail. We will engage with components of theoretical research in Chapters 6-8.

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What goes into Observational Research?

To look for an answer to a question in observational research, we do the following:

Gathering Data: gathering, organising, and representing a body of observational reports in a systematic way;

Finding Patterns: detecting patterns in that body of data; and Generalising: generalising from those patterns in the data and stating them as observational generalisations.

We represent these processes of observational research and their outcomes as follows:

Figure 5.3

Chances are that in courses on research that you might have taken before, the term ‘research methodology’ is used to refer to this aspect of research, to the exclusion of other aspects. The terminology used in discussions of the three processes in Fig. 5.3 include experimentation, randomised sampling, measurement, statistics, meta-analysis, pre-test and post-test, instrumentation, surveys, interviews, focus groups, case studies, ethnography, participant observation, action research, textual analysis, and so on.

Don’t panic if you have not heard some of these terms. If the terminology bugs you, do a Google search on the terms that are new to you.

In addition to gathering data, finding patterns and generalising, observational research involves a fourth component, namely:

Interpreting a given observational report or observational generalisation: The interpretation often involves drawing conclusions about something that is not observable or tangible, and hence not part of the data. Such interpretation is often expressed as: “pattern x because of factor y;” “y is the cause of pattern x;” and so on.

To develop a feel for each of the components of observational research in Fig. 5.2, we will go through the factors that we must pay attention to when we engage with the following research questions:

RQ 5.1: Is bitter gourd (karela in Hindi) an effective cure for diabetes?

RQ 5.2: Is mindfulness meditation an effective cure for clinical depression?

RQ 5.3: Is the ‘phonics’ method of learning to read superior to the ‘whole language’ method?

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5.2 Bitter Gourd and Diabetes

5.2.1 How do we Proceed? The magazine article, “Karela/Bitter Gourd” at https://www.netmeds.com/health-library/post/karelabitter-gourd-karela-juice-uses-benefits-for-diabetic-skin-and-hair-side-effects-tasty-bitter-g says.

“The therapeutic benefits of bitter gourd are widely prized in various traditional medicinal practices. Ayurveda, Chinese and ancient medicinal practitioners in the middle eastern countries vouch by its healing properties especially in dealing with stomach issues. It is strongly endorsed as a wonder medicine for those suffering from type -1 and type - 2 diabetes, thanks to those hypoglycemic properties available in the extracts of bitter gourd.”

A magazine article is not required to provide evidence for an assertion of this kind. However, you might come across an interesting idea for research here. You might state a hypothesis to yourself: “Bitter gourd is an effective cure for diabetes.” How would you proceed to establish the hypothesis? Let is take a look.

5.2.2 Observational Generalisations and Interpretations Our recurrent strategy for engaging with research questions so far has been to begin by clarifying the concepts in the question. In the question:

RQ 5.1: Is bitter gourd (karela in Hindi) an effective cure for diabetes?

the concepts that need defining are those of bitter gourd and diabetes. In fact, upon closer examination, you might also find the need to clarify the concepts denoted by effective and cure. For now, let us assume we understand these two concepts. Let us move to the term diabetes.

Diabetes is a well-studied illness, with clearly defined procedures for detection. The Mayo Clinic website, for instance, lists the following symptoms for diabetes:

• Increased thirst • Frequent urination • Extreme hunger • Unexplained weight loss • Presence of ketones in the urine Ketones: a by-product of the

breakdown of fat and muscle when insulin is insufficient.

• Fatigue • Irritability • Blurred vision • Slow-healing sores • Frequent infections, such as gum or skin infections and vaginal infections

https://www.mayoclinic.org/diseases-conditions/diabetes/symptoms-causes/syc-20371444

We can describe these symptoms in terms of the vocabulary of observational reports, patterns, and observational generalisations. For instance:

Observational report: This particular patient exhibits frequent urination.

Pattern in the sample: The patients in this sample exhibit frequent urination.

Observational generalisation:

The above pattern can be generalised from the sample of patients we have studied to the population that we are interested in but have not studied.

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Notice that an independent observer can agree on the truth of the first two statements upon observing the particular patient (or the sample of patients), assuming the observer shares the same meaning of “frequent” urination as that of the researcher.

The observable attributes and relations listed as symptoms of diabetes exist at what biologists call the phenotypical level. Those attributes and relations have correlates at the molecular level, involving glucose and insulin.

It is important to bear in mind that we are talking about:

a) the observable symptoms of diabetes from Mayo clinic (the phenotypical aspects of the illness that we call diabetes),

b) the attributes of the molecules that these symptoms correlate with,

c) the nature of the correlations between the phenotypical aspects and the attributes of the molecules, and

d) the causal explanations for those correlations.

If you are familiar with statistics, there is a chance that you would take the terms ‘sample’ and ‘population’ as being used in the sense of quantitative research. While it is true that these terms might have that connotation, we are using them without any quantitative implications. For example, suppose you are at an ice cream store to buy a scoop of chocolate ice cream. You might taste a small spoonful of it before you actually buy it. In doing so, you are making a decision based on a small sample (a spoonful of ice cream) from a population (whole bin of ice cream). There are no numbers here. Samples and populations are relevant in both quantitative and qualitative observational research.

We are going to devote a whole chapter on correlation vs causation, but when going through our examples in this chapter, do keep an eye and a ear open to the considerations that arise from that distinction in observational research. These concepts will become clearer as we look at more and more examples in this chapter as well as subsequent chapters.

One cautionary remark. We used the Mayo Clinic list of symptoms as the observable correlates of the more abstract ‘disease’ called diabetes. This was an attempt at what has been called operationalisation in the research methodology literature in psychology and sociology. Operationalisation is the process of identifying the observable (including measurable or countable) correlates of the abstract entity being studied. For example, in Chapter 2 we talked about the legitimacy of the SATs in ‘measuring’ the scholastic aptitude of candidates. Do the questions in these tests actually measure what they intend to measure, namely, scholastic aptitude? Similarly, we asked: “Do the final examination questions of the Bachelor’s Programs in the IITs and the IISERs test the students’ thinking abilities needed for research?” These are question about operationalisation. [Do take a look at Appendix 1 for further details.]

The following table may help clarify what it means to ‘operationalise’ an abstract entity:

Abstract Entity Instruments to ‘Measure’ Observable Correlates

Scholastic aptitude Questions in SATs

Research abilities Questions in the final exam

Time Pendulum clock: number of oscillations of a simple pendulum

Digital clock: number of oscillations of a quartz crystal https://en.wikipedia.org/wiki/Quartz_clock

Weight Weighing machine: Numbers on the dial

Temperature Mercury thermometer: calibration of mercury level

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In the case of diabetes, medical research has already operationalised the ‘disease’, as in the Mayo Clinic list of symptoms. But that may not be the case for the concepts in the research questions that you wish to investigate. You may have to solve that problem on your own.

5.2.3 The Concept of Bitter Gourd We have so far accepted the idea that for a statement to qualify as an academic claim, its concepts must be clearly defined.

Our research question on bitter gourds involves the concepts of bitter gourd and diabetes. The previous section lists the observable symptoms that could be taken as the indication of diabetes. For now, we will assume that we know what we are talking about when we use the term diabetes in the context of research, with the awareness that this assumption may turn out to be untenable when we dig deeper.

The word bitter gourd is associated with a concept in ordinary English, whose translation in Hindi, for example, is karela. You would agree that it is not an academic concept: it has no coherent definition, or even a list of attributes such that a skeptical jury can decide whether or not a given vegetable qualifies as a member of the category that we call bitter gourd. (Note that this remark holds even if we are talking about what the botanical name Momordica charantia denotes (https://en.wikipedia.org/wiki/Momordica_charantia).

Take a look at the photographs in Fig. 5.4, of the different varieties of what has been popularly called bitter gourd.

We have assumed so far that the English word bitter gourd, the Hindi word karela, and the botanical name Momordica charantia refer to the same object.

But this assumption is problematic. For instance, the objects in the picture C in Fig. 5.4, are called bitter gourd in English, and hence would be interpreted as Momordica charantia. However, they are called kantola in Hindi, not karela. And the botanical name for kantola is Momordica dioica, not Momordica charantia. (https://en.wikipedia.org/wiki/Momordica_dioica) This means that even the botanical names would be interpreted differently depending on the researcher’s location on

A

B

C

Figure 5.4

earth (China vs. India, for instance), or on cultural and linguistic differences.

So when we claim that bitter gourd is an effective cure for diabetes, which is the effective cure? Karela or kantola? Momordica charantia or Momordica dioica? Whichever it is, how do we ensure a shared understanding of which one we are refering to? This is

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not an easy question to answer. It is important in research to be aware of such problems, and to make it a habit to clarify concepts as best as we can.

5.2.4 The Correlation between Bitter Gourd and Diabetes

Let us assume that your research design is as outlined below:

A) Take a random sample of individuals diagnosed as having the symptoms of diabetes.

B) Measure their levels of blood sugar using a standardised test.

C) Divide them randomly into two roughly equal samples, sample 1 and sample 2. For a month, patients from sample 1 (experimental group) are given a diet that includes bitter gourd, while patients from sample 2 (control group) are given a diet that excludes bitter gourd. The subjects are not told which diet they are given.

D) Measure their blood sugar levels again at the end of the month, to find out if there is a significant difference between the measurements for sample 1 and sample 2.

The above outline fits a classic paradigm of observational research that includes experiments, randomised sampling, control, specialised instruments, and measurements. Even though the terms pre-test and post-test are typically used in research in education and not in medicine, what we have in samples 1 and 2 is an example of pre-test and post-test: namely, observation before and after the administration of the cure.

Suppose you implement the research plan in terms of A-D above, and find that:

Even though Samples 1 and 2 have the same blood sugar levels before the experiment, Sample 1 has significantly lower blood sugar levels than Sample 2 after the experiment.

What we have identified here is a pattern in the sample. Based on this pattern, we may propose the following observational generalisation on the population of humans with diabetes:

In the human population, people with diabetes who have a significant amount of bitter gourd in their diet have lower levels of glucose in their blood compared to their counterparts who have no bitter gourd in their diet.

This statement involves a systematic relation between two variables, namely: • having or not having bitter gourd in one’s diet, and • having lower or higher glucose levels in one’s blood.

While this conclusion may be perfectly legitimate, it does not, by itself, justify the causal interpretation stated below:

All else being equal, a significant amount of bitter gourd in the diet causes lowered levels of blood sugar in patients diagnosed with diabetes.

We will pick up the issue of causal relations later in the course.

5.2.5 The Issue of Representativeness In Section 5.2.4, we generalised a pattern (the association between bitter gourd in one’s diet and the level of sugar in one’s blood) observed in a sample of the population of human beings with diabetes. We will refer to such a generalisation from a sample to a population as Induction from Sample to Population (ISP). How legitimate is this generalisation in our bitter gourd research? [If you are a math student, do note that we are not talking about mathematical induction.]

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The very first consideration when making observational generalisations is that of the representativeness of the sample. Let us take an example. Suppose you have made a big pot of lemonade, and you want to check if it has enough sugar. You take a spoonful of the lemonade and taste it. If that sample (spoonful of lemonade you tasted) has enough sugar, you will conclude that the population (the whole pot of lemonade) has enough sugar. Whether the population is a small pot or a big barrel of lemonade, a spoonful of lemonade is enough, because we assume that: a spoonful of lemonade from a container of lemonade is representative of the

population of lemonade (whether a small pot or a large barrel). Hence, what is true of the representative sample is true of the population.

To change the scenario, suppose you are at a feast. On the table are five vegetable dishes. You want to find out if the lunch dishes have enough salt. You take a spoonful of one dish, and judge it to have the right amount of salt. Would you conclude, on the basis of this sample, that the population of lunch dishes has enough salt?

Your answer would be no. What is the difference between the two scenarios? Simple. You expect total uniformity in the lemonade-sugar scenario, but considerable variability in the vegetable-salt scenario. For the second scenario, a spoonful from one dish is not a representative sample.

TASK: Can you think of other examples of scenarios in which you consider the sample to be representative of the population? How about scenarios in which you don’t consider the sample to be representative of the population?

Representativeness of the sample is an important consideration in any observational research, whether you are using experimental or non-experimental data, quantitative or qualitative data. If you are using qualitative data (e.g., interviews, case study method, field work, ethnography, action research, participant observation, textual analysis... ) it may be a good idea to find out how meta analysis, for example, in medical research, can be used to check for representativeness.

Returning to bitter gourd and diabetes, let us imagine that the random sample of patients with diabetes you study are all students from a university in your city. Was it a representative sample? Are they representative of the population of the city or the country? How about a population that includes senior citizens with diabetes? Is it legitimate to generalise from a sample of university students to a population of a country? Is it legitimate to generalise from a random sample of individuals from one country to the human population?

5.3 Mindfulness Meditation and Clinical Depression

5.3.1 The Research Question Let us turn to RQ 5.2:

Is mindfulness meditation an effective cure for clinical depression?

This question calls for the clarification of two concepts (apart from those of effective and cure). The first concept is that of clinical depression.

What is clinical depression? In answer to this question, we recommend the video of a lecture by Stanford Professor Robert Sapolsky on clinical depression, at https://www.youtube.com/watch?v=NOAgplgTxfc&t=696s

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[When you watch this video, pay special attention to the relation between psychology and neuroscience, and its consequences to behaviour.]

As you watch the video, keep in mind the questions:

What is clinical depression?

How is it different from non-clinical depression?

The second concept relevant is that of mindfulness meditation, which also calls for the unpacking of the concept of meditation, and the distinction between mindfulness meditation and other types of meditation. For this, we would like you to gather information from the Internet, and do the equivalent of what we did for clinical depression.

5.3.2 The Research Design

Now we come to the hard part. For the bitter gourd and diabetes question, we did the standard protocol of random sampling and blind experiments: the patients do not know whether they are getting the real treatment or a placebo.

If necessary, do a Google search for double blind experiments and placebo effect.

Its equivalent for the meditation question would be to: A) Take a random sample of individuals diagnosed as having symptoms of clinical

depression. B) Divide them randomly into two roughly equal groups. Call them Sample A (the

experimental group) and Sample B (the control group). For two months: Sample A is taken through mindfulness meditation. Sample B is taken though some other kind of meditation. Neither group knows what they are getting. C) At the end of the two months, observe both groups to find out if there is a

substantial difference between them in the symptoms of clinical depression.

Now, for the bitter gourd research, it may be possible to administer the diet in a way that the subjects of neither group would know which diet they are getting. But is this possible with meditation? If not, how would you circumvent the issue?

If the activity in A–C above appears too difficult, do look up Jon Kabat-Zinn’s work on mindfulness meditation (https://en.wikipedia.org/wiki/Jon_Kabat-Zinn)

You might also get some ideas from Michael Posner’s talk on Mindfulness, Meditation, and Reducing Drug Addiction. (A video of the talk is available at https://www.youtube.com/watch?v=ohHP6eT7az0&list=PLXTKx0YCIDygO4IrfIThEmNgwziJ-93os&index=7&t=0s) This talk is not about depression, but you will learn something about research design such that you can extend it to the depression question.

5.4 Phonics vs. Whole Language Pedagogy Within the domain of research on education, there is a specialised area devoted to teaching reading as one of the two components of language literacy. This area has seen an ongoing battle between two positions on teaching to read. One is called ‘phonics’, and the other is called ‘whole language pedagogy’.

Supporters of phonics begin the teaching of reading with the correspondences between sounds and letters. For instance, in English, the letter f in the word fine, the combination of letters ph in physics, and the combination of gh in tough all correspond to

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the same sound. On the other hand, the letter a in bat, bait, star, and ball correspond to four distinct sounds. Having learnt and practiced these correspondences, the learner proceeds to the spelling and pronunciation of words, and then to sentences, paragraphs, stories, and entire chapters in books.

Supporters of the whole language pedagogy begin not with sounds but with meaning. They begin with stories of the kind that children want to hear, whether or not they are familiar with the letters of the alphabet or the spellings of words. A teacher does this by helping children associate meanings of words with written words as unanalysed symbols. Thus, children can associate words with pictures in story books. For instance, a teacher might say the word glonk and point to the drawing in Fig. 5.5. It is fairly easy for a child to learn such sound–image correspondences.

Instead of the drawing, the teacher might point to the visual representation on the right without telling the child that there are five letters in it.

Figure 5.5

G-L-O-N-K

Children can learn the letters, spelling, and the relation between sounds and letters after they have become fluent readers.

The battle between these two groups of researchers is called ‘reading wars’. If you are curious about this, you may want to take a look at: “The Reading Wars: Phonics versus Whole Language” at https://jan.ucc.nau.edu/~jar/Reading_Wars.html

We might approach this controversy from the perspective of the disagreement between (1) and (2):

1) We must choose one of the two teaching methodologies. (Which one?) 2) We should use both the teaching methodologies.

If we opt for (2), additional issues arise: 3) Should the two methodologies be used simultaneously or sequentially? 4) If simultaneous, should one have priority? If yes, which one? 5) If sequential, which methodology should we adopt for early education?

Whatever the question, assuming that the ability to read is necessary for literacy, it is important to clarify what we mean by literacy. Here are five levels of literacy. i) Being aware of the relation between letters and sounds (The first sound in

physics and fan is the same. The first sound in physics and press are different.) ii) Recognising words in their written form iii) Reading words aloud iv) Understanding the meanings of sentences, paragraphs, and larger chunks of

writing v) Reading aloud sentences, paragraphs and larger chunks of writing

Which of these levels of literacy would be relevant for the questions we have raised in (i) – (v) above?

Once the concept of literacy is clear, think about two aspects of the concept: A. Basic Literacy: the skills of reading and writing the script of a particular language.

(e.g., English uses an alphabetic system, while Indian languages use a syllabary. Unlike in English, there is a largely regular correspondence in the script of most Indian languages between the units of the script (‘letters’) and the sounds they represent.)

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B. Higher Order Literacy: the ability to communicate academic concepts, statements, claims, conclusions, arguments, and so on through writing; and the ability to acquire knowledge by reading.

Next: C. Think of a research question that you would like to investigate with respect to the

reading wars on (i)-(v) above, and formulate that question; D. Come up with a research proposal to gather data relevant to that question; and

E. Think of the different kinds of answers your data may point to, and evaluate what each of those answers says about your conclusion.

5.5 Causal Interpretations in Research In Section 5.2.2, we briefly pointed to the distinction between observations and interpretations of those observations. To take an example from real life, whether or not Ardra called Zeno a communist is a matter of observation. Observers who were present at the scene of the speech event can confirm whether or not the statement is true. However, whether or not that utterance constituted an insult to Zeno is a matter of cultural interpretation. In many parts of the USA, it would be taken as an insult; but in communist Russia, it would be seen as a compliment.

The distinction between the methodologies called ‘positivism’ and ‘interpretivism’ in sociology stems from the acknowledgement of this distinction between observation and interpretation. (https://medium.com/@saber/positivism-vs-interpretivism-in-research-1299e4a6877a) Whether or not someone closed one eye leaving the other open, for instance, is a matter of observation; but whether or not that action constituted a wink is a matter of interpretation.

The field of legal theory called Jurisprudence (https://en.wikipedia.org/wiki/Jurisprudence) also makes a distinction between the ‘facts of the matter’ and their ‘legal interpretation’. To take an example, in a criminal court, investigation of the cause of a victim’s death comes under ‘facts of the matter’. And this comes under the jurisdiction of scientific inquiry, left to the expertise of a medical examiner who performs the autopsy. In cases where death is not from a ‘natural cause’, whether or not the accused caused the victim’s death also comes under ‘facts of the matter’, where a specialist in forensics provides ‘expert testimony’. In contrast, whether the act of causing the victim’s death is to be labelled as first degree murder, second degree murder, or manslaughter is for the legal expert to determine, not for the scientist.

In Section 5.2.2, we also briefly signalled that many instances of observational research tend to interpret the observations. Such interpretation is often in terms of causal factors. In fact, the issue of causality is embedded in the very formulation of the three research questions we have explored above:

RQ5.1: Is bitter gourd an effective cure for diabetes? RQ5.2: Is mindfulness meditation an effective cure for clinical depression? RQ5.3: Is the reading pedagogy of phonics superior to that of whole language?

Implicit in the references to effective cure and superior pedagogy is the consideration of causality. We will explore this issue in some detail in a later chapter, as something that lies at the intersection between observational and theoretical research.

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5.6 Wrapping up In this chapter, we commented briefly on a distinction between observational research and theoretical research. Most discussions of ‘methodology’ in ‘research methodology’ courses are limited to that for observational research.

In our discussion of the methodological issues in observational research, we distinguished between observational reports, observational generalisations, and their interpretation.

Some important considerations for observational research include: ~ data gathering strategies, such as experimental and non-experimental

observation; quantitative and qualitative observation; use of instruments; as well as strategies that are specific to the human sciences, such as surveys, interviews, focus groups, case study, field work, ethnography, participant observation; and so on;

~ protocols of sampling, randomised sampling, and control; and ~ representativeness of the sample.

In our examples, we illustrated only a few of these considerations. Our discussion has been at a transdisciplinary but introductory level.

If you have to submit a research project, thesis, or dissertation, you would need to go beyond what we have outlined, and look at discipline-specific considerations. Suppose the methodology you expect to use in your thesis includes field work, action research, case studies, ethnography, or participant observation. You would do well to carefully think about the issue of representativeness, and learn more about the strategy called meta-analysis in clinical case studies in medical research. This strategy solves many of the problems of representativeness that still raise concern in work devoted to the study of human society and culture.

If you go back to the discussion of observational research in the three examples in this chapter, you would notice that each of them calls for a multidisciplinary approach. The diabetes question calls for modern medicine, human biology, molecular biology, chemistry, and ancient medical systems, not to mention analytic philosophy. The clinical depression question calls for modern psychology, neuroscience, cognitive science, psychiatry, and the Buddhist investigation of the mind. (https://www.mindandlife.org/insight/buddhism-modern-science/) The question on reading involves learning theory, pedagogical theory, educational philosophy, linguistics, and so on.

Transdisciplinary and multidisciplinary paradigms of research are central to the investigation of most research questions in the twenty first century. While in-depth knowledge is essential, narrow specialisation without adequate breadth may greatly limit significant breakthroughs in the advancement of human knowledge.

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THE RESEARCH GYM [GYM 5]

This gym has only one question, because the task is really hard even for us seasoned researchers. But we’re looking to be surprised by your fresh thoughts.

TASK: Come up with a research proposal to investigate the following claim:

Those who have taken this course, Introduction to Research, would do significantly better research for their theses and dissertations than those who have not taken the course.

You might need to define the terms in the claim to make their meanings sharper in order to connect them to observations.

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APPENDIX 1:

OBSERVATIONAL REPORTS vs. INTERPRETATIONS OF OBSERVATIONS

In Section 5.2.2, we talked about molecular correlates of illnesses like diabetes. It is important to keep in mind that molecules, and the atoms they are made up of, are unobservable theoretical entities. This statement might be puzzling for someone who is aware that we can acquire information about molecules by using Atomic Force Microscopy and Spectroscopy. Why do we say that atoms and molecules are unobservable, and that they are theoretical entities? That question calls for an extended elucidation.

If two people X and Y are looking at A(pollo) and Z(eno) standing next to each other, and X says, “Zeno is taller than Apollo,” that is clearly an observational report that Y can check by merely look at A and Z. But if A and Z are not standing next to each other, and their heights cannot be compared simply by looking, what would they do if Y disagrees with X? They must resort to measuring the heights of A and Z. Both X and Y can be present when this happens. This would also count as an observational report, verified by both X and Y.

If A and Z are two elephants, and X says that Z is heavier than A, they will have to use a weighing machine to measure the weights of A and Z. If X and Y happen to be physicists, Y would know that X is not talking about mass. This would mean that the weights of A and Z, measured in different locations, one at sea level and the other on a mountain top, are not comparable. Now, a weighing machine has a theory of gravity built into it. The readings (numbers) that X and Y are observing are interpreted as weight through that theory. In other words, if X and Y agree, based on the readings on the instrument, that Z has greater weight than A, they are agreeing on a theoretical interpretation of what they observe.

To take another example, “Is temperature observable?” When we put our finger in a hot or a cold cup of water, we have sensory experiences that we describe as hot or cold. But when we measure the temperature of a person using a mercury thermometer, we are going by a theory of heat that correlates heat with volume. What we see — the mercury level against a calibration — is interpreted as temperature. We do not see temperature.

Similar remarks apply to telling the time by looking at a digital wrist watch. What we see are some numerals on the dial of the watch. We interpret them as time, within a fairly complex theory of physics, in many cases without understanding the theory built into the digital watch.

To take yet another example, suppose someone says that in the English word city as spoken by a person from London, the second vowel is pronounced with a lower tongue position than the first vowel. A phonetician with considerable ‘ear training’ would be able to perceive the difference, and confirm the ‘observation’. She may yet decide to crosscheck what her ear tells her against instrumental data: she might use what is called sound spectroscopy: and look at the spectrograms of the two vowels. And based on the rise and fall of formants in the spectrogram, she can check if it is true that the second vowel in city is indeed lower than the first. This too, is a case of a theoretical interpretation of what we see on a spectrogram. We do not see the vowel height, but the theory tells us how to interpret the dark striations visible on the spectrogram as vowel height.

The data from Raman spectroscopy and Atomic Force Microscopy are parallel. Chemists do not observe molecules, atoms, double bonds and single bonds, or the structure of molecules. They interpret what the machines tell them.

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This remark applies to the DNA molecules in chromosomes as well. Can we see DNA? This is what Michael W. Davidson, curator of the National High Magnetic Field Laboratory at Florida State University says:

“Many scientists use electron, scanning tunneling and atomic force microscopes to view individual DNA molecules …But even with these advanced technologies, DNA appears as a string rather than being resolvable into the individual units from which it is composed.” (https://www.nytimes.com/2008/06/03/science/03qna.html)

So here is what researchers do. Let us take chemistry to illustrate. They accept a theory of atoms and molecules. Some of them have critically examined the theory and judged it to be credible; others have accepted the theory on faith. They use instruments based on those theories, which yield visual representations that they can observe, just like the phonetician observing the spectrograms. They then interpret what they observe in terms of the theory that they have accepted.

This is the basis for saying that atoms and molecules are not observable; rather, they are theoretical entities — entities postulated within a theory.

APPENDIX 2:

‘LITERATURE REVIEWS’ IN RESEARCH

Research students worry about the so-called ‘literature review’, by which they mean a section that they are required to include in their ‘research proposal’, or a requirement in their thesis. It is a set of summaries or a ‘synthesis’ of the articles and books related to the ‘topic’ that they are working on.

We would like to suggest an alternative, one that is characteristic of the kinds of research that we have been doing. In this paradigm, the counterpart of the literature review is an attempt to place the research question and conclusion in a context, to shed light on what is novel about the contribution of the students’ work in the context of what is already known.

This calls for a radically different attitude to reading the research literature itself. Here is what we would like to recommend:

Reflect on what you seek to find out, and try to formulate it as a question. Ask yourself what kinds of considerations you would need to pay attention to in your investigation.

Make a draft of a research proposal. Try to figure out possible answers that the facts point to, and possible conclusions you might draw from the configuration of ideas you have arrived at. Talk to people who might be able to give you feedback, and revise your proposal.

Next, you might start reading relevant articles (just a few at this point) on the question. That will tell you what is known about the topic. Place your question against that backdrop, and proceed to figure out what is not known.

Read more and more on the subject as you proceed with your active research. In sum, we are suggesting that the reading that traditionally comes under “Literature Review”, which forms a separate chapter in many theses,

~ begins only after you have formulated your research question and have already formed a nucleus of careful thinking and planning around it; and you have a set of tentative conclusions based on that nucleus; and

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~ doing the literature review and doing your own thinking go hand in hand in parallel streams.

In particular, we are expressing our reservations about the notion that a student can start doing research only after having read everything available on the subject. This can lead to paralysis, and a ‘researcher’s block’.

We are not saying that you should not read anything before formulating your research question. Far from it. Quite often, your research question can be triggered by what you read — open-ended non-targeted reading of what interests you. Good ideas emerge in a fertile open mind. After you figure out your research question, you can move on to a focussed, purpose-driven literature search and reading around the question.

The actual ‘literature review’ is best woven into your thesis as and when necessary, to provide the background for (a) justifying your claims; (b) pointing to gaps in existing work; and (c) presenting what is novel about your contribution.

If you are doing observational research, in particular, if it requires experimentation and expensive instrumentation, you may not be in a position to adopt the kind of open-ended fluid approach that we are suggesting. If that is the case, you would do well to follow the practical guidelines provided by your thesis advisor.

You might ask at what stage you can begin to do focussed reading. This will have to be your subjective decision, based on your gut feeling or intuition, together with advice from those with more experience in the process of research, including your advisor.