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'Back to Work' Illustration by Seamus Jennings

Rebecca Welshman detects a disturbing subtext which echoes Boris Johnson's Churchillian rhetoric over the Coronavirus pandemic while shifting blame away from his administration.

After seven weeks of lockdown, the country is looking for leadership and answers. With the new "Stay Alert" and "Control the Virus" advice - signed off without the approval of either the Chief Medical Officer Professor Chris Whitty or the Chief Scientific Advisor, Sir Patrick Vallance - the Government is attempting to shift responsibility for the virus onto the public.

While "Stay Alert" might at first seem vague, it is dangerously and subversively precise.

The new slogans advocated by Boris Johnson and designed by the Government's Behavioural Insights Team use the mass psychology theory known as Nudge Theory, which aims to modify behaviour on a large-scale. It has been pointed out that the change in the colour scheme of the COVID-19 messaging from red to green is a psychological prompt from 'stop' to 'go', with the word "Alert" designed to energise us.

But there is more to it.

The Military Resonances

The meaning of "alert" as we know it today has its origins in 1940s Britain. The Dictionary of Neologisms from 1941, in a commentary titled 'Among the New Words', describes the new meaning:

"ALERT. An air-raid alarm. Defined in a glossary in the Outpost, reprinted in Topeka Journal, Dec. 15, 1940. 'Sirens sounded London's first daylight alert ... today'."

"Stay alert" is also used in signage in dangerous conditions. An explorer in late twentieth-century Canada recalled: "We realized how close to nature the residents are when we saw large posters suggesting STAY ALERT, STAY ALIVE, with a drawing of a polar bear". A suitable sign, perhaps, for a large, white, hungry predator with a visual mass almost impossible to miss. The phrase is hardly appropriate for a threat we cannot visualize except through the magnified images of the prickly little Coronavirus microbes themselves that are shown on daily television broadcasts.

"Stay Alert" has long been associated with the outdoors, and more specifically, firsthand military accounts of the Second World War where the phrase and its variants ("remain alert" etc) frequently appear as part of military commands.

In Memoirs of a Flying Tiger Captain Ho Weng Toh, a WWII air force veteran, recalled navigating the difficult terrain of the Himalayas where spaces were too narrow for an aircraft's turning radius, a journey that would take up to six hours: "it was a feat of mental endurance for the pilot to stay alert and cautious for such a long period of time". In his autobiography, the American war veteran James R. Clark writes, "we kept reminding the person who was steering the boat to keep a good distance from the shoreline. We also had to stay alert and on the lookout for the Vietnamese large boats they called sampans".

Another memoir based on a war diary by William Schumann offers a history of the U.S.S. battleship Idaho. Schumann, who served on the ship, was the observer in a Vought OS2U Kingfisher seaplane used for aerial spotting and observation. He recalls: "Sometime around 23.00 hours, general quarters is called. A lookout saw three flashes on the horizon in front of us. What causes the flashes was not determined, but all hands were advised to remain alert." Another autobiographical account recalls being part of the "Stay Alert, Stay Alive" battalion at Fort Lewis.

Shifting Responsibilities

As the UK lockdown is prematurely eased we have been instructed to "Stay Alert" because we are being placed in a dangerous position, where many thousands of lives are once again at risk.

We are being given the green light to go outdoors in unpredictable and potentially harmful conditions. Yet COVID-19 is an invisible threat, which renders the original meaning of "alert" in its highly visual and military context all the more redundant.

This deliberate mismatch between the traditional meaning and its application to the current crisis is designed to sow confusion and instill false confidence;to encourage people to believe that through following these commands they can successfully avoid and control the virus. The wording and colour change of the new signage ultimately aims to shift responsibility for the consequences of these changes from the Government onto the public.

In this way, Johnson and his cabinet are preparing the way to evade scrutiny and blame, with the aim being to get people back to work, whatever the cost. However, the language they use cannot and should not be isolated from its original contexts, and at this uncertain time, it becomes all the more important to keep in mind the origins of the phrasing being used to control us.

It must be a source of disappointment to Boris Johnson that his imagined Churchill moments have been defined by an enemy that no one can see. COVID-19 was not the war that he wanted to lead, and it shows. In this next step in the war against the virus - an attempt to ease the lockdown in order to help the economy - Johnson is putting his troops on the front line against an enemy that can easily overwhelm them.

As Professor Alice Roberts recently tweeted "good luck, herd".

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Dangerous and Subversive - The Government's New 'Stay Alert' COVID-19 Messaging - Byline Times

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After seven weeks of lockdown, the country is looking for leadership and answers. With the new "Stay Alert" and "Control the Virus" advice - signed off without the approval of either the Chief Medical Officer Professor Chris Whitty or the Chief Scientific Advisor, Sir Patrick Vallance - the Government is attempting to shift responsibility for the virus onto the public.

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Self-driving cars were just the start. What's the future of big data-driven technology and design? In a thrilling science talk, Kenneth Cukier looks at what's next for machine learning -- and human knowledge.

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How the spread of coronavirus is changing consumer behaviour

How the spread of coronavirus is changing consumer behaviour. Elizabeth Hotson goes on the hunt for toilet paper and hand sanitizer on the streets of London. Ed Butler speaks to Charlene Chan, marketing researcher and consumer psychology researcher at Nanyang School of Business in Singapore about how feeling a loss of control influences our buying behaviour. Steven Taylor, a clinical psychologist at the University of British Columbia in Canada, tells us what panic buying says about the psychology of pandemics.

(Photo: Shoppers stock up on toilet paper and other supplies as Canadians purchase food and essential items in Markham, Ontario, Canada. Credit: Getty Images)

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BBC World Service - Business Daily, The psychology of panic buying

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How the spread of coronavirus is changing consumer behaviour How the spread of coronavirus is changing consumer behaviour. Elizabeth Hotson goes on the hunt for toilet paper and hand sanitizer on the streets of London. Ed Butler speaks to Charlene Chan, marketing researcher and consumer psychology researcher at Nanyang School of Business in Singapore about how feeling a loss of control influences our buying behaviour.

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Open University

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Psychological research, obedience and ethics

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In this free course, Psychological research, obedience and ethics, you will learn about the importance of ethics in research that is undertaken by psychologists. You will read about the famous ...

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Nudge theory - psychology and the economy 

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Nudge theory - psychology and the economy 

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Coronavirus: How the government hopes to stop you touching your face - BBC News

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People in the UK are being asked to improve their personal hygiene in an effort to limit and slow the spread of coronavirus. But how's the government going about it?

Don't touch your face.

It's something people are thought to do more than 20 times an hour on average, but the official advice is to desist so that coronavirus doesn't spread any faster.

It's not easy to give up unhygienic habits picked up over a lifetime, though.

So the government is using "Nudge theory", a branch of economics designed to get us to do the "right thing" by making it easier, more normal and more obvious.

The Behavioural Insights Team (BIT), a company part-owned by the Cabinet Office, is advising ministers on how best to do it. It's come up with some ideas to prevent face-touching, as featured on its blog.

How about asking friends, family or colleagues to shout "face" every time they see you're about to reach up for a scratch or rub?

Or folding your arms in a locked position, grabbing the biceps to avoid the hands slipping free and heading upwards?

Another suggestion is creating an alternative "habitual" action, such as drumming fingers on legs or playing with a substitute object, such a ball.

"Even if you're very concerned about coronavirus, it's very difficult to stop doing something like face-touching because it's such a habit," says Cynthia McVey, former head of psychology at Glasgow Caledonian University. "There have been reports of people who are worried enough to wear masks who have still removed them so they can rub their nose."

Nudge theory, already widely used by governments, aims to instil better habits - or change social "norms" - rather than focusing on explicit "top-down" advice.

If enough people do something, you feel a sense of oddness, of missing out, of guilt perhaps, if you don't do the same.

"It's more powerful than traditional methods of getting across certain advice," says Kelly Hunstone, chief executive of the Social Change UK agency and a former adviser on behavioural science to the government. "We've left the age of deference and need to communicate differently.

"In some instances we don't trust the experts but the person on the street to show us how to act. It's a bit like using Tripadvisor - you'd rather take the advice of Bob in Essex than a travel operator."

Since the coronavirus crisis began, the government has recommended singing Happy Birthday to yourself twice while washing your hands. It's easier to remember than counting the full 20 seconds this equates to.

We've also been advised to sneeze into our sleeves if there are no tissues around.

Both tips bear the signs of Nudge theory, which emphasises putting across a "clear message".

Nudge theory also assumes people don't always act in their own best interests, or those of society - for reasons such as laziness, selfishness and ignorance. Hence the need to ease the way towards doing what a government wants us to do.

It's an idea that has caught on at local and national level since the book Nudge, by US academics Richard Thaler and Cass Sunstein, was published in 2008.

Some UK councils provide recycling bins that are larger than their normal waste bins, in an effort to get people recycle more.

Tax reminders are sent out saying the "majority" of people in an area have already paid, putting social pressure on recipients to do the same.

And there are now automatic donor cards and automatic enrolment work pensions - where people have to opt out, rather than in. These have been shown to increase take-up rates.

What can be done, though, to deal with the effects of coronavirus on society? The government is urging people not to stockpile toilet paper, pasta and other goods, while supermarkets are putting up notices asking people not to over-purchase and leave others without.

The logic is clear, but Ms Hunstone advocates a more Nudge-y, emotion-led approach.

"It would be more effective to show a picture of Doris Jones, 76, who can't get out easily to visit the supermarket, if you're trying to persuade people not to buy too many cans of baked beans or loo rolls," Ms Hunstone says.

She argues that pictures like this, seen recently on Facebook, could be placed in shops, inducing guilt in those tempted to stuff their trolleys.

"It's also important to use the ego when you're putting out this kind of message and people like to be seen doing what's right," Ms Hunstone adds.

People leaving enough provisions on shelves could photograph their trolleys or baskets and share them on social media, further marginalising those who do not, she says. So, in a way, "annoying" virtue-signallers could encourage the spread of virtuous behaviour in others.

The UK government has faced criticism for adopting a different approach to other countries towards coronavirus, focusing more so far on people following advice than creating a wider "lockdown".

Prime Minister Boris Johnson has signalled a change of approach this week, urging people to reduce social contact with others, but the measures are less severe than in many other European countries, necessitating simple advice.

Staff with the Behavioural Insights Team are honing and testing messages to create the greatest impact.

"Simplicity is key," says Ms Hunstone. It might get boring hearing the same thing again and again. But you have to hear messages like that nine times before people really understand them and they get through. Consistency is absolutely vital."

There have been criticisms that Nudge theory infantilises people, not crediting them with the ability to think for themselves and removing effective choice.

But it's very much in favour in Downing Street. Whether it can stop people touching their faces remains to be seen.

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The government plan to stop you touching your face

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People in the UK are being asked to improve their personal hygiene in an effort to limit and slow the spread of coronavirus. But how's the government going about it? Don't touch your face. It's something people are thought to do more than 20 times an hour on average, but the official advice is to desist so that coronavirus doesn't spread any faster.

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Research Methods: AS Revision Webinar (2016)

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Research Methods - Levels of Measurement (Nominal, Ordinal, Interval, Ratio)

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Inferential testing

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Research Methods - Interpreting Inferential Statistics

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"How Racist Are You?" | part 3 | Jane Elliott's Brown Eye-Blue Eye Experiment

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"How Racist Are You?" | part 2 | Jane Elliott's Brown Eye-Blue Eye Experiment

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"How Racist Are You?" | part 1 | Jane Elliott's Brown Eye-Blue Eye Experiment

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Every so often, the internet is set ablaze with opinion pieces on a familiar question: Are "soft" sciences, like psychology, actually science? Most of the time the argument against psychology as a science comes from people from the so-called harder sciences (you know, people who don't know ish about psychology).

Of course, every once in a while we throw ourselves under the bus by declaring that for our softer sciences to be taken seriously, we must be more like the real sciences. You're still reading this so most likely you are interested in my opinion on this topic. With a quick nod to others who have covered this topic here, here, here, and here, let's review some of the arguments for and against psychology as a science in what follows.
I. Psychologists do unscientific things
Whenever I read a story about how psychology isn't a real science, it is usually accompanied by mentions of some psychologists (and I use the term loosely here) engaging in unscientific things. This includes the stacks and stacks of pseudo-scientific self-help books that claim to reveal the science of X, the mere existence of celebrity psychologists like Dr. Drew or Dr. Phil, and the fraudulent research of now-disgraced psychologists like Dirk Smeesters or Marc Hauser.

It is true that psychology has its fair share of pseudoscience-I mean, the entire diagnostic manual of mental disorders has continued to resist integration with research findings (see here). However, there is real science happening here in psychology-that is, our scientific journals are packed with research summaries where psychological scientists have used the scientific method to test a specific hypothesis.

The argument that a field is not a science just because some of its members aren't scientists doesn't really hold water. Take the case of fraud as an example: Have you been to retraction watch recently? If you go there you will find that scientific fraud is not the domain of just the soft sciences. Fraud affects the sciences, from hard to soft.
II. Psychology doesn't define its terminology well enough to be considered a science
Some people (usually who know little about psychology) argue that psychologists don't define their terms clearly enough to be considered a science. In one example of this, a physicist named Alex Berezow (using a bunch of sciencey terms that my poor psychologist brain struggled to understand) argued that happiness research is a perfect example of a failure to define terms. He states that "the meaning of the word differs from person to person and especially between cultures."

Setting aside the point of cultural variability for a second, I actually think that happiness research is a very bad example of poor definitions in psychology. People who study subjective well-being have spent decades arriving at a definition of the construct that is comprised of three parts-subjective cognitive assessments of one's life as meaningful, positive affect, and negative affect. Importantly, they didn't just come to a definition by writing a random opinion piece in the LA Times about a field they know nothing about. Instead, researchers arrived at this definition based on decades of evidence gathered based on thousands (possibly millions by now) of people reflecting on their happiness (go here for the source of this effort). This hardly seems like a deficit in terminology.
III. Psychology relies too heavily on subjective experience
Much of what bothers people about psychology is the sheer subjectivity of it all. That is, how we perceive any number of social phenomena is likely to vary a great deal from person to person. This problem is at the very core of psychology research, and many people feel that, t o be science, psychologists must uncover universal human psychological processes.

The problem with this logic is that the search for human universals, with very few exceptions, is likely to be a fool's errand. Studying the human experience means asking people how they feel, and those feelings are likely to vary from person to person, situation to situation, and culture to culture. The inherent messiness is the challenge that each psychologist faces in his/her research ( and the fun). That psychological phenomena are often culturally or situationally bound is not evidence of lack of scientific rigor, but rather, acknowledgment of the power of cultures and situations to influence how we perceive and respond to our social environments.

Of course, some psychologists think the subjective isn't sciencey enough, and so they operationalize their variables in ways that are far less bound to the meaning messiness of questionnaire measures. In the realm of happiness, if a researcher is unsatisfied with subjective ratings, he or she might measure the length of telomeres (a marker of cell aging) or the levels of glucocorticoid hormones in the bloodstream. Psychologists do this too, and this sort of work is much closer to what even a physicist or chemist might consider science.

Of course, whether psychologists use subjective self-reports or biological measures ( or mathematical models that "precisely" quantify the golden ratio of positive affect) does not make them any more of a scientist. All biological measures really do is make psychologists look more like "hard" scientists to other "hard" scientists.
IV. Psychology isn't falsifiable
This criticism comes from within our own field as well as from the outside: Psychologists too often publish positive findings-that is findings that support rather than contradict hypotheses. Publication of primarily positive findings suggests that psychologists are more interested in supporting their own beliefs about human experience than in finding truth about that experience. It is because of this trend that one of my colleagues suggested that our field contains more lawyers than scientists ( here).

This criticism is actually a fair one in my book-psychologists are often likely to bury data that does not support their theories about the world ( even famous psychologists like Stanley Milgram are guilty of this). This practice strikes me as unscientific because it renders hypotheses more difficult to falsify. However, the good news is that efforts are underway to pay more attention to negative findings (see here).
What is the psychology behind the "Psychology isn't a Science" argument?
I think there are several basic psychological principles that help explain why this argument strikes a nerve with so many people. I tend to think of it in terms of social comparison. Psychologists like to weigh in on the psychology is a science perspective because we are engaging in upward social comparison. We want a seat at the table with the hard sciences, we want to be published in the most prestigious science journals, and we want a larger share of the grant funding from our government. In contrast, the harder sciences engage in downward social comparison with psychology. Hard sciences seek to maintain their elevated position in the science hierarchy, and sometimes they accomplish this by disparaging the softer sciences.

To close, I would just like to point out that psychology is a very young science, and so to expect it to have the same prestige and admiration as other sciences that have been around for centuries is a little far-fetched. Just like it takes a person time to build respect and prestige amongst his or her co-workers, psychology is going to have to be around for a bit longer before it starts earning the respect and admiration of other sciences. I'm OK with this and if you want to talk with me more about this issue I'll be here: doing science.
This post appeared on Psych-Your-Mind.

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The Psychology of the "Psychology Isn't a Science" Argument

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I consider the arguments suggesting that psychology isn't a science, and find them mostly insufficient.

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Psychology is a science because it employs systematic methods of observation, experimentation, and data analysis to understand and predict behavior and mental processes, grounded in empirical evidence and subjected to peer review.

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Critical values table for the Wilcoxon test

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Critical values table for the Wilcoxon test

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Critical values table for the Mann-Whitney test

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Critical values table for the Mann-Whitney test

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P-value in statistics: Understanding the p-value and what it tells us - Statistics Help

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Inferential Tests, Alpha Probabilities and Critical Values

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A Prezicast on the use of inferential statistical tests in psychology and the social sciences. It is intended as a revision guide for A level (AS and A2) students in Psychology. However, it may also be useful those preparing for A levels in Biology and Geography. The original presentation can be found at http://goo.gl/alHx9.

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Definition

There was a big hubbub some years ago that accused vaccines of causing autism. (Vaccines are a weak form of an infection given to people to prevent them from getting the full infection. Autism is a pervasive developmental disorder that is characterized by difficulties with socializing and communication, and it is believed to affect over 1 in 100 people.) This is, of course, ridiculous since it has been found that the brain structure of those with autism is different than those of other people. No simple shot could alter a person's brain like that. However, some people still didn't know this and freaked out.

My reason for bringing this up is to show you how understanding significance tests, along with your statistical tests, is necessary. Statistically significant means the relationship in the results did not occur by random chance. Most researchers work with samples, defined as a section of the population. A population is defined as the complete collection to be studied. Since a researcher is not looking at everyone, there is a possibility that they will collect, by accident, a sample that will lead them to erroneous conclusions. To avoid this, nearly all statistical tests look for statistical significance.

Using our autism and vaccine example, after several tests had been conducted, the researchers found that there was no relationship. They could make this assertion with confidence because their results were statistically significant despite using a sample. So, even though they didn't test every person who had a vaccine, the researchers found no relationship between those who had the vaccine and those who were diagnosed with autism.

P-Value

When you run a statistical test, you will compute a p-value, which is defined as the significance level value. This value will be represented by a decimal, anywhere between 1.0 to below .01. This value will inform you how likely the null hypothesis, or the prediction that there is no relationship, is true.

The commonly accepted level of the p-value for the relationship to be statistically significant is .05. This means that 1 in 20 times your results will be positive despite there being no actual relationship. In our autism example, there will be some studies that find significant connections between autism and vaccines. This means that 1 in 20 times the results will be positive based on pure probability and not because there is a statistical relationship.

Where does this .05 come from? What is the p-value? It is derived from looking at the bell curve and examining the strength of the relationship and the likelihood that there is no relationship. Back to our autism/vaccine example - if I were to make up some of the p-values, they might be .03, .001, and .5. I listed three, and they are all made up. The first two, .03 and .001, would be statistically significant. The .5 would not be statistically significant.

Critical Regions

Statistical significance comes from the bell curve. In a statistical test, you are looking to see if there is a relationship between the numbers. This relationship can be in the form of scores being similar, like a correlation, or different, like a t-test. When testing for significance, you are testing your data to see if your value falls in the critical region, defined as the statistical value that will allow you to reject the null hypothesis.

What this means is when you perform a statistical test, you will end up with a p-value. If the number falls in the critical regions area, then your relationship is statistically significant, and you are able to reject the null hypothesis. This is because in the critical regions, you are testing to see if the relationship between your scores is strong enough. If the relationship is weak, then your score will fall in the null hypothesis area and not be statistically significant.

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levels & critical regions - video & lesson transcript | study.com

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Statistical Significance: Definition, Levels & Critical Regions - Video & Lesson Transcript | Study.com

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There was a big hubbub some years ago that accused vaccines of causing autism. (Vaccines are a weak form of an infection given to people to prevent them from getting the full infection. Autism is a pervasive developmental disorder that is characterized by difficulties with socializing and communication, and it is believed to affect over 1 in 100 people.)

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Learning Objectives

1. Define Type I and Type II errors

2. Interpret significant and non-significant differences

3. Explain why the null hypothesis should not be rejected when the effect is not significant

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Try the free Interactive e-book for iPhone, iPad, and OS X.

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Hypothesis testing: step-by-step, p-value, t-test for difference of two means - Statistics Help

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Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help

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The Basics: Descriptive and Inferential Statistics

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statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!

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The concept of validity was formulated by Kelly (1927, p. 14), who stated that a test is valid if it measures what it claims to measure.

For example, a test of intelligence should measure intelligence and not something else (such as memory).

A distinction can be made between internal and external validity. These types of validity are relevant to evaluating the validity of a research study/procedure.

Internal validity refers to whether the effects observed in a study are due to the manipulation of the independent variable and not some other factor.

In other words, there is a causal relationship between the independent and dependent variables.

Internal validity can be improved by controlling extraneous variables, using standardized instructions, counterbalancing, and eliminating demand characteristics and investigator effects.

External validity refers to the extent to which the results of a study can be generalized to other settings (ecological validity), other people (population validity), and over time (historical validity).

External validity can be improved by setting experiments in a more natural setting and using random sampling to select participants.

Assessing the Validity of the Test

There are two main categories of validity used to assess the validity of the test (i.e., questionnaire, interview, IQ test, etc.): Content and criterion.

Face validity is simply whether the test appears (at face value) to measure what it claims to. This is the least sophisticated measure of validity.

Tests wherein the purpose is clear, even to naïve respondents, are said to have high face validity. Accordingly, tests wherein the purpose is unclear have low face validity (Nevo, 1985).

A direct measurement of face validity is obtained by asking people to rate the validity of a test as it appears to them. This rater could use a Likert scale to assess face validity. For example:

1. the test is extremely suitable for a given purpose

2. the test is very suitable for that purpose;

3. the test is adequate

4. the test is inadequate

5. the test is irrelevant and, therefore, unsuitable

It is important to select suitable people to rate a test (e.g., questionnaire, interview, IQ test, etc.). For example, individuals who actually take the test would be well placed to judge its face validity.

Also, people who work with the test could offer their opinion (e.g., employers, university administrators, employers). Finally, the researcher could use members of the general public with an interest in the test (e.g., parents of testees, politicians, teachers, etc.).

The face validity of a test can be considered a robust construct only if a reasonable level of agreement exists among raters.

It should be noted that the term face validity should be avoided when the rating is done by an "expert," as content validity is more appropriate.

Having face validity does not mean that a test really measures what the researcher intends to measure, but only in the judgment of raters that it appears to do so. Consequently, it is a crude and basic measure of validity.

A test item such as " I have recently thought of killing myself " has obvious face validity as an item measuring suicidal cognitions and may be useful when measuring symptoms of depression.

However, the implication of items on tests with clear face validity is that they are more vulnerable to social desirability bias. Individuals may manipulate their responses to deny or hide problems or exaggerate behaviors to present a positive image of themselves.

It is possible for a test item to lack face validity but still have general validity and measure what it claims to measure. This is good because it reduces demand characteristics and makes it harder for respondents to manipulate their answers.

For example, the test item " I believe in the second coming of Christ " would lack face validity as a measure of depression (as the purpose of the item is unclear).

This item appeared on the first version of The Minnesota Multiphasic Personality Inventory (MMPI) and loaded on the depression scale.

Because most of the original normative sample of the MMPI were good Christians, only a depressed Christian would think Christ is not coming back. Thus, for this particular religious sample, the item does have general validity but not face validity.

Construct validity was invented by Cronbach and Meehl (1955). This type of validity refers to the extent to which a test captures a specific theoretical construct or trait, and it overlaps with some of the other aspects of validity

Construct validity does not concern the simple, factual question of whether a test measures an attribute.

Instead, it is about the complex question of whether test score interpretations are consistent with a nomological network involving theoretical and observational terms (Cronbach & Meehl, 1955).

To test for construct validity, it must be demonstrated that the phenomenon being measured actually exists. So, the construct validity of a test for intelligence, for example, depends on a model or theory of intelligence.

Construct validity entails demonstrating the power of such a construct to explain a network of research findings and to predict further relationships.

The more evidence a researcher can demonstrate for a test's construct validity, the better. However, there is no single method of determining the construct validity of a test.

Instead, different methods and approaches are combined to present the overall construct validity of a test. For example, factor analysis and correlational methods can be used.

Concurrent Validity

This is the degree to which a test corresponds to an external criterion that is known concurrently (i.e., occurring at the same time).

If the new test is validated by comparison with a currently existing criterion, we have concurrent validity.

Very often, a new IQ or personality test might be compared with an older but similar test known to have good validity already.

This is the degree to which a test accurately predicts a criterion that will occur in the future.

For example, a prediction may be made on the basis of a new intelligence test that high scorers at age 12 will be more likely to obtain university degrees several years later. If the prediction is born out, then the test has predictive validity.

References

Cronbach, L. J., and Meehl, P. E. (1955) Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.

Hathaway, S. R., & McKinley, J. C. (1943). Manual for the Minnesota Multiphasic Personality Inventory. New York: Psychological Corporation.

Kelley, T. L. (1927). Interpretation of educational measurements. New York: Macmillan.

Nevo, B. (1985). Face validity revisited. Journal of Educational Measurement, 22(4), 287-293.

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Validity in Research: Definitions, Types, & Examples

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The concept of validity was formulated by Kelly (1927, p. 14), who stated that a test is valid if it measures what it claims to measure.

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Case Study Method

By Dr. Saul McLeod, updated 2019

What is a case study research method?

Case studies are in-depth investigations of a single person, group, event or community. Typically, data are gathered from a variety of sources and by using several different methods (e.g. observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e. the patient's personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual's past (i.e. retrospective), as well as to significant events which are currently occurring in his or her everyday life.

The case study is not itself a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

What is an example of a case study in psychology?

Case studies are widely used in psychology and amongst the best known were the ones carried out by Sigmund Freud, including Anna O and Little Hans.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses. Even today case histories are one of the main methods of investigation in abnormal psychology and psychiatry.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist or psychiatrist, i.e. someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e. abnormal) behavior or atypical development.

How is a case study conducted?

The procedure used in a case study means that the researcher provides a description of the behavior. This comes from interviews and other sources, such as observation.

The client also reports detail of events from his or her point of view. The researcher then writes up the information from both sources above as the case study, and interprets the information.

The research may also continue for an extended period of time, so processes and developments can be studied as they happen.

Amongst the sources of data the psychologist is likely to turn to when carrying out a case study are observations of a person's daily routine, unstructured interviews with the participant herself (and with people who know her), diaries, personal notes (e.g. letters, photographs, notes) or official document (e.g. case notes, clinical notes, appraisal reports).

The case study method often involves simply observing what happens to, or reconstructing 'the case history' of a single participant or group of individuals (such as a school class or a specific social group), i.e. the idiographic approach.

The interview is also an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person's friends, parents, employer, workmates and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.

Most of this information is likely to be qualitative (i.e. verbal description rather than measurement) but the psychologist might collect numerical data as well.

How to analyze case study data

The data collected can be analyzed using different theories (e.g. grounded theory, interpretative phenomenological analysis, text interpretation, e.g. thematic coding).

All the approaches mentioned here use preconceived categories in the analysis and they are ideographic in their approach, i.e. they focus on the individual case without reference to a comparison group.

Interpreting the information means the researcher decides what to include or leave out. A good case study should always make clear which information is the factual description and which is an inference or the opinion of the researcher.

Strengths of Case Studies

Provides detailed (rich qualitative) information.

Provides insight for further research.

Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of 'averaging'.

Because of their in-depth, multi-sided approach case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research which only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension to experience which is so important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person's life are related to each other.

The method is therefore important for psychologists who adopt a holistic point of view (i.e. humanistic psychologists).

Limitations of Case Studies

Lacking scientific rigour and providing little basis for generalization of results to the wider population.

Researchers' own subjective feeling may influence the case study (researcher bias).

Difficult to replicate.

Time-consuming and expensive.

The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group we can never be sure if the case study investigated is representative of the wider body of "similar" instances. This means the the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e. descriptive) data a lot depends on the interpretation the psychologist places on the information she has acquired.

This means that there is a lot of scope for observer bias and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit the particular theories about behavior (e.g. Little Hans).

This is also true of Money's interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

APA Style References

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine, 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der "Rattenmann"). Jb. psychoanal. psychopathol. Forsch., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE, 10: 151-318.

Little Hans - Freudian Case Study Little Hans - Freudian Case Study Anna O - Freudian Case Study Genie Case Study - Curtiss (1977)

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By Dr. Saul McLeod, updated 2019 What is a case study research method? Case studies are in-depth investigations of a single person, group, event or community. Typically, data are gathered from a variety of sources and by using several different methods (e.g. observations & interviews).

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Tutor2U research methods revision (year 2)

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Tutor2U research methods revision (year 2)

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3.2 - 3.2 Triangulation [Qualitative Research Methods]

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Week 3, Lecture 2 for the online course "Qualitative Research Methods", taught by Gerben Moerman. All rights belong to Coursera and University of Amsterdam. For educational purposes only. Happy learning!

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Content analysis is a method used to analyse qualitative data (non-numerical data). In its most common form it is a technique that allows a researcher to take qualitative data and to transform it into quantitative data (numerical data). The technique can be used for data in many different formats, for example interview transcripts, film, and audio recordings.

The researcher conducting a content analysis will use 'coding units' in their work. These units vary widely depending on the data used, but an example would be the number of positive or negative words used by a mother to describe her child's behaviour or the number of swear words in a film.

The procedure for a content analysis is shown below:

Strengths of content analysis
It is a reliable way to analyse qualitative data as the coding units are not open to interpretation and so are applied in the same way over time and with different researchers

It is an easy technique to use and is not too time consuming

It allows a statistical analysis to be conducted if required as there is usually quantitative data as a result of the procedure
Weaknesses of content analysis
Causality cannot be established as it merely describes the data

As it only describes the data it cannot extract any deeper meaning or explanation for the data patterns arising.

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Content analysis is a method used to analyse qualitative data (non-numerical data). In its most common form it is a technique that allows a researcher to take qualitative data and to transform it into quantitative data (numerical data). The technique can be used for data in many different formats, for example interview transcripts, film, and audio recordings.

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Difference between content and thematic analysis

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Difference between content and thematic analysis

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Key Difference - Content vs Thematic Analysis

When speaking of data analysis in conducting research, there are many types that can be used by researchers. Content analysis and thematic analysis are two such types of analyses used in research. For most researchers, the difference between content and thematic analysis can be quite confusing as both include going through the data to identify patterns and themes. However, it is important to highlight that the key difference between content and thematic analysis is that while in the content analysis, the researcher can focus more on the frequency of occurrence of various categories, in the thematic analysis, it is more about identifying themes and building up the analysis in the most cohesive manner. Some researchers also highlight that thematic analysis can be more in depth and provide a broader understanding than content analysis.

What is Content Analysis?

Content analysis refers to a data analysis technique used in both quantitative and qualitative research. This technique assists the researcher to identify important data from a data corpus. The data can come in different forms. It can be books, pictures, photographs, statues, ideas, papers, behaviors, etc. The aim of the researcher is to analyze the content of each data item. In most content analyses, researchers use coding systems to identify and categorize various data items.

When the content analysis is used for quantitative data analysis, it can be used to identify frequencies of data as well. This is why content analysis is now vastly used in communication and media. Now let us move on to thematic analysis.

What is Thematic Analysis?

Thematic analysis is a data analysis technique used in research. This is mainly used for qualitative researches where the researcher gathers descriptive data in order to answer his research problem. Once the data has been gathered the researcher would go through the data repeatedly with the intention of finding emerging patterns, themes, sub-themes etc. This allows the researcher to categorize the data under different sections. This can be a rather tedious task because the researcher will have to go through the data many times before he finalizes the main themes and sub-themes of the research. This process of going through data is known as 'immersion.'

It is important to highlight in a thematic analysis the main themes that the researcher uses for his final analysis are connected to one another. If the themes remain idle without connecting with one another, it can be difficult to create the final structure and make sense of the research. There are many advantages of using a thematic analysis. Firstly it brings out the rich data that the researcher has gathered in the data gathering phase. Also, it provides a logical structure to the research as well.

What is the difference between Content and Thematic Analysis?

Definitions of Content and Thematic Analysis:

Content Analysis: Content analysis refers to a data analysis technique used in both quantitative and qualitative research.

Thematic Analysis: Thematic analysis is a data analysis technique used in research.

Characteristics of Content and Thematic Analysis:

Type of Research:

Content Analysis: Content analysis can be used in both quantitative and qualitative research.

Thematic Analysis: Thematic analysis is mostly used in qualitative research.

Content Analysis: Data coding is given a lot of prominence as it allows in recognizing the important data items.

Thematic Analysis: Themes are given more prominence.
Image Courtesy: 1. Researchers review cancer data By Rhoda Baer (Photographer) [Public domain or Public domain], via Wikimedia Commons
2. "Bookshelf" by Stewart Butterfield - flickr. [CC BY 2.0] via Commons

Cite This Page
"Difference Between Content and Thematic Analysis." DifferenceBetween.Com. January 6, 2016. < http://www.differencebetween.com/difference-between-content-and-vs-thematic-analysis/ >

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Difference Between Content and Thematic Analysis

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When speaking of data analysis in conducting research, there are many types that can be used by researchers. Content analysis and thematic analysis are two such types of analyses used in research. For most researchers, the difference between content and thematic analysis can be quite confusing as both include going through the data to identify patterns and themes.

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10 greatest case studies (BPS)

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The Psychologist - The British Psychological Society

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Sharing information can even influence our behaviour: participants made different financial decisions depending on whether they had shared an article on investing.

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The concept of validity was formulated by Kelly (1927, p. 14), who stated that a test is valid if it measures what it claims to measure.

For example, a test of intelligence should measure intelligence and not something else (such as memory).

A distinction can be made between internal and external validity. These types of validity are relevant to evaluating the validity of a research study/procedure.

Internal validity refers to whether the effects observed in a study are due to the manipulation of the independent variable and not some other factor.

In other words, there is a causal relationship between the independent and dependent variables.

Internal validity can be improved by controlling extraneous variables, using standardized instructions, counterbalancing, and eliminating demand characteristics and investigator effects.

External validity refers to the extent to which the results of a study can be generalized to other settings (ecological validity), other people (population validity), and over time (historical validity).

External validity can be improved by setting experiments in a more natural setting and using random sampling to select participants.

Assessing the Validity of the Test

There are two main categories of validity used to assess the validity of the test (i.e., questionnaire, interview, IQ test, etc.): Content and criterion.

Face validity is simply whether the test appears (at face value) to measure what it claims to. This is the least sophisticated measure of validity.

Tests wherein the purpose is clear, even to naïve respondents, are said to have high face validity. Accordingly, tests wherein the purpose is unclear have low face validity (Nevo, 1985).

A direct measurement of face validity is obtained by asking people to rate the validity of a test as it appears to them. This rater could use a Likert scale to assess face validity. For example:

1. the test is extremely suitable for a given purpose

2. the test is very suitable for that purpose;

3. the test is adequate

4. the test is inadequate

5. the test is irrelevant and, therefore, unsuitable

It is important to select suitable people to rate a test (e.g., questionnaire, interview, IQ test, etc.). For example, individuals who actually take the test would be well placed to judge its face validity.

Also, people who work with the test could offer their opinion (e.g., employers, university administrators, employers). Finally, the researcher could use members of the general public with an interest in the test (e.g., parents of testees, politicians, teachers, etc.).

The face validity of a test can be considered a robust construct only if a reasonable level of agreement exists among raters.

It should be noted that the term face validity should be avoided when the rating is done by an "expert," as content validity is more appropriate.

Having face validity does not mean that a test really measures what the researcher intends to measure, but only in the judgment of raters that it appears to do so. Consequently, it is a crude and basic measure of validity.

A test item such as " I have recently thought of killing myself " has obvious face validity as an item measuring suicidal cognitions and may be useful when measuring symptoms of depression.

However, the implication of items on tests with clear face validity is that they are more vulnerable to social desirability bias. Individuals may manipulate their responses to deny or hide problems or exaggerate behaviors to present a positive image of themselves.

It is possible for a test item to lack face validity but still have general validity and measure what it claims to measure. This is good because it reduces demand characteristics and makes it harder for respondents to manipulate their answers.

For example, the test item " I believe in the second coming of Christ " would lack face validity as a measure of depression (as the purpose of the item is unclear).

This item appeared on the first version of The Minnesota Multiphasic Personality Inventory (MMPI) and loaded on the depression scale.

Because most of the original normative sample of the MMPI were good Christians, only a depressed Christian would think Christ is not coming back. Thus, for this particular religious sample, the item does have general validity but not face validity.

Construct validity was invented by Cronbach and Meehl (1955). This type of validity refers to the extent to which a test captures a specific theoretical construct or trait, and it overlaps with some of the other aspects of validity

Construct validity does not concern the simple, factual question of whether a test measures an attribute.

Instead, it is about the complex question of whether test score interpretations are consistent with a nomological network involving theoretical and observational terms (Cronbach & Meehl, 1955).

To test for construct validity, it must be demonstrated that the phenomenon being measured actually exists. So, the construct validity of a test for intelligence, for example, depends on a model or theory of intelligence.

Construct validity entails demonstrating the power of such a construct to explain a network of research findings and to predict further relationships.

The more evidence a researcher can demonstrate for a test's construct validity, the better. However, there is no single method of determining the construct validity of a test.

Instead, different methods and approaches are combined to present the overall construct validity of a test. For example, factor analysis and correlational methods can be used.

Concurrent Validity

This is the degree to which a test corresponds to an external criterion that is known concurrently (i.e., occurring at the same time).

If the new test is validated by comparison with a currently existing criterion, we have concurrent validity.

Very often, a new IQ or personality test might be compared with an older but similar test known to have good validity already.

This is the degree to which a test accurately predicts a criterion that will occur in the future.

For example, a prediction may be made on the basis of a new intelligence test that high scorers at age 12 will be more likely to obtain university degrees several years later. If the prediction is born out, then the test has predictive validity.

References

Cronbach, L. J., and Meehl, P. E. (1955) Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.

Hathaway, S. R., & McKinley, J. C. (1943). Manual for the Minnesota Multiphasic Personality Inventory. New York: Psychological Corporation.

Kelley, T. L. (1927). Interpretation of educational measurements. New York: Macmillan.

Nevo, B. (1985). Face validity revisited. Journal of Educational Measurement, 22(4), 287-293.

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Validity in Research: Definitions, Types, & Examples

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The concept of validity was formulated by Kelly (1927, p. 14), who stated that a test is valid if it measures what it claims to measure.

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The term reliability in psychological research refers to the consistency of a quantitative research study or measuring test. For instruments like questionnaires, reliability ensures that responses are consistent across times and occasions. Multiple forms of reliability exist, including test-retest, inter-rater, and internal consistency.

For example, if a person weighs themselves during the day, they would expect to see a similar reading. Scales that measured weight differently each time would be of little use.

The same analogy could be applied to a tape measure that measures inches differently each time it is used. It would not be considered reliable.

If findings from research are replicated consistently, they are reliable. A correlation coefficient can be used to assess the degree of reliability. If a test is reliable, it should show a high positive correlation.

Of course, it is unlikely the same results will be obtained each time as participants and situations vary. Still, a strong positive correlation between the same test results indicates reliability.

There are two types of reliability: internal and external.

Split-Half Method

The split-half method assesses the internal consistency of a test, such as psychometric tests and questionnaires.

There, it measures the extent to which all parts of the test contribute equally to what is being measured.

Here's how it works:

1. A test or questionnaire is split into two halves, typically by separating even-numbered items from odd-numbered items, or half half items vs second half.

2. Each half is scored separately, and the scores are correlated using a statistical method, often Pearson's correlation.

3. The correlation between the two halves gives an indication of the test's reliability. A higher correlation suggests better reliability.

4. To adjust for the test's shortened length (because we've split it in half), the Spearman-Brown prophecy formula is often applied to estimate the reliability of the full test based on the split-half reliability.

The reliability of a test could be improved by using this method. For example, any items on separate halves of a test with a low correlation (e.g., r = .25) should either be removed or rewritten.

The split-half method is a quick and easy way to establish reliability. However, it can only be effective with large questionnaires in which all questions measure the same construct. This means it would not be appropriate for tests that measure different constructs.

For example, the Minnesota Multiphasic Personality Inventory has sub scales measuring differently behaviors such as depression, schizophrenia, social introversion. Therefore the split-half method was not be an appropriate method to assess reliability for this personality test.

Test-Retest

The test-retest method assesses the external consistency of a test. Examples of appropriate tests include questionnaires and psychometric tests. It measures the stability of a test over time.

A typical assessment would involve giving participants the same test on two separate occasions. If the same or similar results are obtained, then external reliability is established.

Here's how it works:

1. A test or measurement is administered to participants at one point in time.

2. After a certain period, the same test is administered again to the same participants without any intervention or treatment in between.

3. The scores from the two administrations are then correlated using a statistical method, often Pearson's correlation.

4. A high correlation between the scores from the two test administrations indicates good test-retest reliability, suggesting the test yields consistent results over time.

This method is especially useful for tests that measure stable traits or characteristics that aren't expected to change over short periods. The disadvantage of the test-retest method is that it takes a long time for results to be obtained. The reliability can be influenced by the time interval between tests and any events that might affect participants' responses during this interval.

Beck et al. (1996) studied the responses of 26 outpatients on two separate therapy sessions one week apart, they found a correlation of .93 therefore demonstrating high test-restest reliability of the depression inventory.

This is an example of why reliability in psychological research is necessary, if it wasn't for the reliability of such tests some individuals may not be successfully diagnosed with disorders such as depression and consequently will not be given appropriate therapy.

The timing of the test is important; if the duration is too brief, then participants may recall information from the first test, which could bias the results.

Alternatively, if the duration is too long, it is feasible that the participants could have changed in some important way which could also bias the results.

The test-retest method assesses the external consistency of a test. This refers to the degree to which different raters give consistent estimates of the same behavior. Inter-rater reliability can be used for interviews.

Inter-Rater Reliability

Inter-rater reliability, often termed inter-observer reliability, refers to the extent to which different raters or evaluators agree in assessing a particular phenomenon, behavior, or characteristic. It's a measure of consistency and agreement between individuals scoring or evaluating the same items or behaviors.

High inter-rater reliability indicates that the findings or measurements are consistent across different raters, suggesting the results are not due to random chance or subjective biases of individual raters.

Statistical measures, such as Cohen's Kappa or the Intraclass Correlation Coefficient (ICC), are often employed to quantify the level of agreement between raters, helping to ensure that findings are objective and reproducible.

Ensuring high inter-rater reliability is essential, especially in studies involving subjective judgment or observations, as it provides confidence that the findings are replicable and not heavily influenced by individual rater biases.

Note it can also be called inter-observer reliability when referring to observational research. Here, researchers observe the same behavior independently (to avoid bias) and compare their data. If the data is similar, then it is reliable.

Where observer scores do not significantly correlate, then reliability can be improved by:

Training observers in the observation techniques and ensuring everyone agrees with them.

Ensuring behavior categories have been operationalized. This means that they have been objectively defined.

For example, if two researchers are observing 'aggressive behavior' of children at nursery they would both have their own subjective opinion regarding what aggression comprises.

In this scenario, they would be unlikely to record aggressive behavior the same, and the data would be unreliable.

However, if they were to operationalize the behavior category of aggression, this would be more objective and make it easier to identify when a specific behavior occurs.

For example, while "aggressive behavior" is subjective and not operationalized, "pushing" is objective and operationalized. Thus, researchers could count how many times children push each other over a certain duration of time.

Validity vs. Reliability In Psychology

In psychology, validity and reliability are fundamental concepts that assess the quality of measurements.

Validity pertains to the accuracy of an instrument. It addresses whether the test measures what it claims to measure. Different forms include content, criterion, construct, and external validity.

Reliability concerns the consistency of an instrument. It evaluates whether the test yields stable and repeatable results under consistent conditions. Types of reliability include test-retest, inter-rater, and internal consistency.

A pivotal understanding is that reliability is a necessary but not sufficient condition for validity.

It means a test can be reliable, consistently producing the same results, without being valid, or accurately measuring the intended attribute.

However, a valid test, one that truly measures what it purports to, must be reliable. In the pursuit of rigorous psychological research, both validity and reliability are indispensable.

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Reliability in Psychology Research: Definitions & Examples

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The term reliability in psychological research refers to the consistency of a quantitative research study or measuring test. For instruments like questionnaires, reliability ensures that responses are consistent across times and occasions. Multiple forms of reliability exist, including test-retest, inter-rater, and internal consistency.

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Psychology as a Science | Simply Psychology

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When people use the term "Scientific" we all have a general schema of what they mean, but when we break it down in the way that we just have done, the picture is less certain. What is science? It depends on your philosophy. Is psychology a science? It depends on your definition.

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internet journal of neuropsychiatry and
psychopharmacology

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The Pacific Neuropsychiatric Institute (PNI) is involved with comprehensive evaluation and management in Neuropsychiatry and Psychopharmacology at the clinical, research, forensic, education and phenomenologic levels.

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Independent and Dependent Variables | Definitions & Examples | Simply Psychology | Simply Psychology

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The independent variable is the variable the experimenter changes or controls and is assumed to have a direct effect on the dependent variable. Two examples of common independent variables are gender and educational level.

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Inferential Tests, Alpha Probabilities and Critical Values

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A Prezicast on the use of inferential statistical tests in psychology and the social sciences. It is intended as a revision guide for A level (AS and A2) students in Psychology. However, it may also be useful those preparing for A levels in Biology and Geography. The original presentation can be found at http://goo.gl/alHx9.

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The ethical foundations of the peer review process have come under heated questioning in recent years. Women and minorities have charged that an old boys' network denies them fair due. Smaller or relatively obscure institutions feel slighted by larger rivals.

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Use this resource to read original reports of research studies

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Use this resource to read original reports of research studies

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An Electronic Resource Developed by Christopher D. Green York University, Toronto, Canada ISSN 1492-3173

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Paradigm Shift Esoteric Agenda Metaphysics Quantum Physics Unity Consciousness

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A description of Thomas Kuhn's concept of the paradigm shift, as described in his 1962 book "The Structure of Scientific Revolutions". No expense was spared in the making of this video. Written, directed, edited and narrated by Nathan Radke

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Contrasts poor and good research interview

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Contrasts poor and good research interview

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Psychological Research: Crash Course Psychology #2