Scientists, data analysts and researchers navigate some seriously murky waters. For professions based on empirical data, ensuring that data is actually, and genuinely, empirical and unbiased is difficult.
Journal Citation Reports
Investigating previously carried out research is an excellent way to ensure that you do not waste time looking for a result already available. Academic journals have traditionally been the number one source for reading up on existing research.
The internet has allowed information sharing to move at a phenomenal rate, but it also has downsides. It is relatively easy to set up a journal or even a network of journals to push theories and research that are not wholly impartial or based on scientific facts.
An excellent research tool is Journal Citation Reports; with this, you can get an unbiased review of a journal to find out where it stands in the landscape of scientific and scholarly journals. It utilises a variety of citation metrics to decide the reliability of a journal. If you are not sure if you can trust a journal or want to ensure that you are submitting your research to a trustworthy source, make this tool your first port of call.
Unbiased Estimators
As much as researchers would like to be able to survey entire populations, it is just not possible, so a more manageable sample size of the population has to be used instead. However, if you use a biased sample, your research will be fundamentally skewed.
Biased samples can result when your sample does not accurately represent the population. For example, a survey conducted midday and midweek in a commuter community will not accurately represent that population. Good practice can go a long way to set yourself up to create unbiased results.
In addition to ensuring your sample represents the studied population, survey questions should be as precise and unambiguous as possible to avoid measurement errors. In certain studies, researchers may employ the Cramer-Rao Lower Bound or the Rao-Blackwell Theorem to prevent biases as much as possible.
Data Quality
If you thought removing bias from your surveys was a challenge, ensuring the quality and accuracy of your data is an even more in-depth process.
One of the hardest parts about ensuring the quality of your data is that there is not always a definite yardstick with which to measure that quality in these cases. It is helpful to break down the data into the following six components and judge whether they meet that brief.
- Conformity: Is your data able to be presented in the format required? If it cannot be reported in the industry standard for its niche, you need to reevaluate it.
- Integrity: When research includes different data sets, can those pieces be linked together to provide context in a way that makes sense?
- Consistency: Is the data consistent across all parts of the research? Inconsistently recorded data, either due to a simple typo or as a result of an error in the research phase, will seriously impact the quality and accuracy of the data.
- Accuracy: Does the data reflect the situation it was recorded for?
- Complete: Is the data comprehensive enough to fulfil the scope of the research?
- Punctuality: Is your data available for when it is required?
Database Integrity
Once you have satisfied yourself that your data is accurate, attention needs to be given to the integrity of how it is stored. Ensuring that the database where your data resides has its own quality checks can even act as an additional measure to confirm its accuracy.
When creating or reviewing your database, it is worthwhile to ask yourself the following three questions:
- Duplication: Is there a way to check for duplicate entries?
- Clean: Has the data passed all the quality checks? Is that recorded anywhere?
- Structure: Is the database organised in a useful and easy way to navigate?
Correcting Biased Data
As you can see, the quest for unbiased data is by no means easy. If you have suspicions that the data you have collected is, in fact, biased, you will need to decide how to handle it.
To decide what to do next, you must first identify the bias’s root cause. Three of the most common reasons for biased data are:
- Selection Bias: This can be prevented by the use of an unbiased estimator mentioned above.
- Systematic Bias: Rigorous data quality checks should be able to identify errors that are consistently being made in how the research is carried out.
- Response Bias: Possibly the element we have the least control over. Your participants’ responses may be skewing the results due to false or inaccurate answers.
Once identified, you will need to decide if you can salvage the data, for example, by adding in additional samples to reflect a population better. If you find that the data cannot be salvaged, view it as part of the process and be sure to learn from what went wrong to prevent similar mistakes in the future.
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