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Why Most Clinical Research Is Not Useful

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John Ioannidis argues that problem base, context placement, information gain, pragmatism, patient centeredness, value for money, feasibility, and transparency define useful clinical research. He suggests most clinical research is not useful and reform is overdue.

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Brochures: Writing for Audience and Purpose | Read Write Think

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5 Critical Components of Primary Source Analysis

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These five components of primary source analysis have the power

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Critical Questions for Informational Writing

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Critical Questions for Informational Writing

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Who wrote this?

Who is the intended audience?

What is the purpose of this information?

Why is the information presented and organized in this way?

What is said?

What is not said?

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What are the different types of informational texts students will interact with in life?

Websites

Social Media Posts

Newspapers

Research papers

Journal Articles

Brochures

Infographics

Podcasts

Speeches

Public Service Announcements

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Play the fake news game! How bad are you? Become a fake news tycoon and get as many followers as you can.

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How to Make a Peanut Butter & Jelly Sandwich * Beyond the Chalkboard

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Be aware of peanut allergies! If any of your students are allergic to peanut butter, you can instead make a cream cheese and jelly sandwich or something similar. If you do have any students who are allergic to peanuts, DO NOT ATTEMPT THIS ACTIVITY WITH PEANUT BUTTER!

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Blog/Social media

Brochure

Infographic

Podcast

Newspaper/Journal Article

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How do I determine truth in a non-neutral world? (How do I find my own opinions within this world?)

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Topic: Informational Writing

Grade: 11th

Time: 50min lessons

Early in the year

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Speaking and Listening (11-12.SL) [ALL]

Standard 11-12.R.10

Standard 11-12.R.12

Standard 11-12.R.13

Standard 11-12.W.2

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English Language Arts Grades 11-12 (2023) Core

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K-12 Core Curriculum - The Utah State Core Standards represent those standards of learning that are essential for all students. They are the ideas, concepts, and skills that provide a foundation on which subsequent learning may be built.

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Objective To determine if using a parachute prevents death or major traumatic injury when jumping from an aircraft.

Design Randomized controlled trial.

Setting Private or commercial aircraft between September 2017 and August 2018.

Participants 92 aircraft passengers aged 18 and over were screened for participation. 23 agreed to be enrolled and were randomized.

Intervention Jumping from an aircraft (airplane or helicopter) with a parachute versus an empty backpack (unblinded).

Main outcome measures Composite of death or major traumatic injury (defined by an Injury Severity Score over 15) upon impact with the ground measured immediately after landing.

Results Parachute use did not significantly reduce death or major injury (0% for parachute v 0% for control; P>0.9). This finding was consistent across multiple subgroups. Compared with individuals screened but not enrolled, participants included in the study were on aircraft at significantly lower altitude (mean of 0.6 m for participants v mean of 9146 m for non-participants; P<0.001) and lower velocity (mean of 0 km/h v mean of 800 km/h; P<0.001).

Conclusions Parachute use did not reduce death or major traumatic injury when jumping from aircraft in the first randomized evaluation of this intervention. However, the trial was only able to enroll participants on small stationary aircraft on the ground, suggesting cautious extrapolation to high altitude jumps. When beliefs regarding the effectiveness of an intervention exist in the community, randomized trials might selectively enroll individuals with a lower perceived likelihood of benefit, thus diminishing the applicability of the results to clinical practice.

Study protocol

Between September 2017 and August 2018, individuals were screened for inclusion in the PArticipation in RAndomized trials Compromised by widely Held beliefs aboUt lack of Treatment Equipoise (PARACHUTE) trial. Prospective participants were approached and screened by study investigators on commercial or private aircraft.

For the commercial aircraft, travel was related to trips the investigators were scheduled to take for business or personal reasons unrelated to the present study. Typically, passengers seated close to the study investigator (typically not known acquaintances) would be approached mid-flight, between the time of initial seating and time of exiting the aircraft. The purpose and design of the study were explained. Owing to difficulty in enrolling patients at several thousand meters above the ground, we expanded our approach to include screening members of the investigative team, friends, and family. For the private aircraft, the boarding of aircraft was done for the explicit purpose of participating in the trial.

All participants were asked whether they would be willing to be randomized to jump from the aircraft at its current altitude and velocity. Potential study participants completed an anonymous survey using a survey app on the screening investigator's phone or tablet. Responses were transmitted to an online database upon landing for later analysis.

We enrolled individuals willing to participate in the trial and meeting inclusion criteria in the study. We randomized patients (1:1) to the intervention or the control. We obtained written informed consent. Participants were then instructed to jump from the aircraft after being provided their assigned device. Jumps were conducted at two sites in the US: Katama Airfield in Martha's Vineyard, MA (conducted by investigators from the Beth Israel Deaconess Medical Center), and the Yankee Air Museum in Belleville, MI (conducted by investigators from the University of Michigan). The same protocol was followed at each site, but the type of aircraft (airplane v helicopter) differed between the two sites.

Study population

Participants aged 18 and over, seated on an aircraft, and deemed to be rational decision makers by the enrolling investigator were eligible. Only participants who were willing to be randomized in the study were ultimately enrolled and randomized. Most of the participants who were randomized were study investigators.

Interventions

Participants were randomized to wear either a parachute (National 360, National Parachute Industries, Inc, Palenville, NY; or Javelin Odyssey, Sun Path Products, Inc, Raeford, NC; supplementary materials fig 1) or an empty backpack (The North Face, Inc, Alameda, CA; or Javelin Odyssey Gearbag, Sun Path Products, Inc). The interventions were not blinded to either participants or study investigators.

Randomization

We used block randomization, stratified by site and sex with a block size of two. The trial statistician created the randomization sequence by using the R package blockrand. The research team had previously assigned unique numeric identifiers to each participant. At both sites, only one team member had access to the list of numeric identifiers. Participants were verbally assigned their treatment, which was done by order of enrolment. Allocation was not concealed to the investigator who assigned the treatment.

Data collection

We collected data on basic demographic characteristics during screening by using paper forms or the survey app. 8 Characteristics included age, sex, ethnic group, height, and weight. We also collected information on participants' medical history including a history of broken bones, acrophobia (fear of heights), previous parachute use, family history of parachute use, and frequent flier status. Flight characteristics included carrier, velocity, altitude, make and model of the aircraft, the individual's seating section, and whether the flight was international or domestic. Velocity and altitude were captured by using flight information provided by aircraft on individual television screens when available, as well as through pilot announcements. When neither was directly available, visual estimations were made by the study investigators.

At the time of each jump, researchers recorded the altitude and velocity of the aircraft, and conducted a follow-up interview with each participant to ascertain vital status and to record any injuries sustained from the free fall within five minutes of impact with the ground, and again at 30 days after impact. We collected data electronically or with paper forms and uploaded the data to an online deidentified, password protected database.

Outcomes

The primary outcome was the composite of death and major traumatic injury, defined by an Injury Severity Score greater than 15, within five minutes of impact. The Injury Severity Score is a commonly used anatomical scoring system to grade the severity of traumatic injuries. 9 Separate scores are assigned to each of six anatomical regions, and the three most highly injured regions contribute to a final score ranging from 0 to 75. Higher scores indicate a more severe injury. Secondary outcomes included death and major traumatic injury assessed at 30 days after impact using the Injury Severity Score, as well as 30 day quality of life assessed by the Short Form Health Survey. The Short Form Health Survey is a multipurpose questionnaire that measures a patient's overall health-related quality of life based on mental and physical functioning. 10

Statistical analysis

The primary efficacy analysis tested the hypothesis that parachute use is superior to the control in preventing death and major traumatic injury. Based on an assumption of an average jump altitude of 4000 meters (typical of skydiving) and the anticipated effect of impact with the Earth at terminal velocity on human tissue, we projected that 99% of the control arm would experience the primary outcome at ground impact with a relative risk reduction of 95% in the intervention arm. A sample size of 14 (7 in each arm) would yield 99% power to detect this difference at a two sided α of 0.05. In anticipation of potential withdrawal after enrolment owing to last minute anxieties, a total sample size of 20 participants was targeted. Analysis was performed on an intention-to-treat basis. We performed secondary subgroup analyses stratified by aircraft type (airplane v helicopter) and previous parachute use through formal tests of statistical interaction.

We summarized continuous variables by mean (standard deviation) and categorical variables by frequency and percentage. We tabulated baseline characteristics of the two trial arms to examine for potential imbalance in variables. We tested for differences between the outcomes of the two trial arms by using Student's t test (continuous variables) and Fisher's exact test (categorical variables). To better understand what drove the willingness to participate in the trial, we also compared characteristics of individuals who were screened but chose not to enroll with individuals who enrolled. Baseline characteristics between those enrolled and not enrolled were compared using the same statistical tests. Confidence intervals for the difference in continuous outcomes between the two arms were constructed using T distributions. We could not calculate confidence intervals for the difference between arms (eg, risk difference, odds ratio, or relative risk) because no events were observed for any of the binary outcomes in either arm.

We performed all analyses by using SAS version 9.4 (SAS Institute Inc, Cary, NC). A P value less than 0.05 was considered statistically significant.

Study population

A total of 92 individuals were screened and surveyed regarding their interest in participating in the PARACHUTE trial. Among those screened, 69 (75%) were unwilling to be randomized or found to be otherwise ineligible by investigators. Figure 1 shows that a total of 23 individuals were deemed eligible for randomization.

Table 1 shows that the baseline characteristics of enrolled participants were generally similar between the intervention and control arms. The median age of randomized participants was 38 years and 13 (57%) were male. Three (13%) of the randomized participants had previous parachute use and nine (39%) had a history of acrophobia. Table 2 shows that participants in the study were similar to those screened but not enrolled with regard to most demographic and clinical characteristics. However, participants were less likely to be on a jetliner, and instead were on a biplane or helicopter (0% v 100%; P<0.001), were at a lower mean altitude (0.6 m, SD 0.1 v 9146 m, SD 2164; P<0.001), and were traveling at a slower velocity (0 km/h, SD 0 v 800 km/h, SD 124; P<0.001) ( table 2).

Among the 12 participants randomized to the intervention arm, the parachute did not deploy in all 12 (100%) owing to the short duration and altitude of falls. Among the 11 participants randomized to receive an empty backpack, none crossed over to the intervention arm. Figure 2 shows a representative jump (additional jumps are shown in supplementary materials fig 2).

Outcomes

Table 3 shows the results for the primary and secondary outcomes. There was no significant difference in the rate of death or major traumatic injury between the treatment and control arms within five minutes of ground impact (0% for parachute v 0% for control; P>0.9) or at 30 days after impact (0% for parachute v 0% for control; P>0.9). Health status as measured by the Short Form Health Survey was similar between groups (43.9, SD 1.8 for parachute v 44.0, SD 2.4 for control; P=0.9; mean difference of 0.1, 95% confidence interval −2.0 to 2.2). In subgroup analyses, there were no significant differences in the effect of parachute use on outcomes when stratified by type of aircraft or previous parachute use (P>0.9 for interaction for both comparisons).

Strengths and weaknesses of this study

A key strength of the PARACHUTE trial was that it was designed and initially powered to detect differences in the combination of death and major traumatic injury. Although the use of softer endpoints, such as levels of fear before and after jumping, or its surrogates, such as loss of urinary continence, could have yielded more power to detect an effect of parachutes, we believe that that our selection of bias-resistant endpoints that are meaningful to all patients increases the clinical relevance of the trial.

The study also has several limitations. First and most importantly, our findings might not be generalizable to the use of parachutes in aircraft traveling at a higher altitude or velocity. Consideration could be made to conduct additional randomized clinical trials in these higher risk settings. However, previous theoretical work supporting the use of parachutes could reduce the feasibility of enrolling participants in such studies. 16

Second, our study was not blinded to treatment assignment. We did not anticipate a strong placebo effect for our primary endpoint, but it is possible that other subjective endpoints would have necessitated the use of a blinded sham parachute as a control.

Third, the individuals screened but not enrolled in the study were limited to passengers unfortunate enough to be seated near study investigators during commercial flights, and might not be representative of all aircraft passengers. The participants who did ultimately enroll, agreed with the knowledge that the aircraft were stationary and on the ground.

Finally, although all endpoints in the study were prespecified, we were unable to register the PARACHUTE trial prospectively. We attempted to register this study with the Sri Lanka Clinical Trials Registry (application number APPL/2018/040), a member of the World Health Organization's Registry Network of the International Clinical Trials Registry Platform. After several rounds of discussion, the Registry declined to register the trial because they thought that "the research question lacks scientific validity" and "the trial data cannot be meaningful." We appreciated their thorough review (and actually agree with their decision).

The PARACHUTE trial satirically highlights some of the limitations of randomized controlled trials. Nevertheless, we believe that such trials remain the gold standard for the evaluation of most new treatments. The PARACHUTE trial does suggest, however, that their accurate interpretation requires more than a cursory reading of the abstract. Rather, interpretation requires a complete and critical appraisal of the study. In addition, our study highlights that studies evaluating devices that are already entrenched in clinical practice face the particularly difficult task of ensuring that patients with the greatest expected benefit from treatment are included during enrolment.

To safeguard this last issue, we see several solutions. First, overcoming such a hurdle requires extreme commitment on the part of the investigators, clinicians, and patients; thankfully, recent examples of such efforts do exist. 17 Second, stronger efforts could be made to ensure that definitive trials are conducted before new treatments become inculcated into routine practice, when greater equipoise is likely to exist. Third, the comparison of baseline characteristics and outcomes of study participants and non-participants should be utilized more frequently and reported consistently to facilitate the interpretation of results and the assessment of study generalizability. 14 Finally, there could be instances where clinical beliefs justifiably prevent a true randomized evaluation of a treatment from being conducted.

Conclusion

Parachute use compared with a backpack control did not reduce death or major traumatic injury when used by participants jumping from aircraft in this first randomized evaluation of the intervention. This largely resulted from our ability to only recruit participants jumping from stationary aircraft on the ground. When beliefs regarding the effectiveness of an intervention exist in the community, randomized trials evaluating their effectiveness could selectively enroll individuals with a lower likelihood of benefit, thereby diminishing the applicability of trial results to routine practice. Therefore, although we can confidently recommend that individuals jumping from small stationary aircraft on the ground do not require parachutes, individual judgment should be exercised when applying these findings at higher altitudes.

View Abstract

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Parachute use to prevent death and major trauma when jumping from aircraft: randomized controlled trial

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Objective To determine if using a parachute prevents death or major traumatic injury when jumping from an aircraft. Design Randomized controlled trial. Setting Private or commercial aircraft between September 2017 and August 2018. Participants 92 aircraft passengers aged 18 and over were screened for participation. 23 agreed to be enrolled and were randomized.

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Overtrust of robots in emergency evacuation scenarios

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Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we performed an experiment where a participant interacts with a robot in a non-emergency task to experience its behavior and then chooses whether to follow the robot’s instructions in an emergency or not. Artificial smoke and fire alarms were used to add a sense of urgency. To our surprise, all 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to safely exit the way they entered.

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Abstract

Summary

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Citation: Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8): e124. https://doi.org/10.1371/journal.pmed.0020124

Published: August 30, 2005

Copyright: © 2005 John P. A. Ioannidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests: The author has declared that no competing interests exist.

Abbreviation: PPV, positive predictive value

Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [ 1-3] to the most modern molecular research [ 4, 5]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [ 6-8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.

Modeling the Framework for False Positive Findings

Several methodologists have pointed out [ 9-11] that the high rate of nonreplication (lack of confirmation) of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p-value less than 0.05. Research is not most appropriately represented and summarized by p-values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p-values. Research findings are defined here as any relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, risk factors, or associations. "Negative" research is also very useful. "Negative" is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null findings.

It can be proven that most claimed research findings are false

As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study), the statistical power of the study, and the level of statistical significance [ 10, 11]. Consider a 2 × 2 table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both true and false hypotheses can be made about the presence of relationships. Let R be the ratio of the number of "true relationships" to "no relationships" among those tested in the field. R is characteristic of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let us also consider, for computational simplicity, circumscribed fields where either there is only one true relationship (among many that can be hypothesized) or the power is similar to find any of the several existing true relationships. The pre-study probability of a relationship being true is R/( R + 1). The probability of a study finding a true relationship reflects the power 1 - β (one minus the Type II error rate). The probability of claiming a relationship when none truly exists reflects the Type I error rate, α. Assuming that c relationships are being probed in the field, the expected values of the 2 × 2 table are given in Table 1. After a research finding has been claimed based on achieving formal statistical significance, the post-study probability that it is true is the positive predictive value, PPV. The PPV is also the complementary probability of what Wacholder et al. have called the false positive report probability [ 10]. According to the 2 × 2 table, one gets PPV = (1 - β) R/( R - βR + α). A research finding is thus more likely true than false if (1 - β) R > α. Since usually the vast majority of investigators depend on a = 0.05, this means that a research finding is more likely true than false if (1 - β) R > 0.05.

What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the globe may further distort this picture and may lead to even smaller probabilities of the research findings being indeed true. We will try to model these two factors in the context of similar 2 × 2 tables.

Bias

First, let us define bias as the combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Let u be the proportion of probed analyses that would not have been "research findings," but nevertheless end up presented and reported as such, because of bias. Bias should not be confused with chance variability that causes some findings to be false by chance even though the study design, data, analysis, and presentation are perfect. Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. In the presence of bias ( Table 2), one gets PPV = ([1 - β] R + uβ R)/( R + α − β R + u − uα + uβ R), and PPV decreases with increasing u, unless 1 − β ≤ α, i.e., 1 − β ≤ 0.05 for most situations. Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure 1. Conversely, true research findings may occasionally be annulled because of reverse bias. For example, with large measurement errors relationships are lost in noise [ 12], or investigators use data inefficiently or fail to notice statistically significant relationships, or there may be conflicts of interest that tend to "bury" significant findings [ 13]. There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data. Regardless, reverse bias may be modeled in the same way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.

Panels correspond to power of 0.20, 0.50, and 0.80.

Testing by Several Independent Teams

Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, it is practically the rule that several research teams, often dozens of them, may probe the same or similar questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation. An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention. The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate. For n independent studies of equal power, the 2 × 2 table is shown in Table 3: PPV = R(1 − βn)/( R + 1 − [1 − α]n − Rβn) (not considering bias). With increasing number of independent studies, PPV tends to decrease, unless 1 - β < a, i.e., typically 1 − β < 0.05. This is shown for different levels of power and for different pre-study odds in Figure 2. For n studies of different power, the term βn is replaced by the product of the terms βi for i = 1 to n, but inferences are similar.

Panels correspond to power of 0.20, 0.50, and 0.80.

Corollaries

A practical example is shown in Box 1. Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true.

Box 1. An Example: Science at Low Pre-Study Odds

Let us assume that a team of investigators performs a whole genome association study to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them. Then R = 10/100,000 = 10 −4, and the pre-study probability for any polymorphism to be associated with schizophrenia is also R/( R + 1) = 10 −4. Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at α = 0.05. Then it can be estimated that if a statistically significant association is found with the p-value barely crossing the 0.05 threshold, the post-study probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 × 10 −4.

Now let us suppose that the investigators manipulate their design, analyses, and reporting so as to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available "data mining" packages actually are proud of their ability to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is only 4.4 × 10 −4. Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.5 × 10 −4, hardly any higher than the probability we had before any of this extensive research was undertaken!

Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Small sample size means smaller power and, for all functions above, the PPV for a true research finding decreases as power decreases towards 1 − β = 0.05. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology (several thousand subjects randomized) [ 14] than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller) [ 15].

Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks 3-20), than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1-1.5) [ 7]. Modern epidemiology is increasingly obliged to target smaller effect sizes [ 16]. Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.

Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study probability that a finding is true (PPV) depends a lot on the pre-study odds (R). Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [ 4, 8, 17], should have extremely low PPV.

Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. Flexibility increases the potential for transforming what would be "negative" results into "positive" results, i.e., bias, u. For several research designs, e.g., randomized controlled trials [ 18-20] or meta-analyses [ 21, 22], there have been efforts to standardize their conduct and reporting. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may be more common when outcomes are unequivocal and universally agreed (e.g., death) rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes) [ 23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank test) [ 24] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (e.g., artificial intelligence methods) and only "best" results are reported. Regardless, even in the most stringent research designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [ 25]. Simply abolishing selective publication would not make this problem go away.

Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. Conflicts of interest and prejudice may increase bias, u. Conflicts of interest are very common in biomedical research [ 26], and typically they are inadequately and sparsely reported [ 26, 27]. Prejudice may not necessarily have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [ 28].

Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention. With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive "positive" results. "Negative" results may become attractive for dissemination only if some other team has found a "positive" association on the same question. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [ 29]. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics [ 29].

These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.

Most Research Findings Are False for Most Research Designs and for Most Fields

In the described framework, a PPV exceeding 50% is quite difficult to get. Table 4 provides the results of simulations using the formulas developed for the influence of power, ratio of true to non-true relationships, and bias, for various types of situations that may be characteristic of specific study designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-study chance that the intervention is effective is eventually true about 85% of the time. A fairly similar performance is expected of a confirmatory meta-analysis of good-quality randomized trials: potential bias probably increases, but power and pre-test chances are higher compared to a single randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to "correct" the low power of single studies, is probably false if R ≤ 1:3. Research findings from underpowered, early-phase clinical trials would be true about one in four times, or even less frequently if bias is present. Epidemiological studies of an exploratory nature perform even worse, especially when underpowered, but even well-powered epidemiological studies may have only a one in five chance being true, if R = 1:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,000-fold (e.g., 30,000 genes tested, of which 30 may be the true culprits) [ 30, 31], PPV for each claimed relationship is extremely low, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.

Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias

As shown, the majority of modern biomedical research is operating in areas with very low pre- and post-study probability for true findings. Let us suppose that in a research field there are no true findings at all to be discovered. History of science teaches us that scientific endeavor has often in the past wasted effort in fields with absolutely no yield of true scientific information, at least based on our current understanding. In such a "null field," one would ideally expect all observed effect sizes to vary by chance around the null in the absence of bias. The extent that observed findings deviate from what is expected by chance alone would be simply a pure measure of the prevailing bias.

For example, let us suppose that no nutrients or dietary patterns are actually important determinants for the risk of developing a specific tumor. Let us also suppose that the scientific literature has examined 60 nutrients and claims all of them to be related to the risk of developing this tumor with relative risks in the range of 1.2 to 1.4 for the comparison of the upper to lower intake tertiles. Then the claimed effect sizes are simply measuring nothing else but the net bias that has been involved in the generation of this scientific literature. Claimed effect sizes are in fact the most accurate estimates of the net bias. It even follows that between "null fields," the fields that claim stronger effects (often with accompanying claims of medical or public health importance) are simply those that have sustained the worst biases.

For fields with very low PPV, the few true relationships would not distort this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would still yield a clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed large and highly significant effects with excitement, as signs of important discoveries. Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.

Of course, investigators working in any field are likely to resist accepting that the whole field in which they have spent their careers is a "null field." However, other lines of evidence, or advances in technology and experimentation, may lead eventually to the dismantling of a scientific field. Obtaining measures of the net bias in one field may also be useful for obtaining insight into what might be the range of bias operating in other fields where similar analytical methods, technologies, and conflicts may be operating.

How Can We Improve the Situation?

Is it unavoidable that most research findings are false, or can we improve the situation? A major problem is that it is impossible to know with 100% certainty what the truth is in any research question. In this regard, the pure "gold" standard is unattainable. However, there are several approaches to improve the post-study probability.

Better powered evidence, e.g., large studies or low-bias meta-analyses, may help, as it comes closer to the unknown "gold" standard. However, large studies may still have biases and these should be acknowledged and avoided. Moreover, large-scale evidence is impossible to obtain for all of the millions and trillions of research questions posed in current research. Large-scale evidence should be targeted for research questions where the pre-study probability is already considerably high, so that a significant research finding will lead to a post-test probability that would be considered quite definitive. Large-scale evidence is also particularly indicated when it can test major concepts rather than narrow, specific questions. A negative finding can then refute not only a specific proposed claim, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. Moreover, one should be cautious that extremely large studies may be more likely to find a formally statistical significant difference for a trivial effect that is not really meaningfully different from the null [ 32-34].

Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [ 35]. Registration would pose a challenge for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.

Finally, instead of chasing statistical significance, we should improve our understanding of the range of R values-the pre-study odds-where research efforts operate [ 10]. Before running an experiment, investigators should consider what they believe the chances are that they are testing a true rather than a non-true relationship. Speculated high R values may sometimes then be ascertained. As described above, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to see how often they are indeed confirmed. I suspect several established "classics" will fail the test [ 36].

Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [ 37], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.

References

12. Kelsey JL, Whittemore AS, Evans AS, Thompson WD (1996) Methods in observational epidemiology, 2nd ed. New York: Oxford U Press. 432 p.

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Why Most Published Research Findings Are False

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There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field.

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To make lists logical, readable, and memorable, the items in those lists must be parallel with one another, both in logic and in grammatical structure. Learn how to make items in a list parallel. Read more free writing and grammar tips at Get It Write Online.

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Objectives:

Writing using concise and effective language

Critical literacy of informational sources

Presenting information in a clear and understandable way

Outline:

Students will choose a nuanced topic they don't have an opinion on but that is talked about a lot. Students will do research in multiple informational formats to understand the reality of this topic. With this, they will create a presentation of the "facts vs. fiction" of their topic, comparing the misconceptions with the reality in a medium of their own choosing. It will be presented in a Gallery Walk at the end of the unit.

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Examine current news stories and how they’re covered by the news media

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Listen to this post as a podcast:
When I worked with student teachers on developing effective lesson plans, one thing I always asked them to revise was the phrase "We will discuss."
We will discuss the video. We will discuss the story. We will discuss our results.
Every time I saw it in a lesson plan, I would add a note: "What format will you use? What questions will you ask? How will you ensure that all students participate?" I was pretty sure that We will discuss actually meant the teacher would do most of the talking; He would throw out a couple of questions like "So what did you think about the video?" or "What was the theme of the story?" and a few students would respond, resulting in something that looked like a discussion, but was ultimately just a conversation between the teacher and a handful of extroverted students; a classic case of Fisheye Teaching.

The problem wasn't them; in most of the classrooms where they'd sat as students, that's exactly what a class discussion looked like. They didn't know any other "formats." I have only ever been familiar with a few myself. But when teachers began contacting me recently asking for a more comprehensive list, I knew it was time to do some serious research.

So here they are: 15 formats for structuring a class discussion to make it more engaging, more organized, more equitable, and more academically challenging. If you've struggled to find effective ways to develop students' speaking and listening skills, this is your lucky day.

I've separated the strategies into three groups. The first batch contains the higher-prep strategies, formats that require teachers to do some planning or gathering of materials ahead of time. Next come the low-prep strategies, which can be used on the fly when you have a few extra minutes or just want your students to get more active. Note that these are not strict categories; it's certainly possible to simplify or add more meat to any of these structures and still make them work. The last group is the ongoing strategies. These are smaller techniques that can be integrated with other instructional strategies and don't really stand alone. For each strategy, you'll find a list of other names it sometimes goes by, a description of its basic structure, and an explanation of variations that exist, if any. To watch each strategy in action, click on its name and a new window will open with a video that demonstrates it.

Enjoy!
a.k.a. Chat Stations
Basic Structure: Stations or posters are set up around the classroom, on the walls or on tables. Small groups of students travel from station to station together, performing some kind of task or responding to a prompt, either of which will result in a conversation.

Variations: Some Gallery Walks stay true to the term gallery, where groups of students create informative posters, then act as tour guides or docents, giving other students a short presentation about their poster and conducting a Q&A about it. In Starr Sackstein's high school classroom, her stations consisted of video tutorials created by the students themselves. Before I knew the term Gallery Walk, I shared a strategy similar to it called Chat Stations, where the teacher prepares discussion prompts or content-related tasks and sets them up around the room for students to visit in small groups.
a.k.a. Values Continuum, Forced Debate, Physical Barometer, This or That
Basic Structure: A statement that has two possible responses-agree or disagree-is read out loud. Depending on whether they agree or disagree with this statement, students move to one side of the room or the other. From that spot, students take turns defending their positions.

Variations: Often a Philosophical Chairs debate will be based around a text or group of texts students have read ahead of time; students are required to cite textual evidence to support their claims and usually hold the texts in their hands during the discussion. Some teachers set up one hot seat to represent each side, and students must take turns in the seat. In less formal variations (which require less prep), a teacher may simply read provocative statements students are likely to disagree on, and a debate can occur spontaneously without a text to refer to (I call this variation This or That in my classroom icebreakers post). Teachers may also opt to offer a continuum of choices, ranging from "Strongly Agree" on one side of the room, all the way to "Strongly Disagree" on the other, and have students place themselves along that continuum based on the strength of their convictions.

Basic Structure: Students are divided into 4 groups. Three of these groups are assigned to represent specific points of view. Members of the fourth group are designated as "provocateurs," tasked with making sure the discussion keeps going and stays challenging. One person from each group (the "speaker") sits in a desk facing speakers from the other groups, so they form a square in the center of the room. Behind each speaker, the remaining group members are seated: two right behind the speaker, then three behind them, and so on, forming a kind of triangle. From above, this would look like a pinwheel. The four speakers introduce and discuss questions they prepared ahead of time (this preparation is done with their groups). After some time passes, new students rotate from the seats behind the speaker into the center seats and continue the conversation.

Variations: When high school English teacher Sarah Brown Wessling introduced this strategy in the featured video (click Pinwheel Discussion above), she used it as a device for talking about literature, where each group represented a different author, plus one provocateur group. But in the comments that follow the video, Wessling adds that she also uses the strategy with non-fiction, where students represent authors of different non-fiction texts or are assigned to take on different perspectives about an issue.
a.k.a. Socratic Circles
Basic Structure: Students prepare by reading a text or group of texts and writing some higher-order discussion questions about the text. On seminar day, students sit in a circle and an introductory, open-ended question is posed by the teacher or student discussion leader. From there, students continue the conversation, prompting one another to support their claims with textual evidence. There is no particular order to how students speak, but they are encouraged to respectfully share the floor with others. Discussion is meant to happen naturally and students do not need to raise their hands to speak. This overview of Socratic Seminar from the website Facing History and Ourselves provides a list of appropriate questions, plus more information about how to prepare for a seminar.

Variations: If students are beginners, the teacher may write the discussion questions, or the question creation can be a joint effort. For larger classes, teachers may need to set up seminars in more of a fishbowl-like arrangement, dividing students into one inner circle that will participate in the discussion, and one outer circle that silently observes, takes notes, and may eventually trade places with those in the inner circle, sometimes all at once, and sometimes by "tapping in" as the urge strikes them.
a.k.a. Affinity Diagramming
Basic Structure: Give students a broad question or problem that is likely to result in lots of different ideas, such as "What were the impacts of the Great Depression?" or "What literary works should every person read?" Have students generate responses by writing ideas on post-it notes (one idea per note) and placing them in no particular arrangement on a wall, whiteboard, or chart paper. Once lots of ideas have been generated, have students begin grouping them into similar categories, then label the categories and discuss why the ideas fit within them, how the categories relate to one another, and so on.

Variations: Some teachers have students do much of this exercise-recording their ideas and arranging them into categories- without talking at first. In other variations, participants are asked to re-combine the ideas into new, different categories after the first round of organization occurs. Often, this activity serves as a good pre-writing exercise, after which students will write some kind of analysis or position paper.
a.k.a. Speed Dating
Basic Structure: Students form two circles, one inside circle and one outside circle. Each student on the inside is paired with a student on the outside; they face each other. The teacher poses a question to the whole group and pairs discuss their responses with each other. Then the teacher signals students to rotate: Students on the outside circle move one space to the right so they are standing in front of a new person (or sitting, as they are in the video). Now the teacher poses a new question, and the process is repeated.

Variations: Instead of two circles, students could also form two straight lines facing one another. Instead of "rotating" to switch partners, one line just slides over one spot, and the leftover person on the end comes around to the beginning of the line. Some teachers use this strategy to have students teach one piece of content to their fellow students, making it less of a discussion strategy and more of a peer teaching format. In fact, many of these protocols could be used for peer teaching as well.

Basic Structure: Another great idea from Sarah Brown Wessling, this is a small-group discussion strategy that gives students exposure to more of their peers' ideas and prevents the stagnation that can happen when a group doesn't happen to have the right chemistry. Students are placed into a few groups of 4-6 students each and are given a discussion question to talk about. After sufficient time has passed for the discussion to develop, one or two students from each group rotate to a different group, while the other group members remain where they are. Once in their new group, they will discuss a different, but related question, and they may also share some of the key points from their last group's conversation. For the next rotation, students who have not rotated before may be chosen to move, resulting in groups that are continually evolving.

Basic Structure: Two students sit facing each other in the center of the room; the remaining students sit in a circle around them. The two central students have a conversation based on a pre-determined topic and often using specific skills the class is practicing (such as asking follow-up questions, paraphrasing, or elaborating on another person's point). Students on the outside observe, take notes, or perform some other discussion-related task assigned by the teacher.

Variations: One variation of this strategy allows students in the outer circle to trade places with those in the fishbowl, doing kind of a relay-style discussion, or they may periodically "coach" the fishbowl talkers from the sidelines. Teachers may also opt to have students in the outside circle grade the participants' conversation with a rubric, then give feedback on what they saw in a debriefing afterward, as mentioned in the featured video.

Basic Structure: One student assumes the role of a book character, significant figure in history, or concept (such as a tornado, an animal, or the Titanic). Sitting in front of the rest of the class, the student responds to classmates' questions while staying in character in that role.

Variations: Give more students the opportunity to be in the hot seat while increasing everyone's participation by having students do hot seat discussions in small groups, where one person per group acts as the "character" and three or four others ask them questions. In another variation, several students could form a panel of different characters, taking questions from the class all together and interacting with one another like guests on a TV talk show.
a.k.a. Pyramid Discussion
Basic Structure: Students begin in pairs, responding to a discussion question only with a single partner. After each person has had a chance to share their ideas, the pair joins another pair, creating a group of four. Pairs share their ideas with the pair they just joined. Next, groups of four join together to form groups of eight, and so on, until the whole class is joined up in one large discussion.

Variations: This structure could simply be used to share ideas on a topic, or students could be required to reach consensus every time they join up with a new group.

Whereas the other formats in this list have a distinct shape-specific activities you do with students-the strategies in this section are more like plug-ins, working discussion into other instructional activities and improving the quality and reach of existing conversations.

One of the limitations of discussion is that rich, face-to-face conversations can only happen when all parties are available, so we're limited to the time we have in class. With a tool like Voxer, those limitations disappear. Like a private voice mailbox that you set up with just one person or a group (but SOOOO much easier), Voxer allows users to have conversations at whatever time is most convenient for each participant. So a group of four students can "discuss" a topic from 3pm until bedtime-asynchronously-each member contributing whenever they have a moment, and if the teacher makes herself part of the group, she can listen in, offer feedback, or contribute her own discussion points. Voxer is also invaluable for collaborating on projects and for having one-on-one discussions with students, parents, and your own colleagues. Like many other educators, Peter DeWitt took a while to really understand the potential of Voxer, but in this EdWeek piece, he explains what turned him around.

A backchannel is a conversation that happens right alongside another activity. The first time I saw a backchannel in action was at my first unconference: While those of us in the audience listened to presenters and watched a few short video clips, a separate screen was up beside the main screen, projecting something called TodaysMeet (update: TodaysMeet has shut down. Use YoTeach! instead.) It looked a lot like those chat rooms from back in the day, basically a blank screen where people would contribute a few lines of text, the lines stacking up one after the other, no other bells or whistles. Anyone in the room could participate in this conversation on their phone, laptop, or tablet, asking questions, offering commentary, and sharing links to related resources without ever interrupting the flow of the presentations. This kind of tool allows for a completely silent discussion, one that doesn't have to move at a super-fast pace, and it gives students who may be reluctant to speak up or who process their thoughts more slowly a chance to fully contribute. For a deeper discussion of how this kind of tool can be used, read this thoughtful overview of using backchannel discussions in the classroom by Edutopia's Beth Holland.
a.k.a. Accountable Talk
Talk moves are sentence frames we supply to our students that help them express ideas and interact with one another in respectful, academically appropriate ways. From kindergarten all the way through college, students can benefit from explicit instruction in the skills of summarizing another person's argument before presenting an alternate view, asking clarifying questions, and expressing agreement or partial agreement with the stance of another participant. Talk moves can be incorporated into any of the other discussion formats listed here.

Whole Brain Teaching is a set of teaching and classroom management methods that has grown in popularity over the past 10 years. One of WBT's foundational techniques is Teach-OK, a peer teaching strategy that begins with the teacher spending a few minutes introducing a concept to the class. Next, the teacher says Teach!, the class responds with Okay!, and pairs of students take turns re-teaching the concept to each other. It's a bit like think-pair-share, but it's faster-paced, it focuses more on re-teaching than general sharing, and students are encouraged to use gestures to animate their discussion. Although WBT is most popular in elementary schools, this featured video shows the creator of WBT, Chris Biffle, using it quite successfully with college students. I have also used Teach-OK with college students, and most of my students said they were happy for a change from the sit-and-listen they were used to in college classrooms.

An oldie but a goodie, think-pair-share can be used any time you want to plug interactivity into a lesson: Simply have students think about their response to a question, form a pair with another person, discuss their response, then share it with the larger group. Because I feel this strategy has so many uses and can be way more powerful than we give it credit for, I devoted a whole post to think-pair-share; everything you need to know about it is right there.

TWO MORE YOU'LL LOVE

Since writing this post, I have learned about two more discussion strategies that teachers are finding to be incredibly effective and powerful for getting students to talk, especially about books and other texts:

This protocol has students come up with their own Thoughts, lingering Questions, and Epiphanies from an assigned reading. Teachers who have used this method say it has generated some of the richest conversations they have ever heard from students!

This strategy places students into one-on-one conversations, getting them to learn each others' names better and create a track record of what they talked about. Excellent for classes where you want to assess for discussion and help students get more comfortable with each other.

So what else do you have? I would like to think this is a pretty complete list, but I'm sure more strategies are out there. If you use a discussion strategy that's not mentioned here, please share it below.

Come back for more.
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The Big List of Class Discussion Strategies

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Here they are: 15 formats for structuring a class discussion to make it more engaging, more organized, more equitable, and more academically challenging.

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My essential question for this unit is a two-pronged approach, focusing on critical media consumption and making informed opinions about topics. I believe these questions will help the students, and myself, think about our texts in a complex and nuanced way because it is forcing us to decide our own view on the topic, compared to the opinion presented. I have three main learning objectives for this unit: students will be able to be critical about information, students will be able to write concisely and effectively, and students will be able to present information to a general audience". These three objectives set up a solid path for success outside of the ELA classroom and within narrative and informational reading/writing. My main focus is on critical literacy(everything highlighted orange) and they build off of this critical literacy a better understanding of how the text works as a whole. This is my way of showing them how to carefully take apart a clock to see how it works, vs just smashing it open. With these skills, they will be able to continue to practice critical literacy both in and outside the classroom. The focus on effective language comes down to the importance of effective communication in day-to-day life. If my students don’t build an arsenal of strategies to create productive conversation then they will be ill-equipped for the workplace and the world at large. Good communication is the basis of society and it is our job to help foster those skills as ELA teachers.

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"There is one question that should be considered above all others. Why am I doing this?" (Beach et. al. 44)

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"There is one question that should be considered above all others. Why am I doing this?" (Beach et. al. 44)

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"How can you best detail the expectations and work of the class in ways that create opportunities for students and you, as well, to dialogue with the texts of your lives?" (Beach et. al. 47)

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"How can you best detail the expectations and work of the class in ways that create opportunities for students and you, as well, to dialogue with the texts of your lives?" (Beach et. al. 47)

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Kittle and Gallagher 72

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Kittle and Gallagher 72

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"As projects increase in complexity, students are expected to compose effectively both individually and on a team: listening to others, sharing thinking, combining understandings, and prioritizing information into coherent, collaborative texts" (Kittle and Gallagher 72)

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"As projects increase in complexity, students are expected to compose effectively both individually and on a team: listening to others, sharing thinking, combining understandings, and prioritizing information into coherent, collaborative texts" (Kittle and Gallagher 72)

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"No matter the career plans of our students, a study of informational writing is relevant" (Kittle and Gallagher 72)

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"No matter the career plans of our students, a study of informational writing is relevant" (Kittle and Gallagher 72)

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"Informational texts have moved far beyond the annual school research paper. Has our teaching?" (Kittle and Gallagher 72)

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"Informational texts have moved far beyond the annual school research paper. Has our teaching?" (Kittle and Gallagher 72)

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Stuff they don’t want you to know

Stuff you should know

Lore(and other Aaron Mahnke productions)

Radio Lab

There is no such thing as fish

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newsmap.jp – このドメインはお名前.comで取得されています。

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お名前.com は GMOインターネットグループ(株) が運営する国内シェアNo.1のドメイン登録サービスです。

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AllSides | Balanced news via media bias ratings for an unbiased news perspective

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See issues and political news with news bias revealed. Non-partisan, crowd-sourced technology shows all sides so you can decide.

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Defining Coherence in Writing

Have you ever read something that was hard to follow, where the writer jumped around from idea to idea and did not have unity or consistency to connect the words, sentences, and paragraphs? That is something that you want to avoid in your own writing. But how do you do that?

Coherence in writing is the logical bridge between words, sentences, and paragraphs. Coherent writing uses devices to connect ideas within each sentence and paragraph. Main ideas and meaning can be difficult for the reader to follow if the writing lacks coherence. In this lesson, you will see some examples and learn some tips for making your writing coherent between words, sentences, and paragraphs.

Coherence Between Words

Between each word, coherence can be created by parallelism. Parallel structure means using similar grammatical constructions between words in sentences. Parallelism is particularly important for words in lists. If you're writing a list of things someone likes to do, then each activity in the list should take the same grammatical form. For instance, if one verb in the list takes on the '-ing' gerund form, like 'running,' then the other verbs in the list should also be in the gerund form. An incoherent structure would be to say:

Sarah likes to jump, running, and skate.

Instead, the list should be parallel like this:

Sarah likes jumping, running, and skating.

Coherence Between Sentences

Coherence can be created between sentences through repetition and transitional devices. Repetition of words across sentences helps to reiterate the same ideas between sentences. One way to use repetition to create coherence is to repeat the same word or phrase at the end of one sentence and the beginning of the next sentence to show how the ideas connect. Here is an example of sentences that create coherence through repetition:

The most important part of an essay is the thesis statement. The thesis statement introduces the argument of the essay. The thesis statement also helps to create a structure for the essay.

In this example, the repetition of the phrase 'thesis statement' helps to unify the three sentences. It is a phrase that ends the first sentence and transitions into the next sentence by starting with that same phrase.

Another way to create coherence between sentences is through transitional devices. There are many types of transitional devices that show time and help ideas flow smoothly. Transitional words, such as 'first', 'later', and 'then', are a few examples of transitional devices that show time to help ideas flow more smoothly. Transitional devices are like signposts that tell the reader what is coming up ahead and where the discussion is going.

Coherence Between Paragraphs

Transitional words can also be used between paragraphs. Words such as:

can not only be used between sentences, but between paragraphs to connect them. Other ways to create coherence between paragraphs include having a consistent paragraph structure and point of view.

The structure of a coherent paragraph includes a topic sentence, which focuses on the main idea. The topic sentence usually comes first in a paragraph. The topic sentence is followed by supporting sentences that develop the idea, and finally, a concluding sentence to tie it all together. Transitional words then bridge the gap between paragraphs, and the structure starts anew with another topic sentence for the next paragraph.

Finally, consistent point of view is a way to create coherence between paragraphs. Point of view is the narrator's position in telling the story. The types of point of view in writing are:

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coherence in writing: definition & examples - video & lesson transcript | study.com

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Coherence in Writing: Definition & Examples - Video & Lesson Transcript | Study.com

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Have you ever read something that was hard to follow, where the writer jumped around from idea to idea and did not have unity or consistency to connect the words, sentences, and paragraphs? That is something that you want to avoid in your own writing. But how do you do that?

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Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

When thinking about proper diction, an author should consider three main categories: tone , mood , and audience .

Audience refers to who will be reading the work. Authors tend to write to a particular audience, whether kids, or young adults, or specialist within a field. The audience can affect the mood and tone of the writing because different audiences have different expectations.
Tonederision ?
refers to the author's attitude-how they feel about their subject and their readers. It expresses something of the author's persona, the aspects of their personality they wish to show to their readers. For example, are they being funny or serious? Are they writing with fondness or with Moodtone, because the author's attitude influences the overall feeling of a text. It's difficult, for instance, to take a jovial refers to the overall atmosphere or feeling of a piece of writing. It is often closely related to tone if the overall mood of the piece ought to be somber, or vice versa. Wuthering Heights by Emily Bront ë would be far less effective as a gothic text if its spooky atmosphere was interrupted by witty , sarcastic commentary in the style of Jane Austen .

Take, for example, this qu ote from Wuthering Heights :

u- haunt me then. The murdered do haunt their murderers. I believe - I know that ghosts have wandered the earth. Be with me always - take any form - drive me mad. Only do not leave me in this abyss, where I cannot find you! Oh, God! It is unutterable! I cannot live without my life! I cannot live without my soul!"

Th is passage displays heightened emotions and dark themes through the use of words like "ghost," "haunt," a nd "abyss," among others. Consider how much less effective this p assage would be if the narration sounded like Pride and Prejudice : "It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife."

Using the appropriate kind of descriptive words, including imagery , or vivid language used to paint a mental picture, can convey mood and tone by helping readers get a clearer sense of what they're reading about and how the author thinks and feels about the subject, and thus what they're supposed to think and feel.

Diction can help authors make audiences feel a certain way, like in the example above. Similarly, different styles of diction may be targeted at different audiences- there's a good reason Wuthering Heights is aimed at teenagers and adults rather than young children, for instance. In addition to the content of the text, the elevated and somewhat antiquated diction would make it very challenging for younger audiences to understand. Conversely, a paper aimed at an audience of academic experts would probably be expected to use more jargon and complicated diction.

Take, for example, this simplistic description of Pluto's orbit from Astronomy.com's Astronomy for Kids educational resource:

"Pluto was the ninth planet from the Sun. It orbits our star at a distance of 3.6 billion miles (5.9 billion kim), nearly forty times as far from the Sun as Earth...It takes Pluto 248 Earth years to go around the Sun one time."

Compare this language with the highly techni cal language used in an Encyclopedia Britannica article on Pluto :

Pluto's mean distance from the Sun, about 5.9 billion km (3.7 billion miles or 39.5 astronomical units), gives it an orbit larger than that of the outermost planet, Neptune. (One astronomical unit [AU] is the average distance from Earth to the Sun-about 150 million km [93 million miles].) Its orbit, compared with those of the planets, is atypical in several ways. It is more elongated, or eccentric, than any of the planetary orbits and more inclined (at 17.1°) to the ecliptic, the plane of Earth's orbit, near which the orbits of most of the planets lie. In traveling its eccentric path around the Sun, Pluto varies in distance from 29.7 AU, at its closest point to the Sun (perihelion), to 49.5 AU, at its farthest point (aphelion). "

These texts, while essentially saying the same thing, are using wildly different language due to the disparity between their intended audiences.

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

When thinking about proper diction, an author should consider three main categories: tone , mood , and audience .

Audience refers to who will be reading the work. Authors tend to write to a particular audience, whether kids, or young adults, or specialist within a field. The audience can affect the mood and tone of the writing because different audiences have different expectations.
Tonederision ?
refers to the author's attitude-how they feel about their subject and their readers. It expresses something of the author's persona, the aspects of their personality they wish to show to their readers. For example, are they being funny or serious? Are they writing with fondness or with Moodtone, because the author's attitude influences the overall feeling of a text. It's difficult, for instance, to take a jovial refers to the overall atmosphere or feeling of a piece of writing. It is often closely related to tone if the overall mood of the piece ought to be somber, or vice versa. Wuthering Heights by Emily Bront ë would be far less effective as a gothic text if its spooky atmosphere was interrupted by witty , sarcastic commentary in the style of Jane Austen .

Take, for example, this qu ote from Wuthering Heights :

u- haunt me then. The murdered do haunt their murderers. I believe - I know that ghosts have wandered the earth. Be with me always - take any form - drive me mad. Only do not leave me in this abyss, where I cannot find you! Oh, God! It is unutterable! I cannot live without my life! I cannot live without my soul!"

Th is passage displays heightened emotions and dark themes through the use of words like "ghost," "haunt," a nd "abyss," among others. Consider how much less effective this p assage would be if the narration sounded like Pride and Prejudice : "It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife."

Using the appropriate kind of descriptive words, including imagery , or vivid language used to paint a mental picture, can convey mood and tone by helping readers get a clearer sense of what they're reading about and how the author thinks and feels about the subject, and thus what they're supposed to think and feel.

Diction can help authors make audiences feel a certain way, like in the example above. Similarly, different styles of diction may be targeted at different audiences- there's a good reason Wuthering Heights is aimed at teenagers and adults rather than young children, for instance. In addition to the content of the text, the elevated and somewhat antiquated diction would make it very challenging for younger audiences to understand. Conversely, a paper aimed at an audience of academic experts would probably be expected to use more jargon and complicated diction.

Take, for example, this simplistic description of Pluto's orbit from Astronomy.com's Astronomy for Kids educational resource:

"Pluto was the ninth planet from the Sun. It orbits our star at a distance of 3.6 billion miles (5.9 billion kim), nearly forty times as far from the Sun as Earth...It takes Pluto 248 Earth years to go around the Sun one time."

Compare this language with the highly techni cal language used in an Encyclopedia Britannica article on Pluto :

Pluto's mean distance from the Sun, about 5.9 billion km (3.7 billion miles or 39.5 astronomical units), gives it an orbit larger than that of the outermost planet, Neptune. (One astronomical unit [AU] is the average distance from Earth to the Sun-about 150 million km [93 million miles].) Its orbit, compared with those of the planets, is atypical in several ways. It is more elongated, or eccentric, than any of the planetary orbits and more inclined (at 17.1°) to the ecliptic, the plane of Earth's orbit, near which the orbits of most of the planets lie. In traveling its eccentric path around the Sun, Pluto varies in distance from 29.7 AU, at its closest point to the Sun (perihelion), to 49.5 AU, at its farthest point (aphelion). "

These texts, while essentially saying the same thing, are using wildly different language due to the disparity between their intended audiences.

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Style Guide Overview MLA Guide APA Guide Chicago Guide OWL Exercises This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice. Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved.

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How to Teach Peer Feedback Between Students - XQ

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A valuable tool for educators, peer feedback helps students think critically, apply their learning to an evaluation model, and refine communication skills.