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Response bias refers to several factors that can lead someone to respond falsely or inaccurately to a question. Self-report questions, such as those asked on surveys or in structured interviews, are particularly prone to this type of bias.

Example: Response biasA job applicant is asked to take a personality test during the recruitment process. One of the questions is “Do you like meeting new people?”

The applicant thinks that, since this is a customer service job, the company is probably looking for someone who enjoys meeting new people. Despite being an introvert at heart, the applicant answers “yes” in an attempt to increase their chances of being hired.

Because respondents are not actually answering the questions truthfully, response bias distorts study results, threatening the validity of your research. Response bias is a common type of research bias.

What is response bias?

Response bias is a general term describing situations where people do not answer questions truthfully for some reason.

This occurs because of the way we integrate and process multiple sources of information when we answer a question in an interview or similar setting. Respondents may answer inaccurately for a variety of reasons:

Desire to conform to perceived social norms (social desirability bias)

Desire to appear favorably to interviewer or other participants while being observed (Hawthorne effect)

Desire to perform in line with the research objectives, perhaps due to having guessed the aims of the study through demand characteristics

Desire to finish survey questions quickly, or lack of interest

In practice, this means that any aspect of a study can potentially cause a respondent to answer in a biased fashion.

Different types of response bias

There are several types of response bias, categorized based on what causes the bias. Common types of response bias are:

Acquiescence bias: Respondent tendency to answer “yes” to every question, regardless of what they really think. This often occurs with surveys that include only or mostly binary response options, like “Yes/No.”

Demand characteristics: Anything that can alert research participants to the goals of the study. The title of the study, the tools and instruments used, or even the researchers’ interactions with the participants can all lead participants to alter their behavior based on what they think the research is about.

Social desirability bias: Respondent tendency to distort responses in order to bring them more in line with social norms and expectations. For example, when asked about their drinking habits, respondents who drink on a daily basis may feel inclined to conceal this, fearing that they may be perceived negatively by others or by the researcher.

Courtesy bias: Respondent tendency to be polite or courteous toward the researcher. It is common in qualitative research designs (e.g., face-to-face interviews). For example, when consumers are asked about their opinion on a product, some may downplay their frustration or lack of satisfaction for fear of being impolite.

Question-order bias: Risk of questions that appear earlier in a survey or questionnaire affecting responses to subsequent questions. Earlier questions can serve to set context, influencing how respondents interpret the questions that follow. For example, asking respondents which basketball team is their favorite and then immediately following up with a question about which sport is their favorite is likely to result in more respondents indicating a preference for basketball.

Extreme responding: Respondent tendency to choose only the highest or lowest response available, regardless of their actual opinion. For example, in a Likert scale survey with response options ranging from 1 to 5, there is a risk that a respondent will only choose the 1s or the 5s throughout the survey.

Response bias examples

In experimental designs, response bias can influence participant behavior due to demand characteristics. Here, the result or response that the researchers expect is accidentally suggested to participants.

Example: Demand characteristicsSuppose you want to test whether study participants who are aware of the study goals behave differently from those who are unaware.

Participants are randomly assigned to either a group told that menstrual cycle symptomatology is the focus of the study or a group to which no interest in menstrual cycle symptoms is communicated.

You notice that people who are aware of the study goals are more likely to report negative typical symptoms like irritability and pain than those who are unaware of the study goals.

You conclude that the reporting of the symptoms is influenced by demand characteristics. In other words, people who were informed about the study’s purpose thought that the researchers wanted to hear about the typical complaints related to the menstrual cycle. As a result they were more likely to report that they had experienced such negative symptoms.

Response bias can also distort the findings of a study.

Example: Acquiescence biasAn admissions officer wants to find out what prospective students think of the live virtual visit feature on the university’s website. A 10-item survey is sent via email to everyone who registered for the live virtual visit in the past 3 months.

The survey includes questions with binary responses such as:

“Was the live virtual visit satisfactory?” Yes/No

“Our live virtual visit feature is easy to use” Agree/Disagree

When the survey is closed, the admissions officer notices that responses are invariably positive. In this case, questions were worded in such a way that the respondents were more likely to respond positively to every question, choosing yes or agree, even if they didn’t necessarily agree with the statements.

How to minimize response bias

Although it may not always be possible to eliminate response bias entirely, there are a few steps you can take to minimize it:

Keep your surveys short and to the point to avoid respondent fatigue.

Use unambiguous language and avoid jargon when writing your survey questions and responses. In this way, your respondents will not lose interest or disengage due to the complexity of your survey.

Use neutral language, particularly in surveys and interviews that probe into sensitive topics like politics, religion, or illegal substances.

Make sure that your questions are interesting and relevant to your respondents.

Use different question formats—e.g., scale, binary, or open-ended. Group questions by topic.

Withhold information that can place demand characteristics on participants or researchers when conducting experimental research. Setting up a double-blind study and using random assignment can prevent this type of bias from affecting your results.

Other types of research bias Frequently asked questions Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

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Nikolopoulou, K. Retrieved July 19, 2023,

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What Is Anchoring Bias?

Anchoring bias describes people’s tendency to rely too heavily on the first piece of information they receive on a topic. Regardless of the accuracy of that information, people use it as a reference point, or anchor, to make subsequent judgments. Because of this, anchoring bias can lead to poor decisions in various contexts, such as salary negotiations, medical diagnoses, and purchases.

Example: Anchoring bias You are considering buying a used car, and you visit a car dealership. The dealer walks you around, showing you all the higher-priced cars, and you start worrying that you can’t afford a car after all.

Next, the car dealer walks you toward the back of the lot, where you see more affordable cars. Having seen all the expensive options, you think these cars seem like a good bargain. In reality, all the cars are overpriced. By showing you all the expensive cars first, the dealer has set an anchor, influencing your perception of the value of a used car.

What is anchoring bias?

Anchoring bias (also known as anchoring heuristic or anchoring effect) is a type of cognitive bias that causes people to favor information they received early in the decision-making process. People hold on to this information, called an anchor, as a reference point and fail to correctly adjust their initial impressions, even after receiving additional information.

Once the anchor is set, subsequent judgments are made by adjusting away from that anchor, while staying within the range set by it. For example, the initial price offered for a used car sets the standard for the rest of the negotiation. Here, prices lower than the initial price seem like a good deal, even if they are still higher than the car’s actual value. As a result, our perception of reality is distorted, and our decisions are biased.

Depending on their sources, anchors can be external or internal.

External anchors are reference points provided by others (for example, the suggested retail price tags we see on many products).

Internal anchors are reference points based on beliefs, experiences, or contextual clues. For example, if your parents followed an active lifestyle and exercised a lot, this experience might set a standard level of exercise for you in adulthood.

NoteIt is important to keep in mind that the more knowledgeable we are about a certain topic, the less likely we are to fall for anchoring bias. When we don’t have enough information to know how to value something, we are more likely to be influenced by anchors.

Why does anchoring bias happen?

Although there is no consensus as to why anchoring bias happens, two mechanisms can help explain this phenomenon:

Anchoring and adjustment applies best to situations where people are influenced by an internal anchor.

Confirmatory hypothesis testing can explain how external anchors influence our judgment.

Anchoring and adjustment

Anchoring and adjustment is the mechanism that explains how people try to answer a general knowledge question when they don’t know the answer.

If people don’t know the correct answer, they try to make an educated guess and adjust from there until they reach a conclusion that seems plausible.

This initial estimation becomes an internal anchor and influences subsequent adjustments.

Because the adjustment is usually insufficient, it results in a biased estimation. In other words, people always end up with an answer that is close to the anchor anyway.

Example: Anchoring and adjustmentSuppose you need to answer the question “How long does it take Mars to orbit the Sun?” but don’t know the correct answer and you’re not allowed to search for it online! You remember that Mars is between Earth and Jupiter, and that it takes 12 years for Jupiter to orbit the Sun.

Based on this, you estimate that the correct answer is somewhere close to 12 years. After thinking some more, you come up with your final answer: 6 years. Unfortunately, your internal anchor (12 years) was too high, and it didn’t allow you to adjust sufficiently so as to approximate the correct answer, which is actually 1.88 years.

Confirmatory hypothesis testing

When we are presented with an external anchor, our first response is to consider the anchor as a possible answer. While we are doing that, we activate existing information in our brain that is consistent with the anchor.

This information is more accessible, and so we use it for estimating the absolute value, a phenomenon called selective accessibility.

In general, after a comparison with a high anchor, people are likely to base their absolute estimate on knowledge indicating that the target object or situation value is fairly high.

However, after a comparison with a low anchor, people are likely to base their absolute estimate on knowledge suggesting that the value is fairly low.

Example: Selective accessibility When asked whether Mahatma Gandhi was younger or older than 86 when he died, people often engage in confirmatory hypothesis testing. In other words, they are influenced by the phrasing of the question and recall information that supports the hypothesis presented to them: that Gandhi was approximately 86 years old when he died. As a result, this information is likely to influence their final answer.

This selective accessibility mechanism works even when anchors are clearly unrealistic. When asked whether Mahatma Gandhi was

older or younger than 140 years old when he died


older or younger than 9 years old when he died

participants were influenced by these implausible anchors.

Participants who received the high implausible anchor estimated on average that Gandhi lived 67 years, whereas participants who received the low implausible anchor thought that he was just 50 years old when he died.

Anchoring bias examples

Example: Anchoring bias and salary negotiations You have passed the first round of interviews for a job, and you are now invited to a second round. During a call, the HR person makes you an offer of $50,000 per year. Considering the role and your previous experience, you know this is too low of an offer.

With that amount as a starting point, you manage to negotiate up to $55,000. You are satisfied that you got more than they initially offered. In reality, the HR person could have offered you more, but they used the anchoring effect against you. By starting with a low value, they influenced your perception of what an acceptable salary would be.

Anchors that are entirely arbitrary and unrelated to the decision can still influence our judgment, especially when we lack the knowledge to make an educated guess.

Example: Anchoring bias and random anchorsIn an experiment, participants were asked to estimate the percentage of African countries in the United Nations (UN) in two ways:

First, they were asked whether the percentage is smaller or larger than a given number (the anchor), which was randomly determined by spinning a wheel.

Next, participants were asked to estimate the exact percentage of African UN member states.

Even though the anchor was entirely arbitrary and irrelevant to the question, it still influenced participants who used it as a standard in their subsequent judgment. As a result, their answers were close to the anchor. For example:

If the anchor was 10, participants’ mean estimate of the true value was 25.

If the anchor was 65, their mean estimate was 45.

This shows that under the anchoring bias, irrelevant anchors are just as impactful as anchors that offer relevant informational cues.

Other types of cognitive bias in decision-making

Apart from anchoring bias, there are two more types of heuristics that people use that can affect their decision-making:

The availability heuristic occurs when we place greater emphasis on information that is easier to recall while forming a judgment.

The representativeness heuristic arises when we estimate the probability of something based on the degree to which it is similar to (or is representative of) a known situation.

Although all of them help us reduce the time and effort needed to form a judgment, they do so in different ways.

Other types of research bias Frequently asked questions

What is the difference between anchoring bias and availability bias?

Although anchoring bias and availability bias are both types of cognitive bias (or heuristics) and may seem similar, they are quite different:

The availability bias refers to people’s tendency to estimate the probability of an outcome (e.g., being struck by lightning), based on how easily they can recall similar events. Because of this, people sometimes mix up ease of recall with probability or frequency and end up believing that some events are far more common than they actually are.

Anchoring bias refers to people’s tendency to give disproportionate weight to the first piece of information they receive in a decision-making context. As a result, this becomes a reference point or anchor that influences people’s perception of subsequent information.

In other words, although both anchoring and availability bias influence our perception, anchoring is related to the order in which we receive the information, while availability is related to ease of recall.

When does anchoring bias occur?

Anchoring bias occurs when you focus on the first piece of information you receive during a decision-making process and fail to consider any other information that follows.

What is anchoring and adjustment bias?

Anchoring and adjustment bias refers to the mechanism underlying cases in which we are influenced by an internal anchor or reference point. When we are faced with a decision or question and we are uncertain about the right option, we try to make an educated guess.

For example, when we are trying to estimate how long it will take us to write a paper. In this case, we start with an initial anchor value that seems reasonable and then adjust until an acceptable answer is found. Because we subconsciously place more importance on the initial value or answer we come up with, we typically fail to adjust sufficiently from there on and our judgment is biased.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

This Scribbr article

Nikolopoulou, K. Retrieved July 19, 2023,

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Furnham, A. & Boo, H. (2011). A Literature Review of the Anchoring Effect. The Journal of Socio-Economics. 40. 35-42. 10.1016/j.socec.2010.10.008.

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Sampling Bias And How To Avoid It

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields.

Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity. In other words, findings from biased samples can only be generalized to populations that share characteristics with the sample.

Causes of sampling bias

Your choice of research design or data collection method can lead to sampling bias. This type of research bias can occur in both probability and non-probability sampling.

Sampling bias in probability samples

In probability sampling, every member of the population has a known chance of being selected. For instance, you can use a random number generator to select a simple random sample from your population.

Although this procedure reduces the risk of sampling bias, it may not eliminate it. If your sampling frame – the actual list of individuals that the sample is drawn from – does not match the population, this can result in a biased sample.

Example of sampling bias in a simple random sampleYou want to study procrastination and social anxiety levels in undergraduate students at your university using a simple random sample. You assign a number to every student in the research participant database from 1 to 1500 and use a random number generator to select 120 numbers.

Although you used a random sample, not every member of your target population –undergraduate students at your university – had a chance of being selected. Your sample misses anyone who did not sign up to be contacted about participating in research. This may bias your sample towards people who have less social anxiety and are more willing to participate in research.

Sampling bias in non-probability samples

A non-probability sample is selected based on non-random criteria. For instance, in a convenience sample, participants are selected based on accessibility and availability.

Non-probability sampling often results in biased samples because some members of the population are more likely to be included than others.

Example of sampling bias in a convenience sampleYou want to study the popularity of plant-based foods amongst undergraduate students at your university. For convenience, you send out a survey to everyone enrolled in Introduction to Psychology courses at your university. They all complete it in exchange for course credits.

Because this is a convenience sample, it is not representative of your target population. People who take this course may be more liberal and drawn towards plant-based foods than others at your university.

Types of sampling bias

Type Explanation Example

Self-selection bias People with specific characteristics are more likely to agree to take part in a study than others. People who are more thrill-seeking are likely to take part in pain research studies. This may skew the data.

Nonresponse bias People who refuse to participate or drop out from a study systematically differ from those who take part.

Undercoverage bias Some members of a population are inadequately represented in the sample. Administering general national surveys online may miss groups with limited internet access, such as the elderly and lower-income households.

Survivorship bias Successful observations, people and objects are more likely to be represented in the sample than unsuccessful ones. In scientific journals, there is strong publication bias towards positive results. Successful research outcomes are published far more often than null findings.

When seeking volunteers to test a novel sleep intervention, you may end up with a sample that is more motivated to improve their sleep habits than the rest of the population. As a result, they may have been likely to improve their sleep habits regardless of the effects of your intervention.

Healthy user bias Volunteers for preventative interventions are more likely to pursue health-boosting behaviors and activities than other members of the population. A sample in a preventative intervention has a better diet, higher physical activity levels, abstains from alcohol, and avoids smoking more than most of the population. The experimental findings may be a result of the treatment interacting with these characteristics of the sample, rather than just the treatment itself.

How to avoid or correct sampling bias

Using careful research design and sampling procedures can help you avoid sampling bias.

Define a target population and a sampling frame (the list of individuals that the sample will be drawn from). Match the sampling frame to the target population as much as possible to reduce the risk of sampling bias.

Make online surveys as short and accessible as possible.

Follow up on non-responders.

Avoid convenience sampling.

Oversampling to avoid bias

Oversampling can be used to avoid sampling bias in situations where members of defined groups are underrepresented (undercoverage). This is a method of selecting respondents from some groups so that they make up a larger share of a sample than they actually do the population.

After all data is collected, responses from oversampled groups are weighted to their actual share of the population to remove any sampling bias.

Example of oversampling to avoid sampling biasA researcher wants to study the political opinions of different ethnic groups in the US and focus in depth on Asian Americans, who make up only 5.6% of the US population. The researcher wants to study each ethnic group separately, but also gather enough data about Asian Americans for precise conclusions.

They gather a nationally representative sample, with 1500 respondents, that oversamples Asian Americans. Random digit dialling is used to contact American households, and disproportionately larger samples are taken from regions with more Asian Americans. Of the 1500 respondents, 336 are Asian American. Based on this sample size, the researcher can be confident in their findings about Asian Americans.

Weighting is applied to ensure that the responses of Asian Americans account for 5.6% of the total. This allows for accurate estimates of the sample as a whole.

Other types of research bias Frequently asked questions about sampling bias Cite this Scribbr article

Bhandari, P. Retrieved July 19, 2023,

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What Is A Vpn And What Is It Used For?

A VPN, or Virtual Private Network is an extremely useful tool for every internet user, no matter how much time you spend online or what sort of stuff you do on the internet. A VPN gives you more security and privacy when you go online – things that are hard to come by in today’s information age. 

Table of Contents

What Is a VPN and How Does It Work?

If you have a general idea of how the internet works, you probably know that your internet service provider (ISP) and some other organizations have ways of tracking your online activity. Your browser’s private browsing mode can’t help protect your data and give you peace of mind, while a VPN can do more than that.

VPN is a technology that protects your online activity by adding an extra level of encryption to your data. It connects your computer or a smartphone to a private network, allowing your data to go through an encrypted “tunnel”.

It travels from your device to some other point on the internet, often in another country, before making its way to the public internet. Your data stays hidden the entire time. All an ISP can see is that you’re connected to a private network. 

When you connect your device to a VPN, it behaves as if it was on the same local network as the VPN. It allows you to access local network resources even if physically you’re accessing the internet from a different country. You can also browse the internet as if you were based at the VPN’s location. For example, if you’re using a USA-based VPN from outside the US, the websites will see your online activity as if it was coming from within the country. 

What IS a VPN Used For?

Some people associate using a VPN with the need to hide your online activity. In reality, a VPN comes with a number of benefits that can help any internet user in their everyday life. Here are a few reasons why you might want to start using a VPN.

Secure Your Connection When Using Public WiFi

When you’re browsing the internet at home, your connection is via a password-protected router and a private WiFi network. However, when you connect to a public WiFi network – whether it’s at a coffee shop, a hotel, or an airport – your traffic isn’t protected anymore. Those networks offer open access to all users, which makes it much easier to intercept the wireless network traffic. 

That’s why it’s a good idea to use a VPN next time you decide to connect to a public WiFi network to send an email or check your Instagram account. 

Take Back Your Online Privacy

Using a VPN is one of the simplest and most effective ways to gain back your online privacy and stop others from collecting your data.

Access Geo-Blocked Content 

One of the most popular reasons for getting a VPN service is to access geo-blocked websites. Sometimes you’ll find that certain content, streaming services, or websites are blocked or have restricted access based on your location. Using a VPN is the easiest way to bypass those restrictions. 

This can be useful if you ever find that certain movies or TV shows aren’t available on your streaming platform in your region. Connecting to a VPN server from the appropriate country can easily fix that. 

When traveling, you might find yourself in a country where entire websites and services are geo-blocked. For instance, if you want to access LinkedIn in Russia, you’ll have to use a VPN or other means of getting around geo-restrictions. 

Bypass Your School or Workplace Firewall

When using the internet at your school or workplace, you’re bound to come across blocked websites that management doesn’t want you to access. They do this by using firewalls that filter all web content and keep you away from the blacklisted sites. 

There’s a number of workarounds that can help you bypass your school or workplace firewall. One of the easiest ways to do it is via the VPN service. When you connect to a VPN and start browsing, all the internet traffic is encrypted, and the firewall can’t see what you’re doing and what sites you’re visiting on the network connection. This way you can view the sites you want no matter the time and place.

Save Money on Shopping

Aside from the obvious benefits you get from using a VPN, you can get some extra perks if you think outside the box. A VPN can help you save money on shopping. Admittedly, it does require some time and effort.

You can use a VPN to connect to different servers across the world and find the cheapest prices. The things you can save money on include streaming services, software subscription plans, flight tickets, and even hotels. In theory, you can find cheaper prices for everything. However, don’t forget that some of the price differences will be covered by exchange rates and bank fees. 

Do You Need a VPN?

While VPNs come with many benefits, they’re not flawless. The biggest downside of using a VPN is a connection speed drop. Since you’re sending your data to another location before it reaches the right server, your VPN connection speed is bound to be slower than your regular internet speed. 

However, if that’s not a big problem for you, or if the connection speed drop isn’t significant, using a VPN is definitely a good idea. All that’s left is to choose the right VPN service for you. Luckily, there are plenty of options out there – from a Windows built-in option, VPN Chrome extensions, best free options for Mac, and the best VPN apps out there.  

New WordPress Feature Gets Tough Response

WordPress announced a new feature for publishing blog posts to Twitter in the form of a tweetstorm. The publishing community initially welcomed the announcement. The initial positive reaction took a downward trend as the potential negative implications of the tweetstorm feature became apparent.

This feature appears to be a part of the Jetpack plugin, the announcement wasn’t explicit on that point.

Engage with a New Audience They Said…

The idea behind the new feature was to present the content to a new audience. Usually that means promoting the content on a platform or website in a way that brings the audience from the platform to your website.

But that wasn’t the case, as expressed in the WordPress announcement.

The end goal was to give Twitter free content:

“By publishing your quality content on Twitter, you can open new lines of engagement and conversation.”

There was nothing in the announcement about how the feature benefits the publisher site itself.

Anyone reading the official WordPress announcement and asking, “What’s in it for me?” would not find an answer. 

WordPress Tweetstorms Enable Endless Content Republishing

A problem with the new feature is that it creates a way for others to republish your content on their own sites. What’s worse, is that there may not be a way to successfully file a Digital Millennium Copyright Act (DMCA) complaint if someone embedded your content via the Tweets, because Twitter allows their content from its platform to be embedded on other sites.

Lack of Content Canonicalization

Another flaw in the feature is that there is no way to place a cross-site canonical to tell the search engines who the original content publisher is.

A cross-site canonical is an HTML meta tag that tells search engines that the content they are indexing is not original to the site and communicates who the original publisher is. That’s a way to communicate who the original publisher is when for example an article is syndicated.

Webmaster Response

The WebmasterWorld community immediately spotted the problems inherent in tweetstorming entire articles from a WordPress site toTwitter.

The first response on the WebmasterWorld discussion (by NickMNS) was drenched in sarcasm:

“Wow that is awesome, now you can post your entire content on Twitter and your users will have all of it there, available to them without ever needing to leave the platform.

As a result your likes will go way up, and your follower count will rise too, what more could anyone ever ask for…!”

“I was going to say it sounds absolutely dreadful, since nothing makes you look more of an idiot than overflowing to multiple tweets, as if you’re too stupid to be able to count to 140 and edit accordingly.”

Others remarked how it was a free content boon for Twitter and how the next bad step would be to replicate it for Pinterest, a platform that many feels dominates Google’s search results.

WordPress Community Reaction

Matt Mullenweg tweeted about it and the response was generally positive… at first.

— Matt Mullenweg (@photomatt) October 13, 2023

The response to his tweet began with praise but took on a more negative tone as time went on.

Some were polite with their feedback:

— Alex Danco (@Alex_Danco) October 13, 2023

Others were more direct:


Michele Neylon (@mneylon) shuddered at the idea:


— Michele Neylon (@mneylon) October 13, 2023

The reactions kept coming.

If you’re even thinking of doing this, please delete your account immediately.

— David Stehle (@davidstehle) October 13, 2023

SocialMediaToday Inspires More Opinions

The opinions of the new tweetstorm feature increased after social media news site, SocialMediaToday, threw fuel on the smoldering discontent with their take on it.

The author of the article, Andrew Hutchinson (@adhutchinson), had rightfully recognized some of the negative implications inherent in the new feature and wrote a great overview of the possible problems associated with it.

His article began with these words:

The response on Twitter agreed with his assessment.

— NetWeave Social Net (@NetWeave) October 14, 2023

— Guy LeCharles Gonzalez 😷 (@glecharles) October 14, 2023

WordPress Tweetstorm Feature Can Be Useful?

Considering the scorn poured on the new feature, there may be a positive way that it can be used.

WordPress may have saved itself some grief if it had better articulated how the feature benefited users.

The WordPress tweetstorm feature can be used to publish shorter content designed to specifically engage Twitter users.

If it’s sufficiently engaging it can build followers. Those followers will later see (and retweet) subsequent non-tweetstorm tweets that publicize content on the site itself.

The tweetstorm feature’s usefulness isn’t about publishing content on Twitter. The benefit is in publishing content specifically for Twitter for the purpose of growing followers and traffic.

One could even put a noindex meta tag on the Twitter-focused content to keep search engines from indexing it.

The Twitter-focused content could be created expressly for the purpose of gaming Twitter for more users and traffic.

So it is possible to use the WordPress tweetstorm feature in a positive way.


There are some positive possibilities to the new WordPress feature but there may need to be a thoughtful strategy behind the use of it.

Publishers may need to heed to practical considerations such as Twitter outranking them in the search engines with their own content.


What Is Secondary Research?

Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research.

Example: Secondary researchYou are interested in how the number and quality of vegan options offered at your campus dining hall have changed over time. You have a friend who graduated a few years ago who was also interested in this topic. You borrow her survey results and use them to conduct statistical analysis.

Secondary research can be qualitative or quantitative in nature. It often uses data gathered from published peer-reviewed papers, meta-analyses, or government or private sector databases and datasets.

When to use secondary research

Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.

Since it is often inexpensive or free to access, secondary research is a low-stakes way to determine if further primary research is needed, as gaps in secondary research are a strong indication that primary research is necessary. For this reason, while secondary research can theoretically be exploratory or explanatory in nature, it is usually explanatory: aiming to explain the causes and consequences of a well-defined problem.

Types of secondary research

Secondary research can take many forms, but the most common types are:

Statistical analysis

There is ample data available online from a variety of sources, often in the form of datasets. These datasets are often open-source or downloadable at a low cost, and are ideal for conducting statistical analyses such as hypothesis testing or regression analysis.

Credible sources for existing data include:

The government

Government agencies

Non-governmental organizations

Educational institutions

Businesses or consultancies

Libraries or archives

Newspapers, academic journals, or magazines

Literature reviews

A literature review is a survey of preexisting scholarly sources on your topic. It provides an overview of current knowledge, allowing you to identify relevant themes, debates, and gaps in the research you analyze. You can later apply these to your own work, or use them as a jumping-off point to conduct primary research of your own.

Structured much like a regular academic paper (with a clear introduction, body, and conclusion), a literature review is a great way to evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

TipA literature review is not a summary. Instead, it critically analyzes, synthesizes, and evaluates sources to give you and/or your audience a clear picture of the state of existing work on your research topic.

Case studies

A case study is a detailed study of a specific subject. It is usually qualitative in nature and can focus on  a person, group, place, event, organization, or phenomenon. A case study is a great way to utilize existing research to gain concrete, contextual, and in-depth knowledge about your real-world subject.

You can choose to focus on just one complex case, exploring a single subject in great detail, or examine multiple cases if you’d prefer to compare different aspects of your topic. Preexisting interviews, observational studies, or other sources of primary data make for great case studies.

Content analysis

Content analysis is a research method that studies patterns in recorded communication by utilizing existing texts. It can be either quantitative or qualitative in nature, depending on whether you choose to analyze countable or measurable patterns, or more interpretive ones. Content analysis is popular in communication studies, but it is also widely used in historical analysis, anthropology, and psychology to make more semantic qualitative inferences.

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Examples of secondary research

Secondary research is a broad research approach that can be pursued any way you’d like. Here are a few examples of different ways you can use secondary research to explore your research topic.

Example: Statistical analysisYou are interested in the characteristics of Americans enrolled in Affordable Care Act coverage. You utilize enrollment data from the US government’s Department of Health and Human Resources to observe how these characteristics change over time. Example: Literature reviewYou are interested in the reactions of campus police to student protest movements on campus. You decide to conduct a literature review of scholarly works about student protest movements in the last 100 years. Example: Case studyYou are interested in the acclimatization process of formerly incarcerated individuals. You decide to compile data from structured interviews with those recently released from a prison facility in your hometown into a case study. Example: Content analysisYou are interested in how often employment issues came up in political campaigns during the Great Depression. You choose to analyze campaign speeches for the frequency of terms such as “unemployment,” “jobs,” and “work.”

Advantages of secondary research

Advantages include:

Secondary data is very easy to source and readily available.

It is also often free or accessible through your educational institution’s library or network, making it much cheaper to conduct than primary research.

As you are relying on research that already exists, conducting secondary research is much less time consuming than primary research. Since your timeline is so much shorter, your research can be ready to publish sooner.

Using data from others allows you to show reproducibility and replicability, bolstering prior research and situating your own work within your field.

Ease of access does not signify credibility. It’s important to be aware that secondary research is not always reliable, and can often be out of date. It’s critical to analyze any data you’re thinking of using prior to getting started, using a method like the CRAAP test.

Secondary research often relies on primary research already conducted. If this original research is biased in any way, those research biases could creep into the secondary results.

Many researchers using the same secondary research to form similar conclusions can also take away from the uniqueness and reliability of your research. Many datasets become “kitchen-sink” models, where too many variables are added in an attempt to draw increasingly niche conclusions from overused data. Data cleansing may be necessary to test the quality of the research.

Other interesting articles

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

Frequently asked questions Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

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George, T. Retrieved July 19, 2023,

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Largan, C., & Morris, T. M. (2023). Qualitative Secondary Research: A Step-By-Step Guide (1st ed.). SAGE Publications Ltd.

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