It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon. Remember, research is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales. Gather research insights.
Review your goals before making any conclusions about your research. Keep in mind how the process you have completed and the data you have gathered help answer your questions.
Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations. Though you're welcome to continue on your mobile screen, we'd suggest a desktop or notebook experience for optimal results. Survey software Leading survey software to help you turn data into decisions. Research Edition Intelligent market research surveys that uncover actionable insights. Customer Experience Experiences change the world. Deliver the best with our CX management software.
Workforce Powerful insights to help you create the best employee experience. What is Research? Research is conducted with a purpose to: Identify potential and new customers Understand existing customers Set pragmatic goals Develop productive market strategies Address business challenges Put together a business expansion plan Identify new business opportunities What are the characteristics of research? Good research follows a systematic approach to capture accurate data.
Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions. The analysis is based on logical reasoning and involves both inductive and deductive methods. Real-time data and knowledge is derived from actual observations in natural settings. There is an in-depth analysis of all data collected so that there are no anomalies associated with it. It creates a path for generating new questions. Existing data helps create more research opportunities. It is analytical and uses all the available data so that there is no ambiguity in inference.
Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. What is the purpose of research? There are three main purposes: Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions.
If the individual-level approach is retained, costs could raise further. The sheer size of the REF itself now seems to lead to organizational-level evaluation. There is a chance that the choice in the UK will not be between peer review and metrics, but between individual-level and organizational-level evaluation.
With the latter, individual performances would be seen as already evaluated by internal institutional procedures and external procedures in journals and second-stream funding. Peer review would become one of several tools for assessing the performance of a university as a whole.
In addition, the REF could become an inspiring example for smaller countries that are concerned about costs and institutional autonomy when they design their performance-based funding schemes for universities. Solutions in some countries are sometimes inspiring other countries.
It seems important to develop further the basis for mutual learning. One such step forward already occurred in an Organisation for Economic Co-operation and Development workshop, initiated by Norway, on performance-based funding for public research in tertiary education institutions in Paris in June This is the most important forerunner of the more academic review de Rijcke et al.
Unlike her successors, Butler could respond in her work to feedback from the participating countries. It seems that academic literature reviews are not able, as a method, to keep trace of continuously changing national PRFS and thereby to inform mutual learning.
I am presently participating as an expert in a Mutual Learning Exercise on Performance Based Funding Systems , which involves fourteen countries and is organized by the European Commission in — In this exercise, representatives of governments are actively contributing, learning from each other and taking home advise and inspiration.
Still, the process has so far shown that national contexts heavily influence the type and design of PRFS and the needs that these funding systems are expected to respond to.
Differences should, therefore, be expected and respected. This does not mean that there is no need for a discussion and clarification across countries of responsible metrics in higher education and research. But rather than trying to formulate best practice statements from the perspective of one or two countries, I have presented a framework for understanding differences with the aim of facilitating mutual learning.
How to cite this article : Sivertsen G Unique, but still best practice? Palgrave Communications. Footnote 3. Google Scholar. Aagaard K How incentives trickle down: Local use of a national bibliometric indicator system. Science and Public Policy ; 42 5 : — Research Evaluation ; 24 2 : — Ahlgren P, Colliander C and Persson O Field normalized citation rates, field normalized journal impact and Norwegian weights for allocation of university research funds.
Scientometrics ; 92 3 : — Organisation and Governance Technopolis: Brighton. Bloch C and Schneider JW Performance-based funding models and researcher behavior: An analysis of the influence of the Norwegian Publication level at the individual level. Research Evaluation ; 25 4 : — Dahler-Larsen P Constitutive effects of performance indicators. Public Management Review ; 16 7 : — Research Evaluation ; 25 2 : — Minerva ; 41 4 : — Research Policy ; 45 1 : — Hicks D Performance-based university research funding systems.
Research Policy ; 41 2 : — Nature ; : — Kulczycki E Assessing publications through a bibliometric indicator: The case of comprehensive evaluation of scientific units in Poland.
Research Evaluation ; 26 1 : 41— On the relationship between research productivity and impact. CAS Google Scholar. Ossenblok T, Engels T and Sivertsen G The representation of the social sciences and humanities in the Web of Science—a comparison of publication patterns and incentive structures in Flanders and Norway —9. Research Evaluation ; 21 4 : — Schneider JW An outline of the bibliometric indicator used for performance-based funding of research institutions in Norway. European Political Science ; 8 3 : — A comparison of the Australian and Norwegian publication-based funding models.
Research Evaluation ; 25 3 : — Sivertsen G Publication-based funding: The Norwegian model. Swedish Government. Swedish Research Council. Swedish Research Council: Stockholm. Wilsdon J et al. Wouters P et al. Download references. You can also search for this author in PubMed Google Scholar. Correspondence to Gunnar Sivertsen. This work is licensed under a Creative Commons Attribution 4. Reprints and Permissions. Sivertsen, G. Unique, but still best practice?
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Download PDF. Subjects Politics and international relations Science, technology and society. Introduction In seven major research assessment exercises, beginning in and concluding with the Research Excellence Framework REF , the UK has used the peer review of individuals and their outputs to determine institutional funding. One of the main recommendations is that: Metrics should support, not supplant, expert judgement. A framework for understanding country differences in the design of PRFS Most European countries have introduced performance based research funding systems PRFS for institutional funding.
Countries can be divided in four categories regarding their use of bibliometrics in PRFS: A The purpose of funding allocation is combined with the purpose of research evaluation. B The funding allocation is based on a set of indicators that represent research activities.
D As in category C, but bibliometrics is not part of the set of indicators. The reasons given have been the following: The evaluations mainly have a formative and advisory function.
Gaming should be avoided in the information given to panels. Direct funding should support institutional autonomy. 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.
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 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.
Research findings from underpowered, early-phase clinical trials would be true about one in four times, or even less frequently if bias is present. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,fold e. 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.
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.
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.
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. 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. Is it unavoidable that most research findings are false, or can we improve the situation? However, there are several approaches to improve the post-study probability.
Better powered evidence, e. 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.
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.
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.
Abstract Summary There is increasing concern that most current published research findings are false. Abbreviation: PPV, positive predictive value. 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.
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 ]. Download: PPT. 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.
Figure 1. Table 2. Testing by Several Independent Teams Several independent teams may be addressing the same sets of research questions. Figure 2. Table 3. Corollaries A practical example is shown in Box 1.
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 , gene polymorphisms are associated with susceptibility to schizophrenia.
Table 4. 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. How Can We Improve the Situation? References 1. BMJ —
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