observational research method

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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Observation Method in Psychology: Naturalistic, Participant and Controlled

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.

Learn about our Editorial Process

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.

On This Page:

The observation method in psychology involves directly and systematically witnessing and recording measurable behaviors, actions, and responses in natural or contrived settings without attempting to intervene or manipulate what is being observed.

Used to describe phenomena, generate hypotheses, or validate self-reports, psychological observation can be either controlled or naturalistic with varying degrees of structure imposed by the researcher.

There are different types of observational methods, and distinctions need to be made between:

1. Controlled Observations 2. Naturalistic Observations 3. Participant Observations

In addition to the above categories, observations can also be either overt/disclosed (the participants know they are being studied) or covert/undisclosed (the researcher keeps their real identity a secret from the research subjects, acting as a genuine member of the group).

In general, conducting observational research is relatively inexpensive, but it remains highly time-consuming and resource-intensive in data processing and analysis.

The considerable investments needed in terms of coder time commitments for training, maintaining reliability, preventing drift, and coding complex dynamic interactions place practical barriers on observers with limited resources.

Controlled Observation

Controlled observation is a research method for studying behavior in a carefully controlled and structured environment.

The researcher sets specific conditions, variables, and procedures to systematically observe and measure behavior, allowing for greater control and comparison of different conditions or groups.

The researcher decides where the observation will occur, at what time, with which participants, and in what circumstances, and uses a standardized procedure. Participants are randomly allocated to each independent variable group.

Rather than writing a detailed description of all behavior observed, it is often easier to code behavior according to a previously agreed scale using a behavior schedule (i.e., conducting a structured observation).

The researcher systematically classifies the behavior they observe into distinct categories. Coding might involve numbers or letters to describe a characteristic or the use of a scale to measure behavior intensity.

The categories on the schedule are coded so that the data collected can be easily counted and turned into statistics.

For example, Mary Ainsworth used a behavior schedule to study how infants responded to brief periods of separation from their mothers. During the Strange Situation procedure, the infant’s interaction behaviors directed toward the mother were measured, e.g.,

• Proximity and contact-seeking
• Contact maintaining
• Avoidance of proximity and contact
• Resistance to contact and comforting

The observer noted down the behavior displayed during 15-second intervals and scored the behavior for intensity on a scale of 1 to 7.

Sometimes participants’ behavior is observed through a two-way mirror, or they are secretly filmed. Albert Bandura used this method to study aggression in children (the Bobo doll studies ).

A lot of research has been carried out in sleep laboratories as well. Here, electrodes are attached to the scalp of participants. What is observed are the changes in electrical activity in the brain during sleep ( the machine is called an EEG ).

Controlled observations are usually overt as the researcher explains the research aim to the group so the participants know they are being observed.

Controlled observations are also usually non-participant as the researcher avoids direct contact with the group and keeps a distance (e.g., observing behind a two-way mirror).

• Controlled observations can be easily replicated by other researchers by using the same observation schedule. This means it is easy to test for reliability .
• The data obtained from structured observations is easier and quicker to analyze as it is quantitative (i.e., numerical) – making this a less time-consuming method compared to naturalistic observations.
• Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. This means a large sample can be obtained, resulting in the findings being representative and having the ability to be generalized to a large population.

Limitations

• Controlled observations can lack validity due to the Hawthorne effect /demand characteristics. When participants know they are being watched, they may act differently.

Naturalistic Observation

Naturalistic observation is a research method in which the researcher studies behavior in its natural setting without intervention or manipulation.

It involves observing and recording behavior as it naturally occurs, providing insights into real-life behaviors and interactions in their natural context.

Naturalistic observation is a research method commonly used by psychologists and other social scientists.

This technique involves observing and studying the spontaneous behavior of participants in natural surroundings. The researcher simply records what they see in whatever way they can.

In unstructured observations, the researcher records all relevant behavior with a coding system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.

Compared with controlled observations, it is like the difference between studying wild animals in a zoo and studying them in their natural habitat.

With regard to human subjects, Margaret Mead used this method to research the way of life of different tribes living on islands in the South Pacific. Kathy Sylva used it to study children at play by observing their behavior in a playgroup in Oxfordshire.

Collecting Naturalistic Behavioral Data

Technological advances are enabling new, unobtrusive ways of collecting naturalistic behavioral data.

The Electronically Activated Recorder (EAR) is a digital recording device participants can wear to periodically sample ambient sounds, allowing representative sampling of daily experiences (Mehl et al., 2012).

Studies program EARs to record 30-50 second sound snippets multiple times per hour. Although coding the recordings requires extensive resources, EARs can capture spontaneous behaviors like arguments or laughter.

EARs minimize participant reactivity since sampling occurs outside of awareness. This reduces the Hawthorne effect, where people change behavior when observed.

The SenseCam is another wearable device that passively captures images documenting daily activities. Though primarily used in memory research currently (Smith et al., 2014), systematic sampling of environments and behaviors via the SenseCam could enable innovative psychological studies in the future.

• By being able to observe the flow of behavior in its own setting, studies have greater ecological validity.
• Like case studies , naturalistic observation is often used to generate new ideas. Because it gives the researcher the opportunity to study the total situation, it often suggests avenues of inquiry not thought of before.
• The ability to capture actual behaviors as they unfold in real-time, analyze sequential patterns of interactions, measure base rates of behaviors, and examine socially undesirable or complex behaviors that people may not self-report accurately.
• These observations are often conducted on a micro (small) scale and may lack a representative sample (biased in relation to age, gender, social class, or ethnicity). This may result in the findings lacking the ability to generalize to wider society.
• Natural observations are less reliable as other variables cannot be controlled. This makes it difficult for another researcher to repeat the study in exactly the same way.
• Highly time-consuming and resource-intensive during the data coding phase (e.g., training coders, maintaining inter-rater reliability, preventing judgment drift).
• With observations, we do not have manipulations of variables (or control over extraneous variables), meaning cause-and-effect relationships cannot be established.

Participant Observation

Participant observation is a variant of the above (natural observations) but here, the researcher joins in and becomes part of the group they are studying to get a deeper insight into their lives.

If it were research on animals , we would now not only be studying them in their natural habitat but be living alongside them as well!

Leon Festinger used this approach in a famous study into a religious cult that believed that the end of the world was about to occur. He joined the cult and studied how they reacted when the prophecy did not come true.

Participant observations can be either covert or overt. Covert is where the study is carried out “undercover.” The researcher’s real identity and purpose are kept concealed from the group being studied.

The researcher takes a false identity and role, usually posing as a genuine member of the group.

On the other hand, overt is where the researcher reveals his or her true identity and purpose to the group and asks permission to observe.

• It can be difficult to get time/privacy for recording. For example, researchers can’t take notes openly with covert observations as this would blow their cover. This means they must wait until they are alone and rely on their memory. This is a problem as they may forget details and are unlikely to remember direct quotations.
• If the researcher becomes too involved, they may lose objectivity and become biased. There is always the danger that we will “see” what we expect (or want) to see. This problem is because they could selectively report information instead of noting everything they observe. Thus reducing the validity of their data.

Recording of Data

With controlled/structured observation studies, an important decision the researcher has to make is how to classify and record the data. Usually, this will involve a method of sampling.

In most coding systems, codes or ratings are made either per behavioral event or per specified time interval (Bakeman & Quera, 2011).

The three main sampling methods are:

Event-based coding involves identifying and segmenting interactions into meaningful events rather than timed units.

For example, parent-child interactions may be segmented into control or teaching events to code. Interval recording involves dividing interactions into fixed time intervals (e.g., 6-15 seconds) and coding behaviors within each interval (Bakeman & Quera, 2011).

Event recording allows counting event frequency and sequencing while also potentially capturing event duration through timed-event recording. This provides information on time spent on behaviors.

• Interval recording is common in microanalytic coding to sample discrete behaviors in brief time samples across an interaction. The time unit can range from seconds to minutes to whole interactions. Interval recording requires segmenting interactions based on timing rather than events (Bakeman & Quera, 2011).
• Instantaneous sampling provides snapshot coding at certain moments rather than summarizing behavior within full intervals. This allows quicker coding but may miss behaviors in between target times.

Coding Systems

The coding system should focus on behaviors, patterns, individual characteristics, or relationship qualities that are relevant to the theory guiding the study (Wampler & Harper, 2014).

Codes vary in how much inference is required, from concrete observable behaviors like frequency of eye contact to more abstract concepts like degree of rapport between a therapist and client (Hill & Lambert, 2004). More inference may reduce reliability.

Coding schemes can vary in their level of detail or granularity. Micro-level schemes capture fine-grained behaviors, such as specific facial movements, while macro-level schemes might code broader behavioral states or interactions. The appropriate level of granularity depends on the research questions and the practical constraints of the study.

Another important consideration is the concreteness of the codes. Some schemes use physically based codes that are directly observable (e.g., “eyes closed”), while others use more socially based codes that require some level of inference (e.g., “showing empathy”). While physically based codes may be easier to apply consistently, socially based codes often capture more meaningful behavioral constructs.

Most coding schemes strive to create sets of codes that are mutually exclusive and exhaustive (ME&E). This means that for any given set of codes, only one code can apply at a time (mutual exclusivity), and there is always an applicable code (exhaustiveness). This property simplifies both the coding process and subsequent data analysis.

For example, a simple ME&E set for coding infant state might include: 1) Quiet alert, 2) Crying, 3) Fussy, 4) REM sleep, and 5) Deep sleep. At any given moment, an infant would be in one and only one of these states.

Macroanalytic coding systems

Macroanalytic coding systems involve rating or summarizing behaviors using larger coding units and broader categories that reflect patterns across longer periods of interaction rather than coding small or discrete behavioral acts. 

Macroanalytic coding systems focus on capturing overarching themes, global qualities, or general patterns of behavior rather than specific, discrete actions.

For example, a macroanalytic coding system may rate the overall degree of therapist warmth or level of client engagement globally for an entire therapy session, requiring the coders to summarize and infer these constructs across the interaction rather than coding smaller behavioral units.

These systems require observers to make more inferences (more time-consuming) but can better capture contextual factors, stability over time, and the interdependent nature of behaviors (Carlson & Grotevant, 1987).

Examples of Macroanalytic Coding Systems:

• Emotional Availability Scales (EAS) : This system assesses the quality of emotional connection between caregivers and children across dimensions like sensitivity, structuring, non-intrusiveness, and non-hostility.
• Classroom Assessment Scoring System (CLASS) : Evaluates the quality of teacher-student interactions in classrooms across domains like emotional support, classroom organization, and instructional support.

Microanalytic coding systems

Microanalytic coding systems involve rating behaviors using smaller, more discrete coding units and categories.

These systems focus on capturing specific, discrete behaviors or events as they occur moment-to-moment. Behaviors are often coded second-by-second or in very short time intervals.

For example, a microanalytic system may code each instance of eye contact or head nodding during a therapy session. These systems code specific, molecular behaviors as they occur moment-to-moment rather than summarizing actions over longer periods.

Microanalytic systems require less inference from coders and allow for analysis of behavioral contingencies and sequential interactions between therapist and client. However, they are more time-consuming and expensive to implement than macroanalytic approaches.

Examples of Microanalytic Coding Systems:

• Facial Action Coding System (FACS) : Codes minute facial muscle movements to analyze emotional expressions.
• Specific Affect Coding System (SPAFF) : Used in marital interaction research to code specific emotional behaviors.
• Noldus Observer XT : A software system that allows for detailed coding of behaviors in real-time or from video recordings.

Mesoanalytic coding systems

Mesoanalytic coding systems attempt to balance macro- and micro-analytic approaches.

In contrast to macroanalytic systems that summarize behaviors in larger chunks, mesoanalytic systems use medium-sized coding units that target more specific behaviors or interaction sequences (Bakeman & Quera, 2017).

For example, a mesoanalytic system may code each instance of a particular type of therapist statement or client emotional expression. However, mesoanalytic systems still use larger units than microanalytic approaches coding every speech onset/offset.

The goal of balancing specificity and feasibility makes mesoanalytic systems well-suited for many research questions (Morris et al., 2014). Mesoanalytic codes can preserve some sequential information while remaining efficient enough for studies with adequate but limited resources.

For instance, a mesoanalytic couple interaction coding system could target key behavior patterns like validation sequences without coding turn-by-turn speech.

In this way, mesoanalytic coding allows reasonable reliability and specificity without requiring extensive training or observation. The mid-level focus offers a pragmatic compromise between depth and breadth in analyzing interactions.

Examples of Mesoanalytic Coding Systems:

• Feeding Scale for Mother-Infant Interaction : Assesses feeding interactions in 5-minute episodes, coding specific behaviors and overall qualities.
• Couples Interaction Rating System (CIRS): Codes specific behaviors and rates overall qualities in segments of couple interactions.
• Teaching Styles Rating Scale : Combines frequency counts of specific teacher behaviors with global ratings of teaching style in classroom segments.

Preventing Coder Drift

Coder drift results in a measurement error caused by gradual shifts in how observations get rated according to operational definitions, especially when behavioral codes are not clearly specified.

This type of error creeps in when coders fail to regularly review what precise observations constitute or do not constitute the behaviors being measured.

Preventing drift refers to taking active steps to maintain consistency and minimize changes or deviations in how coders rate or evaluate behaviors over time. Specifically, some key ways to prevent coder drift include:

• Operationalize codes : It is essential that code definitions unambiguously distinguish what interactions represent instances of each coded behavior. 
• Ongoing training : Returning to those operational definitions through ongoing training serves to recalibrate coder interpretations and reinforce accurate recognition. Having regular “check-in” sessions where coders practice coding the same interactions allows monitoring that they continue applying codes reliably without gradual shifts in interpretation.
• Using reference videos : Coders periodically coding the same “gold standard” reference videos anchors their judgments and calibrate against original training. Without periodic anchoring to original specifications, coder decisions tend to drift from initial measurement reliability.
• Assessing inter-rater reliability : Statistical tracking that coders maintain high levels of agreement over the course of a study, not just at the start, flags any declines indicating drift. Sustaining inter-rater agreement requires mitigating this common tendency for observer judgment change during intensive, long-term coding tasks.
• Recalibrating through discussion : Having meetings for coders to discuss disagreements openly explores reasons judgment shifts may be occurring over time. Consensus on the application of codes is restored.
• Adjusting unclear codes : If reliability issues persist, revisiting and refining ambiguous code definitions or anchors can eliminate inconsistencies arising from coder confusion.

Essentially, the goal of preventing coder drift is maintaining standardization and minimizing unintentional biases that may slowly alter how observational data gets rated over periods of extensive coding.

Through the upkeep of skills, continuing calibration to benchmarks, and monitoring consistency, researchers can notice and correct for any creeping changes in coder decision-making over time.

Reducing Observer Bias

Observational research is prone to observer biases resulting from coders’ subjective perspectives shaping the interpretation of complex interactions (Burghardt et al., 2012). When coding, personal expectations may unconsciously influence judgments. However, rigorous methods exist to reduce such bias.

Coding Manual

A detailed coding manual minimizes subjectivity by clearly defining what behaviors and interaction dynamics observers should code (Bakeman & Quera, 2011).

High-quality manuals have strong theoretical and empirical grounding, laying out explicit coding procedures and providing rich behavioral examples to anchor code definitions (Lindahl, 2001).

Clear delineation of the frequency, intensity, duration, and type of behaviors constituting each code facilitates reliable judgments and reduces ambiguity for coders. Application risks inconsistency across raters without clarity on how codes translate to observable interaction.

Coder Training

Competent coders require both interpersonal perceptiveness and scientific rigor (Wampler & Harper, 2014). Training thoroughly reviews the theoretical basis for coded constructs and teaches the coding system itself.

Multiple “gold standard” criterion videos demonstrate code ranges that trainees independently apply. Coders then meet weekly to establish reliability of 80% or higher agreement both among themselves and with master criterion coding (Hill & Lambert, 2004).

Ongoing training manages coder drift over time. Revisions to unclear codes may also improve reliability. Both careful selection and investment in rigorous training increase quality control.

Blind Methods

To prevent bias, coders should remain unaware of specific study predictions or participant details (Burghardt et al., 2012). Separate data gathering versus coding teams helps maintain blinding.

Coders should be unaware of study details or participant identities that could bias coding (Burghardt et al., 2012).

Separate teams collecting data versus coding data can reduce bias.

In addition, scheduling procedures can prevent coders from rating data collected directly from participants with whom they have had personal contact. Maintaining coder independence and blinding enhances objectivity.

Data Analysis Approaches

Data analysis in behavioral observation aims to transform raw observational data into quantifiable measures that can be statistically analyzed.

It’s important to note that the choice of analysis approach is not arbitrary but should be guided by the research questions, study design, and nature of the data collected.

Interval data (where behavior is recorded at fixed time points), event data (where the occurrence of behaviors is noted as they happen), and timed-event data (where both the occurrence and duration of behaviors are recorded) may require different analytical approaches.

Similarly, the level of measurement (categorical, ordinal, or continuous) will influence the choice of statistical tests.

Researchers typically start with simple descriptive statistics to get a feel for their data before moving on to more complex analyses. This stepwise approach allows for a thorough understanding of the data and can often reveal unexpected patterns or relationships that merit further investigation.

simple descriptive statistics

Descriptive statistics give an overall picture of behavior patterns and are often the first step in analysis.

• Frequency counts tell us how often a particular behavior occurs, while rates express this frequency in relation to time (e.g., occurrences per minute).
• Duration measures how long behaviors last, offering insight into their persistence or intensity.
• Probability calculations indicate the likelihood of a behavior occurring under certain conditions, and relative frequency or duration statistics show the proportional occurrence of different behaviors within a session or across the study.

These simple statistics form the foundation of behavioral analysis, providing researchers with a broad picture of behavioral patterns. 

They can reveal which behaviors are most common, how long they typically last, and how they might vary across different conditions or subjects.

For instance, in a study of classroom behavior, these statistics might show how often students raise their hands, how long they typically stay focused on a task, or what proportion of time is spent on different activities.

contingency analyses

Contingency analyses help identify if certain behaviors tend to occur together or in sequence.

• Contingency tables , also known as cross-tabulations, display the co-occurrence of two or more behaviors, allowing researchers to see if certain behaviors tend to happen together.
• Odds ratios provide a measure of the strength of association between behaviors, indicating how much more likely one behavior is to occur in the presence of another.
• Adjusted residuals in these tables can reveal whether the observed co-occurrences are significantly different from what would be expected by chance.

For example, in a study of parent-child interactions, contingency analyses might reveal whether a parent’s praise is more likely to follow a child’s successful completion of a task, or whether a child’s tantrum is more likely to occur after a parent’s refusal of a request.

These analyses can uncover important patterns in social interactions, learning processes, or behavioral chains.

sequential analyses

Sequential analyses are crucial for understanding processes and temporal relationships between behaviors.

• Lag sequential analysis looks at the likelihood of one behavior following another within a specified number of events or time units.
• Time-window sequential analysis examines whether a target behavior occurs within a defined time frame after a given behavior.

These methods are particularly valuable for understanding processes that unfold over time, such as conversation patterns, problem-solving strategies, or the development of social skills.

observer agreement

Since human observers often code behaviors, it’s important to check reliability . This is typically done through measures of observer agreement.

• Cohen’s kappa is commonly used for categorical data, providing a measure of agreement between observers that accounts for chance agreement.
• Intraclass correlation coefficient (ICC) : Used for continuous data or ratings.

Good observer agreement is crucial for the validity of the study, as it demonstrates that the observed behaviors are consistently identified and coded across different observers or time points.

advanced statistical approaches

As researchers delve deeper into their data, they often employ more advanced statistical techniques.

• For instance, an ANOVA might reveal differences in the frequency of aggressive behaviors between children from different socioeconomic backgrounds or in different school settings.
• This approach allows researchers to account for dependencies in the data and to examine how behaviors might be influenced by factors at different levels (e.g., individual characteristics, group dynamics, and situational factors).
• This method can reveal trends, cycles, or patterns in behavior over time, which might not be apparent from simpler analyses. For instance, in a study of animal behavior, time series analysis might uncover daily or seasonal patterns in feeding, mating, or territorial behaviors.

representation techniques

Representation techniques help organize and visualize data:

• Many researchers use a code-unit grid, which represents the data as a matrix with behaviors as rows and time units as columns.
• This format facilitates many types of analyses and allows for easy visualization of behavioral patterns.
• Standardized formats like the Sequential Data Interchange Standard (SDIS) help ensure consistency in data representation across studies and facilitate the use of specialized analysis software.
• Indeed, the complexity of behavioral observation data often necessitates the use of specialized software tools. Programs like GSEQ, Observer, and INTERACT are designed specifically for the analysis of observational data and can perform many of the analyses described above efficiently and accurately.

Bakeman, R., & Quera, V. (2017). Sequential analysis and observational methods for the behavioral sciences. Cambridge University Press.

Burghardt, G. M., Bartmess-LeVasseur, J. N., Browning, S. A., Morrison, K. E., Stec, C. L., Zachau, C. E., & Freeberg, T. M. (2012). Minimizing observer bias in behavioral studies: A review and recommendations. Ethology, 118 (6), 511-517.

Hill, C. E., & Lambert, M. J. (2004). Methodological issues in studying psychotherapy processes and outcomes. In M. J. Lambert (Ed.), Bergin and Garfield’s handbook of psychotherapy and behavior change (5th ed., pp. 84–135). Wiley.

Lindahl, K. M. (2001). Methodological issues in family observational research. In P. K. Kerig & K. M. Lindahl (Eds.), Family observational coding systems: Resources for systemic research (pp. 23–32). Lawrence Erlbaum Associates.

Mehl, M. R., Robbins, M. L., & Deters, F. G. (2012). Naturalistic observation of health-relevant social processes: The electronically activated recorder methodology in psychosomatics. Psychosomatic Medicine, 74 (4), 410–417.

Morris, A. S., Robinson, L. R., & Eisenberg, N. (2014). Applying a multimethod perspective to the study of developmental psychology. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 103–123). Cambridge University Press.

Smith, J. A., Maxwell, S. D., & Johnson, G. (2014). The microstructure of everyday life: Analyzing the complex choreography of daily routines through the automatic capture and processing of wearable sensor data. In B. K. Wiederhold & G. Riva (Eds.), Annual Review of Cybertherapy and Telemedicine 2014: Positive Change with Technology (Vol. 199, pp. 62-64). IOS Press.

Traniello, J. F., & Bakker, T. C. (2015). The integrative study of behavioral interactions across the sciences. In T. K. Shackelford & R. D. Hansen (Eds.), The evolution of sexuality (pp. 119-147). Springer.

Wampler, K. S., & Harper, A. (2014). Observational methods in couple and family assessment. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 490–502). Cambridge University Press.

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What is an Observational Research: Steps, Types, Pros and Cons

Observational research refers to qualitative and non-experimental studies that seek to systematically observe, record, and analyse a particular society, culture, behaviours and attitudes. It is non-experimental in its observation as it does not manipulate any variables.

The steps are undertaken in conducting Observation research usually include:

• Deciding upon the goals of the study
• Deciding upon the group to be observed
• Choosing a type of observation method to employ
• Earning access to the group
• Establishing a rapport with participants
• Conducting the study by observing and recording behaviour, attitudes and beliefs over a specific time period
• Exiting the observation research setting
• Analysing data that was recorded
• Writing the report and presenting the findings

(Bailey, 1994)

Observational Research is typically dichotomized on the basis of  :

Degree of Structure of the Environment:

• Participant observation : this type of observation research falls under naturalistic observation as it is employed in a natural setting. It calls for a researcher to (either covertly or overtly) participate or immerse themselves in the setting they are studying, becoming a part of the community they are observing and make inferences through their experience. Overt participation implies that the participants aware that they are being observed and studied while covert participation implies that a researcher will act as a member of the group, they are studying without others knowing that they are a researcher. One of the first recorded employments of this method was in the early 19 th century by Joseph Marie who sought to understand Native Americans by becoming one of them stating that, “it is by learning their language that we will become their fellow citizens (Gaille, 2020)”.
• Controlled Observation: When an environment needs to be confined to a structure, a researcher will use a controlled observation design. This  is a type of observation research that is employed mostly in psychological research and in the field of marketing. Controlled observation serves as an exception to the non-experimental criterion of observational research, for this method observes behaviour in a controlled laboratory setting. An ‘un-controlled observation’ simply implies a naturalistic observation employed in an unstructured environment.

Degree of Structure imposed upon the environment by the researcher

• Structured observation: This type of observation employs a specific framework for observing, categorizing, and recording behaviour.
• Unstructured Observation:  This type of observation implies the absence of a specific framework that dictates when, how and what behaviours must be recorded. It alludes to an open-ended approach to a subject in which the researcher records almost everything he observes, sifting through the data at a later stage.

Other types of Observation research include:

• Indirect observation: In a case where the researcher isn’t able to conduct their research in the natural setting of their subject, they must resort to conducting indirect observation. This is the most non-invasive method of research where the researcher will gather primary information by employing techniques in physical tracing such as erosion measures. Erosion Measures refers to studying materials and their condition in order to make conclusive findings; For instance, a researcher may study the floor of a museum to see which exhibits are most popular. Social anthropologists and archaeologists may employ this type of observation research to draw conclusions about historical societies.
• Direct Observation: The opposite of indirect observation, direct observation, would encompass many of the aforementioned types of observation research including naturalistic and participant observation.

Observation research, irrespective of type, come with a plethora of advantages and disadvantages as described below.

Advantages :

• In-expensive: Observational research is relatively in-expensive to conduct as researchers need minimal resources to conduct their observations and no variables can be controlled or manipulated. The sociologist simply observes an already existing phenomena like interest group dynamics or a particular society within its natural setting, hence does not have to allocate many resources to conduct their observation.
• Flexible: Observational research boasts several types within the natural/participant dichotomy such as overt/ covert participation, case studies and archival research allowing one to choose an outline the best suits their research question.
• Greater Ecological Validity: Ecological Validity refers to the real-world application of a studies’ findings: and since observation research takes place in a natural setting, all observations are made ‘from the real world’ leading to greater ecological validity than other methods of research that are conducted in experimental or laboratory set ups where participants may provide inaccurate self-reports of their own behaviours. Observation research eliminates the discrepancy between reported and enacted behaviours by observing the actual behaviour in action.
• Allows change to be recorded: Observing a subject within its natural setting can help researchers capture changing attitudes and mobile dynamics of the subject. For example, if a sociologist is studying the dynamics within a multi-ethnic society, they have the opportunity to watch opinions and attitudes of each social group evolve and change. The researcher will be able to identify recurrent behaviours as well as ones that occur by chance
• Open-ended: Observational research is usually semi-structured, allowing the researchers to work freely within a larger framework. Researchers are free to observe and analyses a plethora of things and the flexibility/ open-endedness of this style of research allows researchers to adapt their research to accommodate more observations that are of value to their research such as interesting phenomena that complement a groups behaviour which researchers did not originally intend to study. This feature would allow a sociologist, for example, to record a new behaviour or attitude of the social group he is studying and include it into his study as it may lend to the research.
• Some advantages exclusive to Participant Observation include:
• Options: Researchers can choose to participate either covertly or overtly, choosing one role that is best suited to the nature of their work. A researcher may choose to study a tribe in the amazon overtly but may choose to study group dynamics within an intersectional environmentalist group by participating in that group disguised as a member. Both of these options lend a sufficient amount of descriptive information for analyses.
• An Inside look into a society or phenomena: Observational research permits researchers to study people in their native environment in order to grasp the subject of their study in manner that would not be understood otherwise. Non-verbal cues and unfiltered responses are recorded in grave detail in a covert participant observation. For instance, if a researcher is observing gender dynamics in a co-ed high school, he will be able to gather information about attitudes and perceptions through gossip exchanges, glances and other non-verbal cues which may not naturally come out if the participants were in a laboratory setting.
• More detailed observations: Spending a great deal of time immersed in a community, social group or culture yields highly specific and ethnographic information. Some researchers spend years living with a society or involved in a social group and this allows them to record, with significant detail, the intricacies of that society or social group.

Also Read: Sampling Methods

Disadvantages

• Small scale: Observational research is most often conducted on a small scale and hence may lack a representative sample which consequently compromises the generalisability of the observations. Researchers may adopt a longitudinal focus while studying, for example, one particular community college. The information found on the students in this community college will be highly specific to that college and not generalizable to all.
• Less reliable: Since observing a phenomenon in its natural setting comes with the presence of innumerable extraneous variables which cannot be controlled, the study is not easily replicable and less reliable than other methods of research including controlled observations. For example, field research may come with a number of variables out of the researchers control including the weather, a group dispute, conflict etc.
• Cannot establish cause and effect relationships : The lack of control the researcher has over the phenomenon being observed makes it significantly difficult to establish causal factors and resulting behaviours. The observations begin to become more descriptive instead of analytical and no significant inferences can be made that can allows for prediction.
• Researchers must be highly skilled & Knowledgeable: Researchers must be skilled and trained to recognise facets of a situation that are sociologically significant. Depending on the nature of research, the Observation method calls for the researcher to adopt one or more roles and depend on several techniques, such as observing with all five senses, in order to gain a comprehensive understanding of the subject being studied. Researchers often spend years doing secondary research, learning a new language and familiarising themselves with a culture in order to participate.
• Time : Observational research is most often time consuming. Researchers choose to spend several months, sometimes years observing the subject of their research in order to gain a comprehensive understanding of the phenomenon that they are studying. As mentioned above, researchers also spend several month planning, researching and preparing for field research.
• Observer-bias: One of the biggest and most recurring issued in observational research is that of Observer bias. Since social reality is relative, observations may end up reflecting a number of biases possessed by the researcher. Several components such as personal beliefs and preferences can cloud a researcher’s perception and his observations may reflect their biases. Researchers often have a hypothesis which may could judgement and make the researcher see only what they want to see in order to confirm their hypothesis. Observation bias is hence, extremely dangerous in the way that it greatly compromises the validity of the results.
• Hawthorne Effect: The behaviour of those being studied is often influenced by the presence of the research which is why covert participation is often preferred by many. Consequently, observations and inferences may blur the actual phenomena leading to inaccurate results.
• External & Distanced observation: Observations are often made from a distance and this could hinder a comprehensive understand of the subject being studied for the researcher may not be able to see or hear any significant events or exchanges. In naturalistic observations, the researcher cannot clarify or inquire into anything being observed, they may only record and subjectively analyse their observations. This leads to non-objective inferences. Furthermore, the only way a researcher conducting a naturalistic observation can get the whole picture is to record data through images, videos and audio recordings which pose a multitude of ethical consequences.
• Difficulty recording : In a participant observational research, the researcher may experience trouble taking notes and providing written accounts of their observations, which often leads to them relying on their memory to reproduce an observation on paper. This can lead to inaccurate observations that may reflect an observer’s bias.
• Access: Gaining access to a particular community or social group is challenging if those comprising those groups are not willing to be studied. Several societies, tribes and social groups are physically inaccessible and may be closed off to outsiders.
• Ethical issues : Covert participation prompts a wide variety of ethical complications as participants of the study are unaware that they are being observed, their behaviours recorded and analysed, hence unable to give consent. Informed Consent being one of the most important aspects of any study, raises a multitude of ethical questions about covert participation observations.
• Microscopic: Most observation research gathers only situation-specific data and is of relatively minimal use (because of its low generalisability and small representative sampling) to the greater body of sociological research. Studying Native Hawaiian approach to gender will yield inferences that are exclusive to the indigenous community of Hawaii and won’t add greatly to existing literature. If a sociologist or researcher, however, chooses to spend more time studying other indigenous communities’ approach to gender, which were colonized such as the Maori community or Native Americans, this can contribute greatly to sociological research but again calls for a significant amount of time to be spent on observation.
• No statistical representation of data: Unless the research design employs a mixed methods approach, most observation research is entirely qualitative and results cannot be represented statistically. Observational research does not allow questionnaires or surveys hence cannot any quantitative data.

Also Read: Qualitative and Quantative Methods

Observation research comes with a myriad of advantages and disadvantages. Obviously, not all pros and cons listed above apply to every research project but several do and it is important to note that this research method must be tailored to the phenomena that you want to study. Each research question will call for a different approach and the observation research style can be moulded to satisfy the studies’ research objectives.

References:

Gaille, Louise. “21 Advantages and Disadvantages of a Participant Observation.” Vittana.org , 3 Feb. 2020, vittana.org/21-advantages-and-disadvantages-of-a-participant-observation. 

McLeod, S. A. (2015, June 06). Observation methods. Simply Psychology. https://www.simplypsychology.org/observation.html

Ciesielska, Malgorzata, et al. “Observation Methods.” Qualitative Methodologies in Organization Studies , 2017, pp. 33–52., doi:10.1007/978-3-319-65442-3_2.

Baker, Lynda M. “Observation: A Complex Research Method.” Library Trends , vol. 55, no. 1, 2006, pp. 171–189., doi:10.1353/lib.2006.0045.

Bailey, K. (1994). Observation in Methods of social research. Simon and

Schuster, 4th ed. The Free Press, New York NY10020. Ch 10. Pp.241-273.

Shivanka Gautam is a student at FLAME University, studying Psychology and Literary & Cultural studies. She has a passion for Critical theory, Cultural Affairs, Political Philosophy and Academia.

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Non-Experimental Research

32 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 methods 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. This type of reactivity is known as the Hawthorne effect . 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 occasionally behaving in ways that are embarrassing.

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 are 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 a famous example of disguised participant observation, Leon Festinger and his colleagues infiltrated a doomsday cult known as the Seekers, whose members believed that the apocalypse would occur on December 21, 1954. Interested in studying how members of the group would cope psychologically when the prophecy inevitably failed, they carefully recorded the events and reactions of the cult members in the days before and after the supposed end of the world. Unsurprisingly, the cult members did not give up their belief but instead convinced themselves that it was their faith and efforts that saved the world from destruction. Festinger and his colleagues later published a book about this experience, which they used to illustrate the theory of cognitive dissonance (Festinger, Riecken, & Schachter, 1956) [1] .

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 deception is being used. The researcher is 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) [2]   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 on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . 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 researchers are 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, additional concerns arise when researchers become active members of the social group they are studying because 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 or participant observation. Often the setting in which the observations are made is not the natural setting. Instead, 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 three 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 or 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) [4] . 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) [5] . 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.

In yet another example (this one in a laboratory environment), Dov Cohen and his colleagues had observers rate the emotional reactions of participants who had just been deliberately bumped and insulted by a confederate after they dropped off a completed questionnaire at the end of a hallway. The confederate was posing as someone who worked in the same building and who was frustrated by having to close a file drawer twice in order to permit the participants to walk past them (first to drop off the questionnaire at the end of the hallway and once again on their way back to the room where they believed the study they signed up for was taking place). The two observers were positioned at different ends of the hallway so that they could read the participants’ body language and hear anything they might say. Interestingly, the researchers hypothesized that participants from the southern United States, which is one of several places in the world that has a “culture of honor,” would react with more aggression than participants from the northern United States, a prediction that was in fact supported by the observational data (Cohen, Nisbett, Bowdle, & Schwarz, 1996) [6] .

When the observations require a judgment on the part of the observers—as in the studies by Kraut and Johnston and Cohen and his colleagues—a process referred to as   coding is typically required . 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 guides different observers to code them in the same way. This difficulty with coding illustrates 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 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.

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 individual’s 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).

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) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

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) [8] . (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, he believed that 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.

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 of 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 both 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 (a possibility suggested by the dissection of HM’s brain following his death) 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 an 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 involves analyzing archival 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) [9] . 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) [10] . 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.

Media Attributions

• What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
• Pappenheim 1882  by unknown is in the  Public Domain .
• Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
• 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. ↵
• Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
• 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. ↵

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

An observational method that involves observing people’s behavior in the environment in which it typically occurs.

When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.

Where the participants are made aware of the researcher presence and monitoring of their behavior.

Refers to when a measure changes participants’ behavior.

In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.

Researchers become active participants in the group or situation they are studying.

Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.

When a researcher makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation.

A part of structured observation whereby the observers use a clearly defined set of guidelines to "code" behaviors—assigning specific behaviors they are observing to a category—and count the number of times or the duration that the behavior occurs.

An in-depth examination of an individual.

A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.

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

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Observation

Observation, as the name implies, is a way of collecting data through observing. This data collection method is classified as a participatory study, because the researcher has to immerse herself in the setting where her respondents are, while taking notes and/or recording. Observation data collection method may involve watching, listening, reading, touching, and recording behavior and characteristics of phenomena.

Observation as a data collection method can be structured or unstructured. In structured or systematic observation, data collection is conducted using specific variables and according to a pre-defined schedule. Unstructured observation, on the other hand, is conducted in an open and free manner in a sense that there would be no pre-determined variables or objectives.

Moreover, this data collection method can be divided into overt or covert categories. In overt observation research subjects are aware that they are being observed. In covert observation, on the other hand, the observer is concealed and sample group members are not aware that they are being observed. Covert observation is considered to be more effective because in this case sample group members are likely to behave naturally with positive implications on the authenticity of research findings.

Advantages of observation data collection method include direct access to research phenomena, high levels of flexibility in terms of application and generating a permanent record of phenomena to be referred to later. At the same time, this method is disadvantaged with longer time requirements, high levels of observer bias, and impact of observer on primary data, in a way that presence of observer may influence the behaviour of sample group elements.

It is important to note that observation data collection method may be associated with certain ethical issues. As it is discussed further below in greater details, fully informed consent of research participant(s) is one of the basic ethical considerations to be adhered to by researchers. At the same time, the behaviour of sample group members may change with negative implications on the level of research validity if they are notified about the presence of the observer.

This delicate matter needs to be addressed by consulting with dissertation supervisor, and commencing the primary data collection process only after ethical aspects of the issue have been approved by the supervisor.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline.

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Observational Research Method explained

Observational Research Method: this article explains the concept of Observational Research Method in a practical way. The article begins with an introduction and the general definition of the term, followed by an explanation of why observational research is important, its advantages and disadvantages, and a practical example. Enjoy reading!

What is observational research?

Observational research is a method of collecting data by simply observing and recording the behavior of individuals, animals or objects in their natural environment.

It offers researchers insights into human and animal behavior, revealing patterns and dynamics that would otherwise go unnoticed.

This article explores the definition, types, advantages, and disadvantages of observational research. Several examples, including its application in market research, will show you how this approach improves our human understanding of the world.

Observational research: collecting insights unobtrusively

Definition of observational research.

Observational studies serve as a means of answering research questions through careful observation of subjects, without any interference or manipulation by the researcher.

Unlike traditional experiments, these studies lack control and treatment groups, allowing researchers to collect data in a natural setting without imposing predetermined conditions.

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Observational studies are generally of a qualitative nature, with both exploratory and explanatory purposes, providing insight into the complexity of particular phenomena.

While quantitative observational studies also exist, they are less common compared to the qualitative studies.

Observational research is widely used in disciplines such as the exact sciences, medicine and social sciences.

Often, ethical or practical considerations prohibit researchers from conducting controlled experiments, leading them to opt for observational studies instead.

The lack of control and treatment groups can pose challenges in drawing conclusions. The risk of confounding variables and observer bias affecting the analysis is high, highlighting the importance of careful interpretation.

Types of observational research

Figure 1 – Types of observational research

Some common types of observational research are:

Naturalistic observation

In naturalistic observation, researchers observe participants in their natural environment, without any interference or disturbance. The aim is to study the behavior and interactions of individuals or groups as they occur in their natural environment.

Structured observation

In structured observation, a predetermined set of behaviors or variables is observed and systematically recorded.

The researchers use specific behavioral categories or measurement tools to collect data .

Participant observation

Participant observation means that the researcher actively participates in the activities or interactions of the participants while they are being observed.

This gives the researcher a deeper insight into the experience and perspectives of the participants.

Covert observation

In the case of a covert observational study, the researcher tries to make himself known to the participants as little as possible.

They observe and record behavior without the participants being aware of the observation. This minimizes the risk of deviant behaviour.

Cross-sectional study

In cross-sectional studies, data is collected at a single point in time or over a short period of time.

The goal is to get a snapshot of the behavior or phenomenon being studied.

Longitudinal study

Longitudinal studies involve following and observing participants over a longer period of time. This makes it possible to identify and analyze changes in behavior or patterns over time.

Choosing the right type of observational study depends on the research question, the aim of the study and the available resources and time. Each type has its own strengths and weaknesses and can be adapted to the specific needs of the research.

Steps in observational research

Below you will find the steps that are followed when setting up an observational research.

Step 1: determine research topic and objectives

The first step involves determining the phenomenon to be observed and the reasons why it is important. Observational studies are especially suitable when an experiment is not an option for practical or ethical reasons. The research topic may also depend on natural behaviour.

As an example, let’s consider a researcher who is interested in the interactions of teens in their social situations. The researcher wants to investigate whether having a smartphone influences the social interactions of the teenagers. Conducting an experiment can be tricky because smartphone use should not be manipulated.

Step 2: choose the type of observation and techniques

Think about what needs to be observed. Does the researcher go in without preconceived notion? Is there another research method that makes more sense to use? Is it important for the analysis that the researcher is present during the observation? If so, a covert observation is already ruled out.

In the example described earlier, several options are possible. The observations could be performed by observing the teens in different situations. It may also be considered to have the observer join a social group and actively participate in their interactions while the group is being observed. Hidden cameras can also be used to record teens’ social interactions in a controlled environment.

Step 3: set up the observational study

There are a number of things to consider before starting the observation.

First, you need to plan ahead. If the participants are observed in a social setting such as community centers or schools, clear agreements should be made and permission should be given. Informed consent might be required. Decide in advance the observational research methods you will use for data collection. Are notes taken? Or video images or audio recordings?

Step 4: before the observation

Once the type of observation has been chosen, the research technique has been decided on and the correct time and place have been determined, it is time to conduct the observation.

In the example, it can be considered to observe two situations, for example one with smartphones and one without smartphones. When conducting the observation, it is important to take confounding variables into account.

Step 5: analyzing data

After completing the observation, it is important to immediately record the first clues, thoughts and impressions. If the observation has been recorded, this recording must be transcribed. Subsequently, a thematic or content analysis must be carried out.

Observations are often exploratory and have an open character. That is why this analysis fits well with this method.

Step 6: discuss next steps

Observational studies are generally exploratory in nature and therefore usually do not immediately yield definitive conclusions. This is mainly because of the risk of observational bias and confounding variables. If the researcher is satisfied with the conclusions that have been reached, it may be useful to switch to another research method, like an experiment.

Examples of observational research

Observational research has led to several revolutionary results that have forever changed our understanding of the world and human behavior.

Some examples of this are:

Development of Darwin’s theory of evolution

Charles Darwin used observational research during his travels on the ship HMS Beagle. Observations of various animal species in their natural environment, such as birds in the Galapagos Islands, allowed Darwin to gather evidence for his theory of evolution.

This revolutionary theory has completely changed the understanding of the origin and diversity of species of creatures.

Discovery of penicillin

Sir Alexander Fleming accidentally discovered the effect of penicillin, a revolutionary antibiotic, through observational research.

He observed that a fungus called Penicillium notatum destroyed bacteria in a petri dish.

This discovery laid the foundation for the development of modern antibiotics and has had an enormous impact on medicine and the treatment of infectious diseases.

Confirmation of Einstein’s theory of relativity

During a solar eclipse in 1919, Arthur Eddington and his team conducted observational research to test the predictions of Einstein’s general theory of relativity.

By observing the positions of stars during the eclipse, they were able to confirm the deflection of light by the sun’s gravity. This experimental evidence supported Einstein’s theory and marked a revolutionary breakthrough in physics.

Research into the effects of smoking on health

One of the most influential observational studies was the study of the relationship between smoking and health problems, particularly lung cancer.

By observing large groups of smokers over a long period of time and collecting data on their smoking behavior and health outcomes, it was shown that there is a strong association between smoking and the risk of lung cancer.

These findings have led to a better understanding of the harmful effects of smoking and have contributed to the promotion of anti-smoking measures and health education.

Pros and cons

Observational research has several advantages and disadvantages that need to be considered before choosing the right research approach.

Advantages of observational research

Authentic behaviour.

By observing people, animals or objects in their natural environment, researchers can study authentic behavior.

That means that the observations take place in real situations and not artificial laboratory conditions.

This allows researchers to study behavior as it actually occurs. This increases scientific validity.

Detailed information

Observational research offers the opportunity to collect detailed information about behaviour, interactions and context.

Researchers can observe specific behaviors such as nonverbal cues, responses to stimuli, and social dynamics. This leads to a deep understanding of the phenomenon being studied.

Flexibility

Observational research can be adapted to different research questions and contexts. Researchers can tailor the observations to the specific situations and variables they want to study. This gives them the flexibility to focus on specific aspects of behaviour, for example.

Disadvantages of observational research

Limited control.

In observational research, researchers have limited control over the conditions and variables they observe. They cannot perform experimental manipulations or control specific environmental factors.

Observer bias

Observer bias refers to the subjective interpretation of the observations by the researcher. Researchers may unconsciously project their own biases, expectations, or interpretations onto the observed behaviors. This could jeopardize the objectivity of the investigation.

Time consuming

Now it’s your turn.

What do you think? Do you recognize the explanation about observational research? Are you familiar with observational research? What do you think are the main benefits of observational research? Have you ever read or experienced an observational study that has given you new insights? Do you have tips or other comments?

Share your experience and knowledge in the comments box below.

More information

• Barick, R. (2021). Research Methods For Business Students . Retrieved 02/16/2024 from Udemy.
• Rosenbaum, P. R. (2005). Observational study . Encyclopedia of statistics in behavioral science.
• Altmann, J. (1974). Observational study of behavior: sampling methods . Behaviour, 49(3-4), 227-266.
• Jepsen, P., Johnsen, S. P., Gillman, M. W., & Sørensen, H. T. (2004). Interpretation of observational studies . Heart, 90(8), 956-960.
• Ligthelm, R. J., Borzì, V., Gumprecht, J., Kawamori, R., Wenying, Y., & Valensi, P. (2007). Importance of observational studies in clinical practice . Clinical therapeutics , 29(6), 1284-1292.

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Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

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Observation Method in Psychology: A Comprehensive Exploration of Research Techniques

From the unobtrusive researcher jotting down notes in a crowded café to the meticulous scientist analyzing hours of video footage, the observation method has long been a cornerstone of psychological research, offering an unparalleled window into the intricacies of human behavior. This powerful approach to understanding the human mind and its manifestations in real-world settings has captivated researchers for generations, providing rich insights that laboratory experiments alone often fail to capture.

Imagine, if you will, a world where our understanding of human nature was limited to self-reports and controlled experiments. We’d be missing out on the subtle nuances of social interactions, the spontaneous expressions of emotion, and the complex dance of human relationships that unfold in natural settings. This is where the observation method truly shines, allowing psychologists to peek behind the curtain of everyday life and uncover the hidden patterns that shape our behavior.

But what exactly do we mean when we talk about observation in psychology? At its core, observational research involves systematically watching and recording behavior in natural or controlled settings. It’s not just about casual people-watching (though that can be fun too!). Instead, it’s a rigorous scientific method that requires careful planning, meticulous documentation, and thoughtful analysis.

The importance of observational research in psychological studies cannot be overstated. It allows us to study behaviors that might be difficult or unethical to manipulate in a lab setting. For instance, how do children naturally develop social skills on a playground? How do couples navigate conflicts in their own homes? These are questions that naturalistic observation in psychology is uniquely positioned to answer.

The history of observational methods in psychology is as fascinating as it is long. From Charles Darwin’s detailed observations of his own children’s emotional expressions to Jane Goodall’s groundbreaking studies of chimpanzees in the wild, observation has been a crucial tool in advancing our understanding of both human and animal behavior. These early pioneers laid the groundwork for what would become a robust and diverse set of observational techniques used in modern psychological research.

Types of Observation Methods in Psychology

Now, let’s dive into the various flavors of observational research. It’s not a one-size-fits-all approach – psychologists have developed a range of techniques to suit different research questions and contexts.

First up, we have naturalistic observation. This is the fly-on-the-wall approach, where researchers observe behavior in its natural habitat without interfering. Picture a psychologist discretely observing children’s play patterns in a public park or studying customer behavior in a bustling shopping mall. The beauty of this method lies in its authenticity – you’re seeing behavior as it naturally occurs, warts and all.

On the flip side, we have participant observation, where the researcher becomes part of the group they’re studying. This method is particularly popular in anthropology and sociology, but it has its place in psychology too. Imagine a researcher joining a support group for anxiety sufferers to gain firsthand insights into their experiences. It’s a delicate balance of participation and observation, requiring researchers to walk the tightrope between involvement and objectivity.

Laboratory observation in psychology offers a more controlled environment. Here, researchers can manipulate certain variables while still observing natural behavior. Think of a study where children are brought into a playroom with various toys, and researchers observe their play patterns and social interactions. It’s a bit like creating a mini-world where certain aspects can be controlled, but behavior can still unfold naturally.

Another important distinction is between structured and unstructured observation. Structured observation in psychology involves a predetermined set of categories or behaviors to look out for. It’s like having a checklist of specific actions or interactions you’re interested in. Unstructured observation, on the other hand, is more open-ended. Researchers might start with a general area of interest but allow their observations to guide them to unexpected discoveries.

Lastly, we have the distinction between covert and overt observation. Covert observation is when subjects don’t know they’re being observed – think hidden cameras or researchers posing as regular members of a group. Overt observation in psychology , by contrast, is when participants are aware they’re being studied. Both approaches have their pros and cons, and the choice often depends on the research question and ethical considerations.

Key Components of the Observational Method

Now that we’ve got a handle on the types of observation, let’s break down the key components that make up a solid observational study. It’s not just about watching and taking notes – there’s a whole science to doing it right!

First things first: selecting the research question and subjects. This is where the rubber meets the road in terms of what you want to study and who you want to study it in. Are you interested in how toddlers develop language skills? Or maybe you’re curious about how people behave in emergency situations? Your research question will guide everything that follows, so it’s crucial to get this step right.

Once you’ve nailed down your question, it’s time to choose the appropriate observation technique. Will naturalistic observation give you the insights you need, or would a more controlled setting be better? Perhaps a mix of methods would work best. It’s like choosing the right tool for the job – you wouldn’t use a hammer to paint a wall, right?

Developing observation protocols and tools is the next critical step. This is where you decide exactly what you’re going to look for and how you’re going to record it. Will you use a checklist of behaviors? A rating scale? Maybe you’ll need specialized software to code complex interactions. Whatever you choose, it needs to be systematic and reliable.

Speaking of reliability, training observers is crucial for consistency. If you have multiple people observing and recording data, you need to make sure they’re all on the same page. This might involve practice sessions, discussions about how to interpret certain behaviors, and regular check-ins to ensure everyone’s still seeing eye to eye.

Finally, there’s the all-important task of recording and documenting observations. In the old days, this might have involved furious scribbling in notebooks. Today, we have a wealth of tools at our disposal, from high-tech video recording systems to sophisticated data analysis software. The key is to capture as much relevant detail as possible without getting bogged down in unnecessary information.

Advantages of the Observation Method in Psychology

Alright, let’s talk about why observation is such a powerhouse in the psychologist’s toolkit. There are some serious perks to this method that make it invaluable in certain research contexts.

First and foremost, observation allows us to capture real-world behaviors in all their messy, complex glory. Unlike lab experiments where conditions are tightly controlled, observational studies let us see how people actually behave in their natural environments. It’s the difference between watching a lion in a zoo and observing one on the African savanna – both have value, but the latter gives you a much more authentic picture of the animal’s true behavior.

Another big advantage is that observation minimizes researcher influence on subjects. When people know they’re part of an experiment, they often change their behavior (consciously or unconsciously). This is known as the Fishbowl Effect in psychology , where being observed alters behavior. Observational methods, especially when covert, can help sidestep this issue.

Observation also allows us to study behaviors that simply can’t be manipulated ethically in a lab setting. Want to understand how people react in a crisis? You can’t exactly set a building on fire for the sake of science. But you can observe and analyze real emergency situations as they naturally occur.

The richness and detail of data gathered through observation is another major plus. You’re not just getting numbers on a scale or ticks in a box – you’re capturing the nuances of facial expressions, the subtleties of body language, the ebb and flow of social interactions. This depth of data can lead to insights that more structured methods might miss.

Last but not least, observational methods are great for discovering unexpected phenomena. When you’re not constrained by predetermined hypotheses or experimental designs, you’re free to notice and explore surprising patterns or behaviors. Some of the most groundbreaking discoveries in psychology have come from researchers simply paying attention to the unexpected.

Limitations and Challenges of Observational Research

Now, let’s not get carried away – observation isn’t a perfect method. Like any research approach, it comes with its own set of challenges and limitations. It’s important to be aware of these so we can interpret observational studies with the appropriate grain of salt.

One of the biggest hurdles is observer bias. No matter how objective we try to be, our own experiences, expectations, and preconceptions can color what we see and how we interpret it. It’s like wearing tinted glasses – they subtly change everything you look at. Researchers need to be constantly vigilant about their own biases and use techniques like multiple observers or blind coding to mitigate this issue.

Reactivity is another potential problem, especially in overt observation. Remember the Fishbowl Effect we mentioned earlier? Even when people know they’re being observed for legitimate research purposes, they might still alter their behavior. It’s like when you suddenly become hyper-aware of how you’re walking when you notice someone watching you on the street.

Ethical considerations loom large in observational studies, particularly when it comes to privacy and consent. How do we balance the need for authentic observation with respect for people’s right to privacy? It’s a thorny issue that researchers grapple with constantly, especially in the age of ubiquitous surveillance technology.

Let’s not sugarcoat it – observational research can be incredibly time-consuming and resource-intensive. Gathering, coding, and analyzing observational data is often a laborious process. It’s not uncommon for researchers to spend hours poring over video footage or field notes to extract meaningful insights.

Another limitation is the difficulty in establishing causality. While observation is great for describing what happens, it’s not always so great at explaining why it happens. We might see a correlation between two behaviors, but without the controlled conditions of an experiment, it’s hard to say definitively that one caused the other.

Finally, there’s the question of generalizability. How much can we extrapolate from observations of a specific group or situation to broader populations or contexts? It’s a bit like trying to understand all of human nature by watching a single family – you might gain some valuable insights, but you’d be missing a lot of the bigger picture.

Applications of the Observation Method in Psychology

Despite these challenges, the observation method continues to be a vital tool across various branches of psychology. Let’s take a whirlwind tour of some of the exciting ways researchers are putting observation to work.

In developmental psychology, observation is king. From studying how infants bond with their caregivers to tracking the complex social dynamics of adolescent peer groups, observational methods provide invaluable insights into how we grow and change over time. It’s through careful observation that we’ve learned so much about crucial concepts like attachment theory and social learning.

Social psychology researchers rely heavily on observational techniques to understand group dynamics, social influence, and interpersonal behavior. Think of classic studies like Zimbardo’s Stanford Prison Experiment or Milgram’s obedience experiments – while not purely observational, these studies leaned heavily on careful observation of participants’ behavior.

In clinical psychology, observation plays a crucial role in assessment and diagnosis. Clinicians often use structured observation techniques to evaluate symptoms, track treatment progress, and understand how mental health issues manifest in real-world settings. It’s not just about what clients say in therapy – it’s about how they behave, interact, and express themselves.

Organizational psychologists use observation to study workplace behavior, team dynamics, and leadership styles. By observing how people actually behave in professional settings (as opposed to how they say they behave), researchers can gain valuable insights into what makes organizations tick.

Cross-cultural psychology is another field where observation shines. By observing behavior across different cultural contexts, researchers can tease apart which aspects of human behavior are universal and which are culturally specific. It’s like being an anthropologist of the mind, exploring the rich diversity of human experience across the globe.

The Future of Observational Research in Psychology

As we wrap up our deep dive into the world of observational research, let’s take a moment to peer into the crystal ball and imagine what the future might hold for this venerable method.

One exciting frontier is the integration of technology into observational studies. Advances in wearable tech, virtual reality, and artificial intelligence are opening up new possibilities for capturing and analyzing behavior. Imagine AI-powered systems that can automatically code facial expressions or body language, or VR environments that allow for controlled observation of behavior in simulated real-world settings.

There’s also a growing trend towards combining observational methods with other research approaches. The empirical method in psychology is all about gathering observable, measurable evidence, and observation is a key part of that. By integrating observation with experimental designs, surveys, and physiological measures, researchers can build a more comprehensive understanding of human behavior.

The rise of big data and machine learning is also likely to impact observational research. As we generate more and more data about our daily lives through social media, smartphones, and other digital technologies, researchers have access to vast troves of observational data. The challenge will be in developing ethical and effective ways to harness this data for psychological insights.

Another area ripe for development is in addressing some of the traditional limitations of observational research. New statistical techniques and research designs are being developed to help establish causality in observational studies. And as our understanding of bias and reactivity grows, we’re getting better at designing studies that minimize these effects.

Conclusion: The Enduring Value of Observation in Psychological Science

As we’ve seen, the observation method in psychology is a powerful and versatile tool for understanding human behavior. From the subtle interactions of infants and caregivers to the complex dynamics of organizational behavior, observation allows us to peer into the rich tapestry of human experience in ways that other methods simply can’t match.

Yes, it has its challenges and limitations. But in many ways, these challenges are also opportunities – pushing us to develop new techniques, technologies, and approaches to better understand the human mind and behavior.

The future of observational research in psychology looks bright indeed. As we continue to refine our methods, integrate new technologies, and combine observation with other research approaches, we’re opening up new vistas of understanding about what makes us tick.

So the next time you find yourself people-watching in a café or observing the subtle dance of social interactions at a party, remember – you’re engaging in a time-honored tradition that has helped shape our understanding of human nature. Who knows? Your casual observations might just spark the next big question in psychological science.

After all, in the grand experiment of understanding human behavior, we’re all observers. And in that sense, the observation method isn’t just a research technique – it’s a fundamental part of what makes us human. Our curiosity, our drive to understand ourselves and others, our capacity for empathy and insight – all of these are rooted in our ability to observe and make sense of the world around us.

So here’s to observation – may it continue to illuminate the fascinating, frustrating, and endlessly complex world of human behavior for generations to come.

References:

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2. Coolican, H. (2017). Research methods and statistics in psychology. Psychology Press.

3. Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. Guilford Press.

4. Robson, C., & McCartan, K. (2016). Real world research. John Wiley & Sons.

5. Bakeman, R., & Quera, V. (2011). Sequential analysis and observational methods for the behavioral sciences. Cambridge University Press.

6. Angrosino, M. (2007). Doing ethnographic and observational research. Sage.

7. McKechnie, L. E. (2008). Observational research. The SAGE encyclopedia of qualitative research methods, 2, 573-575.

8. Pellegrini, A. D. (2004). Observing children in their natural worlds: A methodological primer. Psychology Press.

9. Salkind, N. J. (2010). Encyclopedia of research design (Vol. 1). Sage.

10. Willig, C., & Rogers, W. S. (Eds.). (2017). The SAGE handbook of qualitative research in psychology. Sage.

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