"A Framework for Scientific Papers?" is intended to help students use one framework to structure scientific papers.
There are many ways to do science. One of the strengths of science is that each individual scientist makes different contributions in different ways. Curiosity-based research widens the scope of science to the bounds of the human imagination, and provides vital opportunities for serendipity and opportunism. Moreover, the diversity of science contributes to its evolution. Scientific selection acting on diverse ideas helps to adapt science to the current social, factual, and technological environment. Therefore, it would be impossible and misguided to try to fit science into anysingle process or formula.
However, norms are also important to science. For example, the peer-review process depends on common expectations for methodology and rigor. Scientific norms include statistical conventions that span many scientific fields. Norms can also be confined to specific practices or fields. Across science and within scientific disciplines, some shared acknowledgement of current best practices is essential for advancing research while maintaining reasonable expectations of quality. Common practices in science provide an opportunity to help students practice skills and approaches that they can transfer to many scientific and professional contexts.
"A Framework for Scientific Papers" (AFSP) makes an effort to present the most uncontroversial but useful principles for science practice and communication possible. For example, the Aristotelian logic adopted in "Reasoned Writing" and applied in AFSP is time-honored and ubiquitous. AFSP does not seek to do justice to the history, or current debates in the philosophy of science and scientific reasoning (e.g. Giere, 2001). AFSP also does not seek to address different statistical frameworks that are available for designing experiments and interpreting data (Taper and Ponciano, 2016). Instead, AFSP seeks to present basic principles of reasoning and interpreting statistical tests using available and commonly used methods (e.g. t-tests, ANOVA, etc.).
Most of the recommendations in AFSP generally match the many "guides" for scientific writing available to students. My hope in presenting more normative material is to provide brief, practical advice that can be understood in the context of the reasoning principles introduced in "Reasoned Writing." Therefore, students may be able to gain a deeper understanding of some of the reasons for current scientific practices.
However, some of the instructional approaches that I have found most useful for helping students improve scientific reasoning and writing deviate somewhat from convention. Three important areas where AFSP may seem (or be) different from other sources of guidance are:
1) A focus on quantitative, hypothesis-driven research.
In the interests of simplicity and brevity, AFSP focuses on one framework for scientific papers. The goal of the AFSP module is NOT to survey many kinds of research methods. The goal of AFSP is to present one framework that is specific enough to help students structure scientific papers when time and effort are constrained.
The National Research Council identified six guiding principles that underlie scientific inquiry (Shavelson and Towne, 2002):
1) Pose Significant Questions That Can Be Investigated Empirically.
2) Link Research to Relevant Theory.
3) Use Methods That Permit Direct Investigation of the Question.
4) Provide a Coherent and Explicit Chain of Reasoning.
5) Replicate and Generalize Across Studies.
6) Disclose Research to Encourage Professional Scrutiny and Critique.
Quantitative, hypothesis-driven research satisfies all six principles identified by the NRC, and is therefore an appropriate framework for student inquiry. Hypothesis-driven research is also an important component of the Scientific and Engineering Practices identified by the National Research Council (NRC, 2012).
2) Distinction between "General" and "Measurable" Hypotheses.
The term "hypothesis" is used in many ways. On one hand, "hypothesis" is used for very general models of the world or universe (Giere, 2001; Lovelock and Margulis, 1974). On the other hand, hypotheses refer to specific predictions that can be statistically tested (Sokal and Rohlf, 1987). In my experience, many students are legitimately confused about the many types of hypotheses that they encounter.
When asked to develop a hypothesis, many students write vague statements somewhere between general models and testable predictions. I hypothesize that confusion about the different roles of hypotheses may contribute to the difficulties that students have with developing specific hypotheses. Therefore, AFSP establishes a dichotomy: dividing hypotheses into two separate types that reflect two important roles for hypotheses:
2) "Measurable hypotheses," that are specific, testable predictions of the outcomes of individual experiments.
The General/Measurable dichotomy supports the application of the reasoning principles reviewed in "Reasoned Writing." General Hypotheses are typically tested (supported or rejected) by many experimental studies through induction(although Strong Inference can also be used to test General Hypotheses; Platt 1964). In contrast, Measurable Hypotheses are typically rejected using data from a specific experiment through deduction. Similarly, developing and testing General Hypotheses contributes to evaluation skills, whereas developing and testing Measurable Hypothesis contributes to analytical skills (Bloom et al., 1956).
Using two distinct forms of hypotheses also helps to explicitly structure the very different sections of scientific papers around the common framework of hypotheses. The Introduction and Discussion primarily focus on General Hypotheses, whereas the Methods and Results are structured around Measurable hypotheses.
Practically, the dichotomy of General vs. Measurable hypotheses is useful for helping students write simply and specifically. In my experience, students start developing hypotheses with statements that are closer to General than to Measurable Hypotheses. Distinguishing between General and Measurable hypotheses helps to maintain feedback positive and constructive: students can be commended for making a strong start towards a General Hypothesis, then encouraged to create separate, testable Measurable Hypotheses.
Therefore, AFSP distinguishes between General and Measurable hypotheses even though I readily acknowledge that the explicit distinction and terminology is NOT common in scientific papers. In scientific papers, the roles of hypotheses are often indicated contextually. However, nuanced scientific context is often difficult for students to understand. Using distinct terminology for different types of hypotheses is a simple approach to help students develop useful hypotheses.
The primary goal of AFSP is not to help students write publishable scientific papers, but to understand some of the fundamental elements of scientific reasoning and writing. Therefore, I consider some departure from common scientific conventions to be justified. Clearly-written papers with the General/Measurable hypothesis distinction could easily be revised to submit for publication if desired.
3) Using a reasoned framework to structure the Methods and Results sections.
Books and resources on scientific writing may provide different guidance for writing different sections of a scientific paper. For example, writers may be encouraged to answer the question "Why was the problem studied?" in the Introduction, "How was the problem studied?" in the Methods, "What were the findings?" in the Results and "What do the findings mean?" in the Discussion (Bolt and Bruins, 2012). I hypothesize that structuring different sections around very general questions that are different for each section is confusing for students.
Therefore, AFSP specifies a single type of question ("Why") and a single overall goal (hypothesis testing) for each section of the paper. The questions posed by AFSP are:
INTRODUCTION: WHY does an important GAP in current scientific understanding lead reasonably to the General and Measurable hypotheses?
METHODS: WHY are the chosen methods necessary and appropriate to test the Measurable Hypotheses?
RESULTS: WHY do the data lead to the conclusion to reject or support each Measurable Hypothesis?
DISCUSSION: WHY do the results (i.e. the conclusions about the Measurable Hypotheses) either support existing General Hypotheses or lead us to propose new General Hypotheses?
Although the questions for the Introduction and Discussion may be uncontroversial, the questions for the Methods and Results may seem (at first) to be unconventional. Specifically, the question for the Methods section may seem different from common recommendation for the Methods to be primarily descriptive. Similarly, testing hypotheses in the Results section may seem counter to the common recommendation that the Results present data without interpretation (Holstein et al., 2015). However, I consider the recommendations here in the AFSW module to be consistent with common recommendations for the following reasons:
A) Methods. In my estimation, an explanatory Methods section is more complete than a descriptive Methods section. A purely descriptive Methods section omits important information, even if the section describes all experimental techniques in sufficient detail for rote replication. The reasons for choosing the selected methods instead of alternatives can also represent substantial time, effort and cost. The reasons for selecting methods can also reflect assumptions of a study that may not be explained elsewhere. Therefore, the AFSW module recommends a framework for the Methods section that includes the justification for each method as a part of its presentation. In my estimation, including the reasoning that leads to specific methods is a necessary part of explaining the methods in sufficient detail to be fully understood and replicated.
B) Results. Collecting data as objectively as possible is clearly important for quantitative research. Likewise, presenting data clearly and with as little subjective interpretation as possible is an important goal for the Results section of a scientific paper. However, in my estimation there is a reason that the Results section is called the "Results" section and not simply the "Data" section. The reason is that, for hypothesis-driven studies, Data alone are not Results. The framework presented in AFSW involves three elements that constitute a "result":
1) A Measurable Hypothesis that is specific enough to be testable using data collected and analyzed by the experiment.
2) Data that are objectively collected and analyzed, and suitable for testing the Measurable Hypothesis.
3) The application of the data to testing the Measurable Hypothesis to yield a result.
In a strongly-structured Results section, the process of applying data to a specific and testable Measurable Hypothesis is not interpretation because the conclusion does not require subjective judgment. For example, the hypothesis and data can be expressed as the two premises of a deductive syllogism. If the syllogism is strongly structured in the form of modus tollens, the conclusion directly follows from the premises. Therefore, the conclusion does not require interpretation and reasonably belongs in the Results section.
I acknowledge that some writers and journals prefer to place the conclusions of arguments in the Discussion section. In my estimation, the location of the conclusions is a stylistic issue that does not affect the underlying substance of the reasoning. I consider it clearer to locate the conclusions close to the arguments and data that support them. However, reasonable people differ, and diversity is important for science.
Structuring each section around "Why" questions and consistent, specific reasoning can simplify the process of scientific writing. Therefore, the AFSW module seeks to use the most consistent frameworks possible to structure each section of a scientific paper.
Although there are many ways to conduct research, a comprehensive or comparative review of research techniques is not an objective of the RW/ASSP modules. Instead, the modules seek to help students add ONE framework for structuring scientific papers to their toolbox, with the sincere hope that students will engage in life-long learning to understand the diversity of scientific approaches.