The Basics of Logical Analysis 3: Concluding

I wanted to conclude the line of discussion I was following in my previous posts, with an eye toward the experience a researcher might have in beginning to define a new project, particularly those in areas where the researcher has not done a lot of previous research.  I also wanted to try to make my examples a little more detailed and academic in terms of focus. I’m still going to be working with an example from an area where I have little experience because it’s close to one of the concerns of a writer with whom I’m working.

Down the Rabbit Hole 4: Fractals

The previous post was talking about “going down the rabbit hole” for the way that a question can seem initially simple and small, but takes on detail and scope as it is examined more closely. Another parallel would be fractals, which are patterns/images derived from mathematical operations that are recursively defined in such a way that as you magnify the image, new detail continuously emerges. The Mandelbrot Set is one of the most famous of the fractals. 

Research shares something of this characteristic. It may not be infinitely recursive (though some have argued that it is), but generally, if you examine any issue closely, it will lead to more questions.  This is due to the basic nature of analysis: if we analyze things into separate parts/aspects/issues, each of those separate parts can itself be analyzed into its ow constituent part.  Jorge Luis Borges wrote an essay titled “Avatars of the Tortoise,” in which he argues that infinite regressions “corrupt” reasoning, giving examples, like how to define a word/concept, it is necessary to use other words, and each of those other words then needs to be defined, which requires other words, which then all require their own definition, and so on. I’m not sure that the pattern is infinite (there are, after all only a finite number of words, so for definition at least the regression can’t be infinite), but the multiplication of details can quickly become overwhelming. 

The Nobel Prize-winning psychologist and economist Herbert Simon, whose studied decision-making, coined the term “satisficing,” to speak of how some decisions must be made without a full logical analysis because such analyses take so long and become so detailed.

As my earlier examples of reviewing a restaurant or movie showed, it’s pretty natural to see different aspects in things: the restaurant has food and service and ambiance; the service has courtesy and competence; courtesy has all the different things that different people said and did. It may be simple to say whether you liked the restaurant, but to explain in detail all the different factors that contributed to that decision is another matter altogether.

Fractal: The Barnsley Fern
Each leaf, if expanded, will show similar structure and fine detail to the larger frond.
Image by: DSP-user / CC BY-SA (

A More Focused Example

So far, I was giving pretty general examples, now let’s try to get more focused.

Let’s imagine a hypothetical student, studying business management.  And let’s imagine that this student has what we can call “The Fruit Theory of Management,” in which they assume that giving employees fruit improves performance. (I was going to call it “Apple Theory” but didn’t want this to be confused for a reference to the big corporation.)

The Fruit Theory

On its face, the fruit theory of management is ridiculous, but since I’m talking about a general structure of research, the precise theory in question is not so important (as will hopefully become obvious in a moment). Instead of “giving employees fruit” we could use “giving employees training in XYZ,” or, more generally, “instituting policies ABC.”  ”Giving fruit” can stand in for any possible intervention. And instead of “employees,” we could substitutes almost any group—students, parents, plumbers, etc.—and in each of these cases we could either find a suitable measure of performance, or we could replace “performance” with some other construct to measure (e.g., happiness, health, etc.).  

We can even generalize this to any basic causal pattern: “giving fruit leads to better performance” is a specific example of the general pattern “X causes Y.” Most research is concerned with causal relationships in some way or another, so although I’m going to focus on fruit theory

Studying Fruit Theory

So, we have our business management student who wants to research fruit theory. Generally speaking, a starting point for fruit theory would be to define the theory.

So the student tries to write down a definition (or speaks a definition in conversation with someone). At this point, the process of analysis inevitably has already begun: the words used can themselves be examined individually.  So, if the theory is “giving fruit leads to better performance,” there are elements that can be defined individually. 

For starters, we can ask “what is  fruit?”  In everyday conversation, we know what a fruit is and don’t need definition. But if we’re talking about developing research and examining causal relationships, we want to define things more closely and formally. (Research needs formality and detail so that others can check the research.)  For example, fruit theory might call for fresh, ripe, worm-free fruit that people would enjoy eating (a definition that is not identical with a more general understanding of fruit that includes unripe or wormy or rotten fruit). That might lead us to a whole set of questions of how to identify fruit that people would enjoy eating, which could lead to more general questions of what it means for people to enjoy eating. (Or maybe the real issue is that people enjoy receiving fruit as gifts—that would lead to a different definition of what “fruit” is.)

To study fruit theory, we also need to define what counts as “giving” and what counts as “better performance.”  As for “giving,” there is some question of the specific details of how the transfer is made and whether any conditions are placed on that transaction, including any potentially hidden costs of the transaction. But defining giving is relatively simple compared to the question of “better performance.” Measuring performance a huge array of questions: Whose performance? Are we measuring the performance of the organization as a whole? Or of individuals in it? What kind of performance? What dimensions of performance are we measuring (speed? accuracy? gross sales? net sales? etc.) and over what time periods? Are we measuring cash flow of the business over a month? Or the employee sick days taken over a year? Or are we measuring profitability over a decade? There are any number of different ways to think about the general concept of performance.

To develop research, we might also need to specify further the causal mechanism by which fruit theory works. Does giving fruit work because fruit makes people healthier, and therefore better able to work hard (as the old saying goes “an apple a day keeps the doctor away”)? Is there a physiological causality? Is that physiological causal path one that gives people more energy? Or one that improves their strength? Or one that boosts their mood?  Or maybe the causality is not physiological but psychological: giving employees gifts makes them feel appreciated and they want to work harder as a result?

Answers lead to new questions

Whenever we make a choice of where to focus attention, we can find new questions to pursue. We may start pursuing a question of business, as in fruit theory, but that question might lead into other fields of study.  If we posit a physiological cause for fruit leading to better performance of employees, then we need to study physiology. That study might lead in a variety of directions: maybe fruit theory works because fruit improves health, reducing sick-time lost—that would lead to study of immunology: how and in what ways do apples improve immune response? Or maybe fruit theory works because of some other physiological effect: strength, endurance, mood. Since different foods and substances can impact strength, endurance, and mood, maybe fruit has such effects?  If one thinks that fruit has a physiological effect on mood, one might then be led into questions of which specific biological pathways lead to mood improvement, and perhaps in studying that research, you see that other researchers have identified different kinds of mood improvement, and perhaps debate ways in which physiology affect mood.

New answers pretty much always suggest new questions.  

Preventing Over-analysis

You can take analysis too far. If you constantly analyze everything, you end up with a great mass of questions and no answers.  It can lead to getting swamped in doubt.  There is no rule for this, beyond that at some point it is necessary to pick the point at which you say “I’m satisfied with my answer to this question.”  Such statements close off one potential avenue of study to allow focus on another, and to set limits to what you need to study—limits that are necessary for the practical reason that it’s good to finish a project even if that project is imperfect.

If you say “I’m satisfied that the reason Fruit Theory works is because fruit makes people healthier,” you don’t need to pursue questions of whether and how and how much fruit promotes health, and you can go on to focus on how improved health helps a business.  Or if you say, “I’m satisfied that fruit theory works,” you can go on to study details of implementing fruit theory.  Of course, it’s good to have reasons, and good to be able to explain those reasons: if you’re satisfied that fruit theory works, it’s useful to be able to give evidence and reasoning. In academia, that evidence often comes in the form of other research literature. If you can cite five articles from reputable sources that all say “fruit theory works,” then you can go on to your research in implementation without getting embroiled in any debate about whether fruit theory works—even if the five articles you cite are not yet accepted by all members of the scientific community.


Analysis itself isn’t really that hard in the small-scale—we do it automatically to some extent. But it is something that grows increasingly difficult as we invest more energy into it: the more detail we add to our analysis, the more there is an opportunity to analyze further, which can lead to paralysis or to getting swamped. It is something that wants care; it wants attention to detail. 

The basics of logical analysis 2: Down the rabbit hole

Continuing my discussion of analysis from my previous posts, I look at how analysis can lead to new questions and new perspectives. Just as Alice ducked into the small rabbit hole and found an entire world, so too can stepping into one small question open up a whole world of new questions and ideas.

If you look at things right and apply a bit of imagination, analysis quickly leads to new questions.  Even something that looks small and simple will open up to a vast array of interesting and difficult questions. 

The multiplication of questions that arises from analysis can be good or bad. New questions can be good because they can lead to all sorts of potentially interesting research. But having too many questions can be bad, both because it can interfere with focusing on one project, and because it leads to complexity that can be intimidating. Learning to deal with the expanding complexity that appears with close study is a valuable skill in any intelligence-based endeavor—whether scholar or professional, decisions must be made and action taken, and falling down a rabbit hole of analysis and exploration will sometimes interfere with those decisions and actions.

This post follows up on my previous in which I argued that we analyze automatically and that the work of a researcher includes making our analyses explicit so that we and others can check them.

In this post, in order to show the potential expansion of questions, I’ll look at a couple of examples in somewhat greater detail. While I won’t approach the level of detail that might be expected in a scholarly work meant for experts in a specific field—I want my examples to make sense to people who are not experts and I’m not writing about fields in which I might reasonably called an expert—I hope to at least show how the complexity that characterizes most academic work arises as a natural part of the kind of analysis that we all do automatically.

Looking more closely: Detail appears with new perspectives

In the previous post, I used the example of distinguishing the stem, seeds, skin, and flesh of an apple as a basic analysis (separation into parts), but it was quite simplistic. Now I want to examine how to get more detail in an analysis of this apple.

For starters, we can often see more detail simply by looking more closely (literally): In my previous post, I separated an apple into skin, flesh, seeds, core and stem.  But we could look at each of those in greater detail: the seed, for example, has a dark brown skin that covers it and a white interior.  With a microscope, the seed (and all the rest of the apple) can be seen to be made up of cells.  And with a strong enough microscope, we can see the internal parts of the cells (e.g., mitochondria, nucleus), or even parts of the parts (e.g., the nuclear envelope and nucleolus of the cell’s nucleus). This focus on literally seeing smaller and smaller pieces fails at some point (when the pieces are themselves about the same size as the wavelengths of visible light), but in theory this “looking” more closely leads to the realms of chemistry, atomic and molecular physics, and ultimately to quantum mechanics. Now we don’t necessarily need to know quantum mechanics or even cellular biology to study apples—you don’t necessarily visit all of Wonderland—but those paths are there and can be followed.

In this apple example, each new closer visual focus—each new perspective—revealed further detail that we naturally analyzed as part of what we saw.  But division into physical components is only one avenue of analysis, and others also lead down expansive and detailed courses of study.

So Many Things to See!

We can look at different kinds of apples in a number of different ways. (Not to go all meta here, but we can indeed separate—analyze—distinct ways in which we can analyze apples.)

At the most obvious, perhaps, we can separate apples according to their variety, as can be seen in markets: there are Granny Smiths, Pippins, etc., so that customers can choose apples according to their varied flavors and characters.  Some people like one variety and not another.  These distinctions are often made on the basis of identifying separate characteristics of apples (another analysis): “I like the flavor and smell, but it’s kind mealy and dry;” or “It’s got crisp flesh and strong flavor; it’s not too sweet.” Flavor, texture, appearance (color, shape, etc.), and condition (ripe, overripe, e.g.,) are all distinct criteria that a shopper might consider with respect to an apple.  These aren’t exactly the kind of thing that would be the subject of academic study, but they could certainly lead to more academic questions.

The question of apple variety, for example, could be seen through the lens of biology. There are the questions of which genetic markers distinguish varieties and the ways in which those genetic markers tell us of the relationships between different types of apples and their heritages.  The question of heritage brings up another aspect of apples that could be a study for a biologist: How did a given strain develop? There are wild apples, which developed without human intervention; heirlooms, which develop through selective breeding; and hybrids, which grow from planned crossbreeding.  Combining these questions of genetics and heritage might lead a scholar to study the migration of a specific gene, for example to see if GMO commercial apple farms are spreading their modified genes to wild populations.

Another characteristic of an apple that a shopper might consider at the store is the price.  This is obviously not a matter for biologists, but rather for economists. And an economist might want to look at how apples get priced in different markets.  That might lead to questions of apple distribution and apple growing. Questions of apple growing might lead back to questions of biology, or to other fields of study like agronomy. Questions of distribution might lead to questions of transportation engineering (what’s the best means to transport apples?) or to questions of markets (who are potential producers/distributors/vendors/consumers? what products ‘compete’ with apples?) or questions of government policy (how did the new law affect apple prices?).

So Many Different Perspectives

Different analytical frameworks can be found by imagining different perspectives on apples. In the previous section, I already linked the study of apples into fields like biology and economics and more, but there is wide potential for study of apples in many areas. 

Think about university departments where apples might get studied. Biology, economics, and agronomy are three already suggested. But people in literature departments might study apples in literature—“The apple in literature: From the bible to the novel”. People in history departments could study the history of apples—“Apples on the Silk Road in the 14th century.”  Anthropology: “Apples and the formation of early human agricultural communities.” Ecology/Environmental Science: “Apples and Climate Change.”  

These example titles are a little strained because I have not made a study of apples in these contexts, and therefore I’m throwing out general ideas that are rather simplistic and free of real theoretical considerations.  More complexity would attend a real project.  The student of literature might be looking at different things that apples have symbolized because they want to make a point about changing cultural norms. Or they might look at how apples have been linked to misogynistic representations of women. Such studies, of course, are interested in more than just apples. As we combine interest with apples with other interests, too, new potential ideas being to arise.

Combining Perspectives

Most people have multiple interests and these interests can combine in myriad ways to create a vast array of different questions that could be asked about apples (or any other subject).

Pretty much any scholarly perspective has its own analytical frameworks that structure research. Biology analyzes according to genetic structure, for example. Business analyzes according to market and economic factors. When these frameworks start to overlap—a business analysis using genetic factors, or a genetic analysis driven by specific economic factors—multiple points of intersection appear. Each genetic structure (each type of apple) can be examined with respect to a variety of different economic factors (e.g., flavor, shelf life, durability, appearance). 

This multiplication of different ways of dividing things up (analytically, anyway) can be problematic because it creates a lot of complexity and because it can be confusing/overwhelming, but it can also present opportunities because each new perspective might have some valuable insight to add. 


What seems small and simple to a first glance—a rabbit hole has a small and unassuming entrance—usually opens into a vast and expanding world of questions.

Analysis requires a bit of imagination—imagination to see a whole as composed of parts, imagination to consider different perspectives from which to view an issue, imagination to recognize the different aspects of things.  But a lot of this analysis is pretty automatic: little or no effort is required for the necessary imagination. Still, because it’s so easy and so natural, this process gets discounted—especially if you view “analysis” as something highly specialized that only experts do.

To develop a practice of analysis, all you really need to do is make a point of trying to make your different observations explicit.  Whether you’re judging an apple (taste, appearance, scent, etc.) or a theory (the various assumptions, conclusions, relationships to other theories), chances are good that you’ll pretty automatically respond to different aspects at different times. If you can formalize and record these different observations, you lay the foundation for developing your own analyses.

The Basics of Logical Analysis 1: Seeing Parts of Wholes

In this post, I revisit the general issue of analysis that I discussed in my previous post. There is a measure of overlap because I’m really searching for a way to communicate both the fundamental simplicity of analysis with all its potential complexity.  Maybe the general principle for this post is that analysis is, at its roots, a simple intellectual action: dividing something into different parts, but that this simple action inevitably leads to increasing complexity.

As with so many things in which analysis is involved, this post started out simpler and shorter than it has become. My original plan was to write one short post that just did a better job of explaining the ideas in the previous post. But then, as I thought more closely about it, I found issues that hadn’t been discussed in my previous.  It’s now looking like this will be a series of posts—at least two: this one will discuss the big idea of analysis and relatively simple, everyday examples; the next will look at some examples more closely, in hopes that they feel more like an academic example. I suspect that may end up as two or more posts. In a way, this story encapsulates an aspect of analysis in practice that I want to emphasize here: the more you do it, the more complexity you see, and that leads to expanding projects, that must be reined in for purely practical reasons: basically, if you want to finish a project, you have to stop analyzing everything. (And as I write that, I wonder whether I haven’t sparked the foundation for a third post: how do you stop analyzing once you’ve started. It’s an idea that I touch on briefly in the second post, but maybe it deserves its own? I’ll have to think about that…)

What is “Analysis”

At its root (its etymological foundations), “Analysis” is derived through medieval Latin from the Greek for “unloose” or “take apart.” (In contrast to “synthesis” whose roots lie in the Greek for “put together.”) This sense is generally in line with how the word might get used in a conversation. For example, after [a movie/a TV show/a meal at a restaurant], if one person is talking at length criticizing details of the [movie/etc.], the other might get exasperated and say “Stop picking it apart,” or “stop over-analyzing it.”

It is this basic “picking apart” that concerns me in these posts. It is a basic principle that can manifest informally (as a person might do with a movie/etc.) or one that can manifest as extremely detailed and formalized systems of analysis, as with psychoanalysis, or statistical analysis, or data analysis, or any other field that uses “analysis” in a title.

We Do It Automatically

The kind of analysis that is important in research (and other intellectual work) is something that humans do naturally and automatically—often without even noticing that we’re analyzing.

To apply it in research is to take an automatic, unconscious ability and work to make it conscious and explicit. Splitting things into pieces—into different parts or different aspects—is pretty easy. But making those divisions explicit is hard because of the complexity that tends to develop.

We all automatically split things up into different parts, which is reflected in our languages (including words like “parts,” “pieces,” “components,” “elements,” etc.) and much of our daily lives. We separate the world into all sorts of different categories. We eat food, which includes fruit, vegetables, meat, etc. We work, but have many different kinds of work: homework, housework, yard work, not to mention jobs, which are work. We separate the good from the bad. We divide people up into different groups: family, friends, acquaintances, people we don’t know, etc.

It’s true that many of these divisions are learned, but that doesn’t mean that we don’t naturally make divisions of some sort.

Analysis: Examples

Consider an apple.  It is a whole in itself, but we pretty naturally separate it into a few different parts: stem, skin, flesh, core, seeds.  Our basic sensory apparatus provides distinguishing information: stem, seed, and flesh taste different, smell different, look different, and feel different.  Our basic sensory apparatus is already providing us information about differences in the world that lead to analysis of the apple into its different parts.

Consider a movie.  It is a whole in itself, but we can easily divide it in many different ways that are familiar to cinephiles. We can say “The acting was pretty good, but the script was weak.” Or “The cinematography is great, the writing is great, the direction is ok, but the star annoys me, so I had trouble enjoying it.” We might like what we see (“great cinematography!”), but not what we hear (“poorly written dialogue”). We might like one actor and not another. Again, this is analysis in action, although few would think of this kind of thing as analysis. Unless we were to really get into a lengthy discussion of different aspects of a movie, and then someone might say “stop analyzing it! You’re ruining it for me!” 

Research and Analysis

Research takes this basic ability to distinguish between things and tries to make it explicit and formal. For the researcher, it’s not enough to say that it’s obvious that you have stem, seeds, and flesh, or acting, directing, writing, and cinematography. It’s necessary to begin to formalize.

Formalized analysis is crucial in research because it allows a research community to work together.  Researchers who doesn’t explicitly express their analyses can’t have their researcher reviewed or trusted by others. The need to share and provide explanations and evidence that can be examined leads to detailed discussions (articles books, etc.) that can themselves be analyzed (and will be by other researchers who will look for strengths on which to build and weaknesses to correct).

In practice, research communities develop different analytical frameworks and methods of analysis as a result of the attempt to explain and examine each others’ work. These become increasingly detailed and complex over time, as each successive generation of researchers turns their analytical abilities to the questions of interest. Sometimes entirely new analytical frameworks develop, but these, too, are subject to close examination that leads to complex formal analytical systems. 

Psychoanalysis, for example, depends on familiar analytical divisions: the id, ego, and super-ego represent parts of a large whole. So, too, the conscious and unconscious.  Each different pathology is a part of the large whole of “poor mental health.” And each pathology itself is distinguished by a number of different characteristics that are parts of the pathology. To become a psychoanalyst, is to adopt a specific set of analytical frameworks regarding the psychology of individuals and the nature of psychotherapy as well.   Other theories of psychology and psychotherapy may not be called “psychoanalysis,” but they too adopt different analytical frameworks.

Mathematical analyses separate the world into different symbols that represent different parts of the world and distinct relationships between the parts. Physics, of course, presents the interactions of objects in the world as a set of symbols and mathematical equations. In a business setting, the large-scale system of a factory, for example, might get represented in mathematical equations that separate out machines that produce goods, goods that are produced, rates of production, costs of production, necessary workers, etc.


Analysis happens.  If you examine something closely—an object, an interaction, an idea—you will begin to distinguish different aspects or parts of it.  These distinctions are analysis. To move that analysis into an academic or research setting really only requires that you try to make your analyses explicit as you develop them, so that they can be examined for flaws (by you and by others).

Of course, making analyses explicit and then looking at those analyses with an eye for flaws may be a path to good research, but it is not a path to simplicity.

I’m going to close here and in my next post (or posts), I’ll look with greater detail at some examples to show different ways in which things can be analyzed and to discuss the expansion of complexity, which can be both good and bad.

The Basics of Logical Analysis: Making Judgments

A writer recently expressed doubts to me about making judgments, which is a pretty common reservation: there are good reasons that we don’t want to be overly or inappropriately judgmental.  At the same time, however, life is filled with judgments that we have to make, and we want to make them well.

Life is filled with choices and each choice is a judgment. Are you going to get out of bed now, or will you roll over and pull up the covers? Are you going to go out or stay home? What will you wear? How will you prepare to go out? What will you bring with you? What will you eat? Where will you go? etc. etc.

In positions where experience and/or expertise are required, it is because of the difference between people who can make good judgments and people who make bad ones. You want your doctor/dentist/teacher/lawyer/accountant/auto mechanic/public transit driver/etc. to make good judgments when serving you, for example. You want people making policy, whether for business or organization or government, to make good judgments.  And if you aspire to fill any such role yourself, then you need to be able to make judgments yourself.

Most judgments are complicated, and that’s why people use analysis. The word “analysis” is fraught with a certain mystery or awe—for many, at least—but “analysis” is something that we all do pretty commonly. At its root, the word “analysis” means “to take apart” (in contrast to “synthesis” —put together), and at it’s root, this is what most forms of analysis do: they start to “take apart” the factors that make up a situation where judgment is required.

Consider a really simple example of analysis that most of us have experienced: you go to a store and there are two similar items that might satisfy your basic needs. Let’s say it’s a food item. Most people will perform an informal analysis that might be quite detailed: they compare prices (dimension 1), sizes (dimension 2), ingredients (dimension 3), as well as, perhaps, reputation of producer (dimension 4), aesthetics of packaging (dimension 5).  We could accurately call that comparison a “multi-dimensional analysis,” and it’s one that people do all the time.

This kind of analysis might continue with many of these factors.  With respect to ingredients, we might say “I’m glad they use X in this product, but I’m allergic to Y.” And then we’re analyzing.  An ingredient list, of course, itemizes different constituent parts of a product, so it’s already an analysis of the product.  But we could do the same with the packaging. Indeed, I said “aesthetics of packaging” above, but that’s only one dimension of an evaluative analysis of packaging—in addition to appearance, we might consider materials (paper vs. plastic, for example, is an aspect of packaging that producers absolutely care about; consumers might not be as concerned) and protection of contents. And protection of contents, might itself have different dimensions—for example, preservation of freshness and preservation of form (a cardboard box, for example, will protect the shape of brittle foods—e.g., chips, cookies—better than a plastic bag, but the plastic bag might preserve freshness better).  And if we started studying preservation of freshness, we might start to see different dimensions, again carrying out analysis. I have not studied preservation of freshness, but in my informal off-the-cuff analysis right here, we might consider freshness over weeks, over months, and over years as being different dimensions of preservation. We can imagine packaging that is inferior in the short run, but superior in the long run. For example, a loaf of bread stored in a paper bag will go stale faster than a loaf stored in a plastic bag, but storing a loaf in plastic can make a loaf’s crust less crunchy, which for some breads is a bad thing. (This basic analysis stems from an analysis of desirable qualities of bread—I like crunchy crusts and I like bread that is not stale.)

We analyze almost automatically: we see a movie and like something about it—“I liked the star; I liked the cinematography; etc.—and we have begun the process of analysis. We go to a restaurant and we analyze: “I loved the food, and the service was great, too!”  However, in situations where there is more formality—in educational settings, or when writing and imaging the response of critics—we don’t think of applying the same basic skills that we would apply automatically in our daily life.

Reasons people hesitate to analyze. 

1. We may not feel qualified. As I’ve described it, analysis is a really basic process that we all do, pretty much all the time. But “analysis” is a term typically associated with high levels of expertise. Things like statistical analysis or psychoanalysis or systems analysis are all tasks for experts.  If you doubt yourself—as many do (cf. imposter syndrome—then it is easy to put the tasks of experts outside your own set of abilities.  But, in fact, these formal systems of analysis are no more than extensions of the basic analysis I have described.  The formal details are an outgrowth of repeated attempts to use analysis productively and the recognition that formal systems of analysis are useful.  But those formal systems of analysis all start with the basic willingness to look at something and respond to the complexity that you see. Psychoanalysis, for example, looks at different components of a person’s psychology—id, ego, and super-ego is one analytical axis; conscious and unconscious is another; identification of distinct life-shaping events is another.  Such formal systems of analysis may be detailed and complex, but their use is acquired through practice that starts with trying to identify different issues of significance.  To be an expert in analysis requires practicing analysis, and that means practicing analysis while not yet an expert.  Our analyses, after all, need not commit us to anything. If we feel that an analysis has not helped us, we are perfectly free to ignore it or redo it as we wish.

2. We may feel it is inappropriate.  There are at least two reasons (in addition to the fear of being unqualified): 1. Analysis is often tied to evaluation and negative criticism, which can lead people to avoid it out of a desire to avoid being judgmental. The unfortunate conflation of analysis and negative criticism places analysis in a negative light that it doesn’t deserve. 2. Analysis can also be overdone: not all analysis is useful. Sometimes analysis can be paralyzing: instead of making a decision, we can get stuck thinking more analysis is necessary. And often analysis will focus our attention on negative aspects that we might not have given much consideration. this is not necessarily bad, but it can be an unnecessary damper on enthusiasm. If you enjoy a movie, for example, does it necessarily help you if you suddenly notice a flaw?  Analysis can take attention away form holistic concerns, too.  But these “problems” with analysis are not so much inherent in analysis as they are inherent in misuse. As with drugs or guns, use need not be misuse. There are valuable uses for drugs, for guns, and for analysis. It lies with the practitioner to use with care. 

Research and Analysis

Analysis comes naturally in research. Every choice of topic starts with separating a focal topic from the rest of the world.  If we study “education”, we’re focusing on one part of the world (and leaving out others); if we study “business,” or “history,” or “biology,” again, we’re choosing to separate one aspect of the world from others.  This is not to say that we need imagine any of these ideas as completely distinct from the rest of the world, but only that for various reasons, we are separating out one thing we want to focus on from others that we do not wish to consider. (Or we might have a more sophisticated analysis that separates the world into three general classes—the focal issue, closely related issues, and issues of little direct relevance).  Choices like this are the basis of research, so you want to make them.  

If we ask “how does X affect Y”, a starting place is to literally break out and examine each piece of that sentence: what is X? what is Y? what kind of effects are you imagining?  That is to say that we look at the sentence and separate out different aspects, with each word representing an aspect of the situation in question. The very language that we use reflects our analytical tendencies. Defining different terms used in research is a fundamental process of analyzing a situation into component parts.

Suppose, for example, that we are looking at Montessori education’s (X) effects on students (Y), then  we would naturally want to explain what Montessori education is and who Montessori students are. We would also want to consider what “affects” means in this context, and with a little thought, we can probably find a number of different things that could be relevant to this discussion: maybe Montessori education affects students’ overall success as students, or maybe it affects their emotional health as children, or their ability to make friends, or their long-term success in school, or their success in college, or their success as students of STEM subjects, or their success as students of language arts.  Each of these possible implications for an educational system on its students is one of the factors identified by this very informal process of analysis that I have undertaken.

Or, suppose that we are interested in management practices (X) and business performance (Y).  First we need to look at what we mean by management practices—what counts as a management practice? And if many things do, will we choose to study all of them?  Then, separately, we can look at different dimensions of business performance, starting, perhaps, with profitability, but also including such things as employee morale.

Research starts with casual analysis

Research depends on analysis in many different forms—from finding different aspects of situations to examine to finding different perspectives from which to analyze a situation.  All of these forms essentially spring from the observations that you have as a researcher and your interest in and attention to detail.

In the course of your research, you will probably be motivated to move beyond the initial steps of casual analysis that you would carry out in everyday life—you don’t need to exhaustively list all the different possible characteristics of a movie to decide whether you want to see it (or whether or why you enjoyed it). But don’t be afraid of those first steps: analysis is not something inappropriate or reserved for some special class of analyst. It is one of the foundations of critical thinking, and if you want to come up with original research, your observations of the world, the way that you organize your observations, and the analyses that you come up with are the roots of original research.

So look closely, don’t be afraid to identify specific details, and then see what you can learn from those observations.  At its root, analysis is something we all do. Research is just a move to try to formalize this common practice.