Stop and think about the education you amassed to get to the point you are now—the late nights studying for finals; countless hours preparing for entrance exams; the papers written, edited, and revised to convey just the right message? If you’re a Talking Heads fan you probably hear David Burns asking, “How did I get here?”

In functional terms, a reference manager is a program that allows you to collect, organize, and cite reference materials (e.g., books, journal articles, websites).  It has become an invaluable tool for anyone engaged in modern academic writing.   Through my career I've used a variety of reference managers, and their functionality is an integral part of my work flow which involves reading academic material, collaborating with other authors, and writing across a variety of platforms such as LaTeX, Authorea, and Libre Office.

Practical action research is conducted to improve practice and resolve problems of practice. This involves working within complex scenarios that can be difficult to understand. Complexity can confound otherwise well designed research, creating misunderstanding and misinterpretation within the study itself, and indistinct communication about the study. The effects of complexity can be mitigated through systematic pre-study analysis that clarifies the problem, and provides a transparent decision-trail that is examinable by other researchers and stakeholders. In this article, we present a process for conducting such analysis. The presented method provides a systematic and transparent process for analyzing problems that: a) redounds to a contextually-sensitive, high-resolution articulation of the problem; and b) prioritizes deconstructed problem elements for follow-on research. This method is presented for use as a pre-study task to enhance the effectiveness of practical action research initiatives and transferability of findings.

The process of social science scholarship - research, theoretical, methodological, and conceptual work - does not happen in isolation nor by accident. Scholarship builds on the ideas and efforts of others, challenges established orthodoxies, and provides the insights and evidence for a field of study. It is the collective process by which we've developed high resolution understandings of phenomena, solutions for complex problems, and more effective ways to flourish in our world. There is nothing natural about effective scholarship. Human nature tends toward confirmation bias, affiliatory preferences, and myopia. In other words, scholarship is a process that must be diligently maintained or it will regress back to the default characteristics of human nature. For something this important, there's surprisingly little discussion about the assumptions, norms, and habits under which we operate in scholarly environments. Perhaps it has always been assumed that those engaging in scholarship were naturally enculturated as part of their technical training. Maybe this was true at one point, but over the years, there has been a noticeable decline in many of the long-established informal scientific traditions. And to be clear, I am not referring to the differences between positivist and naturalist orientations, nor between quantitative and qualitative methods of research. What I am referring to here is a decline in a basic understanding of what constitutes good scholarship. This has been supplanted with a deterministic orientation to press a preferred narrative, and the subjugation of scholarship toward that end. That, of course, is not scholarship, it's advocacy. And while advocacy certainly has it's place, the two should not be conflated.The recognition of this decline has changed the way I introduce my  graduate students to research. Rather that assuming that certain assumptions and habits will germinate and develop naturally over time, I have begun explicitly articulating them as a launching point for discussions. As with any thesis, there are statements here that thoughtful people may disagree with; and I welcome any constructive feedback a/or criticisms.Assumptions and HabitsTo claim that you understand an idea you must be able to operationalize it, provide examples of it, and articulate what should follow from it.The scientific method does not prove, it only disproves. A theory only remains tenable through the accumulation of evidence that fails to refute it.  Research can be used selectively to support most any position. Examine the full body of evidence before accepting an evidence-based argument. Demagoguery does not edify nor does it further understanding of ideas. If you disagree with something, then present a better argument.Peer review does not necessarily mean quality. Know what constitutes quality scholarship so you can distinguish the good from the not so good.Scholarship is not immune to narrative, politics, and culture. Mind the exogenous influences when evaluating a body of scholarship.Statistical significance is not the same as practical importance. Know the established benchmarks within a field before evaluating the importance of a claim. Scholarship is grounded in reason. The integrity of an idea is what matters, not the passion that surrounds it.Affiliatory biases inhibit scholarship. Guard against reflexively accepting ideas because they are from within a familiar group or reflexively dismissing them because they are extrinsic to the group.Humility is the inevitable outcome of effective scholarship. Be cautions of those who claim to hold a monopoly on the truth.

Consider what usually happens when a large and complex problem is encountered. All too often there's a paralysis by analysis and nothing meaningful gets done at all. Sometimes solutions are identified from external sources (e.g., academic literature, colleagues, case studies) and applied. Maybe something works, maybe it doesn't. Often the metrics for improvement are fuzzy, so it's hard to tell. People become frustrated, lose interest, and move on to something else.

Correlation is a fundamental concept within statistics that, once understood, provides insight into more complex statistical models and ideas. From a conceptual standpoint, correlation summarizes the measured association between variables, meaning the extent to which one variable is affected by the other. Put another way, correlation is simply a measure of association.

LaTeX is a powerful free and open-source academic writing system; however, it does come with a learning curve. This curve can be especially steep when trying to incorporate bibliographic references and formatting a document to the current writing style of the field - APA 6th edition. Anyone who has submitted a manuscript for publication is familiar with the trials and tribulations of formatting and citing sources. Any tool to help with this process is a welcome one. Although LaTeX comes close to APA compatibility natively, it doesn't quite nail it. This post describes how to set-up and integrate a powerful (and free) LaTeX editor with a powerful (and free) citation manager to assist with formatting and citing. To get started, you will need to download and install two programs.

At some point, we've all likely heard the cautionary assertion that correlation is not causation. It sounds reasonable so we tend to accept the assertion, but what does it really mean? And is it always true?To answer these questions, we first need to understand what the terms mean and how they are distinguished from one another. Correlation is a mathematical representation that summarizes the measured association between variables. In simpler terms, it's a number between -1 and 1 that describes what happens to one variable (let's call this variable y) when another variable changes (let's call this one x). Causation takes correlation a bit further by demanding more from our variables than a basic association. Causation requires that at least part of the the change we see in variable y is actually due to changes in variable x. In other words, a change in one variable has actually caused a change in the other, hence the term causal.First, let's look at how correlations between variables can be misleading. The scatterplot in Fig. 1 shows simulated data from a sample of 50 elementary students, grades 1-6. The plot shows two variables for each student: a measure of shoe size along the x-axis (var.x) and performance on a common math test along the y-axis (var.y). Each point in the plot represents the intersection between those variables for each student in our simulated sample. The association between these variables is clear, as shoe size (x) increases, so do our math scores (y). There is a rather wide range in math scores across shoe sizes, but this range doesn't throw off the overall association demonstrated by the linear increase indicated by the blue line of best fit. To further reinforce this association, we can look at the calculated correlation statistic between shoe size and math performance [r(xy)=.74]. [If this statistic is unfamiliar, see Linear Association and Correlation.] This is a strong correlation, certainly something to take notice of, and provides further evidence for the association between shoe size and math performance within our sample.