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A relationship refers to the correspondence between two variables . When we talk about types of relationships, we can mean that in at least two ways: the nature of the relationship or the pattern of it.

The Nature of a Relationship

While all relationships tell about the correspondence between two variables, there is a special type of relationship that holds that the two variables are not only in correspondence, but that one causes the other. This is the key distinction between a simple correlational relationship and a causal relationship . A correlational relationship simply says that two things perform in a synchronized manner. For instance, there has often been talk of a relationship between ability in math and proficiency in music. In general people who are good in one may have a greater tendency to be good in the other; those who are poor in one may also tend to be poor in the other. If this relationship is true, then we can say that the two variables are correlated. But knowing that two variables are correlated does not tell us whether one causes the other. We know, for instance, that there is a correlation between the number of roads built in Europe and the number of children born in the United States. Does that mean that if we want fewer children in the U.S., we should stop building so many roads in Europe? Or, does it mean that if we don’t have enough roads in Europe, we should encourage U.S. citizens to have more babies? Of course not. (At least, I hope not). While there is a relationship between the number of roads built and the number of babies, we don’t believe that the relationship is a causal one. This leads to consideration of what is often termed the third variable problem . In this example, it may be that there is a third variable that is causing both the building of roads and the birthrate, that is causing the correlation we observe. For instance, perhaps the general world economy is responsible for both. When the economy is good more roads are built in Europe and more children are born in the U.S. The key lesson here is that you have to be careful when you interpret correlations.

If you observe a correlation between the number of hours students use the computer to study and their grade point averages (with high computer users getting higher grades), you cannot assume that the relationship is causal : that computer use improves grades. In this case, the third variable might be socioeconomic status – richer students who have greater resources at their disposal tend to both use computers and do better in their grades. It’s the resources that drives both use and grades, not computer use that causes the change in the grade point average.

Patterns of Relationships

We have several terms to describe the major different types of patterns one might find in a relationship. First, there is the case of no relationship at all. If you know the values on one variable, you don’t know anything about the values on the other. For instance, I suspect that there is no relationship between the length of the lifeline on your hand and your grade point average. If I know your GPA, I don’t have any idea how long your lifeline is.

Then, we have the positive relationship . In a positive relationship, high values on one variable are associated with high values on the other and low values on one are associated with low values on the other. In this example, we assume an idealized positive relationship between years of education and the salary one might expect to be making.

On the other hand a negative relationship implies that high values on one variable are associated with low values on the other. This is also sometimes termed an inverse relationship. Here, we show an idealized negative relationship between a measure of self esteem and a measure of paranoia in psychiatric patients.

These are the simplest types of relationships we might typically estimate in research. But the pattern of a relationship can be more complex than this. For instance, the figure on the left shows a relationship that changes over the range of both variables, a curvilinear relationship. In this example, the horizontal axis represents dosage of a drug for an illness and the vertical axis represents a severity of illness measure. As dosage rises, severity of illness goes down. But at some point, the patient begins to experience negative side effects associated with too high a dosage, and the severity of illness begins to increase again.

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A research-based approach to relationships

Marriage and Couples

Home » Our Mission » Research » Marriage and Couples

The infographic below highlights some of Dr. John Gottman’s most notable research findings on marriage and couple relationships. For a more in-depth review of the three phases of Gottman’s research with marriage and couples, continue reading.

Phase 1: The Discovery of Reliable Patterns of Interaction Discriminating the “Masters” From the “Disasters” of Relationships

In 1976, Dr. Robert Levenson and Dr. John Gottman teamed up to combine the study of emotion with psycho-physiological measurement and a video-recall method that gave us rating dial measures (still applying game theory) of how people felt during conflict. This was the new way of getting the “talk table” numbers. The research also became longitudinal. They made no predictions in the first study, but they were interested in a measure of “physiological linkage,” because a prior study showed that the skin conductance of two nurses was correlated only if they disliked one another. They thought that might be linked to negative affect in couples. Indeed it was.

They were also amazed that in their first study with 30 couples they were able to “predict” the change in marital satisfaction almost perfectly with their physiological measures. The results revealed that the more physiologically aroused couples were (in all channels, including heart rate, skin conductance, gross motor activity, and blood velocity), the more their marriages deteriorated in happiness over a three-year period, even controlling the initial level of marital satisfaction.

The rating dial and their observational coding of the interaction also “predicted” changes in relationship satisfaction. Such large correlations in the data were unprecedented. Furthermore, Gottman and Levenson had preceded the conflict conversation with a reunion conversation (in which couples talked about the events of their day before the conflict discussion), and they had followed the conflict discussion with a positive topic. Gottman and Levenson were amazed to discover that harsh startup by women in the conflict discussion was predictable by the male partner’s disinterest or irritability in the events of the day discussion. They found that the quality of the couple’s friendship, especially as maintained by men, was critical in understanding conflict. Furthermore, the ability to rebound from, or “repair” , conflict to the positive conversation became a marker of emotion regulation ability of couples.

Both Levenson and Gottman had discovered Dr. Paul Ekman and Dr. Wallace Friesen’s Facial Affect Coding System (FACS), and Gottman subsequently developed the Specific Affect Coding System (SPAFF) , which was an integration of FACS and earlier systems in the Gottman lab.

The SPAFF became the main system that Gottman used to code couples’ interaction. At first, it took 25 hours to code 15 minutes of interaction, but later Gottman was able to get the same coding done in just 45 minutes, with no loss of reliability. Gottman also began applying time-series analysis to the analysis of interaction data. He wrote, Time-Series Analysis: A Comprehensive Introduction for Social Scientists , a book on time-series analysis to explain these methods to psychologists, and developed some new methods for analyzing dominance and bi-directionality with James Ringland.

Phase 2: Prediction and the Replication of the Prediction

Soon after, Gottman and Levenson received their first grant together and began attempting to replicate their observations from the first study. The subsequent studies they conducted in their labs with colleagues eventually spanned the entire life course — with the longest of the studies following couples for 20 years, in Levenson’s Berkeley lab.

The Gottman lab at the University of Illinois also studied the linkages between marital interaction, parenting, and children’s social development with Dr. Lynn Katz, and later at the University of Washington involved studying these linkages with infants with Dr. Alyson Shapiro. Gottman developed the concept of “meta-emotion” , which is how people feel about emotion (such as specific emotions like anger), emotional expression, and emotional understanding in general. Meta-emotion mismatches between parents in that study predicted divorce with 80% accuracy.

Gottman and Levenson discovered that couples interaction had enormous stability over time (about 80% stability in conflict discussions separated by 3 years). They also discovered that most relationship problems (69%) never get resolved but are “perpetual problems” based on personality differences between partners.

In seven longitudinal studies, one with violent couples (with Neil Jacobson), the predictions replicated. Gottman could predict whether a couple would divorce with an average of over 90% accuracy, across studies using the ratio of positive to negative SPAFF codes, the Four Horsemen of the Apocalypse (Criticism, Defensiveness, Contempt, and Stonewalling), physiology, the rating dial, and an interview they devised, the Oral History Interview , as coded by Kim Buehlman’s coding system.

Gottman could predict whether or not their stable couples would be happy or unhappy using measures of positive affect during conflict. With Dr. Jim Coan, he discovered that positive affect was used not randomly, but to physiologically soothe the partner. Gottman also discovered that in heterosexual relationships, men accepting influence from their wives was predictive of happy and stable marriages. Bob Levenson also discovered that humor was physiologically soothing and that empathy had a physiological substrate (in research with Dr. Anna Ruef), using the rating dial.

Phase 3: Theory Building, Understanding, and Prevention & Intervention

The third phase of Gottman’s research program was devoted to trying to understand the empirical predictions, and thus building and then testing theory. Ultimately, Gottman aimed to build a theory that was testable or disconfirmable.

Testing theory in the psychological field requires clinical interventions. In 1996, the Gottman lab returned to intervention research with Dr. Julie Schwartz Gottman. John and Julie Gottman designed both proximal and distal change studies. In a proximal change study, one intervenes briefly with interventions designed only to make the second of two conflict discussions less divorce-prone. In one of these studies, they discovered that a 20-minute break, in which couples stopped talking and just read magazines (as their heart rates returned to baseline), dramatically changed the discussion, so that people had access to their sense of humor and affection.

Together with Julie, John Gottman started building the Sound Relationship House Theory . That theory became the basis of the design of clinical interventions for couples in John Gottman’s book,  The Marriage Clinic , and Julie Gottman’s book,  The Marriage Clinic Casebook . In August of 1996, they founded The Gottman Institute to continue to develop evidence-based approaches to improving couples therapy outcomes.

Read more about The Gottman Institute’s mission here .

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Robert Waldinger talks about the findings from an 85-year-long Harvard study of adult life and what the good life really looks like.

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Work out daily? OK, but how socially fit are you?

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Book by Harvard Study of Adult Development director details what research says about value of relationships to physical, mental health

Robert Waldinger , director of the Harvard Study of Adult Development , says one of the biggest surprises they encountered was that what makes people happy is also what helps keep them healthy — relationships. The research project, the longest in-depth study of physical and mental well-being among adults, began in 1938 with 724 participants: 268 Harvard College sophomores and 456 young adults from Boston. It now includes 1,300 descendants of its original participants. The Gazette spoke with Waldinger about his new book, “The Good Life,” which he co-wrote with Marc Schulz. The interview has been edited for length and clarity.

Robert Waldinger

GAZETTE:  One of the conclusions of your book involves how key good relationships are to both physical as well as mental well-being. Were researchers expecting that to be true?

WALDINGER:  As part of the study, we followed our first generation of participants through their entire adult lives — from teenage years all the way into old age. When they reached age 80, we realized we had all these data about their physical and mental health, which we had collected year after year after year.

We started wondering whether we could look back at our participants’ lives in middle age and see what the biggest predictors were of who’s going to be happy and healthy by age 80. We thought that cholesterol level or blood pressure at age 50 would be more important. They were not. It was satisfaction in their relationships, particularly in their marriages, that was the best predictor of a happy and healthy life.

At first, we didn’t believe it; we were wondering how this could be possible. We thought, “It makes sense that if you have happy relationships, you’ll be happier, but how could the quality of your relationships make it more or less likely that you would get coronary artery disease or Type 2 diabetes or arthritis?” We thought maybe this isn’t a real finding, maybe it’s by chance. Then other research groups began to find the same thing. Now it is a very robust finding. It’s very well established that interpersonal connectedness, and the quality of those connections, really impact health, as well as happiness.

“[I]f you are alone and feel stressed and lonely, that’s part of what breaks down your health. That’s why we think loneliness is as dangerous to your health as smoking half a pack of cigarettes a day or being obese.”

GAZETTE:   Is there solid medical evidence that supports how good relationships might affect physical health?

WALDINGER:  Some people might think this finding is very touchy-feely, right? The question you are asking is exactly what researchers were asking, which is, “How does that work? What would the mechanism be by which relationships affect physiology?” We have spent the last 10 years in our lab studying this. The best hypothesis for which there are good data suggests that it is about stress and the regulation of stress by our relationships.

First, stress is a natural part of life. It happens every day to most of us: Something will come along that will stress us, and when that happens, the body goes into fight-or-flight mode. When that happens, you can feel your heart rate increase, your blood pressure goes up, you might start to sweat, and that’s normal because we want the body to prepare itself to meet a challenge. But when the challenge is removed, we want the body to go back to equilibrium. For example, if I have something upsetting happen during the day, and I’m churning or ruminating about it, I go home and talk to my wife or a friend, and if that person is a good listener, I can literally feel my body calm down.

But if you don’t have anyone like that, and many people don’t, if you are isolated or you don’t have a confidant, what we think happens is that the body stays in a kind of low-level fight-or-flight mode, and that means that there are higher levels of circulating stress hormones and higher levels of inflammation, and those things can gradually wear away many body systems. That’s how we think stress can wear down multiple body systems and how good relationships can be protective of our health.

GAZETTE:  How about career and financial success ? Are they as important as good relationships?

WALDINGER:  Certainly, having a job you enjoy or care about and find meaningful is important. Having a job you hate lowers your well-being for sure. But what we know from good studies is that wealth does not increase well-being significantly once we have our basic needs met. Once you get beyond basic financial security, your happiness doesn’t go up very much.

Similarly, fame or high achievement — becoming a Harvard professor or winning the Nobel Prize — won’t make you happier. Maybe the work that got you the Nobel Prize is meaningful to you, and that can make you happy. But the badges of achievement and the badges of wealth don’t make people happy. That’s important to keep in mind because we tell each other a lot of stories about what’s going to make us happy. We get these messages all day long from ads that convey the message that if you just buy this thing, you’ll be happier, or they show people living these beautiful and wealthy lives, and that’s the key to a happy life. It turns out that’s not true.

“What we see in our research is that everybody needs at least one solid relationship, someone whom they feel they can count on in times of need.”

GAZETTE:  Your book highlights the importance of having good relationships with your parents, siblings, neighbors, coworkers, even acquaintances. Can you expand on that?

WALDINGER:  There’s no set number of connections you need to have. If you have everything you need in your family, that’s great. Maybe, you don’t need a wider circle. But what we find is that the benefits of relationships come from anywhere. They certainly come from family, but they can come from friends, from work colleagues, and we even get small bits of well-being from a chat with the person who makes coffee for us in the coffee shop or from a chat with the cashier who checks us out in the grocery store, or the mail carrier. If we have pleasant connections with those people, those also contribute to our well-being.

Some of us are more shy, and some of us are more extroverted. Shy people need fewer relationships whereas an extrovert needs more. What we see in our research is that everybody needs at least one solid relationship, someone whom they feel they can count on in times of need. In one of our questionnaires, we asked our participants, “Who could you call in the middle of the night, if you were sick or scared? List everybody.” Most people could list several people, but some people, even some who were married, couldn’t list anyone. We think that everybody needs at least one person that you know would be there for you.

“Our social life is a living system, and it needs maintenance too. One of the ways you can do it is through tiny actions.”

GAZETTE:  What is the impact of loneliness on your physical health? In your book you write that loneliness could be as dangerous to your health as smoking or being obese.

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WALDINGER:  We think that it operates through this mechanism of chronic stress — that loneliness is a stressor. We evolved to be social creatures because it was safer to be in a group. If you think about when we were trying to survive out in the wilderness, you realize that people who banded together survived longer. We hypothesize that there was genetic selection for being more social. Being alone is a stressor; being isolated is a stressor. Many people feel chronically unsafe when they are lonely. If you’re alone and you’re content, that’s different. But if you are alone and feel stressed and lonely, that’s part of what breaks down your health. That’s why we think loneliness is as dangerous to your health as smoking half a pack of cigarettes a day or being obese. Loneliness has a similar physiologic fingerprint as those other two problems.

GAZETTE:  What if people feel it’s too late in life to have good relationships?

WALDINGER:  What we find in following thousands of people is that many people who thought it was too late for them, who thought, “I’m no good at relationships,” found relationships at a time when they didn’t expect to. We have a story in the book of one man who retired. He didn’t have a good marriage, and he never had friends. He joined a gym, and he found a group of friends who became his tribe, and they started socializing together. And he wrote to us that he was happy in a way he had never been because he had these people in his life. We find people who find love in their 70s and 80s who never expected it. Based on our science, we can say that it’s never too late. And if you think you’re never going to have good relationships, you don’t know that for sure. It’s worth the effort. People can make an effort.

GAZETTE:  What steps should people take to start working on building good relationships?

WALDINGER:  We talk in the book about what we call “social fitness.” The reason we call it that is to frame it as analogous to physical fitness. We think of physical fitness as a practice, as something we do to maintain our bodies. Our social life is a living system, and it needs maintenance too. One of the ways you can do it is through tiny actions. You could think right now, “Whom do I miss? Who would I like to see more of? Who haven’t I been in touch with?” and send them a text, an email, or call them on the phone. You will be amazed at the positive responses that you get for this tiny action.

The piece of advice I’d like to give is that there are some small actions we can take to enliven our social world. The other thing is to think about how you could make new connections, and probably one of the easiest ways to do that is to do something you care about or enjoy doing and do it alongside other people. It could be a bowling league, a gardening club, a knitting group, a political campaign, or working to prevent climate change. Just remember that when you do something you care about in a group, you already have something in common with the people you’re with. It’s a natural place to start conversations, and what we find is that when people repeatedly have casual contact with the same people, that’s the easiest place to start deepening relationships.

One last point I want to make is that nobody is happy all the time. That’s important to know because we can end up believing that if we’re not happy all the time, we’re doing something wrong. No life is happy all the time. Every life is filled with challenge and hard times. This idea about strengthening relationships is a way to increase our happiness, but also to build a safety net that helps us weather those hard times that all of us have in our lives.

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Correlational Research in Psychology: Definition and How It Works

Categories Research Methods

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Correlational research is a type of scientific investigation in which a researcher looks at the relationships between variables but does not vary, manipulate, or control them. It can be a useful research method for evaluating the direction and strength of the relationship between two or more different variables.

When examining how variables are related to one another, researchers may find that the relationship is positive or negative. Or they may also find that there is no relationship at all.

Table of Contents

How Does Correlational Research Work?

In correlational research, the researcher measures the values of the variables of interest and calculates a correlation coefficient, which quantifies the strength and direction of the relationship between the variables. 

The correlation coefficient ranges from -1.0 to +1.0, where -1.0 represents a perfect negative correlation, 0 represents no correlation, and +1.0 represents a perfect positive correlation. 

A negative correlation indicates that as the value of one variable increases, the value of the other variable decreases, while a positive correlation indicates that as the value of one variable increases, the value of the other variable also increases. A zero correlation indicates that there is no relationship between the variables.

Correlational Research vs. Experimental Research

Correlational research differs from experimental research in that it does not involve manipulating variables. Instead, it focuses on analyzing the relationship between two or more variables.

In other words, correlational research seeks to determine whether there is a relationship between two variables and, if so, the nature of that relationship. 

Experimental research, on the other hand, involves manipulating one or more variables to determine the effect on another variable. Because of this manipulation and control of variables, experimental research allows for causal conclusions to be drawn, while correlational research does not. 

Both types of research are important in understanding the world around us, but they serve different purposes and are used in different situations.

Types of Correlational Research

There are three main types of correlational studies:

Cohort Correlational Study 

This type of study involves following a cohort of participants over a period of time. This type of research can be useful for understanding how certain events might influence outcomes.

For example, researchers might study how exposure to a traumatic natural disaster influences the mental health of a group of people over time.

By examining the data collected from these individuals, researchers can determine whether there is a correlation between the two variables under investigation. This information can be used to develop strategies for preventing or treating certain conditions or illnesses.

Cross-Sectional Correlational Study

A cross-sectional design is a research method that examines a group of individuals at a single time. This type of study collects information from a diverse group of people, usually from different backgrounds and age groups, to gain insight into a particular phenomenon or issue.

The data collected from this type of study is used to analyze relationships between variables and identify patterns and trends within the group.

Cross-sectional studies can help identify potential risk factors for certain conditions or illnesses, and can also be used to evaluate the prevalence of certain behaviors, attitudes, or beliefs within a population.

Case-Control Correlational Study

A case-control correlational study is a type of research design that investigates the relationship between exposure and health outcomes. In this study, researchers identify a group of individuals with the health outcome of interest (cases) and another group of individuals without the health outcome (controls).

The researchers then compare the exposure history of the cases and controls to determine whether the exposure and health outcome correlate.

This type of study design is often used in epidemiology and can provide valuable information about potential risk factors for a particular disease or condition.

When to Use Correlational Research

There are a number of situations where researchers might opt to use a correlational study instead of some other research design.

Correlational research can be used to investigate a wide range of psychological phenomena, including the relationship between personality traits and academic performance, the association between sleep duration and mental health, and the correlation between parental involvement and child outcomes. 

To Generate Hypotheses

Correlational research can also be used to generate hypotheses for further research by identifying variables that are associated with each other.

To Investigate Variables Without Manipulating Them

Researchers should use correlational research when they want to investigate the relationship between two variables without manipulating them. This type of research is useful when the researcher cannot or should not manipulate one of the variables or when it is impossible to conduct an experiment due to ethical or practical concerns. 

To Identify Patterns

Correlational research allows researchers to identify patterns and relationships between variables, which can inform future research and help to develop theories. However, it is important to note that correlational research does not prove that one variable causes changes in the other.

While correlational research has its limitations, it is still a valuable tool for researchers in many fields, including psychology, sociology, and education.

How to Collect Data in Correlational Research

Researchers can collect data for correlational research in a few different ways. To conduct correlational research, data can be collected using the following:

• Surveys : One method is through surveys, where participants are asked to self-report their behaviors or attitudes. This approach allows researchers to gather large amounts of data quickly and affordably.
• Naturalistic observation : Another method is through observation, where researchers observe and record behaviors in a natural or controlled setting. This method allows researchers to learn more about the behavior in question and better generalize the results to real-world settings.
• Archival, retrospective data : Additionally, researchers can collect data from archival sources, such as medical, school records, official records, or past polls. 

The key is to collect data from a large and representative sample to measure the relationship between two variables accurately.

Pros and Cons of Correlational Research

There are some advantages of using correlational research, but there are also some downsides to consider.

• One of the strengths of correlational research is its ability to identify patterns and relationships between variables that may be difficult or unethical to manipulate in an experimental study. 
• Correlational research can also be used to examine variables that are not under the control of the researcher , such as age, gender, or socioeconomic status. 
• Correlational research can be used to make predictions about future behavior or outcomes, which can be valuable in a variety of fields.
• Correlational research can be conducted quickly and inexpensively , making it a practical option for researchers with limited resources. 
• Correlational research is limited by its inability to establish causality between variables. Correlation does not imply causation, and it is possible that a third variable may be influencing both variables of interest, creating a spurious correlation. Therefore, it is important for researchers to use multiple methods of data collection and to be cautious when interpreting correlational findings.
• Correlational research relies heavily on self-reported data , which can be biased or inaccurate.
• Correlational research is limited in its ability to generalize findings to larger populations, as it only measures the relationship between two variables in a specific sample.

Frequently Asked Questions About Correlational Research

What are the main problems with correlational research.

Some of the main problems that can occur in correlational research include selection bias, confounding variables. and misclassification.

• Selecting participants based on their exposure to an event means that the sample might be biased since the selection was not randomized.
• Correlational studies may also be impacted by extraneous factors that researchers cannot control.
• Finally, there may be problems with how accurately data is recorded and classified, which can be particularly problematic in retrospective studies.

What are the variables in a correlational study?

In a correlational study, variables refer to any measurable factors being examined for their potential relationship or association with each other. These variables can be continuous (meaning they can take on a range of values) or categorical (meaning they fall into distinct categories or groups).

For example, in a study examining the correlation between exercise and mental health, the independent variable would be exercise frequency (measured in times per week), while the dependent variable would be mental health (measured using a standardized questionnaire).

What is the goal of correlational research?

The goal of correlational research is to examine the relationship between two or more variables. It involves analyzing data to determine if there is a statistically significant connection between the variables being studied.

Correlational research is useful for identifying patterns and making predictions but cannot establish causation. Instead, it helps researchers to better understand the nature of the relationship between variables and to generate hypotheses for further investigation.

How do you identify correlational research?

To identify correlational research, look for studies that measure two or more variables and analyze their relationship using statistical techniques. The results of correlational studies are typically presented in the form of correlation coefficients or scatterplots, which visually represent the degree of association between the variables being studied.

Correlational research can be useful for identifying potential causal relationships between variables but cannot establish causation on its own.

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research . Nurse Researcher . 2016;23(6):20-25. doi10.7748/nr.2016.e1382

Lau F. Chapter 12 Methods for Correlational Studies . University of Victoria; 2017.

Mitchell TR. An evaluation of the validity of correlational research conducted in organizations . The Academy of Management Review . 1985;10(2):192. doi:10.5465/amr.1985.4277939

Seeram E. An overview of correlational research . Radiol Technol . 2019;91(2):176-179.

There Are Three Types of Relationship Histories

New research examines how relationship patterns are linked to happiness..

Posted July 11, 2020 | Reviewed by Abigail Fagan

A large body of research suggests that individuals in long-term committed romantic relationships are happier than those who are not. However, critics of this work, such as fellow Psychology Today blogger and researcher Bella DePaulo , have long argued that this take is overly simplistic . Happily married people may be the happiest. But those who have been divorced or in bad marriages don't necessarily fare so well. She posits that singles have gotten a bad rap, and that in fact, lifelong singles are often quite content and have fulfilling social lives and relationships outside of the romantic context.

But past research on this topic typically just takes a snapshot of people's happiness at a given moment in time — a moment when they may be married or single. How do these relationship histories — marriages, break-ups, singlehood — relate to people's levels of happiness over their adult life? New research by Mariah Purol and colleagues is the first to examine relationship histories over people's entire lifespan and examine their connection to well-being later in life.

What are the patterns of relationship histories?

The researchers examined data from a nationally representative sample of over 7,000 U.S. adults first surveyed in 1968 and followed up with through the present. This allowed the researchers to determine the various changes in people's relationship histories over several decades. First, the researchers wanted to determine if there were certain common types of relationship histories. At any point in the survey, respondents could be single (never married), married, divorced, separated, or widowed. The data revealed three distinct types of people:

• Consistently-married: These people were married to the same person for most of their adult lives, and made up the biggest group, 79% of the sample.
• Consistently-single: These people were single for most of their adult lives. This was the smallest group, representing 8% of the sample.
• Varied-histories: These people moved in and out of different relationship status (e.g., divorced then single, multiple marriages and divorces). This group represented 13% of the sample.

The main factor behind this classification was actually how long people had been in each marital state . For example, the consistently-married group included people who were divorced or widowed after a long marriage . Similarly, the consistently-single group included people who had shorter periods where they were married and then became divorced or widowed, but they were single for most of their adult life.

How is relationship history connected to happiness?

Close to half of the respondents in the survey also completed a measure of well-being. They answered one simple question that asked them to rate how satisfied they felt with their life, as a whole. The researchers then compared the current happiness of these three groups.

The happiest group was the consistently-married group, who, on average, were slightly happier than the consistently-single or varied histories groups. The results also showed that the consistently-single and varied-histories groups were equally satisfied with their lives. Because level of education and gender are associated with marriage and re-marriage rates as well as life satisfaction, the researchers also checked that these differences were not explained by the respondents' educational attainment or gender. It should be noted that these differences in happiness between the consistently-married and the other two groups were fairly small. This makes sense, given the many, many factors that affect well-being, including the quality of the romantic relationships themselves.

The researchers speculate about different reasons why the varied-histories and consistently-single groups were equally happy later in life. Singlehood can make people more socially isolated, but so can divorce. And divorce is a major stressor that can have a lasting impact on your well-being. It is also possible that people who experience more interpersonal difficulties, in general, are less happy and also less likely to find themselves consistently coupled.

This study leaves several questions unanswered.

The data the researchers had available only measured official marital status, so coupled individuals who were not married were lumped together with uncoupled singles. This may minimize differences between these groups, since a "single" person in a long-term committed relationship may be in a more similar situation to a married person than to a single, uncoupled, person.

People are single for different reasons. Some people are conflict-avoidant and find romantic relationships messy and stressful and are just as happy being single , whereas other singles don't mind the messiness as much and really long for a relationship. Research shows that singles with satisfying social lives and close friendships that are important to them value romantic relationships less, while singles whose social lives are lacking are more likely to have a strong desire to couple up. If these individual differences were taken into account, we would likely find subgroups of singles that differed considerably in their overall happiness. We also might find that singles with a strong desire for relationships might end up in less satisfying marriages, and be more likely to get divorced.

The quality of one's romantic relationship is also a major factor in well-being. Not only is marital satisfaction linked to life satisfaction, but the importance of martial satisfaction in determining life satisfaction has been increasing over time.

We still don't know if long-lasting marriages cause people to be happy or not. Because the researchers only looked at life satisfaction at one point in time, they couldn't see how people's satisfaction changed as their relationship status changed. We also don't know if there were other important differences between these relationship trajectory groups — such as differences in personality or psychological adjustment — that explain their differing levels of life satisfaction. But we do know that, on average, people with long-lasting marriages are more likely to experience long-term happiness.

Gwendolyn Seidman, Ph.D., is a professor of psychology at Albright College.

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Correlation Studies in Psychology Research

Determining the relationship between two or more variables.

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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• Characteristics

Potential Pitfalls

Frequently asked questions.

A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables.

A correlation refers to a relationship between two variables. Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation.

There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation. Researchers can present the results using a numerical value called the correlation coefficient, a measure of the correlation strength. It can range from –1.00 (negative) to +1.00 (positive). A correlation coefficient of 0 indicates no correlation.

• Positive correlations : Both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation.
• Negative correlations : As the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation.
• No correlation : There is no relationship between the two variables. A correlation coefficient of 0 indicates no correlation.

Characteristics of a Correlational Study

Correlational studies are often used in psychology, as well as other fields like medicine. Correlational research is a preliminary way to gather information about a topic. The method is also useful if researchers are unable to perform an experiment.

Researchers use correlations to see if a relationship between two or more variables exists, but the variables themselves are not under the control of the researchers.

While correlational research can demonstrate a relationship between variables, it cannot prove that changing one variable will change another. In other words, correlational studies cannot prove cause-and-effect relationships.

When you encounter research that refers to a "link" or an "association" between two things, they are most likely talking about a correlational study.

Types of Correlational Research

There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.

Naturalistic Observation

The naturalistic observation method involves observing and recording variables of interest in a natural setting without interference or manipulation.  

Can inspire ideas for further research

Option if lab experiment not available

Variables are viewed in natural setting

Can be time-consuming and expensive

Extraneous variables can't be controlled

No scientific control of variables

Subjects might behave differently if aware of being observed

This method is well-suited to studies where researchers want to see how variables behave in their natural setting or state.   Inspiration can then be drawn from the observations to inform future avenues of research.

In some cases, it might be the only method available to researchers; for example, if lab experimentation would be precluded by access, resources, or ethics. It might be preferable to not being able to conduct research at all, but the method can be costly and usually takes a lot of time.  

Naturalistic observation presents several challenges for researchers. For one, it does not allow them to control or influence the variables in any way nor can they change any possible external variables.

However, this does not mean that researchers will get reliable data from watching the variables, or that the information they gather will be free from bias.

For example, study subjects might act differently if they know that they are being watched. The researchers might not be aware that the behavior that they are observing is not necessarily the subject's natural state (i.e., how they would act if they did not know they were being watched).

Researchers also need to be aware of their biases, which can affect the observation and interpretation of a subject's behavior.  

Surveys and questionnaires are some of the most common methods used for psychological research. The survey method involves having a  random sample  of participants complete a survey, test, or questionnaire related to the variables of interest.   Random sampling is vital to the generalizability of a survey's results.

Cheap, easy, and fast

Can collect large amounts of data in a short amount of time

Results can be affected by poor survey questions

Results can be affected by unrepresentative sample

Outcomes can be affected by participants

If researchers need to gather a large amount of data in a short period of time, a survey is likely to be the fastest, easiest, and cheapest option.  

It's also a flexible method because it lets researchers create data-gathering tools that will help ensure they get the information they need (survey responses) from all the sources they want to use (a random sample of participants taking the survey).

Survey data might be cost-efficient and easy to get, but it has its downsides. For one, the data is not always reliable—particularly if the survey questions are poorly written or the overall design or delivery is weak.   Data is also affected by specific faults, such as unrepresented or underrepresented samples .

The use of surveys relies on participants to provide useful data. Researchers need to be aware of the specific factors related to the people taking the survey that will affect its outcome.

For example, some people might struggle to understand the questions. A person might answer a particular way to try to please the researchers or to try to control how the researchers perceive them (such as trying to make themselves "look better").

Sometimes, respondents might not even realize that their answers are incorrect or misleading because of mistaken memories .

Archival Research

Many areas of psychological research benefit from analyzing studies that were conducted long ago by other researchers, as well as reviewing historical records and case studies.

For example, in an experiment known as  "The Irritable Heart ," researchers used digitalized records containing information on American Civil War veterans to learn more about post-traumatic stress disorder (PTSD).

Large amount of data

Can be less expensive

Researchers cannot change participant behavior

Can be unreliable

Information might be missing

No control over data collection methods

Using records, databases, and libraries that are publicly accessible or accessible through their institution can help researchers who might not have a lot of money to support their research efforts.

Free and low-cost resources are available to researchers at all levels through academic institutions, museums, and data repositories around the world.

Another potential benefit is that these sources often provide an enormous amount of data that was collected over a very long period of time, which can give researchers a way to view trends, relationships, and outcomes related to their research.

While the inability to change variables can be a disadvantage of some methods, it can be a benefit of archival research. That said, using historical records or information that was collected a long time ago also presents challenges. For one, important information might be missing or incomplete and some aspects of older studies might not be useful to researchers in a modern context.

A primary issue with archival research is reliability. When reviewing old research, little information might be available about who conducted the research, how a study was designed, who participated in the research, as well as how data was collected and interpreted.

Researchers can also be presented with ethical quandaries—for example, should modern researchers use data from studies that were conducted unethically or with questionable ethics?

You've probably heard the phrase, "correlation does not equal causation." This means that while correlational research can suggest that there is a relationship between two variables, it cannot prove that one variable will change another.

For example, researchers might perform a correlational study that suggests there is a relationship between academic success and a person's self-esteem. However, the study cannot show that academic success changes a person's self-esteem.

To determine why the relationship exists, researchers would need to consider and experiment with other variables, such as the subject's social relationships, cognitive abilities, personality, and socioeconomic status.

The difference between a correlational study and an experimental study involves the manipulation of variables. Researchers do not manipulate variables in a correlational study, but they do control and systematically vary the independent variables in an experimental study. Correlational studies allow researchers to detect the presence and strength of a relationship between variables, while experimental studies allow researchers to look for cause and effect relationships.

If the study involves the systematic manipulation of the levels of a variable, it is an experimental study. If researchers are measuring what is already present without actually changing the variables, then is a correlational study.

The variables in a correlational study are what the researcher measures. Once measured, researchers can then use statistical analysis to determine the existence, strength, and direction of the relationship. However, while correlational studies can say that variable X and variable Y have a relationship, it does not mean that X causes Y.

The goal of correlational research is often to look for relationships, describe these relationships, and then make predictions. Such research can also often serve as a jumping off point for future experimental research. 

Heath W. Psychology Research Methods . Cambridge University Press; 2018:134-156.

Schneider FW. Applied Social Psychology . 2nd ed. SAGE; 2012:50-53.

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research .  Nurse Researcher . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382

Carpenter S. Visualizing Psychology . 3rd ed. John Wiley & Sons; 2012:14-30.

Pizarro J, Silver RC, Prause J. Physical and mental health costs of traumatic war experiences among civil war veterans .  Arch Gen Psychiatry . 2006;63(2):193. doi:10.1001/archpsyc.63.2.193

Post SG. The echo of Nuremberg: Nazi data and ethics .  J Med Ethics . 1991;17(1):42-44. doi:10.1136/jme.17.1.42

Lau F. Chapter 12 Methods for Correlational Studies . In: Lau F, Kuziemsky C, eds. Handbook of eHealth Evaluation: An Evidence-based Approach . University of Victoria.

Akoglu H. User's guide to correlation coefficients .  Turk J Emerg Med . 2018;18(3):91-93. doi:10.1016/j.tjem.2018.08.001

Price PC. Research Methods in Psychology . California State University.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

The scope of the study is defined at the start of the study. It is used by researchers to set the boundaries and limitations within which the research study will be performed.

The unit of analysis refers to the main parameter that you’re investigating in your research project or study.

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6.2 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot  manipulate the independent variable because it is impossible, impractical, or unethical. For example, while I might be interested in the relationship between the frequency people use cannabis and their memory abilities I cannot ethically manipulate the frequency that people use cannabis. As such, I must rely on the correlational research strategy; I must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis use is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity. In contrast, correlational studies typically have low internal validity because nothing is manipulated or control but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .  These converging results provide strong evidence that there is a real relationship (indeed a causal relationship) between watching violent television and aggressive behavior.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. A  negative relationship  is one in which higher scores on one variable tend to be associated with lower scores on the other. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

Figure 6.3 Scatterplot Showing a Hypothetical Positive Relationship Between Stress and Number of Physical Symptoms. The circled point represents a person whose stress score was 10 and who had three physical symptoms. Pearson’s r for these data is +.51.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson’s  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

Figure 6.4 Range of Pearson’s r, From −1.00 (Strongest Possible Negative Relationship), Through 0 (No Relationship), to +1.00 (Strongest Possible Positive Relationship)

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

Figure 6.5 Hypothetical Nonlinear Relationship Between Sleep and Depression

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Figure 6.6 Hypothetical Data Showing How a Strong Overall Correlation Can Appear to Be Weak When One Variable Has a Restricted Range.The overall correlation here is −.77, but the correlation for the 18- to 24-year-olds (in the blue box) is 0.

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations.

Some excellent and funny examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who determined how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in (because, again, it was the researcher who determined how much they exercised). Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Key Takeaways

• Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
• Correlation does not imply causation. A statistical relationship between two variables,  X  and  Y , does not necessarily mean that  X  causes  Y . It is also possible that  Y  causes  X , or that a third variable,  Z , causes both  X  and  Y .
• While correlational research cannot be used to establish causal relationships between variables, correlational research does allow researchers to achieve many other important objectives (establishing reliability and validity, providing converging evidence, describing relationships and making predictions)
• Correlation coefficients can range from -1 to +1. The sign indicates the direction of the relationship between the variables and the numerical value indicates the strength of the relationship.
• A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
• A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
• An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
• A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
• A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

2. Practice: For each of the following statistical relationships, decide whether the directionality problem is present and think of at least one plausible third variable.

• People who eat more lobster tend to live longer.
• People who exercise more tend to weigh less.
• College students who drink more alcohol tend to have poorer grades.
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

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We have shown how making formal loans available to a communitychanges both its borrowing and lending networks as well as the relationships that people have through which they share information,even though the information networks are not directly impacted by borrowing money.   This helped us develop new models of how networks form, and show how these can explain the fact that a simple financial loan program has a very widespread impact on the social structure of a community.

We have also examined how the different types of relationships that people have correlate with each other, and how people's positions differ in different networks.  

We have also examined the formation of college students' networks -- both friendship networks and study networks -- and how they change over time.  We have also examined how college students' networks relate to their personalities.   We found substantial homophily:  a tendency of students to be friends with those of the similar ethnicity, gender, and risk aversion; and show that this increases over time.

We have analyzed how banks and other financial institutions' incentives to make risky investments affects the overall financial network.   We found that they take on excessive risk and correlate their risk with other banks.  We provided insight into when it is useful to regulate banks' investments and when and how it is best to bail out banks in distress.   We show how the bailouts can be optimized as a function of cycles in a financial network and have provided definitions of financial centrality that can be useful in stress-testing.

Last Modified: 09/09/2020 Modified by: Matthew O Jackson

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7.2 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research.

What Is Correlational Research?

Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research.

The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). But because they could not manipulate the number of daily hassles their participants experienced, they had to settle for measuring the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 7.2 “Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists” shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 7.2 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data. A third, survey research, is discussed in its own chapter.

Naturalistic Observation

Naturalistic observation is an approach to data collection that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). It could involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are often not aware that they are being studied. Ethically, this is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

Researchers Robert Levine and Ara Norenzayan used naturalistic observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in the United States and Japan covered 60 feet in about 12 seconds on average, while people in Brazil and Romania took close to 17 seconds.

Because naturalistic observation takes place in the complex and even chaotic “real world,” there are two closely related issues that researchers must deal with before collecting data. The first is sampling. When, where, and under what conditions will the observations be made, and who exactly will be observed? Levine and Norenzayan described their sampling process as follows:

Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities. (p. 186)

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.

The second issue is measurement. What specific behaviors will be observed? In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance. Often, however, the behaviors of interest are not so obvious or objective. For example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979). But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

Naturalistic observation has revealed that bowlers tend to smile when they turn away from the pins and toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

sieneke toering – bowling big lebowski style – CC BY-NC-ND 2.0.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This is the issue of interrater reliability. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

Archival Data

Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005). In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988). In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as college students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as college students, the healthier they were as older men. Pearson’s r was +.25.

This is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

• Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
• Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behavior in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.

Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why.

• An educational researcher compares the academic performance of students from the “rich” side of town with that of students from the “poor” side of town.
• A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
• A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
• An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
• A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
• A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4 , 1–39.

Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553.

Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205.

Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110.

Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Non-Experimental Research

29 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson's  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

• Nicholas Cage and Pool Drownings  © Tyler Viegen is licensed under a  CC BY (Attribution)  license
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X  and  Y , are statistically related either because X  causes  Y, or because  Y  causes  X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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In psychedelic therapy, clinician-patient bond may matter most

Study links relationship strength to reduced depression for up to 1 year.

Drug effects have dominated the national conversation about psychedelics for medical treatment, but a new study suggests that when it comes to reducing depression with psychedelic-assisted therapy, what matters most is a strong relationship between the therapist and study participant. 

Researchers analyzed data from a 2021 clinical trial that found psilocybin (magic mushrooms) combined with psychotherapy in adults was effective at treating major depressive disorder. 

Data included depression outcomes and participant reports about their experiences with the drugs and their connection with therapists. Results showed that the stronger the relationship between a participant and clinician – called a therapeutic alliance – the lower the depression scores were one year later. 

“What persisted the most was the connection between the therapeutic alliance and long-term outcomes, which indicates the importance of a strong relationship,” said lead author Adam Levin , a psychiatry and behavioral health resident  in The Ohio State University  College of Medicine . 

Past research has consistently found that as mental health treatments changed, a trusting relationship between clients and clinicians has remained key to better outcomes, said senior author  Alan Davis , associate professor and director of the  Center for Psychedelic Drug Research and Education  in The Ohio State University  College of Social Work . 

“This concept is not novel. What is novel is that very few people have explored this concept as part of psychedelic-assisted therapy,” Davis said. “This data suggests that psychedelic-assisted therapy relies heavily on the therapeutic alliance, just like any other treatment.” 

The study was published recently in the journal PLOS ONE . 

Twenty-four adults who participated in the trial received two doses of psilocybin and 11 hours of psychotherapy. Participants completed the therapeutic alliance questionnaire, assessing the strength of the therapist-participant relationship, three times: after eight hours of preparation therapy and one week after each psilocybin treatment. 

Participants also completed questionnaires about any mystical and psychologically insightful experiences they had during the drug treatment sessions. Their depression symptoms were assessed one week, four weeks, and up to one year after the trial’s end. 

The analysis showed that the overall alliance score increased over time and revealed a correlation between a higher alliance score and more acute mystical and/or psychologically insightful experiences from the drug treatment. Acute effects were linked to lower depression at the four-week point after treatment, but were not associated with better depression outcomes a year after the trial. 

“The mystical experience, which is something that is most often reported as related to outcome, was not related to the depression scores at 12 months,” Davis said. “We’re not saying this means acute effects aren’t important – psychological insight was still predictive of improvement in the long term. But this does start to situate the importance and meaning of the therapeutic alliance alongside these more well-established effects that people talk about.”  

That said, the analysis showed that a stronger relationship during the final therapy preparation session predicted a more mystical and psychologically insightful experience – which in turn was linked to further strengthening the therapeutic alliance. 

“That’s why I think the relationship has been shown to be impactful in this analysis – because, really, the whole intervention is designed for us to establish the trust and rapport that’s needed for someone to go into an alternative consciousness safely,” Davis said. 

Considering that psychedelics carry a stigma as Schedule I drugs under the Controlled Substances Act , efforts to minimize negative experiences in future studies of their therapeutic potential should be paramount – and therapy is critical to creating a supportive environment for patients, the authors said. 

This study ideally will help clearly position psychedelics treatment as a psychotherapeutic intervention moving forward – rather than its primary purpose being administration of a drug, Levin said. 

“This isn’t a case where we should try to fit psychedelics into the existing psychiatric paradigm – I think the paradigm should expand to include what we’re learning from psychedelics,” Levin said. “Our concern is that any effort to minimize therapeutic support could lead to safety concerns or adverse events. And what we showed in this study is evidence for the importance of the alliance in not just preventing those types of events, but also in optimizing therapeutic outcomes.” 

This work was supported by the Center for Psychedelic and Consciousness Research, funded by the Steven & Alexandra Cohen Foundation, the RiverStyx Foundation and private donors. It was also supported by the Center for Psychedelic Drug Research and Education (CPDRE), funded by anonymous donors. 

Additional co-authors are Rafaelle Lancelotta, Nathan Sepeda and Theodore Wagener of Ohio State, and Natalie Gukasyan, Sandeep Nayak, Frederick Barrett and Roland Griffiths of the Center for Psychedelic and Consciousness Research at Johns Hopkins University, where Davis is an affiliate.

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Researcher–researched relationship in qualitative research: Shifts in positions and researcher vulnerability

Målfrid råheim.

1 Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway

Liv Heide Magnussen

2 Department of Occupational Therapy, Physiotherapy and Radiography, Bergen University College, Bergen, Norway

Ragnhild Johanne Tveit Sekse

3 Department of Obstetrics and Gynecology, Haukaland University Hospital, Bergen, Norway

4 Department of Clinical Science, University of Bergen, Bergen, Norway

5 Haraldsplass Deaconess University College, Bergen, Norway

Åshild Lunde

Torild jacobsen.

6 DIALOG, Bergen, Norway

Astrid Blystad

The researcher role is highly debated in qualitative research. This article concerns the researcher-researched relationship.

A group of health science researchers anchored in various qualitative research traditions gathered in reflective group discussions over a period of two years.

Efforts to establish an anti-authoritarian relationship between researcher and researched, negotiation of who actually “rules” the research agenda, and experiences of shifts in “inferior” and “superior” knowledge positions emerged as central and intertwined themes throughout the discussions. The dual role as both insider and outsider, characteristic of qualitative approaches, seemed to lead to power relations and researcher vulnerability which manifested in tangible ways.

Shifting positions and vulnerability surfaced in various ways in the projects. They nonetheless indicated a number of similar experiences which can shed light on the researcher-researched relationship. These issues could benefit from further discussion in the qualitative health research literature.

This article begins and ends in the reflexive turn of qualitative research (Altheide & Johnsen, 1994 ). Reflexivity concerns thoughtful, analytic self-awareness of researchers’ experiences, reasoning, and overall impact throughout the research process. Pre-understanding and openness, closeness and distance, the co-construction and situating of knowledge, trustworthiness and integrity, power relations, and ethical dilemmas are given primacy in the qualitative methodology (Dahlberg, Dahlberg, & Nyström, 2008 ; Finley, 2002 ; Gergen & Gergen, 2000 ; Kvale & Brinkmann, 2009 ; Lincoln & Guba, 1985 ). In this article, we reflect on the role of the researcher in the process of knowledge production as it emerged in a series of reflective group discussions between researchers based in the health sciences. Focusing on our own research experiences, our aim was to explore systematically our experience of fluctuations in “superior” and “inferior” knowledge positions and the related researcher vulnerability that emerged.

The relation between researcher(s) and researched has been a recurrent concern in the methodology literature. The privileged position of the researcher vis à vis the researched has been strongly emphasized. The inherent power imbalance between the parties and the ethical concerns pertaining to this imbalance are commonly dwelled upon, with particular attention to the predetermined asymmetric roles between the researcher and the researched. However, the literature simultaneously emphasizes that qualitative traditions all have “…a common epistemological ground: the researcher determination to minimize the distance and separateness of researcher-participant relationships,” as phrased by Karnieli-Miller, Strier, and Pessach ( 2009 , p. 279). Furthermore, it is argued that defining what knowledge is to count in a concrete researcher–researched encounter is not necessarily the sole privilege of the researcher because participants bring their own agenda to the research situation (Karnieli-Miller et al., 2009 ). In the ethnographic literature, much attention is paid to the complexity of the role of the researcher as observer, as well as the contextual understanding of potentially opposing perspectives between the researcher and researched (Adler & Adler, 2002 ; Angrosino & Mays De Pérez, 2000 ; Hammersley & Atkinson, 1983/1992 ; Vitus, 2008 ). Role conflict related to being both an insider and an outsider, and the experience of resistance from the research participant are related themes that seem to call for further nuancing of the representations of inherent asymmetric relations (see, for instance, Burns, Fenwick, Schmied, & Sheenan, 2012 ; Dwyer & Buckle, 2009 ; Jack, 2008 ; Lalor, Begley, & Devane, 2006 ; Lee, 1993 ; Malacrida, 2007 ).

The insider-outsider perspectives are not new, but have been hotly debated for decades (see for example, Emerson & Pollner, 1988 ; Garfinkel, 1984 ; Lynch & Woolgar, 1988 ; Pollner & Emerson, 2001 ). The debates revolve around researcher positionality, what it means to be an insider or outsider in a given study setting, and how the researcher's status is negotiated throughout the research processes. Laura Nader ( 1969/1972 ) launched the dichotomy of studying up/studying down pertaining to researcher positionality in her classical work, holding that studying up contributes in vital ways to an understanding of the processes by which power and responsibility are exercised. Beyond informing our understanding of patterns of distribution, value, and power, Nader's call for studying up has posed new questions pertaining to the research relationship, and has been widely drawn upon. The researcher “studying up” may experience him- or herself moving into a research field of less “control” or “power,” so the approach calls for new reflections on the issues of access, methodology, attitudes, and ethics (Nader, 1969/1972 , p. 301). Reflections on studying up or down may enhance the understanding of researcher experiences in our article.

We should emphasize that we fundamentally acknowledge the existence of an inherent imbalance in the relation between the researcher(s) and the researched in qualitative health research. Despite this, we will make a modest attempt to add to the debate about whether the researcher is by definition located in a privileged and superior position vis-à-vis the research subjects. Our aim is to use examples from our own research projects to reveal shifts in “superior” and “inferior” positions in researcher–researched relationships, in which ethical dilemmas and vulnerability surface on the part of the researcher. A further aim is to explore whether experiences from projects based in different qualitative traditions can shed additional light on the researcher–researched relationship.

Reflective group discussions

The authors of this article are senior researchers (two professors, four associate professors), all women, who gathered in six reflexive group discussions over a period of 2 years. The group participants had backgrounds in nursing (two), physiotherapy (two), genetic counselling (one), and in acting/drama as a pedagogical tool (one). One of the nurses also held a PhD in social anthropology. They were colleagues in research and/or in the running of a master's programme in health science. The participants reflected on their role as researchers in their earlier research projects. The reflective group discussions took the form of dialogues, aimed at letting multiple voices surface.

The first author developed the project idea, invited the participants, and moderated the group discussions. In order to delve deeper into methodologically important aspects of the researcher role in qualitative health research, it was deemed important that all the group participants were anchored in health science and experienced in traditions in which qualitative approaches are highly valued. Representing a diversity of research designs and traditions was also deemed important, as methodological challenges may surface differently in different designs (see Table I ).

Overview of research projects, which worked as empirical examples in the reflective group discussions, including methods, traditions, and authors.

Complete references are included in the list at the end of the article.

In the first group discussion, we openly shared our experiences as researchers in our own projects. No specific themes were introduced by the moderator, but the participants were encouraged to spontaneously bring up themes they considered important. Each participant then chose one research project/empirical example from which she made drawings revealing important topics of her own experience as a researcher. We did not put any restrictions on ourselves as to the drawings, which were meant as a creative way to come up with preliminary discussion themes. The participants worked in pairs to consider at some length what was communicated in the drawings about the researcher role. The crux of the content was later shared and discussed collectively. The discussion was recorded and transcribed. A preliminary analysis of the concrete researcher experiences and meta-reflections from the discussion was performed by the first author. The transcripts and the drawings were circulated to the group participants before the second group discussion, together with preliminary themes and emerging patterns based on the first author's readings of the transcript. Already, at this stage, shifts in the dynamics of the relations between interviewer and interviewee, and researcher vulnerability, were emerging as preliminary themes.

During the second group discussion, based on the preliminary themes deemed most interesting, a decision was reached to deepen our knowledge about the ways in which our own experiences of research-related relations seemed to move us beyond established knowledge of the imbalance in which the researcher holds a privileged position.

Between the second and third group discussion, transcripts from the first two group discussions were read and analysed by first and last authors, looking for concrete examples and meta-reflections that deepened the key issues chosen by the group. The different examples of researcher experiences revolved around more or less explicitly emerging shifts and ambivalence related to knowing and non-knowing positions of the parties in the phase of co-producing the research material. Examples of negotiations, related to whose agenda was directing the production of the research material, emerged in diverse ways. The examples were categorized under headings highlighting the social status of the participants and the researchers, as well as the knowledge positions of the different parties pertaining to the phenomena under study.

During the third group discussion, the analysis was discussed and key issues further developed. This included discussions pertaining to problematizing the notions of “studying up and down” on the basis of the empirical examples. Literature on researcher reflexivity in qualitative health research was familiar to the participants at the outset of the reflexive group discussions, but a fresh literature search was undertaken at this stage, focusing on themes related to the imbalance in the researcher–researched relationship in qualitative research, and researcher vulnerability.

In the fourth group discussion, we once more worked in pairs to develop our meta-reflections around our own experiences as researchers in the six chosen research projects, summing up and discussing the key issues. Notes were taken during this session as well, and a post-group summary was written.

Between the fourth and the fifth group discussion, the participants worked in pairs or separately to write up their researcher experiences, based on the concrete projects. Drafts of textual presentations were sent to the first author, who wrote a comprehensive preliminary paper that was circulated to the participants.

Discussions during the fifth and the sixth group gatherings concerned revision and refinement of the text. The first and the last author had continuous discussions during the writing process.

The research projects—Differences and common ground

The projects from which the meta-reflections about experiences were drawn were different with regard to aims, research tradition, and research design. However, as stated above, they were all located within the qualitative research tradition of the health sciences, and were epistemologically grounded in the humanistic or social science traditions, as can be seen in Table I .

Two projects (empirical example 1 and 2) were anchored in a phenomenological life-world perspective, and were based on research material produced through in-depth interviews. The projects shared a common interest in exploring phenomena concerning living in “a changed world” related to profound changes in health condition. Both studies involved reflexive practices to create an awareness of the researchers’ pre-understandings. In the first study (Råheim & Håland 2006 ), women with fibromyalgia were interviewed about living with chronic pain. The second study (Sekse, Råheim, Blaaka, & Gjengedal, 2009 ; Sekse, Gjengedal, & Råheim, 2012 ) focused on the experiences of long-term survivors of gynaecological cancer. The researcher–participant relationships in these two projects might be characterized as essentially asymmetrical and “studying down.” Nevertheless, important shifts in who defined the relevant body of knowledge were experienced.

Two studies produced data through focus group interviews (empirical example 3 and 4), research material substantially depending on the interaction within the groups. Both projects aimed to gather knowledge about how to handle challenging cases and ethical dilemmas in professional practice, and they were both anchored in a hermeneutic tradition. One project focused on challenges and problematic aspects of genetic counselling practice (Lunde, Nordin, & Strand, 2014 ). The second (Nilsen, Werner, Maeland, Eriksen, & Magnussen, 2011 ) focused on sick-leave decision-making based on general practitioners’ (GPs’) consultations with patients who have complex health issues. In both studies, the researcher was the group moderator. The relationship between researcher and researched in these two studies can be characterized as asymmetrical, such that the asymmetry worked both ways: the researchers held a “superior” position in relation to the participants in terms of planning and leading the project, while the participants/professionals held a “superior” position pertaining to professionally based knowledge within the actual field of research. These studies also actualize studying the privileged, the experts in the field, or “studying up.”

The fifth study (empirical example 5) was a classical field study anchored in ethnography (Blystad, 1999 ; Blystad & Rekdal, 2004 ). The researcher lived in a Tanzanian pastoral community for a period of more than 2 years, exploring maternal practices related to pregnancy, childbirth, and infant feeding. The aim was to generate knowledge on the perceptions and practices related to the reproductive process in a community with substantial cultural emphasis on fertility but in a context of extreme marginality and a high prevalence of infant death. Shifting between positions in this project is based on experiences with the participant observer role, a role located at the heart of ethnography. That implied continuous shifts between “inferior,” non-knowledgeable, insider positions and “superior,” knowledgeable outsider positions.

The last study (empirical example 6) was a pedagogical project anchored in the context of health education. A model of group-based communication training for medical students was developed with the help of simulated patients (SP) and theatrical devices. Theoretical perspectives were grounded in pedagogy and in theatre science. In the sub-study referred to below (Jacobsen & Baerheim, 2005 ), the researcher simulated a particular patient during the training session while medical students acted as the patient's GP. How the students experienced the communication training and what they learnt was evaluated afterwards. The dual role as researcher and SP provides the starting point for reflections on researcher vulnerability from this project.

Knowledge positions and researcher vulnerability—Shifts and ambivalence

In the following, we will highlight and reflect on shifts related to knowing and not-knowing positions between the researcher and the researched that emerged during discussions. These shifts were intertwined with the power of defining the relevant body of knowledge. In particular, we discuss transitions in terms of who appears to set the agenda or define the terms, and we discuss the vulnerability inherent in the researcher role during the co-production of research material.

Distracted by illness stories

A prime example of partly losing control of the research agenda from the in-depth interview studies (empirical example 1 and 2) was related to an experience of being diverted by stories of illness. Of interest from the researchers’ points of view was the current experience of living with chronic muscle pain (example 1) and living as a long-term survivor of gynaecological cancer (example 2). The study participants, however, seemed to seize the opportunity to tell their “full” illness stories to someone who had the time to listen, stories that were accompanied by strong emotions. The emotions were vital in this context and made it difficult to interrupt participants. It was unclear whether or not the illness stories had been accepted in the participants’ many encounters with health care workers; for some, their illness stories had been ignored. The context of encounters with health care workers in the actual projects seemed vital. Both researchers were experienced qualitative researchers. Nevertheless, the subjects’ wish to reveal a high level of suffering, and the intensity of the illness stories took the researchers by surprise. The researchers felt ambivalent because the lengthy illness stories occupied more time than had been initially planned. These stories moved the focus of the interviews beyond the research agenda, but the ambiguity about when and how to interrupt the interviewees was experienced as challenging.

This illustrates a particular challenge of participants’ bringing their own agenda into the interviews (Karnieli-Miller et al., 2009 ). The narrators in these cases talked about what they felt most strongly, including experiences more or less relevant to the study in question. A need to get the illness story off one's chest, finally to be listened to, might indeed have been a factor motivating the patients to take part in the studies. If the researcher is also a health care worker, this knowledge can further fuel the fire of disclosure. The researcher and health care worker roles can become blurred in the research interview situation (Hewitt, 2007 ; Jack, 2008 ; Tee & Lathlean, 2004 ). The participants’ perceptions of the interviewer, including her professional role, can influence the interaction, and hence the information that is revealed (Richards & Emslie, 2000 ). In one of the projects referred to above, the women who participated did not know about the researcher's professional role as a physiotherapist. In the other, the participants did know that the researcher was also a nurse. The fact that the studies were based in the health care establishment (University Hospital, Faculty of Medicine) might have influenced the participants’ conduct in both studies. Furthermore, the participants could have been motivated to elaborate on the suffering during the interview, as encouragement to reveal personal experiences could have a potential “therapeutic” dimension. Similarities between research interviews and therapeutic encounters have been recognized (Kvale & Brinkmann, 2009 ). Although therapeutic effects are rarely aimed at by researchers, attentive and empathic listening, and encouraging reflections on what is being expressed might be perceived by the participants as encouragement to narrate detailed tales of illness (Hewitt, 2007 ; Hutchinson, Wilson, & Wilson, 1994 ; Lowes & Gill, 2006 ; Richards & Emslie, 2000 ).

During the interview stage, the researcher is dependent on the participants’ willingness not only to take part, but also to share their experiences and thoughts about the topics in question (Karnieli-Miller et al., 2009 ). The researchers considered it important to listen to the illness stories, first and foremost to show respect, but also to gain the trust of the participants, which is essential for a constructive qualitative research encounter. Besides, illness stories might well bring about contextual insights of importance to the understanding of the phenomena to be explored, in our context to the understanding of living with chronic muscle pain or as long-term survivor after cancer. However, including the “full” illness stories had not been planned and it took time away from the key focus of the research.

The balance at play between knowing and non-knowing positions illustrates several points of interest. It is claimed, for instance by Kvale ( 1996 ), Brinkmann and Kvale ( 2005 ) that the empathic, caring, and empowering atmosphere of equality aimed at in qualitative interviews may conceal power differences and hence be ethically questionable. The researcher's dependence on the trust of participants to get their stories can indicate that the dialogue taking place is used as a strategic instrument that works as a cover for the exercise of research-related power. We have indicated that listening to the illness stories of the research participants was important for establishing mutual trust, which might have been a gateway for accomplishing the researchers’ agendas. As such, listening included a strategic element, which we surely acknowledge is a part of qualitative research interviews. However, being guided by respect and ethically sound reasoning, as well as constantly operating through an open and dwelling attitude, contradicts the notion of attentive listening as “a fake.” Indeed, we will argue that it would have been impossible to gain mutual trust and rich descriptions if the researchers had not been genuinely interested in the experiences of the researched. According to phenomenological methodology, a genuine interest coupled with an attitude of openness and wonder that puts pre-understandings at risk, is essential in order to explore lived experience in any depth (Dahlberg et al., 2008 ; Van Manen, 1997 ). However, as we have seen in the cases above, genuine interest and attentive listening also risk paving the way for participants to reveal wells of sensitive information, as well as the risk of moving the interview away from the main research agenda. Difficult ethical choices had to be made during the interview situation. The challenges experienced have some general relevance for the art of in-depth interviewing.

The inherent researcher vulnerability in In-depth interviews

A common theme in the in-depth interview studies relates to researcher as well as participant vulnerability. Hewitt ( 2007 , p. 1151) underscores that moral questions can arise at any time during in-depth interviews, depending on the types of disclosure, unintended consequences of trust and emotional closeness, as well as varying competence in communication skills and ethically sound reasoning on the part of the researcher. Overly intrusive interviews mean exploitation, and might harm participants (Hammersley & Atkinson, 1983/1992 ; Kidd & Finleyson, 2006 ; Richards & Schwartz, 2002 ).

In the in-depth interview studies considered here, the researchers were involved in stories of great emotional intensity. They were challenged to catch and interpret signs and expressions, tones located beneath and between what was literally communicated in words, in order to make choices about the welfare of the woman/participant. In preparation for the interviews, raised awareness of the importance of not being intrusive was practised. However, both researchers in the in-depth interview studies were acquainted with feelings of guilt, and were touched deeply by the participants’ stories. It is claimed that it is necessary to be deeply absorbed by the participants’ expressions and empathically touched by participants’ to, at least partly, understand what might be at stake in the life-worlds of participants (Angel, 2013 ), and that such absorption is also paramount to ethically sound research (Malacrida, 2007 ). However, had we triggered reactions that could add to the women's burden in the long run? If self-disclosure meant re-opening wounds without the opportunity to work them through, it could potentially cause harm. On the other hand, sharing sensitive experiences might invoke relief, self-acknowledgement, and imply a possibility of looking at experiences anew (Hutchinson et al., 1994 ; Lowes & Gill, 2006 ). Participants who agree to take part in a study of this kind will, nevertheless, often be unprepared for what they are consenting to and what they may actually reveal (Richards & Schwartz, 2002 ). The process of qualitative health research is not always predictable for either participants or researchers (Kidd & Finleyson, 2006 ). Furthermore, what participants communicate just after the interview might later be reversed, adding to the complexity of these issues (Murphy & Dingwall, 2004 ). As stated by Hewitt ( 2007 , p. 1157), an acknowledgement of the complexities of researcher–researched relationships in in-depth interview studies implies being sensitive to the risks to participants, a continual concern. We fundamentally acknowledge this complexity, and find that enhanced ethical awareness on the part of the researcher is paramount.

Still, we will argue that there is an unsolvable dilemma implicit in in-depth interview studies, where aiming at rich descriptions is a key concern, often implying disclosure of sensitive topics, while at the same time ensuring that one does no harm to participants. We agree with Rager ( 2005 ), Lalor et al. ( 2006 ), Dunn ( 1991 ), Kidd and Finleyson ( 2006 ), and Malacrida ( 2007 ), who claim that researcher risk vulnerability regarding “compassion stress,” the danger of being emotionally drained. We will add that stress, accompanied by feelings of guilt, is underestimated in qualitative research generally. To explore knowledge about sensitive topics in peoples’ lives entails the “superiority” of the researcher position, but which pertaining to ethically demanding choices and emotional involvement nevertheless implies researcher vulnerability.

The challenge of hierarchy and status in group interviews with professionals

The participants in the group interviews (empirical examples 3 and 4) were highly qualified health professionals, indicating expert knowledge within the research topics of interest, and holding a superior social role compared to the patients in the in-depth interview studies. The researchers held a privileged position in terms of being the ones who were in charge of the research projects’ agenda. The researchers and the researched also possessed a shared body of knowledge by virtue of having similar or related professional roles. At the same time, a certain “inferiority” in terms of professional knowledge existed between the parties; a relatively newly educated genetic counsellor moderated group interviews with experienced genetic counsellors and geneticists (medical doctors specialized in genetics), and an experienced physiotherapist moderated group interviews with GPs. These studies illustrate research situations in which the challenge of interviewing peers and/or professionals enters the picture, another challenge with methodological implications (Coar & Sim, 2006 ). The two group interview studies clearly contained an element of “studying up,” moving the researchers into research fields characterized by less “control,” which again is readily related to challenging attitudes among the researched and difficulties of access to information, as noted by Nader ( 1969/1972 , p. 301). The very fact of using group interviews might, moreover, have increased this particular methodological challenge.

The researchers and moderators of the group discussions did feel that the participants questioned their expertise in the field, which primarily emerged as resistance or lack of responsiveness to some of the questions introduced. Furthermore, a hierarchy based on the classical distinction between objective, fact-related knowledge in contrast to knowledge as subjective and experience-related surfaced in both focus group studies. The researchers were interested in learning about how the informants acted and made judgments in specific challenging situations, and this involved asking for the participants’ experience-based knowledge. However, in the group discussions, the researchers and moderators found it challenging to get the participants to describe and reflect on real-life situations experienced in their own practice. Participants quickly turned to responding formally with generalized replies and fact-based knowledge such as health policy, legislation, and so forth.

It should be acknowledged that the topics of discussion in these projects imply medical assessment of substantial complexity, and to present revealing clinical examples may not be easy. Caution related to the disclosure of patient information may add to the challenge. Notwithstanding these points, the potential danger of being exposed and made vulnerable to peers is inherent in revealing subjective experience from one's own practice, a vulnerability that may be experienced as contradictory to the professional role as a doctor, a geneticist, or a genetic counsellor, and might have been important in our context. The research participants, who had expert knowledge about their professional practice (insiders), might, despite confidentiality and anonymity assertions, have felt slightly threatened by a researcher/moderator (outsider) whose intent was to explore politically and clinically potent challenges inherent in their practice. The study participants might legitimately wonder whether the researcher intended to test their professional competence, and/or place their profession in a “bad” light in the professional community. Hesitation to reveal information to colleagues, not just researchers, concerning one's own ways of solving the challenges discussed may also have been a constraint in the group discussions. In Coar and Sim's study ( 2006 ), in which both interviewer and interviewed were professionals (GPs), several participants regarded the interview as a test of their professional knowledge. Other studies have also noted that participants and professionals believe that their interests and professional identities are threatened during research (see e.g., Enosh & Ben-Ari, 2010 ). The perceptions of the researcher and the researched of the research agenda might thus not always be in harmony. Group interviews may also be challenging for the researcher because of the inherent strengths of a group of individuals, who can directly oppose the researcher's agenda.

In the studies with the GPs and genetic counsellors, minor “battles” seemed to be played out, in which the study participants alternated in terms of who was guiding or guarding the knowledge presented, including moments in which the researchers managed to move the discussion in the direction that was desired for the productive generation of knowledge. Neither the researcher in the sick-leave decision-making study (empirical example 3) nor the researcher in the genetic counselling study (empirical example 4) attempted to force the discussions in a preferred direction. Rather, the researchers repeatedly asked for concrete examples in order to gain knowledge beyond the formal, and made continuous attempts to hear participants dwell on the experienced intricacies of actual decision-making processes.

A more comprehensive understanding of the negotiations taking place about the research agenda involves insight into the context at hand and what might be at stake for both the participants and the researcher (Coar & Sim, 2006 ; Enosh & Ben-Ari, 2010 ; Vitus, 2008 ). We have indicated that the participants in both of the focus group discussion studies might have felt that their professional identities were being scrutinized. One cannot be entirely sure that the researchers and the participants were in full agreement about what the research agenda actually implied, although the aims of the research were shared before the discussions. Negotiations and resistance regarding the discussion of problematic clinical cases are, in the research literature, associated with a challenge of revelation. In such cases, the social status of the parties involved may also emerge as significant (Coar & Sim, 2006 ; Richards & Emslie, 2000 ). In the focus group studies considered here, the symmetry as well as the asymmetry in the researched–researcher relationship represented a dimension of power that the researchers experienced as challenging and as somewhat unpredictable during the course of the research encounters.

The child's role: Being at the mercy of the study participants

The next case reveals examples of researcher vulnerability experienced within a classical ethnographic study (empirical example 5). As pointed out, for instance by Hammersley and Atkinson ( 1983/1992 ), there is a substantial possibility in ethnography for informants to control the information revealed, not least when studies are carried out in foreign contexts. To illustrate the often shifting character of ethnography, Werner and Schoepfle ( 1987 ) have described the participant observer role in fieldwork as a process, starting out with descriptive observation, where taking a child's role is dominant, followed by a more “focused observation” as the cultural knowledge about the field increases, and finally moving into phases of more “selective observation.” Intertwined in such a process are changing relationships between the researcher and the researched.

A classical metaphor for the ethnographic fieldworker is the child who is to be socialized into a particular culture or subculture. As such, the researcher is from the start placed in an “inferior” position pertaining to relevant cultural knowledge. The “innocent” child and ethnographer is simultaneously a conscious and informed researcher working systematically on his or her research agenda. The agenda will be more or less transparent to the study participants, depending on how well a particular research topic can be made sensible in the research setting. An ethnographer's taking on the role of a child has its advantages, especially in the early phases of fieldwork. It allows the ethnographer to pose questions that might appear as uninformed, even naive, and might not be perceived as immediately threatening because they are coming from “a child” who is learning.

Beyond the role of the child, the role of the “insider” is sought within ethnography: being and living among the researched, becoming someone to be trusted and thus allowed access to internal matters. The attempts at gaining mutual trust and reaching a sense of or some degree of closeness to the informants lies at the heart of the ethnographic approach, and depends on considerable time being spent in the field. The “insider” role, however, is continuously articulating with the “outsider” role, which is also inherent in the participant observer role, as the researcher commonly comes from “outside” the studied field.

A particular challenge experienced in the ethnographic study we considered was the study participants’ ways of controlling information, particularly during the early phase of the fieldwork. One area that was perceived as a challenge was that of controlled exclusion: not only the careful sorting of information to be presented to the researcher, but the rigorous denial of access, the distancing or exclusion of the researcher from smaller or larger arenas defined by the researched. Dependent as the ethnographer is on guidance (and possible translation), the potential for control of information passed on to the researcher is more or less limitless, potentially jeopardizing the researcher's project. Despite the fact that the researcher in this project was invited to attend a vast number of relevant events and situations that could provide knowledge about pregnancy and birth-related perceptions and practices, she had, for months, an accompanying feeling of being guided away from core information, and even of being cheated. The experience of being part of a game was not entirely unlike what Angrosino described from his fieldwork (Angrosino & Mays De Pe'rez, 2000 ):

Even in such a highly circumscribed culture …(referring to his field site), people could experiment with styles of interaction and involve the visitor (researcher) in subtle, yet very revealingly subversive power games, games that inevitably shaped both what the ethnographer observed and how he interpreted what he saw. (p. 681)

The potential for the researched to control what a researcher is introduced to is obviously fully within the rights of the study participants, and is a principle located at the very core of any research endeavour. Nevertheless, the informants’ ability to control, deploy, and manipulate again raises questions around the notion of the researcher's exclusive power. Diverse forms of participant resistance have been described in the ethnographic literature (Adler & Adler, 2002 ). Karnieli-Miller et al. ( 2009 , p. 283) refer to the participants’ strategic use of problematic interview behaviour (such as flattery, flirting, and so forth), shift of topics, and even the decision to end the interview or cooperation altogether. On the basis of subtle or overt shifts in power relations between the parties, the awareness of the co-construction of knowledge can become more or less acute. Goodwin, Pope, Mort, and Smith ( 2003 ) write:

The community being researched is not a passive component; it also has a bearing on what the researcher is included in and excluded from. The informants were also agents in the shaping of the data, the data-collecting opportunities, and the course of the fieldwork. (p. 576)

At the same time, in the co-construction of knowledge made possible through the symbiotic relationship between researcher and researched lies one of the substantial advantages of ethnography. The closeness will often, with time, generate an openness and permissiveness, which may imply seemingly endless learning opportunities. However, the dependence on the close relationships with the informants simultaneously sheds light on the precariousness and vulnerability not only of the informants, who may have difficulties controlling the information ultimately generated from the research, but the vulnerability of ethnography as a research approach, as well as the vulnerability of the ethnographer in the process of learning.

In the current study, the researcher gradually gained access to more domains, and later fieldwork revealed the immense impact of her own position for the knowledge gained. She was provided with extensive access to the women's ritual reproductive sphere after being married, giving birth, breastfeeding etc. gaining closeness through the sharing of highly praised bodily transitions, a type of access she had not been granted while still “a girl.” The ethnographic experience also emphasized the fundamental importance of developing trust and close relationships. The potential for control of information is obviously particularly extensive at a point when the researcher knows few of her study participants, and when simultaneously the researcher is relatively uninformed about the field of study, that is, during the phase when the researcher's role as “child” is most prominent. The gaining of closeness to the field is thus part of a process of becoming more knowledgeable about culture and context, the handling of language and codes, and of the continuous building of what is often experienced as true friendship. Karnieli-Miller et al. ( 2009 ) explain that: “to gain access to the participants’ private and intimate experiences—his or her story—the researcher must enhance a sense of rapport with people and needs to build a considerate and sympathetic relationship and sense of mutual trust” (p. 282). This point pertains to all qualitative research endeavours, but is particularly pertinent in ethnography with its common demands for long-term interaction. In the study, we considered the experience of being gradually more at ease with the continued outsider role, the learning process made the researcher more of an insider. “Interaction is always a tentative process,” Angrosino and Mays De Pérez ( 2000 , p. 683) write, referring to the mutual testing out of the perceptions of one's own and the others’ roles that takes place over time in ethnography. As such, the relationship between researcher and ethnographer, and researched, and hence each person's role toward the other, is not fixed and permanent within ethnography; rather, “their behaviors and expectations of each other are part of a dynamic process that continues to grow throughout the course of single research projects” (ibid p. 683). In a similar vein, we have indicated that the role of researchers as interviewers in the in-depth interview studies and in the focus group discussion studies were not fixed during the course of the interviews. Shifts took place both in relation to definition of the relevant body of knowledge, and the particular position of the researcher in knowledge production. Partly due to the time dimension and the demands of participation, the role of the participant observer is indeed far from static or fixed, but is constantly transformed during the course of the fieldwork (Werner & Schoepfle, 1987 ).

The vulnerability in designs with especially demanding inherent dual roles

In the final example, we shed light on how researcher vulnerability seemed to be part and parcel of the dual role of the researcher. In the pedagogical study (empirical example 6), the researcher simultaneously pursued the researcher role and the actor role, portraying a patient during communication training. Two focus group interviews with medical students were conducted after the communication training. The researcher thus shifted from acting the role of a particular patient in front of a group of medical students, to moderating the focus group discussions that evaluated the training from the students’ perspective. The students who participated in the focus groups were either solely a part of the student audience, which was encouraged to comment and suggest “ways to go” in the medical encounter played out in front of them during time-outs led by a teacher and moderator, or they were also involved in the acting as GPs in the simulated encounter.

The character of the SP was a young woman. She was shy, almost nonverbal, someone who gets very easily hurt and starts crying when challenged on personal matters. The topics of the training were “the withdrawn patient” and “breaking bad news” (the patient was told that she has cancer). To portray this patient was demanding, and the actress had to use most of her proficiency and skills as an actor to create a credible character. This created an ambivalent situation; she felt emotionally drained after the performance, and found it difficult to shift from the role of the actress to the role of the researcher who moderated the group interviews. Despite the fact that she was a professional actress and well acquainted with varied responses from audiences, she felt at the mercy of the students’ evaluation in unexpected ways. She found herself wishing for the students’ approval as an actress while simultaneously wanting to be genuinely open to the students’ views of the learning potential of this particular pedagogical practice, with this latter concern demanding the distanced approach of a researcher. Role confusion of both parties could contribute to an unsharpened reflection.

As Malacrida states ( 2007 , pp. 1329–1330), engaging in emotionally challenging research topics and relationships has the potential to unsettle researchers’ well-being, and challenge their self-understanding as researchers. Being in a more emotionally charged research context than initially expected might imply underestimating the strength of the emotional reactions (Dickson-Swift, James, Kippen & Liamputtong, 2008 ; Rager, 2005 ). It puts the researcher at risk of becoming emotionally drained (Dunn, 1991 ; Lalor et al., 2006 ). To take on the dual role as researcher and SP in the development of this particular pedagogical practice exacerbated the emotional challenge, and made it difficult to find a balance between insider–outsider positions (Burns et al., 2012 ; Dwyer & Buckle, 2009 ). Parallels to the vulnerability inherent in the participant observer role in the ethnographic study are present, particularly the feelings of being at the mercy of the participants. The manner in which the researchers opened themselves to exposure placed them in a vulnerable position. In an ethnographic context, the researcher will commonly have a long-lasting relationship with the study participants, which implies opportunities to re-evaluate the course of events and modify ways of approaching demanding topics and situations. This was not the case in the pedagogical project which enhanced the sense of overall vulnerability.

Concluding remarks

In this article, we have made an attempt to shed light on the researcher–researched relationship in different qualitatively anchored studies carried out within health science. We have concentrated on the phase in which the research material is co-produced by the parties, and the researcher is highly dependent on participants’ knowledge about the phenomena under study, and on their willingness to share. Flyvbjerg, cited in Karnieli-Miller et al. ( 2009 , p. 282) argue that the study of power relations should go beyond the normative level and be anchored in the real practices of qualitative research. In this article, we have anchored our analysis of shifts and ambivalence in the researcher–researched relationship by drawing upon concrete examples from our own research. The four main qualitative approaches represented; the phenomenological in-depth interview studies, the focus group discussion studies, the ethnographic study, and the pedagogical study, held a common aim of diminishing the distance between the researcher and the researched, and creating an anti-authoritative researcher–researched relationship. This meant moving into and confronting complex negotiations about the research agenda, about which knowledge was to be counted as relevant, shifts in “inferior” and “superior” knowledge positions, as well as ethical dilemmas. The scenarios that emerged challenged the researchers partly to re-think the research agenda, but it also rendered them vulnerable to substantial emotional stress. The dual role as insider and outsider, participant and researcher, added to the challenge. “Interaction is always a tentative process that involves the continuous testing by all participants of the conceptions they have to the roles of others,” Angrosino and Mays De Pérez ( 2000 , p. 683) write, with reference to ethnography. Researchers’ and participants’ roles are not fixed, but develop during the projects. The empirical examples in this article indicate that these are points of relevance for qualitative research projects, across designs and traditions.

In order to handle shifts in positions between research parties, shifts which are intertwined with ethical dilemmas, the practice of continuous reflexive awareness is paramount. The same holds true for the context of knowledge production; scrutinizing critically what can be at stake in the encounters between researcher and researched, and one's own role in knowledge production. We argue that sharing and discussing these concerns in research teams and groups, where senior researchers as well as novices meet, should be regular practice. The value of reflexive self-awareness among researchers has been contested. Personal disclosure can fall into an infinite regress of excessive self-analysis at the expense of the research aims (Finley, 2002 ; Gergen & Gergen, 2000 ). However, along with Finley ( 2002 , p. 532), we feel that the other pitfall is to avoid reflexivity altogether. Although fraught with ambiguity, a lack of critical awareness about the impact of the research context, perspectives chosen, methodological choices made, and, in this context, the presence of the researcher, might seriously hamper the knowledge claims made. Finally, we support Malacrida ( 2007 , p. 1339) who writes that “reflexive research also should involve emotional care not only for participants but for researchers themselves.”

Authors' contributions

The first author developed the project idea, moderated the group discussions, transcribed the tape-recorded meta-reflections, produced summaries, and had overall responsibility for the project, including the production of the drafts of this article. The first and the last author planned the group discussions together and were discussion partners between the group discussions. All participants were engaged in the group discussions, contributed to developing the core topics, and took part in writing the manuscript. The last author was more involved in writing the article than the authors in the middle.

Conflict of interest and funding

The authors have not received funding or benefits from industry or elsewhere to conduct this study.

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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