Luke – Truth in Research

How will you make your research (and that of others) as truthful as possible?

I will not fabricate or falsify research results.

I will not, knowingly or otherwise, publish or rely on any research results of research which did not actually take place. Fanelli, for instance, found in a meta-analysis of surveys of ‘research misconduct’ that: ‘an average of 1.97%… of scientists admitted to have fabricated, falsified or modified data or results at least once… and up to 33.7% admitted other questionable research practices’ (2009). This commitment is an obvious and easy step in ensuring that your research remains as truthful as possible.

I will attempt to be as honest as possible in my writing about how I achieved my results.

However, there is doubt as to whether simply committing to not directly fabricating research results is enough. Fanelli, for example notes that surveys are unlikely to capture the full extent of fraudulent science, due to the strong social desirability bias of not wanting to expose oneself as a fraud (2009). Rather, the actual process by which bad research practice can happen is much more insidious. For example, in quantitative research, one way in which unconscious bias may slip into your results is through subtly changing research design to give your results statistical significance: ‘Such manipulation… [can] be done, for example, with serendipitous inclusion or exclusion of certain… controls… investigation of… [factors] not originally specified, changes in… definitions, and various combinations of selective or distorted reporting of the results’ (Iadonnis 2005, p. 699). Naturally, everyone ultimately wants to be right, so researchers tend to do research in a way which attempts to confirm their original hypotheses, whether these are “truthful” or not (Lehrer 2010). Equally, in qualitative research, ‘[r]esearchers… are frequently faced with large amounts of data that cannot be easily summarized for presentation in a published article’ (Louw et al. 2014, p.155). This can lead to ‘cherry-picking’, or ‘select[ing] extract[s] that most represent… the paper’s position’, rather than the ‘data as a whole’ (Louw et al. 2014, p. 155). I think that the best way to limit this for both quant and qual research is to be as open as possible in your writing. Decisions about variables and extracts selected, and what constitutes evidence for a theory need to be specified. Making these decisions public, making me accountable through my writing will help me to will help ensure that I follow research best practice and make my research more convincing.

I will commit to doing my best to avoid manipulating my results to get published.   

However, the pressure to publish can also lead to less truthfulness in research. The pressure to get articles published incentivises researchers to ensure that they find what they were looking for in their results (Lehrer 2010). Papers which confirm an effect are simply more likely to be published than those which found nothing (Lehrer 2010). This leads to practices such as those described above, and is a serious problem for academic research, and one which I do not really feel that I have a satisfactory answer to. Personally, I see myself as someone who has the integrity to publish whatever results I find. However, I feel uncomfortable about how this pressure might influence me if I was to carry on to pursue an academic career. This issue requires systematic change in the culture of academic journals to address, however, I will commit to attempting to remain conscious of this issue and to do research which I think is worthwhile regardless of the pressure to publish.


Lehrer, J. (2010). The Truth Wears Off: Is There Something Wrong with the Scientific Method? [Online]. The New Yorker. Available from: [23/03/2017]

Iadonnis, J. P. A. (2005). Why Most Published Research Findings Are False. [Online]. PLoS Medicine. 2. (8). Available from: [Accessed 23/03/2017]

Fanelli D (2009) How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data. [Online]. PLoS ONE. 4. (5). Available from: [Accessed 23/03/2017]

Louw et al. (2014). Picking the Ripe Cherry: Extract Selection In Qualitative Research. [Online]. Proceedings of the International Conference: Doing Research in Applied Linguistics. pp. 155 – 169. Available from: [Accessed 23/03/2017]


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