ByDarko Savic
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Darko Savic Jul 30, 2020
Let's put together the ultimate guide on how best to perform secondary research
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What are your best tips, tricks, and advice on how to perform secondary research?
Secondary research involves interpreting, re-analyzing, or reviewing “past data” - information that has been previously collected by somebody else. This type of data is accessible via research papers, books, articles, etc.
Let's put together our best tips/tricks/advice and create the ultimate how-to guide for secondary research. The contributing bits can be arranged by the number of likes, so the best advice will surface to the top.
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Know your audience and respect their time
Before you start researching/writing, you should have a clear idea of who your readers are. If you want to publish your research in a peer-reviewed journal, your readers are highly educated scientists, who probably know the background of your research. On the other hand, if you are writing for the general public, people may not be well informed on the subject. Therefore, you need to explain every technical term you use with vocabulary that can be understood by the audience. The reader should get a clear picture of what you want to say. Too much-unexplained technicality will either bore or scare your readers. However, saying that be sure to maintain the right amount of topic-specific jargon like the names of molecules, enzymes, and not-so-common processes.
Your review is not a bin where you dump all of your gathered information. Carefully select the information and a few visuals for the review. The primary aim of a review is not to communicate every little detail of the research, but rather to inform people on what has been done, why it has been done, and to realize its future potential and impact. The reader’s time should not be wasted. Therefore, condense the content to the fewest number of words, organize it by sections and lists of bullet points where applicable.
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Understand your goal and the research topic
Define the area of research and list out questions that you want to be answered. This will facilitate focussed research. Write down your naïve ideas or preconceived notions regarding the topic before you start the research. You can create a mind map for starters. You need these naïve ideas because an informed write-up tends to corrupt or bias your mind in thinking that what you read is the only plausible explanation.
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Research keywords
A keyword/phrase is the concept behind your question. The best way to identify keywords is by using the 5W’s and the H approach. Ask questions about the topic at hand – what is the topic? Who is the subject? When, where, and how did it take place? Why is it a concern? The keywords are usually the answers to these questions. If you get a long list of keywords, assign priority to each word.
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Gather information
Research can be performed by using academic peer-reviewed journals, magazines, books, market research reports, and any other form of publicly available information.
While covering up to date advances, do not forget to include the older findings if they are still relevant as they will form the foundation of your review. While reading research papers, look out for the absolute change or the strength of the association in the reports. A very weak change or a weak association need not be blown up to ascertain the effect observed.
Use the references cited in an article to determine the credibility of the information presented. Although not full-proof, this system will give you an idea of how serious the author is. As a general rule of thumb, detailed references are more credible. It is also preferable to use peer-reviewed publications in your review. Peer-reviewed publications are usually validated by peers and thus should contain high-quality research. You can find out whether a publication is peer-reviewed by using online databases, as the search results can be limited to view peer-reviewed publications only.
When you sit down to gather information from the sources at hand, you will end up with a basic research article. However, information is always piling up, and what you know today might be stale tomorrow. If you wish to keep up to date with your research topic, sign up for newsletters, and set keyword email alerts (pubmed, google alerts).
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Dragan Otasevic4 years ago
Some information sources to explore: libgen.is, iris.ai, researchgate.net, academia.edu, zenodo.org, scholar.google.com, wikipedia.org, Medscape.com, pubmed.ncbi.nlm.nih.gov, scielo.org, bioRxiv.org, sci-hub.se. For books: b-ok.org, libgen.is
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Precision and clarity
Writing well doesn’t just mean improving your style but also improving the content. We like to make complex sentences using impossible words and passive tenses. We believe this is how it should be done and makes us seem intelligent. However, in reality, this is a selfish way of writing, which does not take the reader into account. So it is better to construct simple and smaller sentences that are easily understandable. Here are some specific examples of words that you should avoid while writing.
Watch out for weasel words or phrases that sound good but convey little to no information. They create a good first impression but not a long-lasting one. There are a few types of weasel words:
Salt and pepper words: Avoid seasoning your reviews with salt and pepper words. You might feel these words sound technical and convey a gross meaning, but all they do is highlight your uncertainty. Some common salt and pepper words/phrases are various, variety, a number of, fairly, and quite. Sentences that cut these words out become stronger.
Bad: We used a number of methods to isolate the sample.
Better: We isolated the sample using [mention the method] method.
Beholder words: Meanings of the beholder words convey the feelings of the reader. Intelligent readers may not like judgments made for them. Beholder words are of great use in science fiction where you want to lead the reader through your plot but not when you are stating facts. Common beholder words are interestingly, surprisingly, remarkably, and clearly.
Bad: Surprisingly, most rats showed tumor remission after the treatment.
Better: To our surprise, most rats (90%) showed tumor remission after the treatment.
Lazy words: Avoid lazy words to describe quantitative characteristics. They give the impression that the said analysis is undone. Common lazy words are very, extremely, several, exceedingly, many, most, few, vast.
Bad: The two variants were very similar.
Better: The two variants were over 95% similar.
Adverbs: You weaken a sentence when you use adverbs more frequently than you strengthen it.
Bad: Our solution is completely different.
Better: Our solution is different.
Just like the weasel words, the removal of the passive voice would certainly improve your technical writing. However, in rare cases, the passive voice is useful to shift focus onto the right words. For example in cases where the subject is irrelevant.
Okay: The cultures were incubated at 37 °C.
Alternatively, also okay: We incubated the cultures at 37 °C.
The passive voice should be avoided when it hides relevant information or misplaced emphasis. Every time you think of using the passive voice ask yourself:
Is the subject relevant?
Is the subject unclear?
Does the sentence read better in active?
If the answer to all the questions is "yes," then change to active. If only the answer to question 1 is "yes," then mention the subject. If the answer to question 1 and 3 is “No”, only then go with passive.
Other words to avoid:
Studies/study: “Several studies showed that…” Here, the word “studies” is vague because it does not describe the studies in sufficient detail. Do mention the authors’ names or the techniques these studies have used. Sometimes, “study” is used to avoid explaining why you performed that study, or at least, it seems so. Moreover, check if you want to address the studies or the papers that have published those studies. “Studies” and “papers” based on these studies are two separate things. Technically, “paper” implies a formal document that is published, presented, etc. The “study,” on the other hand, is the experiment that was carried out. The paper reports the details of the study, like the methods used in the study, its results, and interpretations derived from the study. Therefore, it is not correct to say “experiments were carried out in the so and so paper”. The experiments were carried out during the study, and not when the paper was written.
This: “This” is usually used without specifying what “this” actually is. Such a construction is ambiguous and some journal style guides do not allow it. In some cases, it is pretty obvious what it means. However, it is always better to specify what “this” means.
Complicates: “Complicates” can be vague in certain cases. It suggests that either you have little idea of what is going on or you are just lazy to explain things in detail. Instead, describe why you think something is complicated.
Prove: You can never “prove” a hypothesis or a mechanism. It can be “tested” and “validated”.
Novel: A pathway, a molecule, their functions, etc. cannot be novel. They already exist and you “reveal” it. On the other hand, an experiment or technique can be novel.
Understand/examine/investigate/dissect: In general, add the specifics to your sentence. Even if you use these words, ensure that you do not use them instead of explaining the experiment that you carried out.
Modulate/coupled/linked/influence/affect/interact/relationship/interplay/dynamics:
Efficient/optimal/significant: Try to attach a number to such words. Mention the p-value. The same goes for the words “increase” and “decrease”. Avoid using “significant” to mean “big” or “major”. It is too easily confused with the results of a statistical test. Even if you are reporting the results of a statistical test, it’s better to report the numerical results instead. In fact, best to avoid "significantly" entirely.
Architecture: “Architecture” is again a vague term. It does not give any idea about the aspect of the architecture you are talking about.
Other words to avoid and explain them explicitly instead are super, better, worse, good, poor, insight, landscape, robust, impactful, belief, unclear, information, paradigm, and issue.
All said, these are just guidelines and not a set of rules. Most of this came from: https://web.archive.org/web/20200107232112/http://matt.might.net/articles/shell-scripts-for-passive-voice-weasel-words-duplicates/
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Formatting and presentation
Here are some pointers from Michael White (a reviewer at Nature) regarding presenting scientific literature:
Using big fonts makes the paper easy to read. Use a font that provides about 12-15 words per line of text.
Use continuous line numbers. It is easy to refer to line numbers when initiating or following a discussion.
A rule of thumb might be to use an acronym if the phrase is used at least five times. Do use common acronyms like DNA, RNA, etc. Avoid inventing acronyms that are unique to your article.
Try to use colors in a way that won’t create problems for the readers with some form of color blindness. First, decide whether your figure needs to have colors. If shades of grey can suffice, avoid colors. If you have to use colors, replace red with magenta and green with turquoise as they can be distinguished by people with red and green forms of color blindness (they are the most common forms of color blindness). Also, use divergent colors when appropriate.
Use declarative titles for your articles as well as any inland images, text boxes, and tables. Instead of a hypothetical title like “Rapamycin and aging” try “Rapamycin decelerates aging”.
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References and citation styles
You need to refer to other peer-reviewed publications and mention them at the end of your write-up. References serve to support your statements and also differentiate your results, interpretation, and speculations from what your peers have reported in the past. Use a reference management tool such as Mendeley, Zotero, Papers, Qiqqa, Sente, paperpile, etc.
There are many citation styles that can be used to write references like MLA, APA, Chicago, Harvard, just to name a few. These guides go into more detail: https://www.citethisforme.com/guides.
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Distinguishing between science and pseudo-science
Scientific methodology is well-suited, perhaps uniquely so, for building reliable knowledge and for avoiding false beliefs. Under the assumption that science has this kind of power, one of the problems with pseudoscience is that it gets unfair credibility by mimicking the surface appearance of science. However, we should not dismiss pseudo-science as utterly useless, uninteresting, or false. It is just not science. The big difference Karl Popper identifies between science and pseudo-science is a difference in attitude. While pseudo-science is set up to look for evidence that supports its claims, science is set up to challenge its claims and look for evidence that might prove it false. In other words, pseudo-science seeks confirmations, and science seeks falsifications. What this means is that you could do a test that shows a scientific claim to be false, but no conceivable test could show a pseudoscientific claim to be false. Sciences are testable, pseudo-sciences are not.
We can find evidence to establish with certainty that a claim is false. However, we can never (due to the problem of induction) find evidence to establish with certainty that a claim is true. So scientists should realize that their best hypotheses and theories are always tentative and some piece of future evidence could prove them false; while the pseudo-scientists are sure that their theories have been proven true. But they haven't been due to the problem of induction again.
Why does this difference between science and pseudo-science matter? As Popper notes, the important difference seems to be in which approach gives better logical justification for knowledge claims. Popper is not saying that science never makes false claims. What he is saying is that the scientific attitude is aimed at locating and removing the false claims, something that does not happen in pseudo-sciences.
Another important detail is just what scientists mean by "theory". A theory is simply a scientific account (or description, or story) about a system or a piece of the world. Typically, a theory will contain a number of hypotheses. The important thing to note is that theories can be rather speculative or extremely well tested, but they are still theories. There is no threshold a theory crosses to become a fact, or truth, or something more-certain-than-a-theory. You cannot be 100% sure that a theory is true. Of course, there is some uncertainty with all scientific theories. Of course, there are certain claims the theory makes that might turn out to be false, but the fact that there is evidence we could demonstrate these claims are false is a scientific virtue.
Most of this advice came from Janet D. Stemwedel.
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Summarize, summarize, summarize
Monitor your word count. The reader’s time should go toward understanding the topic better. It should not be wasted in reading something you felt like writing. Writers tend to be so tied to their material and have spent so much time writing that it can be difficult to cut copy to fit the word count. The following are some tips for cutting back once you are done writing.
Start with the big picture. Look for entire sections first, then paragraphs, sentences, and words. It is much easier to cut an entire section of your writing that just doesn’t seem to fit or seem necessary in a smaller word count. Large sections that you cut now can be saved for later use and may form the basis for follow-up work.
Summarize, summarize, summarize. Summarization is a huge part of the cutting process. Anything (long quotes, descriptions, illustrations that take up space, etc.) that can be summarized in a few sentences and still prove effective should be done.
Cut out repetition. Many writers follow the “presentation model” where they tell what they are going to say, say what they want, and then tell again what they just told the audience. Even while writing a scientific paper, authors repeat sentences, for example from the results in the discussion section. Avoid such repetition.
Chop ancillary topics. Keep your main idea in front of you and omit anything that does not address your main idea.
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Ensuring credibility of information
While searching for secondary information, reliability of information should be checked. Typically sourcing text from peer reviewed journals Clarivate Analytics’ “Impact Factor” is one metric of testing while Ulrich's International Periodicals Directory can provide refereed status and confirm how well indexed is the parent source.
At times academic sources may not be used but credence to information can still be enforced. Reliability of information that is not academically refereed needs to be verified by information timeliness (how dated is the information); if the links on the source working, what are credentials of the author or of the organization that published the information. Sometimes finding domain name owner from the WHOIS data base may provide more information on the source.
Other ways of testing the information truthfulness are. Is the information from an unbiased source? is it the website owner praising his own product or are there multiple reviewers? A book an amazon may have an amazing review of five stars but when there are only handful of reviews, it points to doubtful information. Another measure could be checking information by pasting it in Google - it would confirm if the information appears elsewhere as well? This measure may be a shortcut plagiarism check but it provides evidence of text's uniqueness. Simple grammar and spelling mistakes on the source and website point to sloppy work and also raises the question that if the source has so many mistakes, what are the possibilities of information being accurate.
Credit worthy information needs to be moderate. If someone comes with outlandish claim then odds are that claim is false. Most of the "eye opening" facts prattled on internet are either hoaxes or unverified free text. With simple rules even a novice researcher can eliminate the most glaring examples of wrongful information but with systematic review it is possible to verify most of the information that is passed on as truth.
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Tips for beginners
A
Some suggestions for beginners:
1) Find out who are the most relevant authors on your topic. This is obvious when you are researching on classical themes but new themes have new authorities, know who they are. Sometimes they are no more than 6 or 7 and new knowledge is trying to be built around what they say.
2) Find those 2, 3, 4 *key* books or articles (if the topic is new they would most probably be journal articles) the people in 1) cite and reference in all of their works.
3) There is a (probably old age) mistake I suggest not to repeat. You will find a lot of literature appearing "randomly". For example (an intentionally extreme example): "When people dream (Freud, 1934)...". That would be fine in a paper about narcoleptics, but I've seen it in papers around Giddens.
4) Your reference list must include only the literature mentioned in the paper.
I hope this adds!
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Dealing with a huge amount of information
Fine-tune the knowledge
When it comes to a general topic, it is easy to be overwhelmed by a crazy amount of information. If you type for example “p53 and Cancer “ on PubMed you will obtain – to date- more than 76000 results. 76000! Now, imagine you being a young PhD student whose task is to write a review about it or you are a scientist looking for the next grant and p53 and Cancer seems to be a topic intriguing enough to deserve some money.
How do you tackle this?
Reading 76000 documents is not an option.
So here I will sum up the key points I have sticked to when I needed to prepare my first review article ever about DNA repair which will score roughly 100000 results. You need to direct your research toward a certain direction rather than the other. The topic “DNA repair” is too much for a review, maybe 3 books may contain it.
So the first step is to narrow down the results. Let's say we want to investigate the importance of "DNA repair in Cancer". The results will be half of our previous search: 50000.
Are we missing out on something? No, cause most of the relevant information putting DNA repair in connection to cancer are in those 50000 results.
The second step is the timeline. History is important; however, in science, things may change in the blink of an eye. What is taken for granted today, may be proved wrong in the next couple of years. So now let's assume that what has been researched and proved in the past 5 years is still relevant and true today. Ok, now the results shrink to 13472. In less than two steps we went from 100000 to 1/10 of it.
Are we missing out something now? Not really, cause most of the relevant knowledge doesn’t just “happen”, but is built on previous facts. Hereby, the results from the past 5 years should contain new knowledge that is based on the previous one (“updates” of a previous version). This way, you are basing your actual presentation/ review/ grant proposal on up-to-date facts.
The third step is consulting other reviews. The review is a collection of what is known about a certain topic to date. Thus, it contains the “state of the art”. Using reviews is extremely helpful since:
- they deliver the current state of the art in a straightforward manner
- they collect the most relevant papers that helped to build the state of the art. Those papers can be used if further investigation is needed to know more about the modality of the single experiment and/ or other results that were left out from the review but can still be relevant
- they also highlight not only the state of the art but also the current gaps: what it is missing, what are the challenges faced today
For these reasons, previous reviews are priceless: the state of the art and the unmet need.
These steps can be taken to deliver a presentation about the current state of the art and what unmet needs are still unanswered and more importantly, to check for hypotheses that may help tackle these challenges.
In case you are interested in the review: https://www.mdpi.com/2073-4409/9/7/1665
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Cognitive biases
- There are at least 175 different kinds of cognitive biases that can plague your research. Some of these biases have a context, for example, you come across some of these only during surveys. Nevertheless, it is useful to keep them in mind during any kind of research. Finding the right bias through the Wikipedia list is hard and therefore, Buster Benson came up with categories to group similar or complementary biases together. If we look at them by the problem they are trying to solve, it becomes a lot easier to understand why they exist, how they are useful, and the trade-offs (and resulting mental errors) that they introduce. The problems that biases help us address are information overload, lack of meaning, the need to act fast, and how to know what needs to be remembered for later. We have explained each with help from Benson’s work.
- Too much information: There is too much information in the world and we filter out most of it. Our brain uses tricks to pick out information that is going to be useful in some way. However, the problem we face is that some of the information we filter out is actually useful and important.
- We notice things that are already primed in memory, repeated often or stuff that has recently been loaded in memory. Biases that fall in this category - Availability heuristic, Attentional bias, Illusory truth effect, Mere exposure effect, Context effect, Cue-dependent forgetting, Mood-congruent memory bias, Frequency illusion, Baader-Meinhof Phenomenon, Empathy gap
- We notice bizarre/funny/visually-striking/anthropomorphic things more than non-bizarre/unfunny things. Therefore, we tend to skip over information that we think is ordinary or expected. Biases that fall in this category - Bizarreness effect, Humor effect, Von Restorff effect, Negativity bias, Publication bias, Omission bias
- We notice changes and we generally tend to weigh the significance of the new value by the direction the change happened (positive or negative) more than re-evaluating the new value as if it had been presented alone. Also applies to when we compare two similar things. Biases that fall in this category - Anchoring, Contrast effect, Focusing effect, Framing effect, Weber–Fechner law, Distinction bias
- We notice information that confirms our own existing beliefs. Also, we tend to ignore details that contradict our own beliefs. Biases that fall in this category - Confirmation bias, Congruence bias, Post-purchase rationalization, Choice-supportive bias, Selective perception, Observer-expectancy effect, Experimenter’s bias, Observer effect, Expectation bias, Ostrich effect, Subjective validation, Continued influence effect, Semmelweis reflex, Bucket error, Law of narrative gravity
- We notice flaws in others more easily than flaws in ourselves. Biases that fall in this category - Bias blind spot, Naïve cynicism, Naïve realism
- Not enough meaning: We get only a small part of the information but we need to make some sense of it in order to survive. Therefore, we connect the dots and fill in the gaps with stuff we think we know (it might not be true).
- We find stories and patterns even in sparse data. Based on whatever small information we have after filtering out, we never have the full story. Our brain reconstructs the entire picture to feel complete inside our heads. Biases that fall in this category - Confabulation, Clustering illusion, Insensitivity to sample size, Neglect of probability, Anecdotal fallacy, Illusion of validity, Masked man fallacy, Recency illusion, Gambler’s fallacy, Hot-hand fallacy, Illusory correlation, Pareidolia, Anthropomorphism
- We fill in characteristics from stereotypes, generalities, and prior histories whenever there are new specific pieces or gaps in information. Conveniently, we then forget which parts were real and which were filled in. Biases that fall in this category - Group attribution error, Ultimate attribution error, Stereotyping, Essentialism, Functional fixedness, Moral credential effect, Just-world hypothesis, Argument from fallacy, Authority bias, Automation bias, Bandwagon effect, Placebo effect
- We imagine things and people we are familiar with or fond of as better than things and people we aren’t. This has an effect on the perceived quality and value of the thing we are looking at. When conducting a survey, rather than looking at all the answers provided by the respondent, you place the individual in a certain group only because that individual has one quality or answers one question in a way that you “like”. Biases that fall in this category - Halo effect, In-group bias, Out-group homogeneity bias, Cross-race effect, Cheerleader effect, Well-traveled road effect, Not invented here, Reactive devaluation, Positivity effect
- We simplify probabilities and numbers to make them easier to think about. Our subconscious does not do the math and generally gets things like the likelihood of something happening if any data is missing wrong. Biases that fall in this category - Mental accounting, Normalcy bias, Appeal to probability fallacy, Base rate fallacy, Murphy’s law, Hofstadter’s law, Subadditivity effect, Survivorship bias, Zero-sum bias, Denomination effect, Magic number 7+-2, Swimmer’s body illusion, Money illusion, Conservatism
- We think we know what others are thinking. This means that we might assume that they know what we know, in other cases we might assume they are thinking about us as much as we are thinking about ourselves. We basically model their own mind after our own. Biases that fall in this category - Curse of knowledge, Illusion of transparency, Spotlight effect, Streetlight effect, Illusion of external agency, Illusion of asymmetric insight, Extrinsic incentive error
- We project our current thinking and assumptions onto the past and the future. This is magnified by the fact that our subconscious is not good at imagining how quickly or slowly things will happen or change over time. Biases that fall in this category - Hindsight bias, Outcome bias, Moral luck, Declinism, Telescoping effect, Rosy retrospection, Impact bias, Pessimism bias, Planning fallacy, Time-saving bias, Pro-innovation bias, Projection bias, Restraint bias, Self-consistency bias
- Need to act fast: Without the ability to act fast in uncertainty, we would have perished long ago. However, while acting fast, we make decisions that are sometimes unfair, self-serving, or counter-productive.
- In order to act, we need to be confident and feel what we do is important. In reality, most of this confidence can be classified as overconfidence, but without it, we might not act at all. Biases that fall in this category - Overconfidence effect, Egocentric bias, Optimism bias, Social desirability bias, Third-person effect, Forer effect, Barnum effect, Illusion of control, False consensus effect, Dunning-Kruger effect, Hard-easy effect, Illusory superiority, Lake Wobegone effect, Self-serving bias, Actor-observer bias, Fundamental attribution error, Defensive attribution hypothesis, Trait ascription bias, Effort justification, Risk compensation, Peltzman effect, Armchair fallacy
- In order to stay focused, we favor the immediate, relatable thing in front of us over the delayed and distant. We value stuff more in the present than in the future and relate more to stories of individuals we know than anonymous individuals. Biases that fall in this category - Hyperbolic discounting, Appeal to novelty, Identifiable victim effect
- We are motivated to complete things that we have already invested time and energy in. This helps us finish things, even if we come across more and more reasons to give up. Biases that fall in this category - Sunk cost fallacy, Irrational escalation, Escalation of commitment, Loss aversion, IKEA effect, Processing difficulty effect, Generation effect, Zero-risk bias, Disposition effect, Unit bias, Pseudocertainty effect, Endowment effect, Backfire effect
- We are motivated to preserve our autonomy and status in a group, and we avoid irreversible decisions in order to avoid mistakes. If we must choose, we tend to choose the option that is perceived as the least risky or that preserves the status quo. For example, we tend to write on a topic in a way that appeals to the readers. This can be something about yourself or some phenomenon that everybody in the field accepts but may or may not be the truth. Biases that fall in this category - System justification, Reactance, Reverse psychology, Decoy effect, Social comparison bias, Status quo bias, Abilene paradox, Law of the instrument, Law of the hammer, Maslow’s hammer, Golden hammer, Chesterton’s fence, Hippo problem
- We favor options that appear simple or that have more complete information over more complex, ambiguous options, even if the latter is ultimately a better use of time and energy. Biases that fall in this category - Ambiguity bias, Information bias, Belief bias, Rhyme as reason effect, Bike-shedding effect, Law of Triviality, Delmore effect, Conjunction fallacy, Occam’s razor, Less-is-better effect, Sapir-Whorf-Korzybski hypothesis
- We can only afford to remember the bits that are most likely to prove useful in the future out of all the information out there. We need to make constant bets and trade-offs around what we try to remember and what we forget. However, some of the things we remember make the system more biased to the other biases.
- We edit and reinforce some memories after we get a new piece of information. During that process, memories can become stronger, but various details can also get accidentally swapped. We sometimes accidentally add detail to the memory that is not there before. We may even reinforce it. Biases that fall in this category - Misattribution of memory, Source confusion, Cryptomnesia, False memory, Suggestibility, Spacing effect
- We discard specifics to form generalities using implicit associations, stereotypes, and prejudices. Biases that fall in this category - Implicit associations, Implicit stereotypes, Stereotypical bias, Prejudice, Fading affect bias
- It is difficult to reduce events and lists to generalities, so instead, we pick out a few items to represent the whole. Biases that fall in this category - Peak–end rule, Leveling and sharpening, Misinformation effect, Duration neglect, Serial recall effect, List-length effect, Modality effect, Memory inhibition, Part-list curing effect, Primacy effect, Recency effect, Serial position effect, Suffix effect
- We store memories differently based on how they were experienced. Our brain only encodes information that it deems important at the time, but this decision can be affected by other circumstances like what else is happening simultaneously, how is the information presenting itself, can we easily find the information again if we need to, etc., ultimately changing the significance of the original memory. Biases that fall in this category - Picture superiority effect, Levels of processing effect, Testing effect, Absent-mindedness, Next-in-line effect, Tip of the tongue phenomenon, Google effect, Self-relevance effect
A few biases that specifically plague questionnaire-aided research
- Acquiescence bias: or the yes-saying bias is to agree with whatever you are presented with without giving it critical thought. This can be because the writer is a friend, a valued colleague, or a supervisor.
- Habituation: People tend to provide the same answers to the questions that have similar words. This is an autopilot-kind of response where people are lazy and do not pay attention to the subtle differences in the questions.
- Sponsor bias: Rather than presenting the facts or well-known opinions in the field, people align their thoughts to the sponsoring organization’s interests.
- Culture bias: Interpreting through a cultural lens, judging everything solely by the standards of your culture. Try to understand the activities of an individual using the standards of the individual’s culture.
- Question-order bias: When one question influences your response to the succeeding questions. Typically, when you are asked to rate 2 competitive products as successive questions. Your rate of the first product is higher than the second one. To avoid this, stick to the criteria provided and rate the products according to it.
- Leading questions: Again, questions are used to drive the respondent to the answers that help validate your hypothesis. To avoid this, use the same questionnaire and do not add or delete any of the questions during research.
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General comments
Darko Savic4 years ago
this guide needs a good section on cognitive biases and logical fallacies. The cognitive biases part can be a summary of this article https://medium.com/better-humans/cognitive-bias-cheat-sheet-55a472476b18