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Are you still performing competitor research like you did three, four, even five years ago? Are you crafting your (or your client’s) SEO plan the same way based on what you find? If so, then it’s time to update your strategy. The world of SEO has changed dramatically, and your competitive research should reflect that change. Here’s what you should be looking for when researching competitors, and how to use that information to create a penalty-proof SEO plan.


Just because the competitor is ranking well with tons of spammy, irrelevant links doesn’t mean you should follow suit.  If they haven’t been penalized yet, they might get it in the future. Instead, find out the following.

What is Your Competitor’s Overall Strategy? What Are the Best Links They Have Obtained?

If you or your client has a strong website, aim to get the best links your competitor has obtained as soon as possible. Start with gathering their best links, and then go for even better ones. If Google continues to penalize websites with lots of low quality links, then the ones with fewer but higher quality links (like yours) will be able to shine through.

One easy way to spot your competitors best backlinks is by using tools like CognitiveSEO. Their visual backlink explorer displays high quality links with larger dots.

You can also use the Domain Trustworthiness graph to see the highest authority domains linking to your competitors.

If you prefer an exportable chart, you can filter the backlinks of your competitors by authority and only view the high authority links.

Depending on your industry, you’ll likely find the highest authority links are from .gov or .edu sites, the top blogs in the industry, media networks, and other well-respected sites.

In order to gain these links for yourself, you will need to:

Build relationships with the top industry blogs that will publish your content as a guest or regular contributor.

Build strong profiles on local sites and properly encourage customers to write reviews.

Sign up for HARO and similar networks that connect you to journalists so you can contribute your knowledge in exchange for a mention and a link.


Gone are the days when 300 words of keyword-optimized text were all you needed to get a page ready for your target keywords. Google is not going to reward you for having thousands upon thousands of over-optimized articles on your website. They are going to reward you for having hundreds of high-quality, reader-focused blog posts. Not only that, but they are also going to reward you for having authoritative authors creating that content. When you’re researching the competitor’s content, here are the questions you need to ask.

Who are the authors?

If author rank isn’t playing a role in rankings now, it certainly will be in the future (according to Matt Cutts at least). A part of your competitor research should be focused on who the authors of the competitor’s content are, and how strong are they as authors. Find their Google+ profiles and look at the other sites they’ve written for, how often they are creating content, how many followers they have on Google+, and how much engagement they receive on their Google+ posts.

One tool that can help with this process is the SEOchat Author Links Crawler (free, but currently in beta). You can use it to see what authors are linking to specific sites in order to connect with influential people in your industry. Reach out to them and see if you can get them to create content for you.

What is the length of the average piece of content?

While length isn’t everything, it can certainly help you determine how in-depth competitors go with their content. If their content ranks well in search, and it is generally 1,000 – 2,000 words per piece, but your content or your client’s is only 500 – 600 words per piece, you might want to look into creating more extensive pieces of content.

How much engagement do they receive?

One tool that can help you see the content that receives the most engagement on social media – Twitter in particular – is Topsy. Use the following URL and replace chúng tôi with your competitor’s domain.

You will then see their content links along with the number of tweets each link has received.

Social Media

Having a strong social media presence can be a great asset, especially since you don’t want to put all of your eggs in an organic search basket. If social media isn’t a part of your competitor research, then make sure it does! Ask the following questions.

What networks do the competitors use?

Not what networks do competitors have a profile on, but what networks do they actively use. You’ll generally find that most are active on Facebook, Twitter, LinkedIn, Google+, and Pinterest (usually in that order). Also be on the lookout for any niche-specialized social networks and forums that the competitors are using on a regular basis. To find this out, you can simply use Google to search for the competitor’s name – their top social profiles will generally come up in the first couple of pages.

How many followers / fans do they have?

Do you need 1,000 fans, 10,000 fans, or a million fans? Find out by seeing how many people the competitors have in their social networks. If you’re looking for a selling point of why a business should be on social media, this number could play an important role as it shows the number of potential customers that can be found on social media.

Aside from visiting each of the competitor’s social profiles and noting their audience size, one great tool to use to quickly see a group of competitor’s followers and fans is Rival IQ. It allows you to compare the size of your competitor’s audience on Twitter, Facebook, and Google+.

You can also use tools like TwitterCounter to see the growth of your competitor’s Twitter following over the time span of up to three months for free, or up to six months with a preview of their premium service by paying with a tweet.

One of the things this will quickly reveal is if your competitor has had any major growth spikes, which is sometimes indicative of a purchase of followers.

Of course, don’t stop there…

How much quality engagement do they receive?

Anyone can buy fans and followers. They can even buy engagement. But they can’t buy true interactions. If you are looking to craft your social media strategy based off of how competitors are using social media, be sure that you are modeling yours and your client’s on a competitor that is receiving true engagement from their audience.

How often do they update each of their networks?

This revolves back to content, except in this case, it’s social media content. How many times do competitors update each of their social networks, and how do they do it? Do they ask questions, post links, and/or share photos and videos? And most importantly, what types of updates get the most engagement from their audiences?

You may not be able to tell their ROI, but there’s a good chance that the more exposure they get on a status update, the more likely that update is to convert compared to one with little to no response.

What do you include as part of your competitive research strategy?

You're reading The New Rules Of Competitive Research

Types Of Bias In Research

Research bias results from any deviation from the truth, causing distorted results and wrong conclusions. Bias can occur at any phase of your research, including during data collection, data analysis, interpretation, or publication. Research bias can occur in both qualitative and quantitative research.

Understanding research bias is important for several reasons.

Bias exists in all research, across research designs, and is difficult to eliminate.

Bias can occur at any stage of the research process.

Bias impacts the validity and reliability of your findings, leading to misinterpretation of data.

It is almost impossible to conduct a study without some degree of research bias. It’s crucial for you to be aware of the potential types of bias, so you can minimize them.

Example: Bias in researchSuppose that you are researching whether a particular weight loss program is successful for people with diabetes. If you focus purely on whether participants complete the program, you may bias your research.

For example, the success rate of the program will likely be affected if participants start to drop out (attrition). Participants who become disillusioned due to not losing weight may drop out, while those who succeed in losing weight are more likely to continue. This in turn may bias the findings towards more favorable results. 

Accounting for the differences between people who remain in a study and those who withdraw is important so as to avoid bias.

Information bias

Information bias, also called measurement bias, arises when key study variables are inaccurately measured or classified. Information bias occurs during the data collection step and is common in research studies that involve self-reporting and retrospective data collection. It can also result from poor interviewing techniques or differing levels of recall from participants.

The main types of information bias are:

Example: Information bias in researchYou are researching the correlation between smartphone use and musculoskeletal symptoms among high school students.

Over a period of four weeks, you ask students to keep a journal, noting how much time they spent on their smartphones along with any symptoms like muscle twitches, aches, or fatigue.

At the end of the study, you compare the self-reports with the usage data registered on their smartphones. You notice that for usage of less than three hours a day, self-reports tended to overestimate the duration of smartphone use. Conversely, for usage of more than three hours a day, self-reports tended to underestimate the duration of smartphone use. This goes to show that information bias can operate in more than one direction within a study group.

Recall bias

Recall bias is a type of information bias. It occurs when respondents are asked to recall events in the past and is common in studies that involve self-reporting.

As a rule of thumb, infrequent events (e.g., buying a house or a car) will be memorable for longer periods of time than routine events (e.g., daily use of public transportation). You can reduce recall bias by running a pilot survey and carefully testing recall periods. If possible, test both shorter and longer periods, checking for differences in recall.

Example: Recall bias in researchYou are conducting a case-control study examining the association between the diet of young children and the diagnosis of childhood cancer. You examine two groups:

A group of children who have been diagnosed, called the case group

A group of children who have not been diagnosed, called the control group

Since the parents are being asked to recall what their children generally ate over a period of several years, there is high potential for recall bias in the case group.

The best way to reduce recall bias is by ensuring your control group will have similar levels of recall bias to your case group. Parents of children who have childhood cancer, which is a serious health problem, are likely to be quite concerned about what may have contributed to the cancer.

Thus, if asked by researchers, these parents are likely to think very hard about what their child ate or did not eat in their first years of life. Parents of children with other serious health problems (aside from cancer) are also likely to be quite concerned about any diet-related question that researchers ask about.

Therefore, these parents can be expected to recall their children’s diet in a way that is more comparable with parents of children who have cancer. In contrast, parents of children who have no health problems or parents of children with only minor health problems are less likely to be concerned with carefully recalling their children’s eating habits.

Observer bias

Observer bias is the tendency of research participants to see what they expect or want to see, rather than what is actually occurring. Observer bias can affect the results in observational and experimental studies, where subjective judgment (such as assessing a medical image) or measurement (such as rounding blood pressure readings up or down) is part of the data collection process.

Example: Observer bias in researchYou and a colleague are investigating communication behavior in a hospital. You are observing eight doctors and two nurses, seeking to discover whether they prefer interruptive communication mechanisms—face-to-face discussion or telephone calls—over less interruptive methods, such as emails. You and your colleague follow the 10 members of staff for a month. Each time you observe someone making a call, walking to a colleague to ask something, etc., you make a note.

Based on discussions you had with other researchers before starting your observations, you are inclined to think that medical staff tend to simply call each other when they need specific patient details or have questions about treatments.

At the end of the observation period, you compare notes with your colleague. Your conclusion was that medical staff tend to favor phone calls when seeking information, while your colleague noted down that medical staff mostly rely on face-to-face discussions. Seeing that your expectations may have influenced your observations, you and your colleague decide to conduct semi-structured interviews with medical staff to clarify the observed events.

Note:Observer bias and actor–observer bias are not the same thing.

Observer bias arises from the opinions and expectations of the observer, influencing data collection and recording, while actor–observer bias has to do with how we interpret the same behavior differently depending on whether we are engaging in it or others are.

Performance bias

Performance bias is unequal care between study groups. Performance bias occurs mainly in medical research experiments, if participants have knowledge of the planned intervention, therapy, or drug trial before it begins.

Studies about nutrition, exercise outcomes, or surgical interventions are very susceptible to this type of bias. It can be minimized by using blinding, which prevents participants and/or researchers from knowing who is in the control or treatment groups. If blinding is not possible, then using objective outcomes (such as hospital admission data) is the best approach.

Example: Performance bias in researchYou are investigating whether a high-protein diet can help people lose weight. The experimental group is provided with a high-protein meal plan, while the control group follows their regular diet. The control group does not know that the study is about the link between protein and weight loss, but they can easily guess that it’s about nutrition. If participants know this beforehand, they can potentially change their behavior, e.g., by increasing their protein intake or seeking to eat more healthily than they normally do.

Regression to the mean (RTM)

Regression to the mean (RTM) is a statistical phenomenon that refers to the fact that a variable that shows an extreme value on its first measurement will tend to be closer to the center of its distribution on a second measurement.

Medical research is particularly sensitive to RTM. Here, interventions aimed at a group or a characteristic that is very different from the average (e.g., people with high blood pressure) will appear to be successful because of the regression to the mean. This can lead researchers to misinterpret results, describing a specific intervention as causal when the change in the extreme groups would have happened anyway.

Example: Regression to the mean (RTM)You are researching a new intervention for people with depression.

In general, among people with depression, certain physical and mental characteristics have been observed to deviate from the population mean.

This could lead you to think that the intervention was effective when those treated showed improvement on measured post-treatment indicators, such as reduced severity of depressive episodes.

However, given that such characteristics deviate more from the population mean in people with depression than in people without depression, this improvement could be attributed to RTM.

In order to differentiate between RTM and true improvement, consider introducing a control group, such as an untreated group of similar individuals or a group of similar individuals in an alternative treatment.

Interviewer bias

Interviewer bias stems from the person conducting the research study. It can result from the way they ask questions or react to responses, but also from any aspect of their identity, such as their sex, ethnicity, social class, or perceived attractiveness.

Interviewer bias distorts responses, especially when the characteristics relate in some way to the research topic. Interviewer bias can also affect the interviewer’s ability to establish rapport with the interviewees, causing them to feel less comfortable giving their honest opinions about sensitive or personal topics.

Example: Interviewer bias in researchSuppose you are interviewing people about how they spend their free time at home.

Participant: “I like to solve puzzles, or sometimes do some gardening.”

You: “I love gardening, too!”

In this case, seeing your enthusiastic reaction could lead the participant to talk more about gardening.

Establishing trust between you and your interviewees is crucial in order to ensure that they feel comfortable opening up and revealing their true thoughts and feelings. At the same time, being overly empathetic can influence the responses of your interviewees, as seen above.

A better approach here would be to use neutral responses that still show that you’re paying attention and are engaged in the conversation. Some examples could include “Thank you for sharing” or “Can you tell me more about that?”

Publication bias

Publication bias occurs when the decision to publish research findings is based on their nature or the direction of their results. Studies reporting results that are perceived as positive, statistically significant, or favoring the study hypotheses are more likely to be published due to publication bias.

Example: Publication bias in researchA researcher testing a drug for Alzheimer’s discovers that there is no statistically significant difference between the patients in the control and the treatment groups. Fearing that this will impact their chances for securing funding and career promotion, they decide not to publish their findings.

Researcher bias

Researcher bias occurs when the researcher’s beliefs or expectations influence the research design or data collection process. Researcher bias can be deliberate (such as claiming that an intervention worked even if it didn’t) or unconscious (such as letting personal feelings, stereotypes, or assumptions influence research questions).

The unconscious form of researcher bias is associated with the Pygmalion effect (or Rosenthal effect), where the researcher’s high expectations (e.g., that patients assigned to a treatment group will succeed) lead to better performance and better outcomes.

NoteAlthough researcher bias and observer bias may seem similar, they are not the same thing. Observer bias affects how behaviors are recorded or measurements are taken, often in the data collection and interpretation stages. Researcher bias is a broader term and can influence any part of the research design.

Researcher bias is also sometimes called experimenter bias, but it applies to all types of investigative projects, rather than only to experimental designs.

Example: Researcher bias Suppose you want to study the effects of alcohol on young adults. If you are already convinced that alcohol causes young people to behave in a reckless way, this may influence how you phrase your survey questions. Instead of being neutral and non-judgmental, they run the risk of reflecting your preconceived notions around alcohol consumption. As a result, your survey will be biased.

Good question: What are your views on alcohol consumption among your peers?

Bad question: Do you think it’s okay for young people to drink so much?

Response bias

Response bias is a general term used to describe a number of different situations where respondents tend to provide inaccurate or false answers to self-report questions, such as those asked on surveys or in structured interviews.

This happens because when people are asked a question (e.g., during an interview), they integrate multiple sources of information to generate their responses. Because of that, any aspect of a research study may potentially bias a respondent. Examples include the phrasing of questions in surveys, how participants perceive the researcher, or the desire of the participant to please the researcher and to provide socially desirable responses.

Response bias also occurs in experimental medical research. When outcomes are based on patients’ reports, a placebo effect can occur. Here, patients report an improvement despite having received a placebo, not an active medical treatment.

Example: Response biasYou are researching factors associated with cheating among college students.

While interviewing a student, you ask them:

“Do you think it’s okay to cheat on an exam?”

Since cheating is generally regarded as a bad thing, the word itself is negatively charged. Here, the student may feel the need to hide their true feelings, conforming to what is considered most socially acceptable—that cheating is not okay.

Common types of response bias are:

Acquiescence bias

Acquiescence bias is the tendency of respondents to agree with a statement when faced with binary response options like “agree/disagree,” “yes/no,” or “true/false.” Acquiescence is sometimes referred to as “yea-saying.”

This type of bias occurs either due to the participant’s personality (i.e., some people are more likely to agree with statements than disagree, regardless of their content) or because participants perceive the researcher as an expert and are more inclined to agree with the statements presented to them.

Example: Acquiescence bias in researchSuppose you are researching introversion and extroversion among students. You include the following question in your survey:

Q: Are you a social person?



People who are inclined to agree with statements presented to them are at risk of selecting the first option, even if it isn’t fully supported by their lived experiences.

In order to control for acquiescence, consider tweaking your phrasing to encourage respondents to make a choice truly based on their preferences. Here’s an example:

Q: What would you prefer?

A quiet night in

A night out with friends

Demand characteristics

Demand characteristics are cues that could reveal the research agenda to participants, risking a change in their behaviors or views. Ensuring that participants are not aware of the research objectives is the best way to avoid this type of bias.

Example: Demand characteristicsA researcher is investigating whether a spinal operation can reduce back pain complaints. Patients are interviewed by the surgeon who conducted the operation six weeks, three months, and one year post-op, and their levels of pain are assessed.

Sensing this, the patients downplayed any complaints in an effort to please the researcher. The researcher’s frowns served as cues (demand characteristics) that helped participants figure out that the research agenda was lessened pain.

Social desirability bias

Social desirability bias is the tendency of participants to give responses that they believe will be viewed favorably by the researcher or other participants. It often affects studies that focus on sensitive topics, such as alcohol consumption or sexual behavior.

Example: Social desirability biasYou are designing an employee well-being program for a technology start-up. You want to gauge employees’ interest in various activities and components that could be included in this program.

You are conducting face-to-face semi-structured interviews with a number of employees from different departments. When asked whether they would be interested in a smoking cessation program, there was widespread enthusiasm for the idea.

However, when you leave the building at the end of the day, you run into a few members of the interview group smoking outside. You overhear them saying how they don’t like the idea of the smoking cessation program, but they felt they couldn’t really say it because smoking is considered a bad habit in this day and age.

Note that while social desirability and demand characteristics may sound similar, there is a key difference between them. Social desirability is about conforming to social norms, while demand characteristics revolve around the purpose of the research.

Courtesy bias

Courtesy bias stems from a reluctance to give negative feedback, so as to be polite to the person asking the question. Small-group interviewing where participants relate in some way to each other (e.g., a student, a teacher, and a dean) is especially prone to this type of bias.

Example: Courtesy biasYou are researching cases of disrespectful behavior towards women who gave birth at hospitals. If you ask women about their experiences while in or near the facility in which they received care, it is possible that some women may avoid giving negative feedback.

Courtesy bias, including fear of repercussions, may lead some women to avoid sharing any negative experiences. Conducting interviews to capture women’s experiences of disrespect in a more neutral setting is the best approach here.

Question order bias

Question order bias occurs when the order in which interview questions are asked influences the way the respondent interprets and evaluates them. This occurs especially when previous questions provide context for subsequent questions.

When answering subsequent questions, respondents may orient their answers to previous questions (called a halo effect), which can lead to systematic distortion of the responses.

Example: Question order biasIf a respondent is asked how satisfied he is with his marriage, this increases the probability that he will also take his marriage into account when answering the question about his satisfaction with life in general.

In this case, you can minimize question order bias by asking general questions (satisfaction with life) prior to specific ones (marriage).

Extreme responding

Extreme responding is the tendency of a respondent to answer in the extreme, choosing the lowest or highest response available, even if that is not their true opinion. Extreme responding is common in surveys using Likert scales, and it distorts people’s true attitudes and opinions.

Disposition towards the survey can be a source of extreme responding, as well as cultural components. For example, people coming from collectivist cultures tend to exhibit extreme responses in terms of agreement, while respondents indifferent to the questions asked may exhibit extreme responses in terms of disagreement.

Example: Extreme respondingYou want to find out what students think of on-campus counseling services via a survey using Likert scales. There are 40 questions, with potential responses ranging from “strongly agree” to “strongly disagree.”

In your pilot study, you notice that a number of respondents only select the extreme options for each question. To mitigate this, you decide to shorten the questionnaire and diversify the questions. Instead of solely using Likert scales, you also add some multiple-choice and open questions.

Selection bias

Selection bias is a general term describing situations where bias is introduced into the research from factors affecting the study population.

Common types of selection bias are:

Example: Selection biasYou are investigating elderly people’s self-perceived physical health in your city. You go outside a public pool and interview elderly people as they exit.

Collecting your data only from senior citizens at the pool will lead to selection bias in your data. In this case, you are excluding elderly people who are not willing or able to maintain an active lifestyle.

Sampling or ascertainment bias

Sampling bias occurs when your sample (the individuals, groups, or data you obtain for your research) is selected in a way that is not representative of the population you are analyzing. Sampling bias threatens the external validity of your findings and influences the generalizability of your results.

The easiest way to prevent sampling bias is to use a probability sampling method. This way, each member of the population you are studying has an equal chance of being included in your sample.

Sampling bias is often referred to as ascertainment bias in the medical field.

Example: Sampling (or ascertainment) biasYou are researching the likelihood of heart disease in your area. You decide to collect your data by interviewing people entering and leaving a local shopping mall.

This collection method does not include people who are bedridden or very ill from heart disease. Because many of them are more likely confined at their homes or in a hospital, and not walking around a mall, your sample is biased.

Attrition bias

Attrition bias occurs when participants who drop out of a study systematically differ from those who remain in the study. Attrition bias is especially problematic in randomized controlled trials for medical research because participants who do not like the experience or have unwanted side effects can drop out and affect your results.

You can minimize attrition bias by offering incentives for participants to complete the study (e.g., a gift card if they successfully attend every session). It’s also a good practice to recruit more participants than you need, or minimize the number of follow-up sessions or questions.

Example: Attrition bias in researchUsing a longitudinal design, you investigate whether a stress management training program can help students with anxiety regulate their stress levels during exams.

You provide a treatment group with weekly one-hour sessions over a two-month period, while a control group attends sessions on an unrelated topic. You complete five waves of data collection to compare outcomes: a pretest survey, three surveys during the program, and a posttest survey.

During your study, you notice that a number of participants drop out, failing to attend the training sessions or complete the follow-up surveys. You check the baseline survey data to compare those who leave against those who remain, finding that participants who left reported significantly higher levels of anxiety than those who stayed. This means your study has attrition bias.

Self-selection or volunteer bias

Self-selection bias (also called volunteer bias) occurs when individuals who volunteer for a study have particular characteristics that matter for the purposes of the study.

Closely related to volunteer bias is nonresponse bias, which occurs when a research subject declines to participate in a particular study or drops out before the study’s completion.

Example: Self-selection (or volunteer) bias in researchYou want to study whether fish consumption can reduce the risk of cognitive decline in elderly people. In order to recruit volunteers, you place posters in the area around the hospital where the experiment will take place.

Considering that the hospital is located in an affluent part of the city, volunteers are more likely to have a higher socioeconomic standing, higher education, and better nutrition than the general population.

This means that volunteer bias may affect your findings as the participants will differ significantly from non-participants in ways that relate to the study objectives (i.e., the relationship between nutrition and cognitive decline).

Survivorship bias

Survivorship bias occurs when you do not evaluate your data set in its entirety: for example, by only analyzing the patients who survived a clinical trial.

This strongly increases the likelihood that you draw (incorrect) conclusions based upon those who have passed some sort of selection process—focusing on “survivors” and forgetting those who went through a similar process and did not survive.

Note that “survival” does not always mean that participants died! Rather, it signifies that participants did not successfully complete the intervention.

Example: Survivorship bias in researchYou are researching which factors contribute to a successful career as an entrepreneur. Looking into the résumés of well-known entrepreneurs, you notice that most of them were college dropouts. This could make you think that having a good idea and leaving college to pursue it is all that it takes to set off your career.

However, most college dropouts do not become billionaires. In fact, there are many more aspiring entrepreneurs who dropped out of college to start companies and failed than succeeded.

When you focus on the people who left school and succeeded, ignoring the far larger group of dropouts who did not, you are succumbing to survivorship bias. This means that a visible “successful” subgroup is mistaken as an entire group due to the “failure” subgroup’s not being visible.

Nonresponse bias

Nonresponse bias occurs when those who do not respond to a survey or research project are different from those who do in ways that are critical to the goals of the research. This is very common in survey research, when participants are unable or unwilling to participate due to factors like lack of the necessary skills, lack of time, or guilt or shame related to the topic.

You can mitigate nonresponse bias by offering the survey in different formats (e.g., an online survey, but also a paper version sent via post), ensuring confidentiality, and sending them reminders to complete the survey.

Example: Nonresponse bias in researchYou are investigating the average age of people in your city who have a landline in their homes. You attempt to conduct a phone survey of 1,000 individuals, dialed randomly from the population of landline-owning residents. After 1,000 attempts, you are in possession of 746 valid responses, while 254 individuals never answered the phone. Is this sample representative?

You notice that your surveys were conducted during business hours, when the working-age residents were less likely to be home.

If working-age respondents are underrepresented in your sample, then the average among the 746 valid age responses will skew older than the true population average. In this case, the difference between the biased average and the true, but unobserved, average age among all landline owners is due to nonresponse bias.

Undercoverage bias

Undercoverage bias occurs when you only sample from a subset of the population you are interested in. Online surveys can be particularly susceptible to undercoverage bias. Despite being more cost-effective than other methods, they can introduce undercoverage bias as a result of excluding people who do not use the internet.

Example: Undercoverage bias in researchYou are running a web survey on self-reported health, focusing on smoking habits and binge drinking. However, the method of your survey means that you are systematically excluding non-internet users. Undercoverage would not be a problem here if internet users did not differ from non-internet users.

However, you know from previous studies that the proportion of non-internet use has a positive relationship with age and a negative relationship with education level. This means that you run a risk of excluding older and less educated respondents from your sample. Since the differences between internet users and non-internet users can play a significant role in influencing your study variables, you will not be able to draw valid conclusions from your web survey.

Cognitive bias

Cognitive bias refers to a set of predictable (i.e., nonrandom) errors in thinking that arise from our limited ability to process information objectively. Rather, our judgment is influenced by our values, memories, and other personal traits. These create “ mental shortcuts” that help us process information intuitively and decide faster. However, cognitive bias can also cause us to misunderstand or misinterpret situations, information, or other people.

Example: Cognitive biasWhen asked if their favorite team will win a game, most people will be positive. However, fewer people will still claim that they had predicted a win when asked again after their team has lost. Most people will likely say that they knew it all along the team was going to lose.

Because of cognitive bias, people often perceive events to be more predictable after they happen.

When something happens, it often seems so obvious that we should have seen it coming, and that may distort our memories so that our earlier predictions conform with this belief. In other words, we tell ourselves “I knew it all along”.

Although there is no general agreement on how many types of cognitive bias exist, some common types are:

Anchoring bias

Anchoring bias is people’s tendency to fixate on the first piece of information they receive, especially when it concerns numbers. This piece of information becomes a reference point or anchor. Because of that, people base all subsequent decisions on this anchor. For example, initial offers have a stronger influence on the outcome of negotiations than subsequent ones.

Example: Anchoring bias Anchoring bias can greatly influence the estimated value of a product.

If a car salesperson starts negotiations at $12,000, you’ll likely think you’re getting a good deal when you eventually agree with him to lower the price to $10,500. The real value of the car may be near $10,000, but the first figure you heard influenced your estimation of its value.

Framing effect

Framing effect refers to our tendency to decide based on how the information about the decision is presented to us. In other words, our response depends on whether the option is presented in a negative or positive light, e.g., gain or loss, reward or punishment, etc. This means that the same information can be more or less attractive depending on the wording or what features are highlighted.

Example: Framing effectThe framing effect strongly influences our acceptance of information. A medical procedure with a 90% chance of survival sounds more appealing than one with a 10% chance of mortality. This is because most people prefer an outcome that is presented in a positive rather than a negative way, even though it is the same information or issue, simply cast in a different light.

Actor–observer bias

Actor–observer bias occurs when you attribute the behavior of others to internal factors, like skill or personality, but attribute your own behavior to external or situational factors.

In other words, when you are the actor in a situation, you are more likely to link events to external factors, such as your surroundings or environment. However, when you are observing the behavior of others, you are more likely to associate behavior with their personality, nature, or temperament.

Example: Actor–observer bias in researchSuppose you are researching road rage. You are interviewing people about their driving behavior, as well as the behavior of others.

One interviewee recalls a morning when it was raining heavily. They were rushing to drop off their kids at school in order to get to work on time. As they were driving down the highway, another car cut them off as they were trying to merge. They tell you how frustrated they felt and exclaim that the other driver must have been a very rude person.

At another point, the same interviewee recalls that they did something similar: accidentally cutting off another driver while trying to take the correct exit. However, this time, the interviewee claimed that they always drive very carefully, blaming their mistake on poor visibility due to the rain.

This interview was influenced by actor–observer bias. Your interviewee attributed internal factors (rudeness) to others and external factors (rain) to themselves while describing identical behavior (driving dangerously).

Availability heuristic

Availability heuristic (or availability bias) describes the tendency to evaluate a topic using the information we can quickly recall to our mind, i.e., that is available to us. However, this is not necessarily the best information, rather it’s the most vivid or recent. Even so, due to this mental shortcut, we tend to think that what we can recall must be right and ignore any other information.

Example: Availability heuristic After reading many news stories about shark attacks, you start thinking these attacks always happen. When you go on holiday, you hesitate to swim in the ocean, because you are convinced it’s risky.

Confirmation bias

Confirmation bias is the tendency to seek out information in a way that supports our existing beliefs while also rejecting any information that contradicts those beliefs. Confirmation bias is often unintentional but still results in skewed results and poor decision-making.

Example: Confirmation bias in researchYou are a social scientist researching how military families handle long-term, overseas family separation.

Let’s say you grew up with a parent in the military. Chances are that you have a lot of complex emotions around overseas deployments. This can lead you to over-emphasize findings that “prove” that your lived experience is the case for most families, neglecting other explanations and experiences.

As a researcher, it’s critical to make evidence-based decisions when supporting or rejecting a hypothesis and to avoid acting with confirmation bias towards a given outcome.

Halo effect

The halo effect refers to situations whereby our general impression about a person, a brand, or a product is shaped by a single trait. It happens, for instance, when we automatically make positive assumptions about people based on something positive we notice, while in reality, we know little about them.

Example: Halo effectWhen we meet someone for the first time, the information we have about them is limited, so we will use any information available to assess their character. In that process, we focus e.g., on observable behaviors like body language and facial expressions. We may notice that they seem friendly because they smile a lot and from that assume other positive characteristics about that person, such as that they must be kind or intelligent.

The Baader-Meinhof phenomenon

The Baader-Meinhof phenomenon (or frequency illusion) occurs when something that you recently learned seems to appear “everywhere” soon after it was first brought to your attention. However, this is not the case. What has increased is your awareness of something, such as a new word or an old song you never knew existed, not their frequency.

Example: The Baader-Meinhof phenomenon Soon after hearing the word “simulacrum” in a philosophy class, you experience immediately encountering it again: as the title of a theatrical play, a video game, and a metal band by the same name, over a short period. You start to think this is a strange coincidence. When your brain learns something new and interesting, you simply tend to notice it more.

How to avoid bias in research

While very difficult to eliminate entirely, research bias can be mitigated through proper study design and implementation. Here are some tips to keep in mind as you get started.

Clearly explain in your methodology section how your research design will help you meet the research objectives and why this is the most appropriate research design.

In quantitative studies, make sure that you use probability sampling to select the participants. If you’re running an experiment, make sure you use random assignment to assign your control and treatment groups.

Account for participants who withdraw or are lost to follow-up during the study. If they are withdrawing for a particular reason, it could bias your results. This applies especially to longer-term or longitudinal studies.

Use triangulation to enhance the validity and credibility of your findings.

Phrase your survey or interview questions in a neutral, non-judgmental tone. Be very careful that your questions do not steer your participants in any particular direction.

Consider using a reflexive journal. Here, you can log the details of each interview, paying special attention to any influence you may have had on participants. You can include these in your final analysis.

Other types of research bias Frequently asked questions about research bias

New Crypto: The Ultimate List Of The Top Trending New Cryptocurrencies In 2023

Embark on an enlightening journey through the premier compendium unveiling new crypto coins and new trending cryptocurrencies in the market in 2023. In this in-depth article, we will provide a comprehensive exploration of a wide array of new crypto coins, ranging from new crypto presales and trending altcoins to exciting new cryptocurrencies and the captivating world of memecoins. Tailored for both seasoned crypto enthusiasts and those embarking on their crypto journey, this guide is your helpful resource, providing valuable insights and helping you maneuver through the dynamic realm of new cryptocurrencies. Come along as we navigate the ever-changing terrain of trending crypto, discussing the latest trends, new crypto projects, and burgeoning fresh opportunities that lie ahead.

List of Trending Cryptocurrencies:

ApeMax (APEMAX) – ApeMax is an innovative and trending new crypto available at presale to eligible buyers, and making waves thanks to its state of the art boost-to-earn mechanism empowering holders to stake on entities that they like.

Ethereum (ETH) – decentralized blockchain platform that enables the development and execution of smart contracts and decentralized applications. It has played a pivotal role in driving innovation within the blockchain and cryptocurrency ecosystem.

Wall Street Memes (WSM) – Exciting and fun new meme coin inspired by the famous Wall Street Bets movement and with a young and growing community of fans.

Solana (SOL) – High-performance blockchain platform designed for decentralized applications and crypto projects. Solana focuses on fast transaction processing speeds, low fees, and scalability.

Pepe Coin (PEPE) – New meme coin inspired by the green internet frog cartoon and with a market cap that quickly rose to several hundreds of million of dollars according to CoinGecko data.

Bitcoin (BTC) – World’s first decentralized digital currency, created in 2009 by an anonymous person or group of people using the pseudonym Satoshi Nakamoto. It operates on a peer-to-peer network and allows for secure and direct transactions without the need for intermediaries

Dogecoin (DOGE) – Cryptocurrency introduced in 2013 as a light-hearted and meme-inspired digital token. It features the Shiba Inu dog from the “Doge” meme as its mascot and gained popularity for its active online community and charitable initiatives.

Shiba Inu (SHIB) – Shiba Inu coin, also known as SHIB, is a cryptocurrency inspired by the Shiba Inu dog breed and introduced as an alternative to Dogecoin. It gained popularity for its community-driven nature and is known for its high levels of volatility.

Aptos (APT) – New proof-of-stake (PoS) blockchain developed by former facebook employees and with a focus on high throughput and on creating a system with strong security for smart contracts.

Sui (SUI) – Layer 1 (L1) blockchain introduced in 2023, utilizing its native token called Sui. By leveraging Move, a Rust-based programming language, Sui facilitates swift transactions, immediate processing, and enhanced scalability.

Which cryptocurrency is trending right now?

Several new and old cryptocurrencies are trending at the moment. New meme inspired tokens such as ApeMax, Wall Street Memes, and Pepe Coin are trending amongst crypto presale and altcoin enthusiasts. More traditional crypto buyers are looking at older tokens such as Bitcoin and Ethereum, while meme coin fans tend to watch Shiba Inu and Dogecoin.

What is the 3 most popular cryptocurrency?

Based on historical data, Bitcoin has been and continues to rank as the most popular cryptocurrency, followed by Ethereum. The position of third most popular crypto is hotly contested and changes from month to month depending on new crypto market trends and events. ApeMax is an interesting new crypto coin generating buzz in the cryptocurrency space due to its innovative features.

What crypto is trending on Google?

Many crypto coins and projects are trending on Google. In the realm of crypto presales, Wall Street Memes and ApeMax are amongst the trending crypto coins on Google and best new crypto presales. Pepe Coin, Shiba Inu, and Dogecoin are some of the trending crypto meme tokens on Google. Crypto purists searching for the older tokens will find that Bitcoin and Ethereum are subjects of discussion in trending crypto searches.

Will crypto rise again in 2023?

Oracle Says Google Knowingly ‘Broke The Rules’ With Java

“This case is about Google’s use, in Google’s business, of somebody else’s property without permission,” said Michael Jacobs, an attorney for Oracle, in his opening remarks to the jury.

Oracle sued Google 18 months ago, arguing that its Android operating system infringes Java patents and copyrights that Oracle acquired when it bought Sun Microsystems. Google denies any wrongdoing and says it doesn’t need a license for the parts of Java it used.

Judge William Alsup, who is hearing the case, warned both sides on Monday that they’ll need to show good cause for any evidence submitted at trial to be kept from the public, and that unflattering details about either side might emerge.

“Unless it’s the recipe for Coca-Cola, it’s going to be public,” Alsup said. “If it reveals something embarrassing about the way one of these companies works, too bad. That’s going to be out there for the public to see.”

Most of the opening day was taken up with jury selection, but Jacobs had time to deliver Oracle’s opening statement before the proceedings wrapped up. Google will give its opening statement Tuesday morning.

Jacobs cited several emails to and from Google executives that he said would show that Google knew it needed a license for Java and that, having failed to negotiate one, it developed Android with Java anyway.

Google’s use of Oracle’s intellectual property wasn’t a mistake or the result of any confusion, Jacobs told the jury.

“The decision to use Oracle’s intellectual property in Android was taken at the highest levels, with a lot of comprehension and awareness about what was going on,” he said.

Google had to develop Android quickly, and it had to attract developers to be successful, he said. “How did they meet those requirements? The answer is with components of Java.”

Other companies such as eBay, Cisco Systems and General Electric bought licenses to use Java, but Google “broke the basic set of rules governing the Java community,” Jacobs said.

Some big Silicon Valley names are expected to be called to testify in the trial. Oracle’s witness list includes its CEO, Larry Ellison, Google CEO Larry Page, Google Executive Chairman Eric Schmidt and former Sun CEOs Scott McNealy and Jonathan Schwartz.

Before jury selection took place Monday, Alsup had to settle some last-minute disputes between the two sides.

Google thought it would be unfair if Oracle were allowed to tell the jury it paid $7.4 billion to buy Sun, because it might inflate the value of Java in the minds of the jurors.

“They’ve been dying to throw that number around,” Robert Van Nest, an attorney for Google, told the judge.

Alsup ruled against him but nevertheless cautioned Oracle to be careful how it used such figures. “The idea that you can throw big numbers around in front of the jury and somehow jack up the damages award if there is one … that’s not going to be allowed,” Alsup said.

The trial will be held in three phases: first to hear the copyright claims, then the patent claims, and then any damages Oracle might be awarded. Oracle is seeking about $1 billion in damages and an injunction to block Google from shipping any infringing code.

The lawsuit is seen by many as a test case for whether software APIs (application programming interfaces) can be subject to copyright.

The trial resumes at 8 a.m. Pacific Time Tuesday morning. It’s being held at the U.S. District Court in San Francisco.

Writing Strong Research Questions

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper, dissertation, or thesis.

All research questions should be:

Focused on a single problem or issue

Researchable using primary and/or secondary sources

Feasible to answer within the timeframe and practical constraints

Specific enough to answer thoroughly

Complex enough to develop the answer over the space of a paper or thesis

Relevant to your field of study and/or society more broadly

How to write a research question

You can follow these steps to develop a strong research question:

Choose your topic

Do some preliminary reading about the current state of the field

Narrow your focus to a specific niche

Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Research objectives Research question formulations

Describing and exploring

What are the characteristics of X?

How has X changed over time?

What are the causes of X?

How has X dealt with Y?

Explaining and testing

What is the relationship between X and Y?

What is the role of X in Y?

What is the impact of X on Y?

How does X influence Y?

Evaluating and acting

How effective is X?

How can X be improved?

Using your research problem to develop your research question

Example research problem Example research question(s)

Teachers at the school do not have the skills to recognize or properly guide gifted children in the classroom. What practical techniques can teachers use to better identify and guide gifted children?

Young people increasingly engage in the “gig economy,” rather than traditional full-time employment. However, it is unclear why they choose to do so. What are the main factors influencing young people’s decisions to engage in the gig economy?

Note that while most research questions can be answered with various types of research, the way you frame your question should help determine your choices.

What makes a strong research question?

Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Criteria Explanation

Focused on a single topic Your central research question should work together with your research problem to keep your work focused. If you have multiple questions, they should all clearly tie back to your central aim.

Answerable using credible sources Your question must be answerable using quantitative and/or qualitative data, or by reading scholarly sources on the topic to develop your argument. If such data is impossible to access, you likely need to rethink your question.

Not based on value judgements Avoid subjective words like good, bad, better and worse. These do not give clear criteria for answering the question.

Is X or Y a better policy?

How effective are X and Y policies at reducing rates of Z?

Feasible and specific

Criteria Explanation

Answerable within practical constraints Make sure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific.

Uses specific, well-defined concepts All the terms you use in the research question should have clear meanings. Avoid vague language, jargon, and too-broad ideas.

What effect does social media have on people’s minds?

What effect does daily use of Twitter have on the attention span of 16-year-olds at your local high school?

Does not demand a conclusive solution, policy, or course of action Research is about informing, not instructing. Even if your project is focused on a practical problem, it should aim to improve understanding rather than demand a ready-made solution.

If ready-made solutions are necessary, consider conducting action research instead. Action research is a research method that aims to simultaneously investigate an issue as it is solved. In other words, as its name suggests, action research conducts research and takes action at the same time.

What should the government do about low voter turnout?

What are the most effective communication strategies for increasing voter turnout among those aged 18-30?

Complex and arguable

Criteria Explanation

Cannot be answered with yes or no Closed-ended, yes/no questions are too simple to work as good research questions—they don’t provide enough scope for robust investigation and discussion.

Has there been an increase in those experiencing homelessness in the US in the past ten years?

How have economic and political factors affected patterns of experiencing homelessness in the US over the past ten years?

Cannot be answered with easily-found facts If you can answer the question through a single Google search, book, or article, it is probably not complex enough. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation prior to providing an answer.

Relevant and original

Criteria Explanation

Addresses a relevant problem Your research question should be developed based on initial reading around your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline.

Contributes to a timely social or academic debate The question should aim to contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on.

Has not already been answered You don’t have to ask something that nobody has ever thought of before, but your question should have some aspect of originality. For example, you can focus on a specific location, or explore a new angle.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

Academic style

Vague sentences


Style consistency

See an example

Using sub-questions to strengthen your main research question

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

Less complex than the main question

Focused only on 1 type of research

Presented in a logical order

Here are a few examples of descriptive and framing questions:

Descriptive: According to current government arguments, how should a European bank tax be implemented?

Descriptive: Which countries have a bank tax/levy on financial transactions?

Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

Research questions quiz

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Other interesting articles

If you want to know more about the research process, methodology, research bias, or statistics, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about research questions

Are You Making The Most Of The New Facebook Business Timeline?

A checklist and examples of good practice for 9 new features

It’s now around a month since Facebook business timelines were introduced. We explained the main marketing features here.

In this post I hope to give some guidance to help you review your site and to combine looking at some examples to learn from, plus showing what some are missing. So from top to bottom, I’ll mark what are potentially the most important.

1. Use the cover photo for promotions or to encourage opt-in? (Could be important)

Well, most new pages have a cover photo, mostly a visual that fits the brand, so a tick for this one. There are some interesting promotional ideas. Here’s one example where Firebox are using the photo more tactically for a promotion encouraging liking through an app.

The arrow pointing to the app to sign-up is reminiscent of the old gated Like pages. It may be pushing the terms-of-service, but who’s going to check… This is what Facebook say your cover photo can’t contain:

Price or purchase information, such as “40% off” or “Download it at our website”

Contact information, such as web address, email, mailing address or other information intended for your Page’s About section

References to user interface elements, such as Like or Share, or any other Facebook site features

Calls to action, such as “Get it now” or “Tell your friends”

So perhaps a safer option is the people picture since that’s what Facebook is all about:

2. Integrate your Website through a Link in About (some value)

Browsing different Facebook pages, this is surprisingly rare. To me it’s worthwhile as a call-to-action, above the fold, to browse a store or find out more, so I don’t see it does any harm?

3. Pin to the top left (Important)

Most, but not all are doing this – either for current promotions, or to encourage opt-in – through a gated Facebook App in the panel.

Here’s a nice example a Smart Insights expert member was telling me about. It’s marked by the flag, top left:

5. Create a magazine (Nice if you have the right assets)

One of the nice things about the timeline is that Facebook is that it’s like a magazine and despite Google’s recent efforts, far better than Google+ since it’s two column and supports spanning across them, these are our next two tips. As a print magazine publisher, chúng tôi are great at this.

6. Use full-width features

7. Use photo albums

We know from Pinterest and infographics how people love visuals. ASOS do a great job of keeping columns consistent and using photo albums.

8. Don’t forget events and questions

These are hidden away top left and I’ve seen few recently, perhaps because of the design, perhaps because you need a largish audience to make them work. We had a post from Marie Page on the value of online Facebook events if you’d like to know more.

9. Create milestones (minor)

This is a corporate comms type of thing, but can be fun too. Milestones can start from 1000AD if you can think of a company connection then! Some companies have also used them more recently to engage or show recent announcements.

A lot of companies start when they started using Facebook it seems, so it’s worth digging into those archived photos if you have an interesting story to tell.

I hope you find these ideas useful, here are the official Facebook Timeline Help notes.

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