field experiment vs naturalistic observation

Naturalistic Observation: Definition, Examples, and Advantages

Categories Research Methods

Naturalistic observation is a psychological research method that involves observing and recording behavior in the natural environment. Unlike experiments, researchers do not manipulate variables. This research method is frequently used in psychology to help researchers investigate human behavior.

This article explores how naturalistic observation is used in psychology. It offers examples and the potential advantages and disadvantages of this type of research. 

Table of Contents

What Is Naturalistic Observation?

In naturalistic observation, the researcher observes the participants’ behavior in their natural setting, taking notes on their behavior and interactions. The researcher may use various tools, such as video or audio recordings, to help capture the behavior accurately. The researcher may also use coding systems or other quantitative measures to systematically record observed behavior.

Naturalistic observation can be used to investigate a wide range of psychological phenomena, such as social interaction patterns, parental behavior, or animal behavior. 

Types of Naturalistic Observation

Naturalistic observation can be:

Unstructured or Structured

The observer can either watch and record everything that happens, or they can have a checklist or form to guide their observations.

Participant or Non-Participant

The observer can be an active participant, or they can remain separate from the subject and view from the sidelines.

Overt or Covert

The observer can either openly watch and record the subjects’ behaviors, or they can keep their presence hidden from the individual or group.

The specific type of naturalistic observation that researchers use depends on the situation, what they are researching, and the resources available. No matter the type, the observation must occur in a natural setting rather than in an experimental lab.

How to Collect Data in Naturalistic Observation

There are a number of methods that researchers might utilize to record data about the behaviors and events they observe. Some of these include:

• Note-taking : Research may opt to take notes about what they witness. This approach tends to be unstructured, allowing the observers to determine what they think is relevant and to include insights that may be helpful.
• Tally counts : In other cases, research may take a more structured approach where they count the frequency of a behavior.
• Audiovisual recordings : In other cases, research may want recordings of participant behavior. This not only allows researchers to refer to the recordings later, it can also be useful for sharing with others.

How Data Is Sampled in Naturalistic Observation

While naturalistic observation is not an experimental design, researchers still want to ensure that the data they collect represents what is happening in the group. To do this, researchers must collect a representative sample. When a sample is representative, it means that it accurately reflects what is happening in a given population.

To do this, researchers may utilize three primary sampling approaches:

Event Sampling

Event sampling involves the researcher creating a set of predefined categories and behaviors they will observe. This method is useful when the researcher wants to collect data on specific behaviors or events, allowing for more precise data collection.

Using this approach, the research would note every occurrence of a specific behavior.

Situation Sampling

Situation sampling involves observing participants in more than one situation. This approach can give researchers more insight and allow them to determine if certain behaviors only occur in specific contexts or settings. 

Time Sampling

Time sampling is a type of systematic observation that involves the researcher observing and recording the subjects’ behavior at predetermined intervals. This method is useful when the researcher wants to collect data on the frequency and duration of specific behaviors.

Each method of data collection has its strengths and weaknesses, and the choice of method depends on the research question and the nature of the subjects being observed.

Examples of Naturalistic Observation

It can be helpful to look at a few different examples to learn more about how naturalistic observation can be used:

• Researchers might observe children in a classroom to learn more about their social interaction patterns. 
• Naturalistic observation can also be used to study animal behavior in their natural habitat, such as observing chimpanzees in the wild to understand their social behavior.

Researchers use this research method in various fields, including animal researchers and anthropologists. 

The work of zoologist Konrad Lorenz, for example, relied on the use of naturalistic observation. Lorenz observed the behavior of ducklings after they hatched and noted that they became attached to the first possible parent figure they saw, a phenomenon known as imprinting. Once imprinted on a parent figure, the duckling would follow and learn from their parent.

From his naturalistic observations, Lorenz hypothesized that there was a critical period immediately after hatching where ducklings needed to imprint on a parent. Based on his observations, Lorenz conducted further experiments that confirmed his hypothesis.

More Examples of Naturalistic Observation

Naturalistic observation is a research method commonly used in various areas of psychology. 

Social Psychology

Naturalistic observation can provide valuable insights into people’s behavior in different social situations. By observing people’s behavior in a crowded public place like a shopping mall or train station, researchers can better understand how social norms are established and maintained and how people interact in various social groups.

Consumer Research

Consumer research is another area where naturalistic observation can be used effectively. By observing shoppers in a grocery store or shopping mall, researchers can study how people make purchasing decisions in real-life situations.

Researchers can gain valuable insights into consumer behavior by analyzing what catches their attention, how they interact with different products, and how they decide what to buy.

Developmental Psychology

Observing children playing in a playground or a classroom can help researchers understand how children develop and learn new skills in natural settings.

Researchers can gain insights into the developmental process by observing children as they interact with each other and learn social skills or as they learn new concepts and skills in a classroom.

Cognitive Psychology

Naturalistic observation can be used to study how people think and process information in real-life situations. For example, observing people using a computer program can help researchers understand how people navigate through it and solve problems.

Similarly, observing people in a conversation can provide insights into how they process and respond to information in real time.

Advantages of Naturalistic Observation

Naturalistic observation offers a number of benefits that can make it a good choice for research. 

Ecological Validity

One of the strengths of naturalistic observation is its ability to capture behavior in a natural setting, providing a more accurate and comprehensive picture of how people or animals behave in their everyday environment.

It is often more realistic than lab research, so it can give insight into how people behave authentically in everyday settings and situations.

Inspiration for Additional Research

Naturalistic observation can also generate new hypotheses and insights that may not be captured in other research methods. 

Research That Can’t Be Done in a Lab

Naturalistic observation allows the study of behaviors that cannot be replicated in a lab. Naturalistic observation is sometimes the only approach for studying behaviors that cannot be reproduced in a lab due to ethical reasons.

For example, researchers might use this approach to research prison behavior or the social impact of domestic violence on emotional health. Those are not situations they can manipulate in a lab, but they can observe the impact on people who have had those experiences.

Disadvantages of Naturalistic Observation

While naturalistic can be a valuable tool, it is not appropriate for every situation. Some potential downsides include: 

Bias and Lack of Control

Naturalistic observation is limited by its lack of environmental control and the potential for observer bias. Researchers must be careful to minimize the influence of their presence on the behavior being observed and to use systematic and objective methods for recording and analyzing the data. 

Inability to Infer Cause and Effect

Naturalistic observation is also limited by its inability to establish causality between variables.

Naturalistic Observation vs. Case Study

Naturalistic observation and case studies are both research methods used in psychology but differ in their approach and purpose. Naturalistic observation involves observing and recording the behavior of individuals or groups in their natural environment without any intervention or manipulation by the researcher.

On the other hand, a case study is an in-depth analysis of a single individual or a small group of individuals, often conducted through interviews, surveys, and other forms of data collection.

The key difference between naturalistic observation and a case study is that the former focuses more on observing and recording behaviors and interactions as they occur naturally, while the latter focuses on gathering detailed information about a specific individual or group.

Naturalistic observation is often used to study social interactions, group dynamics, and other natural behaviors in real-world settings. In contrast, case studies often explore complex psychological phenomena such as mental illness, personality disorders, or unusual behaviors.

Both naturalistic observation and case studies have their strengths and limitations. The choice of method depends on the research question, the level of detail needed, and the feasibility of conducting the study in a particular setting.

Naturalistic Observation Ideas

There are many potential ideas for studies that involve naturalistic observation. A few ideas include:

• Observe the behavior of animals in their natural habitats, studying their patterns of movement, foraging, and communication
• Observe human behavior in public spaces, such as parks or coffee shops, documenting patterns of social interaction and communication
• Focus on the behavior of individuals within specific social groups or communities, studying their interactions and relationships over time
• Watch the behavior of children in a classroom setting could provide insights into their learning and socialization processes

Frequently Asked Questions

Why do we use naturalistic observation.

Naturalistic observation is important because it allows researchers to better understand how individuals behave in their everyday lives. By observing behavior in a natural setting, researchers can obtain a more accurate representation of how people act and interact with each other in their normal environment. 

This method is particularly useful when studying social behavior, as it allows researchers to capture the complexity and nuances of social interactions that might not be apparent in a laboratory setting.

Naturalistic observation can also offer valuable insights into the development of certain behaviors, such as those related to child development or the formation of social groups.

What is the most famous example of naturalistic observation?

The most famous example of naturalistic observation is probably Jane Goodall’s study of chimpanzees in the wild. Goodall spent years observing the behavior of chimpanzees in Tanzania, documenting their social interactions, tool use, and other aspects of their lives. Her work helped to revolutionize our understanding of these animals and their place in the natural world.

In conclusion, naturalistic observation is a powerful research method that can be used effectively in various areas within psychology. Researchers can gain valuable insights into human behavior and cognition by observing people’s behavior in natural settings.

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2 Research Methods in Social Psychology

Social psychologists are interested in the ways that other people affect thought, emotion, and behavior. To explore these concepts requires scientific research methods. Following a brief overview of traditional research designs, this module introduces how complex experimental designs, field experiments, naturalistic observation, experience sampling techniques, survey research, subtle and nonconscious techniques such as priming, and archival research and the use of big data may each be adapted to address social psychological questions. This module also discusses the importance of obtaining a representative sample along with some ethical considerations that social psychologists face.

Learning Objectives

• Describe the key features of basic and complex experimental designs.
• Describe the key features of field experiments, naturalistic observation, and experience sampling techniques.
• Describe survey research and explain the importance of obtaining a representative sample.
• Describe the implicit association test and the use of priming.
• Describe use of archival research techniques.
• Explain five principles of ethical research that most concern social psychologists.

Introduction

Are you passionate about cycling? Norman Triplett certainly was. At the turn of last century, he studied the lap times of cycling races and noticed a striking fact: riding in competitive races appeared to improve riders’ times by about 20-30 seconds every mile compared to when they rode the same courses alone. Triplett suspected that the riders’ enhanced performance could not be explained simply by the slipstream caused by other cyclists blocking the wind. To test his hunch, he designed what is widely described as the first experimental study in social psychology (published in 1898!)—in this case, having children reel in a length of fishing line as fast as they could. The children were tested alone, then again when paired with another child. The results? The children who performed the task in the presence of others out-reeled those that did so alone.

Although Triplett’s research fell short of contemporary standards of scientific rigor (e.g., he eyeballed the data instead of measuring performance precisely; Stroebe, 2012), we now know that this effect, referred to as “ social facilitation ,” is reliable—performance on simple or well-rehearsed tasks tends to be enhanced when we are in the presence of others (even when we are not competing against them). To put it another way, the next time you think about showing off your pool-playing skills on a date, the odds are you’ll play better than when you practice by yourself. (If you haven’t practiced, maybe you should watch a movie instead!)

Research Methods in Social Psychology

One of the things Triplett’s early experiment illustrated is scientists’ reliance on systematic observation over opinion, or anecdotal evidence . The scientific method usually begins with observing the world around us (e.g., results of cycling competitions) and thinking of an interesting question (e.g., Why do cyclists perform better in groups?). The next step involves generating a specific testable prediction, or hypothesis (e.g., performance on simple tasks is enhanced in the presence of others). Next, scientists must operationalize the variables they are studying. This means they must figure out a way to define and measure abstract concepts. For example, the phrase “perform better” could mean different things in different situations; in Triplett’s experiment it referred to the amount of time (measured with a stopwatch) it took to wind a fishing reel. Similarly, “in the presence of others” in this case was operationalized as another child winding a fishing reel at the same time in the same room. Creating specific operational definitions like this allows scientists to precisely manipulate the independent variable , or “cause” (the presence of others), and to measure the dependent variable , or “effect” (performance)—in other words, to collect data. Clearly described operational definitions also help reveal possible limitations to studies (e.g., Triplett’s study did not investigate the impact of another child in the room who was not also winding a fishing reel) and help later researchers replicate them precisely.

Laboratory Research

As you can see, social psychologists have always relied on carefully designed laboratory environments to run experiments where they can closely control situations and manipulate variables (see  the NOBA module on Research Designs  for an overview of traditional methods). However, in the decades since Triplett discovered social facilitation, a wide range of methods and techniques have been devised, uniquely suited to demystifying the mechanics of how we relate to and influence one another. This module provides an introduction to the use of complex laboratory experiments, field experiments, naturalistic observation, survey research, nonconscious techniques, and archival research, as well as more recent methods that harness the power of technology and large data sets, to study the broad range of topics that fall within the domain of social psychology. At the end of this module, we will also consider some of the key ethical principles that govern research in this diverse field.

There are several key features to experiments including the manipulation of independent variables and the use of random assignment. Researchers can infer causality from experimental studies because experiments involve randomly assigning participants to one or more conditions of the experiment. This process prevents individual differences amongst the participants to alter the results. Imagine that a researcher was interested in examining whether academic failure reduced creativity. The independent variable in this study was academic failure because they believed this variable would exert an influence on creativity. To operationalize these variables, the researchers administered an achievement test to participants and randomly assigned participants to receive a failing grade, a passing grade, or no feedback (which served as the control). Following the achievement test and feedback, all participants were asked to list as many creative uses as they could for a brick (commonly referred to as the Guilford alternative uses task. Thus, the dependent variable for this study was creativity, operationalized as the participant’s ability to come up with high numbers of alternative uses for a brick. Unlike correlational studies, experiments can determine causality and thus, are often preferred by social psychologists. A correlational study examining the same hypothesis could examine the link between failing academic grades and individual performances on the Guildford alternative uses task, but because participants were not random assigned a failing grade, any relationship found between grades and creativity has a reverse causality problem. Getting more “F”s could make people less creative or being less creative could have led to more “F”s. An experiment rules out the reverse-causality issue.

Some experiments have more complex designs. The use of complex experimental designs , with multiple independent and/or dependent variables, has grown increasingly popular because they permit researchers to study both the individual and joint effects of several factors on a range of related situations. Moreover, thanks to technological advancements and the growth of social neuroscience , an increasing number of researchers now integrate biological markers (e.g., hormones) or use neuroimaging techniques (e.g., fMRI) in their research designs to better understand the biological mechanisms that underlie social processes.

We can dissect the fascinating research of Dov Cohen and his colleagues ( 1996 ) on “culture of honor” to provide insights into complex lab studies. A culture of honor is one that emphasizes personal or family reputation. In a series of lab studies, the Cohen research team invited dozens of university students into the lab to see how they responded to aggression. Half were from the Southern United States (a culture of honor) and half were from the Northern United States (not a culture of honor; this type of setup constitutes a participant variable of two levels). Region of origin was independent variable #1. Participants also provided a saliva sample immediately upon arriving at the lab; (they were given a cover story about how their blood sugar levels would be monitored over a series of tasks).

The participants completed a brief questionnaire and were then sent down a narrow corridor to drop it off on a table. En route, they encountered a confederate at an open file cabinet who pushed the drawer in to let them pass. When the participant returned a few seconds later, the confederate, who had re-opened the file drawer, slammed it shut and bumped into the participant with his shoulder, muttering “asshole” before walking away. In a manipulation of an independent variable—in this case, the insult—some of the participants were insulted publicly (in view of two other confederates pretending to be doing homework) while others were insulted privately (no one else was around). In a third condition—the control group—participants experienced a modified procedure in which they were not insulted at all.

Although this is a fairly elaborate procedure on its face, what is particularly impressive is the number of dependent variables the researchers were able to measure. First, in the public insult condition, the two additional confederates (who observed the interaction, pretending to do homework) rated the participants’ emotional reaction (e.g., anger, amusement, etc.) to being bumped into and insulted. Second, upon returning to the lab, participants in all three conditions were told they would later undergo electric shocks as part of a stress test, and were asked how much of a shock they would be willing to receive (between 10 volts and 250 volts). This decision was made in front of two confederates who had already chosen shock levels of 75 and 25 volts, presumably providing an opportunity for participants to publicly demonstrate their toughness. Third, across all conditions, the participants rated the likelihood of a variety of ambiguously provocative scenarios (e.g., one driver cutting another driver off) escalating into a fight or verbal argument. And fourth, in one of the studies, participants provided saliva samples, one right after returning to the lab, and a final one after completing the questionnaire with the ambiguous scenarios. Later, all three saliva samples were tested for levels of cortisol (a hormone associated with stress) and testosterone (a hormone associated with aggression).

The results showed that people from the Northern United States were far more likely to laugh off the incident (only 35% having anger ratings as high as or higher than amusement ratings), whereas the opposite was true for people from the South (85% of whom had anger ratings as high as or higher than amusement ratings). Also, only those from the South experienced significant increases in cortisol and testosterone following the insult (with no difference between the public and private insult conditions). Finally, no regional differences emerged in the interpretation of the ambiguous scenarios; however, the participants from the South were more likely to choose to receive a greater shock in the presence of the two confederates.

Field Research

Because social psychology is primarily focused on the social context—groups, families, cultures—researchers commonly leave the laboratory to collect data on life as it is actually lived. To do so, they use a variation of the laboratory experiment, called a field experiment . A field experiment is similar to a lab experiment except it uses real-world situations, such as people shopping at a grocery store. One of the major differences between field experiments and laboratory experiments is that the people in field experiments do not know they are participating in research, so—in theory—they will act more naturally. In a classic example from 1972 , Alice Isen and Paula Levin wanted to explore the ways emotions affect helping behavior. To investigate this, they observed the behavior of people at pay phones (I know! Pay phones! ). Half of the unsuspecting participants (determined by random assignment ) found a dime planted by researchers (I know! A dime! ) in the coin slot, while the other half did not. Presumably, finding a dime felt surprising and lucky and gave people a small jolt of happiness. Immediately after the unsuspecting participant left the phone booth, a confederate walked by and dropped a stack of papers. Almost 100% of those who found a dime helped to pick up the papers. And what about those who didn’t find a dime? Only 1 out 25 of them bothered to help.

In cases where it’s not practical or ethical to randomly assign participants to different experimental conditions, we can use naturalistic observation —unobtrusively watching people as they go about their lives. Consider, for example, a classic demonstration of the “ basking in reflected glory ” phenomenon: Robert Cialdini and his colleagues used naturalistic observation at seven universities to confirm that students are significantly more likely to wear clothing bearing the school name or logo on days following wins (vs. draws or losses) by the school’s varsity football team ( Cialdini et al., 1976 ). In another study, by Jenny Radesky and her colleagues ( 2014 ), 40 out of 55 observations of caregivers eating at fast food restaurants with children involved a caregiver using a mobile device. The researchers also noted that caregivers who were most absorbed in their device tended to ignore the children’s behavior, followed by scolding, issuing repeated instructions, or using physical responses, such as kicking the children’s feet or pushing away their hands.

A group of techniques collectively referred to as experience sampling methods represent yet another way of conducting naturalistic observation, often by harnessing the power of technology. In some cases, participants are notified several times during the day by a pager, wristwatch, or a smartphone app to record data (e.g., by responding to a brief survey or scale on their smartphone, or in a diary). For example, in a study by Reed Larson and his colleagues ( 1994 ), mothers and fathers carried pagers for one week and reported their emotional states when beeped at random times during their daily activities at work or at home. The results showed that mothers reported experiencing more positive emotional states when away from home (including at work), whereas fathers showed the reverse pattern. A more recently developed technique, known as the electronically activated recorder , or EAR, does not even require participants to stop what they are doing to record their thoughts or feelings; instead, a small portable audio recorder or smartphone app is used to automatically record brief snippets of participants’ conversations throughout the day for later coding and analysis. For a more in-depth description of the EAR technique and other experience-sampling methods, see the NOBA module on  Conducting Psychology Research in the Real World .

Survey Research

In this diverse world, survey research offers itself as an invaluable tool for social psychologists to study individual and group differences in people’s feelings, attitudes, or behaviors. For example, the World Values Survey II was based on large representative samples of 19 countries and allowed researchers to determine that the relationship between income and subjective well-being was stronger in poorer countries ( Diener & Oishi, 2000 ). In other words, an increase in income has a much larger impact on your life satisfaction if you live in Nigeria than if you live in Canada. In another example, a nationally-representative survey in Germany with 16,000 respondents revealed that holding cynical beliefs is related to lower income (e.g., between 2003-2012 the income of the least cynical individuals increased by $300 per month, whereas the income of the most cynical individuals did not increase at all). Furthermore, survey data collected from 41 countries revealed that this negative correlation between cynicism and income is especially strong in countries where people in general engage in more altruistic behavior and tend not to be very cynical ( Stavrova & Ehlebracht, 2016 ).

Of course, obtaining large, cross-cultural, and representative samples has become far easier since the advent of the internet and the proliferation of web-based survey platforms—such as Qualtrics—and participant recruitment platforms—such as Amazon’s Mechanical Turk. And although some researchers harbor doubts about the representativeness of online samples, studies have shown that internet samples are in many ways more diverse and representative than samples recruited from human subject pools (e.g., with respect to gender; Gosling et al., 2004 ). Online samples also compare favorably with traditional samples on attentiveness while completing the survey, reliability of data, and proportion of non-respondents ( Paolacci et al., 2010 ).

Subtle/Nonconscious Research Methods

The methods we have considered thus far—field experiments, naturalistic observation, and surveys—work well when the thoughts, feelings, or behaviors being investigated are conscious and directly or indirectly observable. However, social psychologists often wish to measure or manipulate elements that are involuntary or nonconscious, such as when studying prejudicial attitudes people may be unaware of or embarrassed by. A good example of a technique that was developed to measure people’s nonconscious (and often ugly) attitudes is known as the implicit association test (IAT) ( Greenwald et al., 1998 ). This computer-based task requires participants to sort a series of stimuli (as rapidly and accurately as possible) into simple and combined categories while their reaction time is measured (in milliseconds). For example, an IAT might begin with participants sorting the names of relatives (such as “Niece” or “Grandfather”) into the categories “Male” and “Female,” followed by a round of sorting the names of disciplines (such as “Chemistry” or “English”) into the categories “Arts” and “Science.” A third round might combine the earlier two by requiring participants to sort stimuli into either “Male or Science” or “Female and Arts” before the fourth round switches the combinations to “Female or Science” and “Male and Arts.” If across all of the trials a person is quicker at accurately sorting incoming stimuli into the compound category “Male or Science” than into “Female or Science,” the authors of the IAT suggest that the participant likely has a stronger association between males and science than between females and science. Incredibly, this specific gender-science IAT has been completed by more than half a million participants across 34 countries, about 70% of whom show an implicit stereotype associating science with males more than with females ( Nosek et al., 2009 ). What’s more, when the data are grouped by country, national differences in implicit stereotypes predict national differences in the achievement gap between boys and girls in science and math. Our automatic associations, apparently, carry serious societal consequences.

Another nonconscious technique, known as priming , is often used to subtly manipulate behavior by activating or making more accessible certain concepts or beliefs. Consider the fascinating example of terror management theory (TMT) , whose authors believe that human beings are (unconsciously) terrified of their mortality (i.e., the fact that, some day, we will all die; Pyszczynski et al., 2003 ). According to TMT, in order to cope with this unpleasant reality (and the possibility that our lives are ultimately essentially meaningless), we cling firmly to systems of cultural and religious beliefs that give our lives meaning and purpose. If this hypothesis is correct, one straightforward prediction would be that people should cling even more firmly to their cultural beliefs when they are subtly reminded of their own mortality.

In one of the earliest tests of this hypothesis, actual municipal court judges in Arizona were asked to set a bond for an alleged prostitute immediately after completing a brief questionnaire. For half of the judges the questionnaire ended with questions about their thoughts and feelings regarding the prospect of their own death. Incredibly, judges in the experimental group that were primed with thoughts about their mortality set a significantly higher bond than those in the control group ($455 vs. $50!)—presumably because they were especially motivated to defend their belief system in the face of a violation of the law ( Rosenblatt et al., 1989 ). Although the judges consciously completed the survey, what makes this a study of priming is that the second task (sentencing) was unrelated, so any influence of the survey on their later judgments would have been nonconscious. Similar results have been found in TMT studies in which participants were primed to think about death even more subtly, such as by having them complete questionnaires just before or after they passed a funeral home ( Pyszczynski et al., 1996 ).

To verify that the subtle manipulation (e.g., questions about one’s death) has the intended effect (activating death-related thoughts), priming studies like these often include a manipulation check following the introduction of a prime. For example, right after being primed, participants in a TMT study might be given a word fragment task in which they have to complete words such as COFF_ _ or SK _ _ L. As you might imagine, participants in the mortality-primed experimental group typically complete these fragments as COFFIN and SKULL, whereas participants in the control group complete them as COFFEE and SKILL.

The use of priming to unwittingly influence behavior, known as social or behavioral priming ( Ferguson & Mann, 2014 ), has been at the center of the recent “replication crisis” in Psychology ( see the NOBA module on replication). Whereas earlier studies showed, for example, that priming people to think about old age makes them walk slower ( Bargh, Chen, & Burrows, 1996 ), that priming them to think about a university professor boosts performance on a trivia game ( Dijksterhuis & van Knippenberg, 1998 ), and that reminding them of mating motives (e.g., sex) makes them more willing to engage in risky behavior ( Greitemeyer, Kastenmüller, & Fischer, 2013 ), several recent efforts to replicate these findings have failed (e.g., Harris et al., 2013 ; Shanks et al., 2013 ). Such failures to replicate findings highlight the need to ensure that both the original studies and replications are carefully designed, have adequate sample sizes, and that researchers pre-register their hypotheses and openly share their results—whether these support the initial hypothesis or not.

Archival Research

Imagine that a researcher wants to investigate how the presence of passengers in a car affects drivers’ performance. She could ask research participants to respond to questions about their own driving habits. Alternately, she might be able to access police records of the number of speeding tickets issued by automatic camera devices, then count the number of solo drivers versus those with passengers. This would be an example of archival research . The examination of archives, statistics, and other records such as speeches, letters, or even tweets, provides yet another window into social psychology. Although this method is typically used as a type of correlational research design—due to the lack of control over the relevant variables—archival research shares the higher ecological validity of naturalistic observation. That is, the observations are conducted outside the laboratory and represent real world behaviors. Moreover, because the archives being examined can be collected at any time and from many sources, this technique is especially flexible and often involves less expenditure of time and other resources during data collection.

Social psychologists have used archival research to test a wide variety of hypotheses using real-world data. For example, analyses of major league baseball games played during the 1986, 1987, and 1988 seasons showed that baseball pitchers were more likely to hit batters with a pitch on hot days ( Reifman et al., 1991 ). Another study compared records of race-based lynching in the United States between 1882-1930 to the inflation-adjusted price of cotton during that time (a key indicator of the Deep South’s economic health), demonstrating a significant negative correlation between these variables. Simply put, there were significantly more lynchings when the price of cotton stayed flat, and fewer lynchings when the price of cotton rose ( Beck & Tolnay, 1990 ; Hovland & Sears, 1940 ). This suggests that race-based violence is associated with the health of the economy.

More recently, analyses of social media posts have provided social psychologists with extremely large sets of data (“ big data ”) to test creative hypotheses. In an example of research on attitudes about vaccinations, Mitra and her colleagues ( 2016 ) collected over 3 million tweets sent by more than 32 thousand users over four years. Interestingly, they found that those who held (and tweeted) anti-vaccination attitudes were also more likely to tweet about their mistrust of government and beliefs in government conspiracies. Similarly, Eichstaedt and his colleagues ( 2015 ) used the language of 826 million tweets to predict community-level mortality rates from heart disease. That’s right: more anger-related words and fewer positive-emotion words in tweets predicted higher rates of heart disease.

In a more controversial example, researchers at Facebook attempted to test whether emotional contagion—the transfer of emotional states from one person to another—would occur if Facebook manipulated the content that showed up in its users’ News Feed ( Kramer et al., 2014 ). And it did. When friends’ posts with positive expressions were concealed, users wrote slightly fewer positive posts (e.g., “Loving my new phone!”). Conversely, when posts with negative expressions were hidden, users wrote slightly fewer negative posts (e.g., “Got to go to work. Ugh.”). This suggests that people’s positivity or negativity can impact their social circles.

The controversial part of this study—which included 689,003 Facebook users and involved the analysis of over 3 million posts made over just one week—was the fact that Facebook did not explicitly request permission from users to participate. Instead, Facebook relied on the fine print in their data-use policy. And, although academic researchers who collaborated with Facebook on this study applied for ethical approval from their institutional review board (IRB), they apparently only did so after data collection was complete, raising further questions about the ethicality of the study and highlighting concerns about the ability of large, profit-driven corporations to subtly manipulate people’s social lives and choices.

Research Issues in Social Psychology

The question of representativeness.

Along with our counterparts in the other areas of psychology, social psychologists have been guilty of largely recruiting samples of convenience from the thin slice of humanity—students—found at universities and colleges ( Sears, 1986 ). This presents a problem when trying to assess the social mechanics of the public at large. Aside from being an overrepresentation of young, middle-class Caucasians, college students may also be more compliant and more susceptible to attitude change, have less stable personality traits and interpersonal relationships, and possess stronger cognitive skills than samples reflecting a wider range of age and experience ( Peterson & Merunka, 2014 ; Visser, Krosnick, & Lavrakas, 2000) . Put simply, these traditional samples (college students) may not be sufficiently representative of the broader population. Furthermore, considering that 96% of participants in psychology studies come from western, educated, industrialized, rich, and democratic countries (so-called WEIRD cultures ; Henrich, Heine, & Norenzayan, 2010 ), and that the majority of these are also psychology students , the question of non-representativeness becomes even more serious.

Of course, when studying a basic cognitive process (like working memory capacity) or an aspect of social behavior that appears to be fairly universal (e.g., even cockroaches exhibit social facilitation!), a non-representative sample may not be a big deal. However, over time research has repeatedly demonstrated the important role that individual differences (e.g., personality traits, cognitive abilities, etc.) and culture (e.g., individualism vs. collectivism) play in shaping social behavior. For instance, even if we only consider a tiny sample of research on aggression, we know that narcissists are more likely to respond to criticism with aggression ( Bushman & Baumeister, 1998 ); conservatives, who have a low tolerance for uncertainty, are more likely to prefer aggressive actions against those considered to be “outsiders” ( de Zavala et al., 2010 ); countries where men hold the bulk of power in society have higher rates of physical aggression directed against female partners ( Archer, 2006 ); and males from the southern part of the United States are more likely to react with aggression following an insult ( Cohen et al., 1996 ).

Ethics in Social Psychological Research

For better or worse (but probably for worse), when we think about the most unethical studies in psychology, we think about social psychology. Imagine, for example, encouraging people to deliver what they believe to be a dangerous electric shock to a stranger (with bloodcurdling screams for added effect!). This is considered a “classic” study in social psychology. Or, how about having students play the role of prison guards, deliberately and sadistically abusing other students in the role of prison inmates. Yep, social psychology too. Of course, both Stanley Milgram’s ( 1963 ) experiments on obedience to authority and the Stanford prison study ( Haney et al., 1973 ) would be considered unethical by today’s standards, which have progressed with our understanding of the field. Today, we follow a series of guidelines and receive prior approval from our institutional research boards before beginning such experiments. Among the most important principles are the following:

• Informed consent: In general, people should know when they are involved in research, and understand what will happen to them during the study (at least in general terms that do not give away the hypothesis). They are then given the choice to participate, along with the freedom to withdraw from the study at any time. This is precisely why the Facebook emotional contagion study discussed earlier is considered ethically questionable. Still, it’s important to note that certain kinds of methods—such as naturalistic observation in public spaces, or archival research based on public records—do not require obtaining informed consent.
• Privacy: Although it is permissible to observe people’s actions in public—even without them knowing—researchers cannot violate their privacy by observing them in restrooms or other private spaces without their knowledge and consent. Researchers also may not identify individual participants in their research reports (we typically report only group means and other statistics). With online data collection becoming increasingly popular, researchers also have to be mindful that they follow local data privacy laws, collect only the data that they really need (e.g., avoiding including unnecessary questions in surveys), strictly restrict access to the raw data, and have a plan in place to securely destroy the data after it is no longer needed.
• Risks and Benefits: People who participate in psychological studies should be exposed to risk only if they fully understand the risks and only if the likely benefits clearly outweigh those risks. The Stanford prison study is a notorious example of a failure to meet this obligation. It was planned to run for two weeks but had to be shut down after only six days because of the abuse suffered by the “prison inmates.” But even less extreme cases, such as researchers wishing to investigate implicit prejudice using the IAT, need to be considerate of the consequences of providing feedback to participants about their nonconscious biases. Similarly, any manipulations that could potentially provoke serious emotional reactions (e.g., the culture of honor study described above) or relatively permanent changes in people’s beliefs or behaviors (e.g., attitudes towards recycling) need to be carefully reviewed by the IRB.
• Deception: Social psychologists sometimes need to deceive participants (e.g., using a cover story) to avoid demand characteristics by hiding the true nature of the study. This is typically done to prevent participants from modifying their behavior in unnatural ways, especially in laboratory or field experiments. For example, when Milgram recruited participants for his experiments on obedience to authority, he described it as being a study of the effects of punishment on memory! Deception is typically only permitted (a) when the benefits of the study outweigh the risks, (b) participants are not reasonably expected to be harmed, (c) the research question cannot be answered without the use of deception, and (d) participants are informed about the deception as soon as possible, usually through debriefing.
• Debriefing: This is the process of informing research participants as soon as possible of the purpose of the study, revealing any deceptions, and correcting any misconceptions they might have as a result of participating. Debriefing also involves minimizing harm that might have occurred. For example, an experiment examining the effects of sad moods on charitable behavior might involve inducing a sad mood in participants by having them think sad thoughts, watch a sad video, or listen to sad music. Debriefing would therefore be the time to return participants’ moods to normal by having them think happy thoughts, watch a happy video, or listen to happy music.

As an immensely social species, we affect and influence each other in many ways, particularly through our interactions and cultural expectations, both conscious and nonconscious. The study of social psychology examines much of the business of our everyday lives, including our thoughts, feelings, and behaviors we are unaware or ashamed of. The desire to carefully and precisely study these topics, together with advances in technology, has led to the development of many creative techniques that allow researchers to explore the mechanics of how we relate to one another. Consider this your invitation to join the investigation.

Text Attribution

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When performance on simple or well-rehearsed tasks is enhanced when we are in the presence of others.

An argument that is based on personal experience and not considered reliable or representative.

A method of investigation that includes systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses.

A possible explanation that can be tested through research.

How researchers specifically measure a concept.

The variable the researcher manipulates and controls in an experiment.

The variable the researcher measures but does not manipulate in an experiment.

A setting in which the researcher can carefully control situations and manipulate variables.

An experiment with two or more independent variables.

An interdisciplinary field concerned with identifying the neural processes underlying social behavior and cognition.

The individual characteristics of research subjects - age, personality, health, intelligence, etc.

A fake description of the purpose and/or procedure of a study, used when deception is necessary in order to answer a research question.

An actor working with the researcher. Most often, this individual is used to deceive unsuspecting research participants. Also known as a “stooge.”

An experiment that occurs outside of the lab and in a real world situation.

Assigning participants to receive different conditions of an experiment by chance.

Unobtrusively watching people as they go about the business of living their lives.

The tendency for people to associate themselves with successful people or groups.

Systematic ways of having participants provide samples of their ongoing behavior. Participants' reports are dependent (contingent) upon either a signal, pre-established intervals, or the occurrence of some event.

A methodology where participants wear a small, portable audio recorder that intermittently records snippets of ambient sounds around them.

A method of research that involves administering a questionnaire to respondents in person, by telephone, through the mail, or over the internet.

A computer-based categorization task that measures the strength of association between specific concepts over several trials.

The process by which exposing people to one stimulus makes certain thoughts, feelings or behaviors more salient.

A theory that proposes that humans manage the anxiety that stems from the inevitability of death by embracing frameworks of meaning such as cultural values and beliefs.

A measure used to determine whether or not the manipulation of the independent variable has had its intended effect on the participants.

A field of research that investigates how the activation of one social concept in memory can elicit changes in behavior, physiology, or self-reports of a related social concept without conscious awareness.

A type of research in which the researcher analyses records or archives instead of collecting data from live human participants.

A type of descriptive research that involves measuring the association between two variables, or how they go together.

The degree to which a study finding has been obtained under conditions that are typical for what happens in everyday life.

The analysis of large data sets.

Participants that have been recruited in a manner that prioritizes convenience over representativeness.

Cultures that are western, educated, industrialized, rich, and democratic.

Subtle cues that make participants aware of what the experimenter expects to find or how participants are expected to behave.

An Introduction to Social Psychology Copyright © 2022 by Thomas Edison State University is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is naturalistic observation in behavioral science, what is naturalistic observation.

Naturalistic Observation is a method of data collection used extensively in the field of psychology and social sciences. It involves the systematic and detailed observation and recording of behaviors in the subject’s real-world setting as opposed to a structured laboratory environment. The observer maintains a non-interventionist mode through the entirety of the study, allowing for a natural unfolding of behaviors without the interference of any external stimulus or prompts.

The key purpose of naturalistic observation is to conduct research in a realistic environment to avoid a manipulated or artificial situation that can potentially impact the behavior of the subjects. This method enables researchers to study behaviors in their most organic form, without the influence of laboratory controls or settings. The resultant data is high in ecological validity as it is drawn from real world scenarios and not artificially induced situations.

Benefits and Limitations of naturalistic observation

Naturalistic observation carries a number of advantages. Primarily, it offers strong external validity—ability to generalize findings—as the observations are made in naturally occurring social settings. It allows the capturing of complex behaviours that may not be feasible in a structured, experimental research setting. A diverse array of behaviors can be studied, including those that may be ethically or practically difficult to recreate in a lab environment.

Limitations

Despite its advantages, the naturalistic observation method also has limitations. It lacks control over external variables that may influence the behavior being observed. The observer’s presence can lead to reactivity, where subjects behave differently because they know they are being watched. Observer bias can be another issue where the observer subconsciously interprets or records information in a way that aligns with their expectations or hypotheses. Furthermore, this method can be time-consuming and expensive considering the length of observation required to gather substantial substantive data.

Examples of naturalistic observation

Naturalistic observation is used in numerous arenas within psychology and social science. Here are few examples:

Educational Psychology

In the field of educational psychology, observations of students in a classroom setting can provide insights about student behavior, teacher-student interactions, and learning outcomes. For instance, a researcher could utilize naturalistic observation to understand the disruptive behavior of a particular student.

Consumer Behavior

Naturalistic observation plays an important role in marketing research, particularly in studies of consumer behavior. Observing shoppers in a retail environment can reveal patterns regarding purchasing behavior, preferences, or the influence of environmental factors on shopping habits.

Animal Behavior Studies

Researchers studying animal behavior often use naturalistic observation to record behaviors in their natural habitats. These studies provide valuable insights about social structures, mating patterns, predation and other behaviors among various animal species.

The role of technology in naturalistic observation

With the advancement of technology, the process of conducting naturalistic observations has been simplified and improved. Digital cameras, video recorders, motion-sensitive devices, and computer software can aid in capturing, recording, and analyzing the observed behaviors effectively and efficiently. The use of this technology also eliminates or reduces the risk of observer bias and enhances the reliability of data gathered.

To conclude, naturalistic observation plays a crucial role in research within psychology and social sciences, adding utility to both descriptive and experimental research designs. It aids in gathering rich, ecological valid data from natural settings, providing deep insights about human or animal behaviors in their usual environments. But while powerful, it is prudent to be aware of the method’s limitations, and when possible, supplement it with other research methods to achieve a comprehensive understanding of the phenomenon in question.

Related Behavioral Science Terms

Belief perseverance, crystallized intelligence, extraneous variable, representative sample, factor analysis, egocentrism, stimulus generalization, reciprocal determinism, divergent thinking, convergent thinking, social environment, decision making, related articles.

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Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Chapter 1: Research Methods

Back to chapter, naturalistic observations, previous video 1.2: case studies, next video 1.4: surveys.

People tend to act differently when they know they are being observed. If researchers are interested in studying realistic phenomenon, they must strive to be as inconspicuous as possible when carrying out their research.

Specifically, they’ll employ naturalistic observation —a research method of unobtrusively watching behaviors in their natural setting—to ensure people are acting accurately, as they would normally.

For example, researchers may be interested in exploring how many times people actually pick up litter off the ground in the park compared to how often they complain about the clutter and don’t help. They could attach cameras to trees in a park—which the park-goers would soon forget about—and watch their behavior from a distance.

Because the participants are unaware of the researchers’ presence, they’ll behave as they would any other day. One person may complain how annoying it is that other people don’t pick up after themselves, but never throw away a piece of trash either.

However, if they saw any of the researchers observing them, they may alter their behavior—and pick up more trash—to impress others.

Thus, one of the major benefits of naturalistic observation is its high ecological validity —the extent to which the results from a study mirror what happens in everyday life.

Naturalistic observation research also has its drawbacks. For instance, because researchers are watching behavior “in the field,” they have no way of knowing when the behavior they want to occur—people entering the park and interacting with trash—will occur. As a result, this type of work can take time to carry out.

In addition, if researchers are expecting a certain result, they may do something, like accidentally move the cup out of sight. This action may then eliminate the option for some participants to even pick it up, and the researcher would be guilty of observer bias —unconsciously skewing the results to meet their predictions. To avoid this issue, researchers come up with strict criteria to categorize behaviors and calculate the inter-rater reliability —the degree of agreement in ratings between multiple observers.

Despite the limitations, naturalistic observations do allow researchers to draw rigorous conclusions from real-world situations that may otherwise go unnoticed.

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, as many individuals do not feel comfortable answering a question honestly. But do you think hand washing after every trip to the restroom is really that universal?

But if we are committed to finding out the facts about hand washing, we have other options available to us. Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting.

To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway.

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa. As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth. The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

This text is adapted from OpenStax, Psychology. OpenStax CNX.

What Is Naturalistic Observation? Definition and Examples

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• Ph.D., Psychology, Fielding Graduate University
• M.A., Psychology, Fielding Graduate University
• B.A., Film Studies, Cornell University

Naturalistic observation is a research method used in psychology and other social sciences in which research participants are observed in their natural environments. Unlike lab experiments that involve testing hypotheses and controlling variables, naturalistic observation simply requires recording what is observed in a specific setting.

Kay Takeaways: Naturalistic Observation

• Naturalistic observation is a research method in which people or other subjects are observed in their natural setting.
• Psychologists and other social scientists use naturalistic observation to study specific social or cultural settings that couldn’t be investigated in other ways, such as prisons, bars, and hospitals.
• Naturalistic observation has some drawbacks, including the inability to control for variables and a lack of replicability.

Naturalistic Observation Applications

Naturalistic observation involves observing subjects of interest in their normal, everyday setting . It is sometimes referred to as field work because it requires researchers to go out into the field (the natural setting) to collect data on their participants. Naturalistic observation traces its roots back to anthropology and animal behavior research. For example, cultural anthropologist Margaret Mead used naturalistic observation to study the daily lives of different groups in the South Pacific.

The approach doesn't always require researchers to observe people in such exotic environments, however. It can be conducted in any kind of social or organizational setting , including offices, schools, bars, prisons, dorm rooms, online message boards, or just about any other place where people can be observed. For example, psychologist Sylvia Scribner used naturalistic observation to investigate how people make decisions in various professions. To do so, she accompanied people—from milk men, to cashiers, to machine operators—as they went about their regular work routines.

Naturalistic observation is valuable when a researcher wants to learn more about people in a specific social or cultural setting but can’t gather the information any other way. Sometimes studying people in a lab can impact their behavior, be cost prohibitive, or both. For example, if a researcher wishes to study the behavior of shoppers in the weeks leading up to the Christmas holiday, it would be impractical to construct a store in the lab. Plus, even if the researcher did so, it would be unlikely to elicit the same response from participants as shopping at a store in the real world. Naturalistic observation offers the opportunity to observe shoppers’ behavior, and based on researchers' observations of the situation, has the potential to generate new ideas for specific hypotheses or avenues of research.

The method requires researchers to immerse themselves in the setting being studied. This typically involves taking copious field notes. Researchers may also interview specific people involved in the situation, collect documents from the setting, and make audio or video recordings. In her research on decision-making in different occupations, for instance, Scribner not only took detailed notes, she also gathered every scrap of written material her participants read and produced, and photographed the equipment they used.

Scope of the Observation

Before going into the field, researchers conducting naturalistic observation must define the scope of their research. While the researcher may want to study everything about the people in the chosen setting, this may not be realistic given the complexities of human behavior. As a result, the researcher must focus observations on the specific behaviors and responses they are most interested in studying.

For example, the researcher might choose to collect quantitative data by counting the number of times a specific behavior occurs. So, if the researcher is interested in dog owners' interactions with their dogs, they might tally the number of times the owner talks to their dog during a walk. On the other hand, much of the data collected during naturalistic observation, including notes, audio and video recordings and interviews, are qualitative data that require the researcher to describe, analyze, and interpret what was observed.

Sampling Methods

Another way researchers can limit the scope of a study is by using a specific sampling method. This will enable them to gather a representative sample of data on the subjects’ behavior without having to observe everything the subject does at all times. Sampling methods include:

• Time sampling, which means the researcher will observe subjects at different intervals of time. These intervals could be random or specific. For example, the researcher could decide to only observe subjects every morning for an hour.
• Situation sampling, which means the researcher will observe the same subjects in various situations. For instance, if a researcher wants to observe the behavior of Star Wars fans' responses to the release of the most recent movie in the franchise, the researcher might observe fans’ behavior at the red carpet of the movie's premiere, during screenings, and on online Star Wars message boards.
• Event sampling , which means the researcher will only record specific behaviors and ignore all others. For example, when observing interactions between children on a playground, the researcher might decide they’re only interested in observing how children decide to take turns on the slide while ignoring behavior on the other playground equipment.

Pros and Cons of Naturalistic Observation

There are a number of advantages to naturalistic observation. These include:

• Studies have greater external validity because the researcher’s data comes directly from observing subjects in their natural environment.
• Observing people in the field can lead to glimpses of behavior that could never occur in a lab, possibly leading to unique insights.
• The researcher can study things that would be impossible or unethical to reproduce in a lab. For example, while it would be unethical to study the way people cope with the aftermath of violence by manipulating exposure in a lab, researchers can gather data on this subject by observing participants in a support group.

Despite its value in certain situations, naturalistic observation can have a number of drawbacks, including:

• Naturalistic observation studies typically involve observing a limited number of settings . As a result, the subjects being studied are limited to certain ages, genders, ethnicities, or other characteristics, which means a study’s findings cannot be generalized to the population as a whole.
• Researchers can’t control for different variables like they can in a lab, which makes naturalistic observation studies less reliable and more difficult to replicate.
• Lack of control over external variables also makes it impossible to determine the cause of the behaviors the researcher observes.
• If subjects know they’re being observed, it has the potential to change their behavior.
• Cherry, Kendra. Naturalistic Observation in Psychology.” V erywellMind , 1 October, 2019. https://www.verywellmind.com/what-is-naturalistic-observation-2795391
• Cozby, Paul C. Methods in Behavioral Research . 10th ed., McGraw-Hill. 2009.
• McLeod, Saul A. “Observation Methods.” Simply Psychology , 6 June 2015. https://www.simplypsychology.org/observation.html
• Immersion Definition: Cultural, Language, and Virtual
• Social Cognitive Theory: How We Learn From the Behavior of Others
• What Is Behaviorism in Psychology?
• How Our Aligning Behavior Shapes Everyday Life
• What Is Direct Observation?
• Definition of Self-Fulfilling Prophecy in Sociology
• What Is Participant Observation Research?
• What Are Health Disparities?
• Assessing a Situation, in Terms of Sociology
• Anthropology vs. Sociology: What's the Difference?
• What Is International Economics?
• What Is Derived Demand? Definition and Examples
• What Is Groupthink? Definition and Examples
• What Is Deindividuation in Psychology? Definition and Examples
• What Is Neoliberalism? Definition and Examples
• What Is a Microaggression? Everyday Insults With Harmful Effects

6.5 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational research designs that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method 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). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply 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 not aware that they are being studied. Such an approach is called disguised naturalistic observation.  Ethically, this method 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. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity  refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are, flirting, having sex, wearing next to nothing, screaming at each other, and at times acting like complete fools in front of the entire nation.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that is collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation, the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers. In contrast with undisguised participant observation,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second passive deception is being used. The researcher is passively deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further,  disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [1]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins (published in  Social Psychology Quarterly ) on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [2] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researcher is in a much better position to understand the viewpoint and experiences of the people they are studying when they are apart of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation when researchers because active members of the social group they are studying, additional concerns arise that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation. Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic and participant observation. Often the setting in which the observations are made is not the natural setting, rather the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation. Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [3] . 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 Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. 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.  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.

As another 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) [4] . 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.

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 difficulty with coding is the issue of interrater reliability, as mentioned in Chapter 4. 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.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interested which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study  is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individuals’ depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

www.youtube.com/watch?v=KkaXNvzE4pk

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [5] , who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [6] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.1 Anna O. “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: http://en.wikipedia.org/wiki/File:Pappenheim_1882.jpg

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample to individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation. However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods.

The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with internal and external validity. Case studies lack the proper controls that true experiments contain. As such they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (indeed questioning into the possibility of a separate brain lesion began after HM’s death and dissection of his brain) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So as with all observational methods case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically a very abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity, with case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research is the use of  archival research  which involves analyzing 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) [7] . 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) [8] . 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 undergraduate 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 undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured 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

• There are several different approaches to observational research including naturalistic observation, participant observation, structured observation, case studies, and archival research.
• Naturalistic observation is used to observe people in their natural setting, participant observation involves becoming an active member of the group being observed, structured observation involves coding a small number of behaviors in a quantitative manner, case studies are typically used to collect in-depth information on a single individual, and archival research involves analysing existing data.
• Describe one problem related to internal validity.
• Describe one problem related to external validity.
• Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• 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. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• 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. ↵

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Naturalistic Observation | Definition, Guide & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 13 March 2023.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering with or influencing any variables in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Table of contents

What is naturalistic observation, types of naturalistic observation methods, how to collect data, data sampling, advantages of naturalistic observation, disadvantages of naturalistic observation, frequently asked questions about naturalistic observation.

In naturalistic observations, you study your research subjects in their own environments to explore their behaviours without any outside influence or control. It’s a research method used in field studies.

Traditionally, naturalistic observation studies have been used by animal researchers, psychologists, ethnographers , and anthropologists. Naturalistic observations are helpful as a hypothesis -generating approach, because you gather rich information that can inspire further research.

Based on his naturalistic observations, he believed that these birds imprinted on the first potential parent in their surroundings, and they quickly learned to follow them and their actions.

Naturalistic observation is especially valuable for studying behaviours and actions that may not be replicable in controlled lab settings.

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Naturalistic observations can be:

• Covert or overt: You either hide or reveal your identity as an observer to the participants you observe.
• Participant or non-participant: You participate in the activity or behaviour yourself, or you observe from the sidelines.

There are four main ways of using naturalistic observations.

Importantly, all of these take place in naturalistic settings rather than experimental laboratory settings. While you may actively participate in some types of observations, you refrain from influencing others or interfering with the activities you are observing too much.

You can use a variety of data collection methods for naturalistic observations.

Audiovisual recordings

Nowadays, it’s common to collect observations through audio and video recordings so you can revisit them at a later stage or share them with other trained observers. It’s best to place these recording devices discreetly so your participants aren’t distracted by them.

However, make sure you receive informed consent in a written format from each participant prior to recording them.

Note-taking

You can take notes while conducting naturalistic observations. Note down anything that seems relevant or important to you based on your research topic and interests in an unstructured way.

Tally counts

If you’re studying specific behaviours or events, it’s often helpful to make frequency counts of the number of times these occur during a certain time period. You can use a tally count to easily note down each instance that you observe in the moment.

There’s a lot of information you can collect when you conduct research in natural, uncontrolled environments. To simplify your data collection, you’ll often use data sampling.

Data sampling allows you to narrow down the focus of your data recording to specific times or events.

Time sampling

You record observations only at specific times. These time intervals can be randomly selected (e.g., at 8:03, 10:34, 12:51) or systematic (e.g., every 2 hours). You record whether your behaviours of interest occur during these time periods.

Event sampling

You record observations only when specific events occur. You may use a tally count to note the frequency of the event or take notes each time you see the event occurring.

Naturalistic observation is a valuable tool because of its flexibility, external validity, and suitability for research topics that can’t be studied in a lab.

Flexibility

Because naturalistic observation is a non-experimental method, you’re not bound to strict procedures. You can avoid using rigid protocols and also change your methods midway if you need to.

Ecological validity

Naturalistic observations are particularly high in ecological validity, because you use real life environments instead of lab settings. People don’t always act in the same ways in and outside the lab. Your participants behave in more authentic ways when they are unaware they’re being observed.

Naturalistic observations help you study topics that you can’t in the lab for ethical reasons.  You can also use technology to record conversations, behaviours, or other noise, provided you have consent or it’s otherwise ethically permissible .

The downsides of naturalistic observation include its lack of scientific control, ethical considerations, and potential for research bias from observers and subjects.

Lack of control

Since you perform research in natural environments, you can’t control the setting or any variables . Without this control, you won’t be able to draw conclusions about causal relationships . You also may not be able to replicate your findings in other contexts, with other people, or at other times.

Ethical considerations

Most people don’t want to be observed as they’re going about their day without their explicit consent or awareness. It’s important to always respect privacy and try to be unobtrusive. It’s also best to use naturalistic observations only in public situations where people expect they won’t be alone.

Observer bias

Because you indirectly collect data , there’s always a risk of observer bias in naturalistic observations. Your perceptions and interpretations of behaviour may be influenced by your own experiences and inaccurately represent the truth. This type of bias is particularly likely to occur in participant observation methods.

Subject bias

When you observe subjects in their natural environment, they may sometimes be aware they’re being observed. As a result, they may change their behaviours to act in more socially desirable ways to confirm your expectations.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

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True, Natural and Field Experiments An easy lesson idea for learning about experiments.

Travis Dixon September 29, 2016 Research Methodology

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There is a difference between a “true experiment” a “field experiment” and  a “natural experiment”. These separate experimental methods are commonly used in psychological research and they each have their strengths and limitations.

True Experiments

Berry’s classic study compared two cultures in order to understand how economics, parenting and cultural values can influence behaviour. But what type of method would we call this?

A true experiment is one where:

• have randomly assigned participants to a condition (if using independent samples)

Repeated measures designs don’t need random allocation because there is no allocation as all participants do both conditions.

One potential issue in laboratory experiments is that they are conducted in environments that are not natural for the participants, so the behaviour might not reflect what happens in real life.

Field Experiments

A field experiment is one where:

• the researcher conducts an experiment by manipulating an IV,
• …and measuring the effects on the DV in a natural environment.

They still try to minimize the effects of other variables and to control for these, but it’s just happening in a natural environment: the field.

• Natural Experiment

A natural experiment is one where:

• the independent variable is naturally occurring. i.e. it hasn’t been manipulated by the researcher.

There are many instances where naturally occurring events or phenomenon may interest researchers. The issue with natural experiments is that it can’t be guaranteed that it is the independent variable that is having an effect on the dependent variable.

• Quantitative Research Methods Glossary
• Let’s STOP the research methods madness!
• What makes an experiment “quasi”?

Activity Idea

Students can work with a partner to decide if the following are true, field or natural experiments.

If you cant’ decide, what other information do you need?

• Berry’s cross-cultural study on conformity ( Key Study: Conformity Across Cultures (Berry, 1967)
• Bandura’s bobo doll study ( Key Study: Bandura’s Bobo Doll (1963)
• Rosenzweig’s rat study ( Key Study: Animal research on neuroplasticity (Rosenzweig and Bennett, 1961)

Let’s make it a bit trickier:

• Key Study: London Taxi Drivers vs. Bus Drivers (Maguire, 2006)
• Key Study: Evolution of Gender Differences in Sexual Behaviour (Clark and Hatfield, 1989)
• Key Study: Serotonin, tryptophan and the brain (Passamonti et al., 2012)
• Saint Helena Study : television was introduced on the island of Saint Helena in the Atlantic ocean and the researchers measured the behaviour of the kids before and after TV was introduced.
• Light Therapy : the researchers randomly assigned patients with depression into three different groups. The three groups received different forms of light therapy to treat depression (red light, bright light, soft light). The lights were installed in the participants’ bedrooms and were timed to come on naturally. The effects on depression were measured via interviews.

What are the strengths and limitations of:

• True Experiment 
• Field Experiment 

Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.

Natural Observation

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• Cite this reference work entry

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• Ivy Chong 3  

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Direct observation ; Field observation ; Naturalistic observation

Studying or assessing individuals as they interact in their typical environment.

Description

During an observation, there is no experimental manipulation, and data are recorded under the conditions that occur in the environment. Specifically, natural observation is a technique used in research and assessment, which is similar to direct observation, where information regarding an individual or individuals is gathered immediately while the individual’s behavior occurs naturally their everyday environment, rather than a laboratory or clinical setting [ 1 ]. Information gathered during observations may be used in order to identify environmental variables necessary to create an artificial laboratory or clinical environment in which to assess or conduct research in, to identify areas which need assessment or research, or may garner the information necessary when research or assessment is being conducted.

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Goodwin, C. J. (2003). Research in psychology: Methods and design (3rd ed.). Hoboken, NJ: Wiley.

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Hoge, R. D. (1985). The validity of direct observation measures of pupil classroom behavior. Review of Educational Research, 55 (4), 469–483.

Miller, D. B. (1977). Roles of naturalistic observation in comparative psychology. American Psychologist, 32 (3), 211–219.

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Scott Center for Autism Treatment, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL, USA

Jessica Sparling & Ivy Chong

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Sparling, J., Chong, I. (2011). Natural Observation. In: Goldstein, S., Naglieri, J.A. (eds) Encyclopedia of Child Behavior and Development. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79061-9_1916

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Study Notes

Types of Experiment: Overview

Last updated 6 Sept 2022

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Different types of methods are used in research, which loosely fall into 1 of 2 categories.

Experimental (Laboratory, Field & Natural) & N on experimental ( correlations, observations, interviews, questionnaires and case studies).

All the three types of experiments have characteristics in common. They all have:

• an independent variable (I.V.) which is manipulated or a naturally occurring variable
• a dependent variable (D.V.) which is measured
• there will be at least two conditions in which participants produce data.

Note – natural and quasi experiments are often used synonymously but are not strictly the same, as with quasi experiments participants cannot be randomly assigned, so rather than there being a condition there is a condition.

Laboratory Experiments

These are conducted under controlled conditions, in which the researcher deliberately changes something (I.V.) to see the effect of this on something else (D.V.).

Control – lab experiments have a high degree of control over the environment & other extraneous variables which means that the researcher can accurately assess the effects of the I.V, so it has higher internal validity.

Replicable – due to the researcher’s high levels of control, research procedures can be repeated so that the reliability of results can be checked.

Limitations

Lacks ecological validity – due to the involvement of the researcher in manipulating and controlling variables, findings cannot be easily generalised to other (real life) settings, resulting in poor external validity.

Field Experiments

These are carried out in a natural setting, in which the researcher manipulates something (I.V.) to see the effect of this on something else (D.V.).

Validity – field experiments have some degree of control but also are conducted in a natural environment, so can be seen to have reasonable internal and external validity.

Less control than lab experiments and therefore extraneous variables are more likely to distort findings and so internal validity is likely to be lower.

Natural / Quasi Experiments

These are typically carried out in a natural setting, in which the researcher measures the effect of something which is to see the effect of this on something else (D.V.). Note that in this case there is no deliberate manipulation of a variable; this already naturally changing, which means the research is merely measuring the effect of something that is already happening.

High ecological validity – due to the lack of involvement of the researcher; variables are naturally occurring so findings can be easily generalised to other (real life) settings, resulting in high external validity.

Lack of control – natural experiments have no control over the environment & other extraneous variables which means that the researcher cannot always accurately assess the effects of the I.V, so it has low internal validity.

Not replicable – due to the researcher’s lack of control, research procedures cannot be repeated so that the reliability of results cannot be checked.

• Laboratory Experiment
• Field experiment
• Quasi Experiment
• Natural Experiment
• Field experiments

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8 Observational Research

This chapter derived from 6.5 Observational Research by Paul C. Price, Rajiv Jhangiani, I-Chant A. Chiang, Dana C. Leighton, & Carrie Cuttler is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.   [1]

Learning Objectives

By the end of this chapter, students must be able to:

• Explain different types of observational research methods
• Choose an appropriate type of method for a particular situation

WHAT IS OBSERVATIONAL RESEARCH?

The term  observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such, we cannot arrive at causal conclusions using this approach. The data that is collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational research designs that will be described below.

NATURALISTIC  VS CONTRIVED OBSERVATION

Naturalistic observation  is an observational method 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). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr. Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzees’ social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply 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 not aware that they are being studied. Such an approach is called  disguised naturalistic observation.  Ethically, this method 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” is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

Contrived observation occurs in an artificial environment, such as a lab setting. For example, researchers may wish to measure people’s physiological responses to an ad that is being screened in the university researcher’s classroom.

Source: DW News. [2]

DISGUISED VS UNDISGUISED OBSERVATION

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher’s presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity.  Reactivity  refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are, flirting, having sex, wearing next to nothing, screaming at each other, and at times acting like complete fools in front of the entire nation.

PARTICIPANT VS MECHANICAL OBSERVATION

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that is collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation,  the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers. In contrast with  undisguised participant observation,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First, no informed consent can be obtained and second passive deception is being used. The researcher is passively deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation. 

One of the primary benefits of participant observation is that the researcher is in a much better position to understand the viewpoint and experiences of the people they are studying when they are a part of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation when researchers because active members of the social group they are studying, additional concerns arise that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant-observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Exhibit 1: Different types of equipment are used to study physiological/neurological responses in humans.

Eye-tracking equipment, Neuromorphic camera/sensor equipment  © 2022  Western Sydney University taken by   Sally Tsoutas Western Sydney University Photographer  is licensed under an   Attribution-NonCommercial-NoDerivatives 4.0 International

In many situations, the means of observation are mechanical rather than human. This involves video cameras, traffic counters, checkout scanners, smartphones, and a range of devices that measure physiological responses. These devices include eye-tracking monitors, pupilometers, psychogalvanometers, voice pitch analyzers, and instruments to measure neurological signals.

STRUCTURED VS UNSTRUCTURED OBSERVATION

In structured observation, the emphasis is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest. For example, a marketer may be interested in the number of people entering a mall, or the number of times people stop to take a look at a displayed ad. Unstructured observation, on the other hand, is flexible and more informal. There is no checklist for the researcher to follow. The researcher may observe all aspects of a phenomenon and then provides details about things that he/she found to be relevant in understanding a situation. This technique can be more subjective than the structured approach.

When the observations require a judgment on the part of the observers (e.g., do customers look happy while shopping in a store?) 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. In one study, for example, researchers 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.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, oftentimes the environment is structured to encourage the behaviors of interest which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real-world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

OBSERVATION OF PHYSICAL EVIDENCE

Market researchers are able to gather data by observing physical evidence which can provide key insights. Artefacts – such as food cans in garbage bins and counting of physical inventories are key methods that can be used. Content analysis of ads, newspapers, images, or memos can help researchers understand certain aspects of a phenomenon.

• Price, PC, Jhangiani, R Chiang, ICA, Leighton, DC & Cuttler, C 2017, 'Observational research' in Research methods in psychology, Pressbooks, chapter 6.5, viewed 2 March 2022, <https://opentext.wsu.edu/carriecuttler/chapter/observational-research/>. ↵
• DW News 2013, Market researchers observe where we look: made in Germany, online video, 4 September, viewed 3 March 2022, <https://www.youtube.com/watch?v=Hatmm84sqm0>. ↵

Customer Insights Copyright © 2022 by Aila Khan is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Frequently asked questions

What is the difference between an observational study and an experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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