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Category Commons WikiProject. Branches of biology. Biology Botanical terms Ecological terms Plant morphology terms. One study employed a Random-Response technique on part of its sample to control for non-response bias, and found no evidence for it  see Discussion for further details.
When asked if they had personal knowledge of a colleague who fabricated or falsified research data, or who altered or modified research data Table S3 , questions, 1, 6, 7, 10, 20, 21, 29, 32, 34, 37, 45, 54 between 5. Meta-analysis yielded a pooled weighted estimate of Between 6. When surveys asked about more generic questions e.
In discussing their results, three studies  ,  ,  considered them to be conservative, four  ,  ,  ,  suggested that they overestimated the actual occurrence of misconduct, and the remaining 13 made no clear statement. Five of the included studies asked respondents what they had done to correct or prevent the act of misconduct they had witnessed.
Around half of the alleged cases of misconduct had any action taken against them Table 2. No study asked if these actions had the expected outcome. Mailed surveys had also higher response rates than handed-out surveys Mean: When the three methodological factors above where controlled for, a significant effect was found for surveys targeted at medical and clinical researchers, who reported higher percentages of misconduct than respondents in biomedical research and other fields Table 3.
The effect of this parameter would remain significant if Bonferroni-corrected for multiple comparisons. Self-report admission rates varied between 1. Reports on colleagues' misconduct varied between Weighted pooled estimates on non-logit-trasformed data yielded self- and non-self- admission rates of 2. Plots show the weighted pooled estimate obtained when the corresponding study was left out of the analysis.
Results of the regression analysis were robust to the leave-one-study-out test: the four significant variables remained statistically significant when anyone of the studies was excluded Table S4. The largest portion of variance was explained when Titus et al. This is the first meta-analysis of surveys asking scientists about their experiences of misconduct. Over the years, the rate of admissions declined significantly in self-reports, but not in non-self-reports. Once these factors were controlled for, surveys conducted among clinical, medical and pharmacological researchers appeared to yield higher rates of misconduct than surveys in other fields or in mixed samples.
All the above results were robust with respect to inclusion or exclusion of any particular study, with perhaps one exception: Martinson et al. How reliable are these numbers? And what can they tell us on the actual frequency of research misconduct? Below it will be argued that, while surveys asking about colleagues are hard to interpret conclusively, self-reports systematically underestimate the real frequency of scientific misconduct.
Therefore, it can be safely concluded that data fabrication and falsification —let alone other questionable research practices- are more prevalent than most previous estimates have suggested. The procedure adopted to standardize data in the review clearly has limitations that affect the interpretation of results. In this latter case, the frequencies reported in surveys would tend to overestimate the prevalence of biased or falsified data in the literature.
This section will discuss ethical issues raised by large-scale surveys in less developed countries that take biological and physiological measures. The first determination is whether or not the activity can be considered research. Surgery 1—7. They direct activities related to research and development, and coordinate activities such as testing, quality control, and production. Each report has been subjected to a rigorous and independent peer-review process and it represents the position of the National Academies on the statement of task. Finally, we aim to interpret the major trajectories according to the leading contributors to representative components.
The history of science, however, shows that those responsible of misconduct have usually committed it more than once  ,  , so the latter case might not be as likely as the former. In any case, many of the included studies asked to recall at least one incident, so this limitation is intrinsic to large part of the raw data. As explained in the introduction, any boundary defining misconduct will be arbitrary, but intention to deceive is the key aspect. The accuracy of self-reports on scientific misconduct might be biased by the effect of social expectations.
In self-reports on criminal behaviour, social expectations make many respondents less likely to admit a crime they committed typically, females and older people and make others likely to report a crime they have not really committed typically, young males . In the case of scientists, however, social expectations should always lead to underreporting, because a reputation of honesty and objectivity is fundamental in any stage of a scientific career.
The opposite scientists admitting misconduct they didn't do appears very unlikely. Indeed, there seems to be a large discrepancy between what researchers are willing to do and what they admit in a survey. Among research trainees in biomedical sciences at the University of California San Diego, 4. Mailed surveys yielded lower frequencies of misconduct than handed out surveys. Which of the two is more accurate? Mailed surveys were often combined with follow-up letters and other means of encouraging responses, which ensured higher response rates. However, the accuracy of responses to sensitive questions is often independent of response rates, and depends strongly on respondents' perception of anonymity and confidentiality  , .
Questionnaires that are handed to, and returned directly by respondents might better entrust anonymity than surveys that need to be mailed or emailed. Therefore, we cannot rule out the possibility that handed out surveys are more accurate despite the lower response rates. This latter interpretation would be supported by one of the included studies: a handed out survey that attempted to measure non-response bias using a Random-Response RR technique on part of its sample .
Contrary to author's expectations, response and admission rates were not higher with RR compared to DR, suggesting that in this handed out survey non-response bias was absent. The effect of social expectations in surveys asking about colleagues is less clear, and could depend on the particular interests of respondents.
In general, scientists might tend to protect the reputation of their field, by minimizing their knowledge of misconduct . On the other hand, certain categories of respondents e. Surveys on colleagues' behaviour might tend to inflate estimates of misconduct also because the same incident might be reported by many respondents.
One study controlled for this factor by asking only one researcher per department to recall cases that he had observed in that department in the past three years . It found that falsification and fabrication had been observed by 5. In the sensitivity analysis run on the regression model, exclusion of this study caused the single largest increase in explained variance, which further suggests that findings of this study are unusual.
Another critical factor in interpreting survey results is the respondents' perception of what does and does not constitute research misconduct. To some extent, they were arguably right. But the fuzzy boundary between removing noise from results and biasing them towards a desired outcome might be unknowingly crossed by many researchers  ,  , .
In a sample of biostatisticians, who are particularly well trained to see this boundary, more than half said they had personally witnessed false or deceptive research in the preceding 10 years . This effect was empirically proven in academic economists  and in a large sample of biomedical researchers in a survey assessing their adherence to Mertonian norms  , and may help to explain the lower frequency with which misconduct is admitted in self-reports: researchers might be overindulgent with their behaviour and overzealous in judging their colleagues.
The decrease in admission rates observed over the years in self-reports but not in non-self-reports could be explained by a combination of the Mohammed Ali effect and social expectations. The level and quality of research and training in scientific integrity has expanded in the last decades, raising awareness among scientists and the public .
However, there is little evidence that researchers trained in recognizing and dealing with scientific misconduct have a lower propensity to commit it  ,  , . Therefore, these trends might suggest that scientists are no less likely to commit misconduct or to report what they see their colleagues doing, but have become less likely to admit it for themselves. Once methodological differences were controlled for, cross-study comparisons indicated that samples drawn exclusively from medical including clinical and pharmacological research reported misconduct more frequently than respondents in other fields or in mixed samples.
To the author's knowledge, this is the first cross-disciplinary evidence of this kind, and it suggests that misconduct in clinical, pharmacological and medical research is more widespread than in other fields. This would support growing fears that the large financial interests that often drive medical research are severely biasing it  ,  , .
However, as all survey-based data, this finding is open to the alternative interpretation that respondents in the medical profession are simply more aware of the problem and more willing to report it. This could indeed be the case, because medical research is a preferred target of research and training programs in scientific integrity, and because the severe social and legal consequences of misconduct in medical research might motivate respondents to report it. However, the effect of this parameter was not robust to one of the sensitivity analyses, so it would need to be confirmed by independent studies before being conclusively accepted.
The lack of statistical significance for the effect of country, professional position and other sample characteristics is not strong evidence against their relevance, because the high between-study variance caused by methodological factors limited the power of the analysis the regression had to control for three methodological factors before testing any other effect. However, it suggests that such differences need to be explored at the study level, with large surveys designed specifically to compare groups.
A few of the included studies had done so and found, for example, that admission rates tend to be higher in males compared to females  and in mid-career compared to early career scientists  , and that they tend to differ between disciplines  , . If more studies attempted to replicate these results, possibly using standardized methodologies, then a meta-analysis could reveal important correlates of scientific misconduct. In conclusion, several surveys asking scientists about misconduct have been conducted to date, and the differences in their results are largely due to differences in methods.
Only by controlling for these latter can the effects of country, discipline, and other demographic characteristics be studied in detail. Therefore, there appears to be little scope for conducting more small descriptive surveys, unless they adopted standard methodologies. On the other hand, there is ample scope for surveys aimed at identifying sociological factors associated with scientific misconduct. Overall, admission rates are consistent with the highest estimates of misconduct obtained using other sources of data, in particular FDA data audits  , .
I wish to thank Nicholas Steneck, Tom Tregenza, Gavin Stewart, Robin Williams and two anonymous referees for comments that helped to improve the manuscript, and Moyra Forrest for helping to search the literature. Conceived and designed the experiments: DF. Performed the experiments: DF. Analyzed the data: DF.
Wrote the paper: DF. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract The frequency with which scientists fabricate and falsify data, or commit other forms of scientific misconduct is a matter of controversy. Introduction The image of scientists as objective seekers of truth is periodically jeopardized by the discovery of a major scientific fraud. Methods Searching Electronic resources were searched during the first two weeks of August Citation databases.
Scientific journals. Grey literature databases. Internet search engines. Selection Only quantitative survey data assessing how many researchers have committed or observed colleagues committing scientific misconduct in the past were included in this review. Validity assessment Surveys that did not sample respondents at random, or that did not provide sufficient information on the sampling methods employed where given a quality score of zero and excluded from the meta-analysis. Data abstraction For each question, the percentage of respondents who recalled committing or who observed i. Quantitative data synthesis The main outcome of the meta-analysis was the percentage proportion of respondents that recalled committing or that knew of a colleague committing the specified behaviour at least once in the given recall period.
Publication bias-Sensitivity analysis The popular funnel-plot-based methods to test for publication bias in meta-analysis are inappropriate and potentially misleading when the number of included studies is small and heterogeneity is large  , . Results Flow of included studies Electronic search produced an initial list of references. Download: PPT. Study characteristics Table 1 lists the characteristics of included studies and their quality score for inclusion in meta-analysis.
Quantitative data analysis Scientists admitting misconduct. Figure 2. Forrest plot of admission rates of data fabrication, falsification and alteration in self reports. Figure 3. Kenneth G. Manton is a leading researcher in this field. Demographers since the last half of the twentieth century have become increasingly involved with the design of surveys and the analysis of survey data, especially that pertaining to fertility or morbidity and mortality.
Various kinds of physical measurements such as height and weight , physiological measurements for example, of blood pressure and cholesterol levels , nutritional status as assessed by analysis of blood or urine and other methods , physical performance for example, hand-grip strength or ability to pick a coin up from the floor , and genetic makeup as determined by analysis of DNA have been added to surveys, including those conducted by Kaare Christensen, Noreen Goldman, Maxine Weinstein, and Zeng Yi. These biological measurements biomarkers can be used as covariates in demographic analyses in much the same way that social and economic information is used.
Skeletal remains are the source of information about prehistoric populations regarding sex, age at death, lifetime morbidity and nutrition, as well as, for women, number of children born.
Hence, a main focus of paleodemography is determining how to extract more information from bones. This requires a sophisticated understanding of biology as well as facility with methods of using physical indicators to determine sex and to estimate age at death and other variables. A promising recent advance has been the development, by Ursula Wittwer-Backofen and Jutta Gampe, of methods to count annual rings deposited in teeth as a way of determining age at death. Roughly similar methods can be used to estimate the age of animals in the wild, with teeth used for mammals and otoliths, ear bones, for fish.
Lesions in bones and minerals in teeth and bones can shed light on health and nutritional histories. Information about human population development for the long period during which written records were scarce or nonexistent thus hinges on biological information.
Carey, James R. Applied Demography for Biologists. New York : Oxford University Press. Finch, Caleb E. Vaupel, and Kevin Kinsella, eds. Washington, D. Hoppa, Robert D. Paleodemography: Age Distributions from Skeletal Samples. Cambridge, Eng. Kingsland, Sharon E.
Modeling Nature. Chicago: University of Chicago Press. Kohler, Hans-Peter, Joseph L. Rodgers, and Kaare Christensen. Manton, Kenneth G. Odense, Denmark: Odense University Press. Vaupel, James W. Carey, Kaare Christensen, Thomas E. Johnson, Anatoli I. Yashin, Niels V.
Holm, Ivan A. Iachine, Vaino Kannisto, Aziz A. Khazaeli, Pablo Liedo, Valter D. Longo, Zeng Yi, Kenneth G. Manton, and James W. Wachter, Kenneth W.