observation type of research

• Privacy Policy

Buy Me a Coffee

Home » Observational Research – Methods and Guide

Observational Research – Methods and Guide

Table of Contents

Observational Research

Definition:

Observational research is a type of research method where the researcher observes and records the behavior of individuals or groups in their natural environment. In other words, the researcher does not intervene or manipulate any variables but simply observes and describes what is happening.

Observation

Observation is the process of collecting and recording data by observing and noting events, behaviors, or phenomena in a systematic and objective manner. It is a fundamental method used in research, scientific inquiry, and everyday life to gain an understanding of the world around us.

Types of Observational Research

Observational research can be categorized into different types based on the level of control and the degree of involvement of the researcher in the study. Some of the common types of observational research are:

Naturalistic Observation

In naturalistic observation, the researcher observes and records the behavior of individuals or groups in their natural environment without any interference or manipulation of variables.

Controlled Observation

In controlled observation, the researcher controls the environment in which the observation is taking place. This type of observation is often used in laboratory settings.

Participant Observation

In participant observation, the researcher becomes an active participant in the group or situation being observed. The researcher may interact with the individuals being observed and gather data on their behavior, attitudes, and experiences.

Structured Observation

In structured observation, the researcher defines a set of behaviors or events to be observed and records their occurrence.

Unstructured Observation

In unstructured observation, the researcher observes and records any behaviors or events that occur without predetermined categories.

Cross-Sectional Observation

In cross-sectional observation, the researcher observes and records the behavior of different individuals or groups at a single point in time.

Longitudinal Observation

In longitudinal observation, the researcher observes and records the behavior of the same individuals or groups over an extended period of time.

Data Collection Methods

Observational research uses various data collection methods to gather information about the behaviors and experiences of individuals or groups being observed. Some common data collection methods used in observational research include:

Field Notes

This method involves recording detailed notes of the observed behavior, events, and interactions. These notes are usually written in real-time during the observation process.

Audio and Video Recordings

Audio and video recordings can be used to capture the observed behavior and interactions. These recordings can be later analyzed to extract relevant information.

Surveys and Questionnaires

Surveys and questionnaires can be used to gather additional information from the individuals or groups being observed. This method can be used to validate or supplement the observational data.

Time Sampling

This method involves taking a snapshot of the observed behavior at pre-determined time intervals. This method helps to identify the frequency and duration of the observed behavior.

Event Sampling

This method involves recording specific events or behaviors that are of interest to the researcher. This method helps to provide detailed information about specific behaviors or events.

Checklists and Rating Scales

Checklists and rating scales can be used to record the occurrence and frequency of specific behaviors or events. This method helps to simplify and standardize the data collection process.

Observational Data Analysis Methods

Observational Data Analysis Methods are:

Descriptive Statistics

This method involves using statistical techniques such as frequency distributions, means, and standard deviations to summarize the observed behaviors, events, or interactions.

Qualitative Analysis

Qualitative analysis involves identifying patterns and themes in the observed behaviors or interactions. This analysis can be done manually or with the help of software tools.

Content Analysis

Content analysis involves categorizing and counting the occurrences of specific behaviors or events. This analysis can be done manually or with the help of software tools.

Time-series Analysis

Time-series analysis involves analyzing the changes in behavior or interactions over time. This analysis can help identify trends and patterns in the observed data.

Inter-observer Reliability Analysis

Inter-observer reliability analysis involves comparing the observations made by multiple observers to ensure the consistency and reliability of the data.

Multivariate Analysis

Multivariate analysis involves analyzing multiple variables simultaneously to identify the relationships between the observed behaviors, events, or interactions.

Event Coding

This method involves coding observed behaviors or events into specific categories and then analyzing the frequency and duration of each category.

Cluster Analysis

Cluster analysis involves grouping similar behaviors or events into clusters based on their characteristics or patterns.

Latent Class Analysis

Latent class analysis involves identifying subgroups of individuals or groups based on their observed behaviors or interactions.

Social network Analysis

Social network analysis involves mapping the social relationships and interactions between individuals or groups based on their observed behaviors.

The choice of data analysis method depends on the research question, the type of data collected, and the available resources. Researchers should choose the appropriate method that best fits their research question and objectives. It is also important to ensure the validity and reliability of the data analysis by using appropriate statistical tests and measures.

Applications of Observational Research

Observational research is a versatile research method that can be used in a variety of fields to explore and understand human behavior, attitudes, and preferences. Here are some common applications of observational research:

• Psychology : Observational research is commonly used in psychology to study human behavior in natural settings. This can include observing children at play to understand their social development or observing people’s reactions to stress to better understand how stress affects behavior.
• Marketing : Observational research is used in marketing to understand consumer behavior and preferences. This can include observing shoppers in stores to understand how they make purchase decisions or observing how people interact with advertisements to determine their effectiveness.
• Education : Observational research is used in education to study teaching and learning in natural settings. This can include observing classrooms to understand how teachers interact with students or observing students to understand how they learn.
• Anthropology : Observational research is commonly used in anthropology to understand cultural practices and beliefs. This can include observing people’s daily routines to understand their culture or observing rituals and ceremonies to better understand their significance.
• Healthcare : Observational research is used in healthcare to understand patient behavior and preferences. This can include observing patients in hospitals to understand how they interact with healthcare professionals or observing patients with chronic illnesses to better understand their daily routines and needs.
• Sociology : Observational research is used in sociology to understand social interactions and relationships. This can include observing people in public spaces to understand how they interact with others or observing groups to understand how they function.
• Ecology : Observational research is used in ecology to understand the behavior and interactions of animals and plants in their natural habitats. This can include observing animal behavior to understand their social structures or observing plant growth to understand their response to environmental factors.
• Criminology : Observational research is used in criminology to understand criminal behavior and the factors that contribute to it. This can include observing criminal activity in a particular area to identify patterns or observing the behavior of inmates to understand their experience in the criminal justice system.

Observational Research Examples

Here are some real-time observational research examples:

• A researcher observes and records the behaviors of a group of children on a playground to study their social interactions and play patterns.
• A researcher observes the buying behaviors of customers in a retail store to study the impact of store layout and product placement on purchase decisions.
• A researcher observes the behavior of drivers at a busy intersection to study the effectiveness of traffic signs and signals.
• A researcher observes the behavior of patients in a hospital to study the impact of staff communication and interaction on patient satisfaction and recovery.
• A researcher observes the behavior of employees in a workplace to study the impact of the work environment on productivity and job satisfaction.
• A researcher observes the behavior of shoppers in a mall to study the impact of music and lighting on consumer behavior.
• A researcher observes the behavior of animals in their natural habitat to study their social and feeding behaviors.
• A researcher observes the behavior of students in a classroom to study the effectiveness of teaching methods and student engagement.
• A researcher observes the behavior of pedestrians and cyclists on a city street to study the impact of infrastructure and traffic regulations on safety.

How to Conduct Observational Research

Here are some general steps for conducting Observational Research:

• Define the Research Question: Determine the research question and objectives to guide the observational research study. The research question should be specific, clear, and relevant to the area of study.
• Choose the appropriate observational method: Choose the appropriate observational method based on the research question, the type of data required, and the available resources.
• Plan the observation: Plan the observation by selecting the observation location, duration, and sampling technique. Identify the population or sample to be observed and the characteristics to be recorded.
• Train observers: Train the observers on the observational method, data collection tools, and techniques. Ensure that the observers understand the research question and objectives and can accurately record the observed behaviors or events.
• Conduct the observation : Conduct the observation by recording the observed behaviors or events using the data collection tools and techniques. Ensure that the observation is conducted in a consistent and unbiased manner.
• Analyze the data: Analyze the observed data using appropriate data analysis methods such as descriptive statistics, qualitative analysis, or content analysis. Validate the data by checking the inter-observer reliability and conducting statistical tests.
• Interpret the results: Interpret the results by answering the research question and objectives. Identify the patterns, trends, or relationships in the observed data and draw conclusions based on the analysis.
• Report the findings: Report the findings in a clear and concise manner, using appropriate visual aids and tables. Discuss the implications of the results and the limitations of the study.

When to use Observational Research

Here are some situations where observational research can be useful:

• Exploratory Research: Observational research can be used in exploratory studies to gain insights into new phenomena or areas of interest.
• Hypothesis Generation: Observational research can be used to generate hypotheses about the relationships between variables, which can be tested using experimental research.
• Naturalistic Settings: Observational research is useful in naturalistic settings where it is difficult or unethical to manipulate the environment or variables.
• Human Behavior: Observational research is useful in studying human behavior, such as social interactions, decision-making, and communication patterns.
• Animal Behavior: Observational research is useful in studying animal behavior in their natural habitats, such as social and feeding behaviors.
• Longitudinal Studies: Observational research can be used in longitudinal studies to observe changes in behavior over time.
• Ethical Considerations: Observational research can be used in situations where manipulating the environment or variables would be unethical or impractical.

Purpose of Observational Research

Observational research is a method of collecting and analyzing data by observing individuals or phenomena in their natural settings, without manipulating them in any way. The purpose of observational research is to gain insights into human behavior, attitudes, and preferences, as well as to identify patterns, trends, and relationships that may exist between variables.

The primary purpose of observational research is to generate hypotheses that can be tested through more rigorous experimental methods. By observing behavior and identifying patterns, researchers can develop a better understanding of the factors that influence human behavior, and use this knowledge to design experiments that test specific hypotheses.

Observational research is also used to generate descriptive data about a population or phenomenon. For example, an observational study of shoppers in a grocery store might reveal that women are more likely than men to buy organic produce. This type of information can be useful for marketers or policy-makers who want to understand consumer preferences and behavior.

In addition, observational research can be used to monitor changes over time. By observing behavior at different points in time, researchers can identify trends and changes that may be indicative of broader social or cultural shifts.

Overall, the purpose of observational research is to provide insights into human behavior and to generate hypotheses that can be tested through further research.

Advantages of Observational Research

There are several advantages to using observational research in different fields, including:

• Naturalistic observation: Observational research allows researchers to observe behavior in a naturalistic setting, which means that people are observed in their natural environment without the constraints of a laboratory. This helps to ensure that the behavior observed is more representative of the real-world situation.
• Unobtrusive : Observational research is often unobtrusive, which means that the researcher does not interfere with the behavior being observed. This can reduce the likelihood of the research being affected by the observer’s presence or the Hawthorne effect, where people modify their behavior when they know they are being observed.
• Cost-effective : Observational research can be less expensive than other research methods, such as experiments or surveys. Researchers do not need to recruit participants or pay for expensive equipment, making it a more cost-effective research method.
• Flexibility: Observational research is a flexible research method that can be used in a variety of settings and for a range of research questions. Observational research can be used to generate hypotheses, to collect data on behavior, or to monitor changes over time.
• Rich data : Observational research provides rich data that can be analyzed to identify patterns and relationships between variables. It can also provide context for behaviors, helping to explain why people behave in a certain way.
• Validity : Observational research can provide high levels of validity, meaning that the results accurately reflect the behavior being studied. This is because the behavior is being observed in a natural setting without interference from the researcher.

Disadvantages of Observational Research

While observational research has many advantages, it also has some limitations and disadvantages. Here are some of the disadvantages of observational research:

• Observer bias: Observational research is prone to observer bias, which is when the observer’s own beliefs and assumptions affect the way they interpret and record behavior. This can lead to inaccurate or unreliable data.
• Limited generalizability: The behavior observed in a specific setting may not be representative of the behavior in other settings. This can limit the generalizability of the findings from observational research.
• Difficulty in establishing causality: Observational research is often correlational, which means that it identifies relationships between variables but does not establish causality. This can make it difficult to determine if a particular behavior is causing an outcome or if the relationship is due to other factors.
• Ethical concerns: Observational research can raise ethical concerns if the participants being observed are unaware that they are being observed or if the observations invade their privacy.
• Time-consuming: Observational research can be time-consuming, especially if the behavior being observed is infrequent or occurs over a long period of time. This can make it difficult to collect enough data to draw valid conclusions.
• Difficulty in measuring internal processes: Observational research may not be effective in measuring internal processes, such as thoughts, feelings, and attitudes. This can limit the ability to understand the reasons behind behavior.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Case Study – Methods, Examples and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

Survey Research – Types, Methods, Examples

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.

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.

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. 

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).

Microanalytic coding systems

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

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.

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.

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.

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.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

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.

Share This Book

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Observational Research: What is, Types, Pros & Cons + Example

Researchers can gather customer data in a variety of ways, including surveys, interviews, and research. But not all data can be collected by asking questions because customers might not be conscious of their behaviors. 

It is when observational research comes in. This research is a way to learn about people by observing them in their natural environment. This kind of research helps researchers figure out how people act in different situations and what things in the environment affect their actions.

This blog will teach you about observational research, including types and observation methods. Let’s get started.

What is observational research?

Observational research is a broad term for various non-experimental studies in which behavior is carefully watched and recorded.

The goal of this research is to describe a variable or a set of variables. More broadly, the goal is to capture specific individual, group, or setting characteristics.

Since it is non-experimental and uncontrolled, we cannot draw causal research conclusions from it. The observational data collected in research studies is frequently qualitative observation , but it can also be quantitative or both (mixed methods).

Types of observational research

Conducting observational research can take many different forms. There are various types of this research. These types are classified below according to how much a researcher interferes with or controls the environment.

Naturalistic observation

Taking notes on what is seen is the simplest form of observational research. A researcher makes no interference in naturalistic observation. It’s just watching how people act in their natural environments. 

Importantly, there is no attempt to modify factors in naturalistic observation, as there would be when comparing data between a control group and an experimental group.

Case studiesCase studies

A case study is a sort of observational research that focuses on a single phenomenon. It is a naturalistic observation because it captures data in the field. But case studies focus on a specific point of reference, like a person or event, while other studies may have a wider scope and try to record everything that happens in the researcher’s eyes. 

For example, a case study of a single businessman might try to find out how that person deals with a certain disease’s ups and down or loss.

Participant observation

Participant observation is similar to naturalistic observation, except that the researcher is a part of the natural environment they are studying. In such research, the researcher is also interested in rituals or cultural practices that can only be evaluated by sharing experiences. 

For example, anyone can learn the basic rules of table Tennis by going to a game or following a team. Participant observation, on the other hand, lets people take part directly to learn more about how the team works and how the players relate to each other.

It usually includes the researcher joining a group to watch behavior they couldn’t see from afar. Participant observation can gather much information, from the interactions with the people being observed to the researchers’ thoughts.

Controlled observation

A more systematic structured observation entails recording the behaviors of research participants in a remote place. Case-control studies are more like experiments than other types of research, but they still use observational research methods. When researchers want to find out what caused a certain event, they might use a case-control study.

Longitudinal observation

This observational research is one of the most difficult and time-consuming because it requires watching people or events for a long time. Researchers should consider longitudinal observations when their research involves variables that can only be seen over time. 

After all, you can’t get a complete picture of things like learning to read or losing weight in a single observation. Longitudinal studies keep an eye on the same people or events over a long period of time and look for changes or patterns in behavior.

Observational research methods

When doing this research, there are a few observational methods to remember to ensure that the research is done correctly. Along with other research methods, let’s learn some key research methods of it:

Have a clear objective

For an observational study to be helpful, it needs to have a clear goal. It will help guide the observations and ensure they focus on the right things.

Get permission

Get permission from your participants. Getting explicit permission from the people you will be watching is essential. It means letting them know that they will be watched, the observation’s goal, and how their data will be used.

Unbiased observation

It is important to make sure the observations are fair and unbiased. It can be done by keeping detailed notes of what is seen and not putting any personal meaning on the data.

Hide your observers

In the observation method, keep your observers hidden. The participants should be unaware of the observers to avoid potential bias in their actions.

Documentation

It is important to document the observations clearly and straightforwardly. It will allow others to examine the information and confirm the observational research findings.

Data analysis

Data analysis is the last method. The researcher will analyze the collected data to draw conclusions or confirm a hypothesis.

Pros and cons of observational research

Observational studies are a great way to learn more about how your customers use different parts of your business. There are so many pros and cons of observational research. Let’s have a look at them.

• It provides a practical application for a hypothesis. In other words, it can help make research more complete.
• You can see people acting alone or in groups, such as customers. So, you can answer a number of questions about how people act as customers.
• There is a chance of researcher bias in observational research. Experts say that this can be a very big problem.
• Some human activities and behaviors can be difficult to understand. We are unable to see memories or attitudes. In other words, there are numerous situations in which observation alone is inadequate.

Example of observational research

The researcher observes customers buying products in a mall. Assuming the product is soap, the researcher will observe how long the customer takes to decide whether he likes the packaging or comes to the mall with his decision already made based on advertisements.

If the customer takes their time making a decision, the researcher will conclude that packaging and information on the package affect purchase behavior. If a customer makes a quick decision, the decision is likely predetermined. 

As a result, the researcher will recommend more and better advertisements in this case. All of these findings were obtained through simple observational research.

How to conduct observational research with QuestionPro?

QuestionPro can help with observational research by providing tools to collect and analyze data. It can help in the following ways:

Define the research goals and question types you want to answer with your observational study . Use QuestionPro’s customizable survey templates and questions to do a survey that fits your research goals and gets the necessary information. 

You can distribute the survey to your target audience using QuestionPro’s online platform or by sending a link to the survey. 

With QuestionPro’s real-time data analysis and reporting features, you can collect and look at the data as people fill out the survey. Use the advanced analytics tools in QuestionPro to see and understand the data and find insights and trends. 

If you need to, you can export the data from QuestionPro into the analysis tools you like to use. Draw conclusions from the collected and analyzed data and answer the research questions that were asked at the beginning of the research.

For a deeper understanding of human behaviors and decision-making processes, explore the realm of Behavioral Research .

To summarize, observational research is an effective strategy for collecting data and getting insights into real-world phenomena. When done right, this research can give helpful information and help people make decisions. 

QuestionPro is a valuable tool that can help with observational research by letting you create online surveys, analyze data in real time, make surveys your own, keep your data safe, and use advanced analytics tools.

To do this research with QuestionPro, researchers need to define their research goals, do a survey that matches their goals, send the survey to participants, collect and analyze the data, visualize and explain the results, export data if needed, and draw conclusions from the data collected.

By keeping in mind what has been said above, researchers can use QuestionPro to help with their observational research and gain valuable data. Try out QuestionPro today!

FREE TRIAL         LEARN MORE

Frequently Asked Questions (FAQ)

Observational research is a method in which researchers observe and systematically record behaviors, events, or phenomena without directly manipulating them.

There are three main types of observational research: naturalistic observation, participant observation, and structured observation.

Naturalistic observation involves observing subjects in their natural environment without any interference.

MORE LIKE THIS

21 Best Customer Advocacy Software for Customers in 2024

Apr 19, 2024

10 Quantitative Data Analysis Software for Every Data Scientist

Apr 18, 2024

11 Best Enterprise Feedback Management Software in 2024

17 Best Online Reputation Management Software in 2024

Apr 17, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

6.6: Observational Research

• Last updated
• Save as PDF
• Page ID 19655

• Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton
• Kwantlen Polytechnic U., Washington State U., & Texas A&M U.—Texarkana

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.

• 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. ↵

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups

What is observational research?

Uses for observational research, observations in research, the different types of observational research, conducting observational studies, uses with other methods, challenges of observational studies.

• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Observational research

Observational research is a social research technique that involves the direct observation of phenomena in their natural setting.

An observational study is a non-experimental method to examine how research participants behave. Observational research is typically associated with qualitative methods , where the data ultimately require some reorganization and analysis .

Contemporary research is often associated with controlled experiments or randomized controlled trials, which involve testing or developing a theory in a controlled setting. Such an approach is appropriate for many physical and material sciences that rely on objective concepts such as the melting point of substances or the mass of objects. On the other hand, observational studies help capture socially constructed or subjective phenomena whose fundamental essence might change when taken out of their natural setting.

What is an example of observational research?

For example, imagine a study where you want to understand the actions and behaviors of single parents taking care of children. A controlled experiment might prove challenging, given the possibility that the behaviors of parents and their children will change if you isolate them in a lab or an otherwise unfamiliar context.

Instead, researchers pursuing such inquiries can observe participants in their natural environment, collecting data on what people do, say, and behave in interaction with others. Non-experimental research methods like observation are less about testing theories than learning something new to contribute to theories.

The goal of the observational study is to collect data about what people do and say. Observational data is helpful in several fields:

• market research
• health services research
• educational research
• user research

Observational studies are valuable in any domain where researchers want to learn about people's actions and behaviors in a natural setting. For example, observational studies in market research might seek out information about the target market of a product or service by identifying the needs or problems of prospective consumers. In medical contexts, observers might be interested in how patients cope with a particular medical treatment or interact with doctors and nurses under certain conditions.

Researchers may still be hung up on science being all about experiments to the point where they may overlook the empirical contribution that observations bring to research and theory. With that in mind, let's look at the strengths and weaknesses of observations in research .

Strengths of observational research

Observational research, especially those conducted in natural settings, can generate more insightful knowledge about social processes or rituals that one cannot fully understand by reading a plain-text description in a book or an online resource. Think about a cookbook with recipes, then think about a series of videos showing a cook making the same recipes. Both are informative, but the videos are often easier to understand as the cook can describe the recipe and show how to follow the steps at the same time. When you can observe what is happening, you can emulate the process for yourself.

Observing also allows researchers to create rich data about phenomena that cannot be explained through numbers. The quality of a theatrical performance, for example, cannot easily be reduced to a set of numbers. Qualitatively, a researcher can analyze aspects gleaned from observing that performance and create a working theory about the quality of that performance. Through data analysis, the researcher can identify patterns related to the aesthetics and creativity of the performance to provide a framework to judge the quality of other performances.

Weaknesses of observational research

Science is about organizing knowledge for the purposes of identifying the aspects of a concept or of determining cause-and-effect relationships between different phenomena. Experiments look to empirically accomplish these tasks by controlling certain variables to determine how other variables change under changing conditions. Those conducting observational research, on the other hand, exert no such control, which makes replication by other researchers difficult or even impossible when observing dynamic environments.

Observational studies take on various forms. There are various types of observational research, each of which has strengths and weaknesses. These types are organized below by the extent to which an experimenter intrudes upon or controls the environment.

Naturalistic observation

Naturalistic observation refers to a method where researchers study participants in their natural environment without manipulating variables or intervening in any way. It provides a realistic snapshot of behavior as it occurs in real-life settings, thereby enhancing ecological validity.

Examples of naturalistic observation include people-watching in public places, observing animal behaviors in the wild, and longitudinally studying children's social development at school. This method can reveal insights about behavior and relationships that might not surface in experimental designs, such as patterns of social interaction, routines, or responses to environmental changes.

Participant observation

Participant observation is similar to naturalistic observation, except that the researcher is part of the natural environment they are observing. In such studies, the researcher is also interested in rituals or cultural practices where they can only determine their value by actually experiencing them firsthand. For example, any individual can understand the basic rules of baseball by watching a game or following a team. Participant observation, on the other hand, allows for direct participation to develop a better sense of team dynamics and relationships among fellow players.

Most commonly, this process involves the researcher inserting themselves into a group to observe behavior that otherwise would not be accessible by observing from afar. Participant observation can capture rich data from the interactions with those who are observed to the reflections of the researchers themselves.

Controlled observation

A more structured observation involves capturing the behaviors of research participants in an isolated environment. Case-control studies have a greater resemblance to experimental research while still relying on observational research methods. Researchers may utilize a case-control study when they want to establish the causation of a particular phenomenon.

For example, a researcher may want to establish a structured observation of a control group and an experimental group, each with randomly assigned research participants, to observe the effects of variables such as distractions on people completing a particular task. By subjecting the experimental group to distractions such as noise and lights, researchers can observe the time it takes participants to complete a task and determine causation accordingly.

Longitudinal study

Among the different types of observational research, this observational method is quite arduous and time-consuming as it requires observation of people or events over extended periods. Researchers should consider longitudinal observations when their inquiry involves variables that can only be observed over time. After all, variables such as literacy development or weight loss cannot be fully captured in any particular moment of observation. Longitudinal studies keep track of the same research participants or events through multiple observations to document changes to or patterns in behavior.

A cohort study is a specific type of longitudinal study where researchers observe participants with similar traits (e.g., a similar risk factor or biological characteristic). Cohort studies aim to observe multiple participants over time to identify a relationship between observed phenomena and a common characteristic.

All forms of observational or field research benefit extensively from the special capabilities of qualitative research tools like ATLAS.ti . Our software can accommodate the major forms of data , such as text, audio, video, and images . The ATLAS.ti platform can help you organize all your observations , whatever method you employ.

Whatever your research, make it happen with ATLAS.ti.

Powerful analytical tools at your fingertips. Try for free by clicking here.

Like any other study design, observational studies begin by posing research questions . Inquiries common when employing observational methods include the study of different cultures, interactions between people from different communities, or people in particular circumstances warranting further study (e.g., people coping with a rare disease).

Generally, a research question that seeks to learn more about a relatively unfamiliar phenomenon would be best suited for observational research. On the other hand, quantitative methods or experimental research methods may be more suitable for inquiries where the theory about a social phenomenon is fairly established.

Study design

Study design for observational research involves thinking about who to observe, where they should be observed, and what the researcher should look for during observation. Many events can occur in a natural, dynamic environment in a short period, so it is challenging to document everything. If the researcher knows what they want to observe, they can pursue a structured observation which involves taking notes on a limited set of phenomena.

The actual data collection for an observational study can take several forms. Note-taking is common in observational research, where the researcher writes down what they see during the course of their observation. The goal of this method is to provide a record of the events that are observed to determine patterns and themes useful for theoretical development.

Observation can also involve taking pictures or recording audio for a richer understanding of social phenomena. Video recorded from observations can also provide data that the researcher can use to document the facial expressions, gestures, and other body language of research participants.

Note that there are ethical considerations when conducting observational research. Researchers should respect the privacy and confidentiality of their research participants to ensure they are not adversely affected by the research. Researchers should obtain informed consent from participants before any observation where possible.

Observational studies can be supplemented with other methods to further contextualize the research inquiry. Researchers can conduct interviews or focus groups with research participants to gather data about what they recall about their actions and behaviors in a natural setting. Focus groups, in particular, provide further opportunities to observe participants interacting with each other. In both cases, these research methods are ideal where the researcher needs to follow up with research participants about the evidence they've collected regarding their behaviors or actions.

As with many other methods in qualitative research , conducting an observational study is time-consuming. While experimental methods can quickly generate data , observational research relies on documenting events and interactions in detail that can be analyzed for theoretical development.

Unstructured data

One common critique of observational research is that it lacks the structure inherent to experimental research, which has concepts such as selection bias and interrater reliability to ensure research quality. On the other hand, qualitative research relies on the assumption that the study and its data are presented transparently and honestly . Under this principle, researchers are responsible for convincing their audiences that the assertions they make are connected empirically to the observations they have made and the data they have collected.

Researcher bias

In most qualitative research, but especially in observational research, the most important data collection instrument is the researcher themselves. This raises issues of bias and subjectivity influencing the collection and interpretation of the data.

Later in this guide, there will be discussion of reflexivity , a concept where the researcher comprehensively accounts for their place in the research relative to others in the environment. For now, it's important to know that social science researchers can and do adequately address critiques of researcher bias to maintain the empirical nature of their observational research.

Conduct your observational study with ATLAS.ti

From the inception of your study to disseminating to your research audience, get it all done with our powerful software platform. Start with a free trial.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• What Is Qualitative Observation? | Definition & Examples

What Is Qualitative Observation? | Definition & Examples

Published on 18 March 2023 by Tegan George .

Qualitative observation is a research method where the characteristics or qualities of a phenomenon are described without using any quantitative measurements or data. Rather, the observation is based on the observer’s subjective interpretation of what they see, hear, smell, taste, or feel.

Qualitative observations can be done using various methods, including direct observation, interviews , focus groups , or case studies . They can provide rich and detailed information about the behaviour, attitudes, perceptions, and experiences of individuals or groups.

Table of contents

When to use qualitative observation, examples of qualitative observation, types of qualitative observations, advantages and disadvantages of qualitative observations, frequently asked questions.

Qualitative observation is a type of observational study , often used in conjunction with other types of research through triangulation . It is often used in fields like social sciences, education, healthcare, marketing, and design. This type of study is especially well suited for gaining rich and detailed insights into complex and/or subjective phenomena.

A qualitative observation could be a good fit for your research if:

• You are conducting exploratory research . If the goal of your research is to gain a better understanding of a phenomenon, object, or situation, qualitative observation is a good place to start.
• When your research topic is complex, subjective, or cannot be examined numerically. Qualitative observation is often able to capture the complexity and subjectivity of human behaviour, particularly for topics like emotions, attitudes, perceptions, or cultural practices. These may not be quantifiable or measurable through other methods.
• You are relying on triangulation within your research approach. Qualitative observation is a solid addition to triangulation approaches, where multiple sources of data are used to validate and verify research findings.

Prevent plagiarism, run a free check.

Qualitative observation is commonly used in marketing to study consumer behaviour, preferences, and attitudes towards products or services.

During the focus group, you focus particularly on qualitative observations, taking note of the participants’ facial expressions, body language, word choice, and tone of voice.

Qualitative observation is often also used in design fields, to better understand user needs, preferences, and behaviours. This can aid in the development of products and services that better meet user needs.

You are particularly focused on any usability issues that could impact customer satisfaction. You run a series of testing sessions, focusing on reactions like facial expressions, body language, and verbal feedback.

There are several types of qualitative observation. Here are some of the most common types to help you choose the best one for your work.

Qualitative observations are a great choice of research method for some projects, but they definitely have their share of disadvantages to consider.

Advantages of qualitative observations

• Qualitative observations allow you to generate rich and nuanced qualitative data – aiding you in understanding a phenomenon or object and providing insights into the more complex and subjective aspects of human experience.
• Qualitative observation is a flexible research method that can be adjusted based on research goals and timeline. It also has the potential to be quite non-intrusive, allowing observation of participants in their natural settings without disrupting or influencing their behaviour.
• Qualitative observation is often used in combination with other research methods, such as interviews or surveys , to provide a more complete picture of the phenomenon being studied. This triangulation can help improve the reliability and validity of the research findings.

Disadvantages of qualitative observations

• Like many observational studies, qualitative observations are at high risk for many research biases , particularly on the side of the researcher in the case of observer bias . These biases can also bleed over to the participant size, in the case of the Hawthorne effect or social desirability bias .
• Qualitative observations are typically based on a small sample size , which makes them very unlikely to be representative of the larger population. This greatly limits the generalisability of the findings if used as a standalone method, and the data collection process can be long and onerous.
• Like other human subject research, qualitative observation has its share of ethical considerations to keep in mind and protect, particularly informed consent, privacy, and confidentiality.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Data analysis in qualitative observation often involves searching for any recurring patterns, themes, and categories in your data. This process may involve coding the data, developing conceptual frameworks or models, and conducting thematic analysis . This can help you generate strong hypotheses or theories based on your data.

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

George, T. (2023, March 18). What Is Qualitative Observation? | Definition & Examples. Scribbr. Retrieved 15 April 2024, from https://www.scribbr.co.uk/research-methods/qualitative-observations/

Is this article helpful?

Tegan George

Other students also liked, what is participant observation | definition & examples, naturalistic observation | definition, guide & examples, what is a cohort study | definition & examples.

• Research Process

What is Observational Study Design and Types

• 4 minute read
• 110.7K views

Table of Contents

Most people think of a traditional experimental design when they consider research and published research papers. There is, however, a type of research that is more observational in nature, and it is appropriately referred to as “observational studies.”

There are many valuable reasons to utilize an observational study design. But, just as in research experimental design, different methods can be used when you’re considering this type of study. In this article, we’ll look at the advantages and disadvantages of an observational study design, as well as the 3 types of observational studies.

What is Observational Study Design?

An observational study is when researchers are looking at the effect of some type of intervention, risk, a diagnostic test or treatment, without trying to manipulate who is, or who isn’t, exposed to it.

This differs from an experimental study, where the scientists are manipulating who is exposed to the treatment, intervention, etc., by having a control group, or those who are not exposed, and an experimental group, or those who are exposed to the intervention, treatment, etc. In the best studies, the groups are randomized, or chosen by chance.

Any evidence derived from systematic reviews is considered the best in the hierarchy of evidence, which considers which studies are deemed the most reliable. Next would be any evidence that comes from randomized controlled trials. Cohort studies and case studies follow, in that order.

Cohort studies and case studies are considered observational in design, whereas the randomized controlled trial would be an experimental study.

Let’s take a closer look at the different types of observational study design.

The 3 types of Observational Studies

The different types of observational studies are used for different reasons. Selecting the best type for your research is critical to a successful outcome. One of the main reasons observational studies are used is when a randomized experiment would be considered unethical. For example, a life-saving medication used in a public health emergency. They are also used when looking at aetiology, or the cause of a condition or disease, as well as the treatment of rare conditions.

Case Control Observational Study

Researchers in case control studies identify individuals with an existing health issue or condition, or “cases,” along with a similar group without the condition, or “controls.” These two groups are then compared to identify predictors and outcomes. This type of study is helpful to generate a hypothesis that can then be researched.

Cohort Observational Study

This type of observational study is often used to help understand cause and effect. A cohort observational study looks at causes, incidence and prognosis, for example. A cohort is a group of people who are linked in a particular way, for example, a birth cohort would include people who were born within a specific period of time. Scientists might compare what happens to the members of the cohort who have been exposed to some variable to what occurs with members of the cohort who haven’t been exposed.

Cross Sectional Observational Study

Unlike a cohort observational study, a cross sectional observational study does not explore cause and effect, but instead looks at prevalence. Here you would look at data from a particular group at one very specific period of time. Researchers would simply observe and record information about something present in the population, without manipulating any variables or interventions. These types of studies are commonly used in psychology, education and social science.

Advantages and Disadvantages of Observational Study Design

Observational study designs have the distinct advantage of allowing researchers to explore answers to questions where a randomized controlled trial, or RCT, would be unethical. Additionally, if the study is focused on a rare condition, studying existing cases as compared to non-affected individuals might be the most effective way to identify possible causes of the condition. Likewise, if very little is known about a condition or circumstance, a cohort study would be a good study design choice.

A primary advantage to the observational study design is that they can generally be completed quickly and inexpensively. A RCT can take years before the data is compiled and available. RCTs are more complex and involved, requiring many more logistics and details to iron out, whereas an observational study can be more easily designed and completed.

The main disadvantage of observational study designs is that they’re more open to dispute than an RCT. Of particular concern would be confounding biases. This is when a cohort might share other characteristics that affect the outcome versus the outcome stated in the study. An example would be that people who practice good sleeping habits have less heart disease. But, maybe those who practice effective sleeping habits also, in general, eat better and exercise more.

Language Editing Plus Service

Need help with your research writing? With our Language Editing Plus service , we’ll help you improve the flow and writing of your paper, including UNLIMITED editing support. Use the simulator below to check the price for your manuscript, using the total number of words of the document.

Clinical Questions: PICO and PEO Research

Paper Retraction: Meaning and Main Reasons

You may also like.

Descriptive Research Design and Its Myriad Uses

Five Common Mistakes to Avoid When Writing a Biomedical Research Paper

Making Technical Writing in Environmental Engineering Accessible

To Err is Not Human: The Dangers of AI-assisted Academic Writing

When Data Speak, Listen: Importance of Data Collection and Analysis Methods

Choosing the Right Research Methodology: A Guide for Researchers

Why is data validation important in research?

Writing a good review article

Input your search keywords and press Enter.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.1; 2022 Dec

Direct observation methods: A practical guide for health researchers

Gemmae m. fix.

a VA Center for Healthcare Organization and Implementation Research, Bedford and Boston, MA, USA

b General Internal Medicine, Boston University School of Medicine, Boston, MA, USA

c Department of Psychiatry, Harvard Medical School, Boston, MA, USA

Mollie A. Ruben

d Department of Psychology, University of Maine, Orono, ME, USA

Megan B. McCullough

e Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA

To provide health research teams with a practical, methodologically rigorous guide on how to conduct direct observation.

Synthesis of authors’ observation-based teaching and research experiences in social sciences and health services research.

This article serves as a guide for making key decisions in studies involving direct observation. Study development begins with determining if observation methods are warranted or feasible. Deciding what and how to observe entails reviewing literature and defining what abstract, theoretically informed concepts look like in practice. Data collection tools help systematically record phenomena of interest. Interdisciplinary teams--that include relevant community members-- increase relevance, rigor and reliability, distribute work, and facilitate scheduling. Piloting systematizes data collection across the team and proactively addresses issues.

Observation can elucidate phenomena germane to healthcare research questions by adding unique insights. Careful selection and sampling are critical to rigor. Phenomena like taboo behaviors or rare events are difficult to capture. A thoughtful protocol can preempt Institutional Review Board concerns.

This novel guide provides a practical adaptation of traditional approaches to observation to meet contemporary healthcare research teams’ needs.

Graphical abstract

• • Health research study designs benefit from observations of behaviors and contexts
• • Direct observation methods have a long history in the social sciences
• • Social science approaches should be adapted for health researchers’ unique needs
• • Health research observations should be feasible, well-defined and piloted
• • Multidisciplinary teams, data collection tools and detailed protocols enhance rigor

1. Introduction

Health research studies increasingly include direct observation methods [ [1] , [2] , [3] , [4] , [5] ]. Observation provides unique information about human behavior related to healthcare processes, events, norms and social context. Behavior is difficult to study; it is often unconscious or susceptible to self-report biases. Interviews or surveys are limited to what participants share. Observation is particularly useful for understanding patients’, providers’ or other key communities’ experiences because it provides an “emic,” insider perspective and lends itself to topics like patient-centered care research [ 1 , 5 , 6 ]. This insider perspective allows researchers to understand end users’ experiences of a problem. For example, patients may be viewed as “non-compliant,” while observations can reveal daily lived experiences that impede adherence to recommended care [ [7] , [8] , [9] , [10] ]. Observation can examine the organization and structure of healthcare delivery in ways that are different from, and complementary to, methods like surveys, interviews, or database reviews. However, there is limited guidance for health researchers on how to use observation.

Observation has a long history in the social sciences, with participant observation as a defining feature of ethnography [ [11] , [12] , [13] ]. Observation in healthcare research differs from the social sciences. Traditional social science research may be conducted by a single individual, while healthcare research is often conducted by multidisciplinary teams. In social science studies, extended time in the field is expected [ 11 ]. In contrast, healthcare research timelines are often compressed and conducted contemporaneous with other work. Compared to social science research questions, healthcare studies are typically targeted with narrowly defined parameters.

These disciplinary differences may pose challenges for healthcare researchers interested in using observation. Given observation’s history in the social sciences there is a need to tailor observation to the healthcare context, with attention to the dynamics and needs of the research team. This paper provides contemporary healthcare research teams a practical, methodologically rigorous guide on when and how to conduct observation.

This article synthesizes the authors’ experiences conducting observation in social science and health services research studies, key literature and experiences teaching observation. The authors have diverse training in anthropology (GF, MM), systems engineering (BK) and psychology (MR). To develop this guide, we reflected on our own experiences, identified literature in our respective fields, found common considerations across these, and had consensus-reaching discussions. We compiled this information into a format initially delivered through courses, workshops, and conferences. In keeping with this pedagogical approach, the format below follows the linear process of study development.

Following the trajectory of a typical health research project, from study development through data collection, analysis and dissemination ( Fig. 1 ), we describe how to design and conduct observation in healthcare related settings. We conclude with data analysis, dissemination of findings, and other key guidance. Importantly, while illustrated as a linear process, many steps inform each other. For example, analysis and dissemination, can inform data collection.

Direct observation across a health research study.

3.1. Study development

3.1.1. study design and research questions.

In developing research using observation, the first step is determining if observation is appropriate. Observation is ideal for studies about naturally occurring behaviors, actions, or events. These include explorations of patient or provider behaviors, interactions, teamwork, clinical processes, or spatial arrangements. The phenomena must be feasible to collect. Sensitive or taboo topics like substance use or sexual practices are better suited to other approaches, like one-on-one interviews or anonymous surveys. Additionally, the phenomena must occur frequently enough to be captured. Trying to observe rare events requires considerable time while yielding little data. Early in the study design process, the scope and resources should be considered. The project budget and the timeline need to account for staffing, designing data collection tools, and pilot testing.

Research questions establish the study goals and inform the methods to accomplish them. In a study examining patients’ experiences of recovery from open heart surgery, the ethnographic study design included medical record data, in-depth interviews, surveys, and observations of patients in their homes, collected over three months following surgery [ 7 ]. By observing patients in their homes GF saw how the household shaped post-surgical diet and exercise. Table 1 provides additional examples of healthcare studies using observation, often as part of a larger, mixed-method design [ 14 , 15 ].

Example studies that use observation.

3.1.2. Data collection procedures

The phenomena to observe should be clearly defined. Research team discussions create a unified understanding of the phenomena, clarify what to observe and record, and ensure data collection consistency. This explication specifies what to look for during observation. For example, a team might operationalize the concept of patient-centered care into specific actions, like how the provider greets the patient. Further, additional nuances within broader domains (e.g., patient-centered care) could be identified while observations are ongoing. The team may identify unanticipated ways that providers enact patient-centered care (e.g., raising non-clinical, but relevant psychosocial topics- like vacations or hobbies- prior to gathering biomedical information). It is also important to look for negative instances, or behaviors that did not happen that should have, or surprising, unexpected findings. A surprise finding during observation was the impetus for further analysis examining how HIV providers think about their patients. While observing HIV care, a provider made an unexpected, judgmental comment about patients who seek pre-exposure prophylaxis (PrEP) to prevent HIV. This statement was documented in the fieldnotes (see 3.1.3 for a further description of fieldnotes) and later discussed with the team, leading to review of other study data and an eventual paper (see Fix et al 2018) [ 1 ]. Leaving room, both literally on the template and conceptually, can provide space for new, unexpected insights.

The sampling strategy outlines the frequency and duration of what is observed and recorded. It requires determining the unit of observation and the observation period. Units of observation are sometimes called “slices” of data. Ambady and Rosenthal [ 20 ] coined the term thin slices, using brief exposures of behavior (6s, 15s, and 30s) to predict teacher effectiveness. While thin slices are predominantly used in psychology, healthcare researchers can apply this concept by recording data for set blocks of time in a larger process, such as recording emergency department activity for the first 15 minutes of each hour.

The unit of observation can be a person (e.g., patient, provider), their behavior (e.g., smiling, eye rolling), an event (e.g., shift change) or interaction (e.g., clinical encounter). Using interactions as the unit of observation requires consideration for repeat observations of some individuals. For example, a fixed number of providers may be repeatedly observed with different patients.

Observation frequency will depend on the frequency of the phenomena. Enough data is needed for variation while also achieving “saturation,” a concept from qualitative methods, which means the point in data collection when no new information is obtained [ 21 ]. For quantitative studies, when examining the relationship between a direct observation measure (e.g., patient smiling) and an outcome (e.g., patient satisfaction), effect sizes from past research should dictate the number of interactions needed to achieve power to detect an effect. The duration of observation (the data slice) can be constrained using parameters as broad as a clinic workday, to distinct events like a clinical encounter.

Observation data can be collected on a continuous, rolling basis, or at predefined intervals. Continuous sampling is analogous to a motion picture—the recorded data mirrors the flow of information captured in a video [ 22 ]. Continuous observation is ideal for understanding what happens throughout an event. It is labor intensive and time-consuming and may result in a small number of observations, although each observation can yield considerable data. For example, a team may want to know about the patient-centeredness of patient-provider interactions. Continuous sampling of a clinical encounter could start when the patient arrives through when they leave, with detailed data collected about both the verbal and nonverbal communication. This could be considered an N of one observation but would yield substantial data. This information could be collected over a continuous day of encounters across several providers and patients, resulting in a considerable amount of data for a small group of people.

In contrast, instantaneous sampling can be conceptualized as snapshots, and is analogous to the thin slice methodology. Psychology research sometimes uses random intervals, while in healthcare research it may be preferable to use predetermined criteria or intervals [ 23 ]. Instantaneous sampling is economical and data collection can happen flexibly across a variety of individuals or times of day or weeks. Disadvantages include losing some of the context that is gained through continuous sampling.

3.1.3. Data collection tools

Data collection tools enable systematic observations, codifying what to observe and record. These tools vary from open-ended to highly structured, depending on the research question(s) and what is known a priori. We describe below three general tool categories—descriptive fieldnotes, semi-structured templates, and structured templates.

3.1.3.1. Descriptive fieldnotes

Descriptive fieldnotes, common in anthropology, are open-ended notes recorded with minimal a priori fields. Descriptive fieldnotes are ideal for research questions where less is known. An almost blank page is used to record the phenomena of interest. Key information such as date, time, location, people present and who recorded the information are useful for later analysis. These notes are jotted sequentially in real-time to maximize data collection, and are filled out and edited later for clarity and details. The flexible and open format facilitates the capture of unanticipated events or interactions.

Descriptive fieldnotes describe in detail what is observed (e.g., who is present, paraphrased statements), while leaving out interpretation. Analytic notes, that interpret what is being observed, can accompany the descriptive notes (e.g., the doctor is frowning and seems skeptical of what the patient is saying), but these analytic notes should be clearly marked as interpretation. One author (GF) demarcates interpretive portions of her fieldnotes using [closed brackets] to identify this portion of the fieldnote as distinct from the descriptive data. Interpretive notes should explain why the observer thinks this might be the case, using supporting data from the observation. Building on the example above, an accompanying interpretive note might say, “[the doctor raised their eyebrows, and does not seem to believe what the patient is saying, similar to what was observed in another encounter- see site 5 fieldnote). This information can be valuable during analysis to contextualize what was recorded and used in a later report or paper. Observation experience builds comfort and expertise with the open-ended, unstructured format.

3.1.3.2. Semi-structured templates

A semi-structured template comprises both open-ended and structured fields ( Fig. 2 ). It includes the same key information described above (i.e., date, time, etc.), then provides prompts for a priori concepts underlying the research questions, often derived from a theoretical model. These literature-based, theoretical concepts should be clearly defined and operationalized. For example, drawing from Street et al’s [ 24 ] framework for patient-centered communication, we can use their six functions (fostering the patient-clinician relationship, exchanging information, responding to emotions, managing uncertainty, making decisions, and enabling self-management) to develop categories for semi-structured coding a template. Like descriptive fieldnotes, the template also provides open-ended space for capturing contextual details about the a priori data recorded in the structured section.

Semi-Structured Observation Template.

3.1.3.3. Structured templates

A structured template in the form of a checklist or recording sheet captures specific, pre-determined phenomena. Structured templates are most useful when the phenomena are known. These templates are commonly used in psychology and engineering. Structured observations are more deductive and based on theoretical models or literature-based concepts. The template prompts the observer to record whether a phenomenon occurred, its frequency, and sometimes its duration or quality. See Keen [ 5 ] or Roter [ 25 ] for example structured templates for recording patient-centered care or patient-provider communication.

All templates should include key elements like the date, time and observer. Descriptive fieldnotes and semi-structured templates should be briefly filled out during the observation, and then written more thoroughly immediately afterwards. Setting aside time during data collection, such as a few hours at the end of each day, facilitates completion of this step. Recording information immediately, rather than weeks or months later, enhances data quality by minimizing recall bias. If written too much later, the recorder might fill in holes in their memory with inaccurate information. Further, small details, written while memories are fresh, may seem unremarkable but later provide critical insights.

For the semi-structured and structured templates, which contain prepopulated fields, there should be an accompanying “codebook” of definitions describing the parameters for each field. For example, building on the previous example using Street et al’s constructs, the code “responding to emotions” could identify instances where patients appear to be sad or worried and the provider responds to these emotions (also termed empathic opportunities and empathic responses) by eliciting, exploring, and validating the patients’ emotions [ 25 , 26 ]. This process operationally defines each concept and facilitates more reliable data capture. If space allows, the codebook can be included in the template and referenced during data collection. Codebooks should be updated through team discussion and as observations are piloted. Definitions from the codebook can be used in later reports and manuscripts.

3.2. Piloting

Given the real-world context within which observation data is collected, pilot-testing helps ensure that ideas work in practice. Piloting provides an opportunity to ensure the research plan works and reduce wasted resources. For example, piloting could reveal issues with the sampling plan (e.g., the phenomena do not happen frequently enough), staffing capacity (e.g., there are too many people to follow) or the codebook (e.g., few of the items specified in the data collection template are observed). Further, piloting gives the team a chance to systematize data collection and address issues before they interfere with the overall study integrity. This process guides what refinements need to be made to the data collection procedures. Piloting should be done at least once in a setting comparable to the intended setting.

3.3. Collecting data, analysis and dissemination

Healthcare studies are commonly conducted by interdisciplinary teams. The observation team should include at minimum two people, including someone with prior observation experience. Having more than one person collecting data increases capacity, distributes the workload and facilitates scheduling flexibility. Multiple observers complement each other’s perspectives and can provide diverse analytic insights. The observers should be engaged early in the research process. Having regular debriefing meetings during data collection ensures data quality and reliability in data collection. Adding key members of relevant communities to the team, such as patients or providers, can further enhance the relevance and help the research team think about the implications of the work.

Observational data collection often takes place in fast-paced clinical settings. For paper-based data collection, consolidating the materials on a clipboard and/or using colored papers or tabs, facilitates access. An electronic tablet to enter information directly bypasses the need for later, manual data entry.

Data analysis should be considered early in the research process. The analytic plan will be informed by both the principles of the epistemological tradition from which the overall study design is drawn and the research questions. Studies using observation are premised on a range of epistemological traditions. Analytical approaches, standards, and terminology differ between anthropologically informed qualitative observations recorded using descriptive fieldnotes versus structured, quantitative checklists premised on psychological or systems engineering principles. A full description of analysis is thus beyond the scope of this guide. Analytic strategies can be found in discipline-specific texts, such as Musante and DeWalt [ 27 ], anthropology; Suen and Ary [ 28 ], psychology; or Lopetegui et al [ 29 ], systems engineering. Regardless of discplinary tradition, analytic decisions should be made based on the study design, research question(s), and objective(s).

Dissemination is a key, final step of the research process. Observation data lends itself to a rich description of the phenomena of interest. In health research, this data is often part of a larger mixed methods study. The observation protocol should be described in a manuscript’s methods section; the results should report on what was observed. Similar to reporting of interview data, the observed data should include key descriptors germane to the research question, like actors, site number, or setting. See Fix et al [ 1 ] and McCullough et al [ 4 ] for examples on how to include semi-structured, qualitative observation data in a manuscript and Waisel et al [ 17 ] and Kuhn et al [ 19 ] for examples of reporting structured, quantitative data in a manuscript.

3.4. Institutional review boards

Healthcare Institutional Review Boards may be unfamiliar with observation. Being explicit about data collection can proactively address concerns. The protocol should detail which individuals will be observed, if and how they will be consented and what will and will not be recorded. Using a reference like the Health Insurance Portability and Accountability Act (HIPAA) identifiers (e.g., name, street address) can guide what identifiable information is collected. The protocol should also describe how the team will protect data, especially while in the field (e.g., “immediately after data collection, written informed consents will be taken to an office and locked in a filing cabinet”).

There are unique risks in studies using observation because data is collected in “the field.” Precautions attentive to these settings protect both participants and research team members. A detailed protocol should describe steps to address potential issues, including rare or distressing events, or what to do if a team member witnesses a clinical emergency or a participant discloses trauma. Additionally, team members may need to debrief after distressing experiences.

4. Discussion & conclusion

4.1. discussion.

The ability to improve healthcare is limited if real-world data are not taken into account. Observation methods can elucidate phenomena germane to healthcare’s most vexing problems. Considerable literature documents the discrepancy between what people report and their behavior [ [30] , [31] , [32] ]. Direct observation can provide important insights into human behavior. In their ethnographic evaluation of an HIV intervention, Evans and Lambert [ 31 ] found, “observation of actual intervention practices can reveal insights that may be hard for [participants] to articulate or difficult to pinpoint, and can highlight important points of divergence and convergence from intervention theory or planning documents.” Further, they saw ethnographic methods as a tool to understand “hidden” information in what they call “private contexts of practice.” While in Rich et al.’s work [ 32 ], asthmatic children were asked about exposure to smoking. Despite not reporting smoking in the home, videos recorded by the children—part of the study design—documented smokers outside their home. The use of observation can help explain research questions as diverse as patients’ health behaviors [ 7 , 10 , 32 ], healthcare delivery [ 3 , 4 ] or the outcomes of a clinical trial [ 9 , 33 ].

A common critique in healthcare research is that observing behavior will change behavior, a concept known as the Hawthorne Effect. Goodwin’s study [ 34 ], using direct observation of physician-patient interactions, explicitly examined this phenomena and found a limited effect. We authors have observed numerous instances of unexpected behavior of healthcare employees such as making disparaging comments about patients, eye rolling, or eating in sterile areas. Thus, those of us who conduct observation often say that if behavior change were as easy as observing people, we could simply place observers in problematic healthcare settings.

The descriptions above on how to use observation are applicable to fields like health services research and implementation and improvement sciences which have similarly adapted other social science approaches.[ [35] , [36] , [37] , [38] , [39] , [40] ] Notably, unlike the social sciences, many health researchers work in teams and thus this guide is written for team-based work. Yet, health researchers sometimes also conduct observations without support from a larger team. While this may be done because of resource constraints, it may raise concerns about the validity of the observations. First, social sciences have a long history of solo researchers collecting and analyzing data, yielding robust, rigorous findings [ 13 , [41] , [42] , [43] ]. Using strategies, such as those outlined above (i.e., writing detailed, descriptive fieldnotes immediately; keeping interpretations separate from the data; looking for negative cases) can enhance rigor. Further, constructs like validity are rooted in quantitative, positivist epistemologies and need to be adapted for naturalistic study designs, like those that include direct observation [ 44 ].

4.2. Innovation

Social science-informed research designs, such as those that include observation, are needed to tackle the dynamic, complex, “wicked problem” that impede high quality healthcare [ 45 ]. Thoughtful, rigorous use of observation tailored to the unique context of healthcare can provide important insights into healthcare delivery problems and ultimately improve healthcare.

Additionally, observation provides several ways to involve key communities, like patients or providers, as participants. Observing patient participants can provide information about healthcare processes or structures, and inform research about patient experiences of care or the extent of patient-centeredness. With the movement towards engaging end users in research, these individuals can contribute more meaningfully [ 46 , 47 ]. As team members, they can define the problem, inform what to observe, how to observe, help interpret data and disseminate findings.

4.3. Conclusion

Observation’s long history in the social sciences provides a robust body of work with strategies that can be inform healthcare research. Yet, traditional social science approaches, such as extended, independent fieldwork may be untenable in healthcare settings. Thus, adapting social science approaches can better meet healthcare researchers’ needs.

This paper provides an innovative, yet practical adaptation of social science approaches to observation that can be feasibly used by health researchers. Team meetings, developing data collection tools and protocols, and piloting, each enhance study quality. During development, teams should determine if observation is an appropriate method. If so, the team should then discuss what and how to collect the data, as described above. Piloting improves data collection procedures. While many aspects of observation can be tailored to health research, analysis is informed by epistemological traditions. Having clear steps for health researchers to follow can increase the rigor or credibility of observation.

Rigorous utilization of observation can enrich healthcare research by adding unique insights into complex problems. This guide provides a practical adaptation of traditional approaches to observation to meet healthcare researchers’ needs and transform healthcare delivery.

This work was supported by the US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development. Dr. Fix is a VA HSR&D Career Development awardee at the Bedford VA (CDA 14-156). Drs. Fix, Kim and McCullough are employed at the Center for Healthcare Organization and Implementation Research, where Dr. Ruben was a postdoctoral fellow. The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Competing Interest

All authors declared no conflict of interests.

Acknowledgements

This work has been previously presented as workshops at the 2015 Veteran Affairs Health Services Research & Development / Quality Enhancement Research Initiative National Meeting (Philadelphia, PA) and the 2016 Academy Health Annual Research Meeting (Boston, MA). We would like to acknowledge Dr. Shihwe Wang for participating in the 2015 workshop; Dr. Adam Rose for encouragement and helpful comments; and the VA Anthropology Group for advancing the utilization of direct observation in the US Department of Veteran Affairs. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

4 Types of Observational Research

Observational research typically happens in the users’ home, workplace, or natural environment and not in a lab or controlled setting.

With this research, you can understand how people naturally interact with products and people and the challenges they face.

It can provide inspiration and ideas for opportunities for improvement and innovation.

While it may seem like observation is as simple and uniform as watching and taking notes, there are some subtle differences that can affect the type of data you collect. The role the observer plays forms a continuum from completely removed to completely engaged with the participant.

As you plan your next observational research project and choose the right type for it to be successful, consider the following:

Ethics of Observing . On both ends of the spectrum (a fully detached or fully engaged observer), you face ethical considerations, as those being observed aren’t aware of it. For that reason, most observational research you’ll conduct falls somewhere in between. Think about quantifying the results . While observational research is typically associated with qualitative methods, you can still quantify the occurrences of behaviors or statements made by the participants to get an idea about the frequency of customer attitudes and actions. Improve the reliability and validity of your observations. Consider having multiple independent researchers observe and code their notes. Using multiple observers with differing perspectives (e.g. product manager and researcher) helps identify areas of agreement and disagreement and makes your observational data more trustworthy and reliable.

Keep these caveats in mind as you chose a role for an observational research project. The four types of observational roles we discuss here are based on the distinctions made by the sociologist Raymond Gold in 1958 but apply to any field of research.

1. Complete Observer

This is a detached observer where the researcher is neither seen nor noticed by participants. It’s one way of minimizing the Hawthorne Effect as participants are more likely to act natural when they don’t know they’re being observed.

While this was once considered an objective role for the ethnographer, it’s fallen out of favor because it’s the role most likely to raise ethical questions about possible deception. How would you feel if you found out someone was watching you, but you didn’t know? Sort of Big Brotherish, most likely.

However, in public places like coffee shops, office building lobbies, airports, subway stations, or even public bathrooms the complete observer role may be the only means to collect the type of data you need. And with the ubiquity of video cameras, remote observation remains a viable option.

2. Observer as Participant

Here the researcher is known and recognized by the participants and in many cases, the participants know the research goals of the observer.

There is some interaction with the participants but the interaction is limited. The researcher’s aim is to play a neutral role as much as possible.

This approach is generally used when “following a customer home” to understand how someone uses software products to accomplish goals.

3. Participant as Observer

Here the researcher is fully engaged with the participants. She is more of a friend or colleague than a neutral third party. While there is full interaction with participants, they still known that this is a researcher.

This method is often used when studying remote indigenous populations or inner-city cultures. There’s an anthropologist joke [pdf] that a household photo of a native village consists of a married couple, their parents, and a graduate student.

4. Complete Participant

This is a fully embedded researcher, almost like a spy. Here the observer fully engages with the participants and partakes in their activities.

Participants aren’t aware that observation and research is being conducted, even though they fully interact with the researcher. This has sometimes been referred to as “going native,” in reference to performing indigenous fieldwork.

In customer research, this is like a secret shopper or the show Undercover Boss . The idea is that the best way to understand a type of role, people, or culture is to experience it firsthand. Want to understand Burning Man ? Then go as a complete participant.

Gathering authentic qualitative data can be a challenge in UX research; one way to do so is with observation outside of a controlled environment where participants are more likely to act natural.

There are four types of observational research you can do, ranging from detached observation with no participation on your part (complete observer) to immersing yourself completely in the environment (complete participant). Which you choose depends on your goals, timeframe, and properly balancing the ethical considerations.

You might also be interested in

• Tools and Resources
• Customer Services
• Original Language Spotlight
• Alternative and Non-formal Education 
• Cognition, Emotion, and Learning
• Curriculum and Pedagogy
• Education and Society
• Education, Change, and Development
• Education, Cultures, and Ethnicities
• Education, Gender, and Sexualities
• Education, Health, and Social Services
• Educational Administration and Leadership
• Educational History
• Educational Politics and Policy
• Educational Purposes and Ideals
• Educational Systems
• Educational Theories and Philosophies
• Globalization, Economics, and Education
• Languages and Literacies
• Professional Learning and Development
• Research and Assessment Methods
• Technology and Education
• Share This Facebook LinkedIn Twitter

Article contents

Observing schools and classrooms.

• Alison LaGarry Alison LaGarry University of North Carolina at Chapel Hill
• https://doi.org/10.1093/acrefore/9780190264093.013.983
• Published online: 29 July 2019

Qualitative observation is an attempt to view and interpret social worlds by immersing oneself in a particular setting. Observation draws on theoretical assumptions associated with the interpretivist paradigm. Thus, researchers who engage in qualitative observations believe that the world cannot be fully known, but must be interpreted. Observation is one way for researchers to seek to understand and interpret situations based on the social and cultural meanings of those involved. In the field of education, observation can be a meaningful tool for understanding the experiences of teachers, students, caregivers, and administrators.

Rigorous qualitative research is long-term, and demands in-depth engagement in the field. In general, the research process is cyclical, with the researcher(s) moving through three domains: prior-to-field, in-field, and post- or inter-field. Prior to entering the field, the researcher(s) examine their assumptions about research as well as their own biases, and obtain approval from an Institutional Review Board. This is also the time when researcher(s) make decisions about how data will be collected. Upon entering the field of study, the researcher(s) work to establish rapport with participants, take detailed “jottings,” and record their own feelings or preliminary impressions alongside these quick notes. After leaving an observation, the researcher(s) should expand jottings into extended field notes that include significant detail. This should be completed no later than 48 hours after the observation, to preserve recall. At this point, the researcher may return to the field to collect additional data. Focus should move from observation to analysis when the researcher(s) feel that they have reached theoretical data saturation.

• education research
• qualitative
• observation
• ethnography

Introduction

Observation, as a concept, can refer to many things. Yet, in terms of social research and ethnography, observation is the act of “record[ing] the ongoing experiences of those observed, through their symbolic world” (Denzin, 2017 , p. 185). It is an attempt to view and interpret social worlds by immersing oneself in a particular setting—a way to “see from the inside” (Emerson, Fretz, & Shaw, 2011 , p. 3). Observation draws on theoretical assumptions of the interpretivist paradigm, and is associated with methodologies such as ethnography, narrative inquiry, discourse analysis, grounded theory, phenomenology, and symbolic interactionism. It is one of many ways for researchers to understand situations based on the meanings of those involved. The particular approach to observation presented here considers the process and implications of observations in educational settings such as schools and classrooms.

The Interpretivist Paradigm

All research methods and methodologies are based on assumptions about reality and knowledge. In order to understand how one might study a particular research question or explore a phenomenon, it is important for researchers to examine their beliefs about whether the world around them can be objectively known. Researchers who approach their work from the interpretivist paradigm believe that the world cannot be objectively understood, and does not exist independently of thoughts or ideas. Since there is no objective truth, the world must be interpreted (Glesne, 2016 ). Further, the goal of such research is not just to interpret the social world, but to do so through the lens of actors in that particular setting or context. Through observation, then, qualitative researchers “access . . . others’ interpretations of some social phenomenon” and also use their own lens to interpret the actions and motivations of others (Glesne, 2016 , p. 9).

Because interpretivist qualitative research, as described in this article, is centered on interpretation, it is not considered “objective” research. Throughout the observation process, the researcher’s identity and subjectivity are always implicated. Interpretivist research engages participants’ multiple ways of knowing and making meaning, at the same time engaging socially constructed meanings agreed upon by society. Thus, while interpretations may be unique to individuals, to some degree, it is also possible to access the “perspectives of several members of the same social group about some phenomena,” which can “suggest some cultural patterns of thought and action for that group as a whole” (Glesne, 2016 , p. 9). In order to collect substantial evidence of such cultural patterns, interpretivist researchers prioritize significant, long-term engagement in the field. While one might observe and use the techniques described in this article on a short-term or ad hoc basis, sustained presence in the field and interaction with participants are vital for interpreting cultural understandings unique to the context.

Nearly every researcher has experienced schooling in some manner, making informal “insider” status somewhat universal for researchers who choose to study education. This amplifies researcher subjectivity such that most researchers entering the field have an a priori vision of what the student experience is like, and how educators are, or should be, in an educational setting. For those who have experienced traditional schooling, their experience is not insignificant, spanning more than a decade of their lives. Additionally, some education researchers are former educators, adding a further layer of knowledge and experience that influences how they engage in observation-based qualitative research. All this is to say that the cultural meanings that each of us bring to bear on educational research are heavily laden with our own schooling experiences and the social powers that shape them. This can be both a benefit and a reason for increased attentiveness or caution.

Another concern regarding observation in the field of education is that there are significant contextual implications for observations in classrooms. Thus, the term is doubly fraught with meaning. Generally, when teachers (or students) think about being observed, they assume judgement. While a fear or wariness about researcher judgement is not uncommon in observational research, the apprenticeship model for teachers invokes observation as a form of evaluation with real professional consequences. This is the case for pre service teachers and in-service teachers alike. In conjunction with student achievement, observation ratings may also be tied to teacher performance evaluations and merit pay. This discursive and symbolic conundrum can be problematic for qualitative researchers both in terms of gaining entry into the field, and also in terms of managing their own biases toward judgement. In conducting observation in classrooms, the aura of evaluation is ever-present. This is not to say that observation, as associated with educational evaluation, is bad. There are vast benefits to apprenticeship, directed feedback, legitimate peripheral participation (Lave & Wenger, 1991 ), and experiential learning (Dewey, 1938 ). When it comes to qualitative research, however, there is a necessary translation that must occur to orient both the reflexive approach of the researcher, and the understanding of the teacher or students being observed.

While interpretivist participant observation engages the subjectivity of the researcher, novice researchers are encouraged to take field notes as objectively as possible, reserving analysis and interpretation for a later phase. That said, our experiences as researchers in the field always engage some level of analysis as we integrate what we see and experience into our own extant frames of reference. Denzin ( 2017 ) reminded researchers that participant observation “entails a continuous movement between emerging conceptualizations of reality and empirical observations. Theory and method combine to allow the simultaneous generation and verification of theory” (p. 186). This article presents a methodological perspective on how one might conduct participant observation in educational settings, while paying particular attention to the movement between empirical or “objective” observation, subjective interpretation, and further evaluation. While the article focuses primarily on observation rather than analysis, it is necessary to consider how a researcher navigates the continuous push in the field to detach (concrete observation) and connect (understanding emerging concepts). The article thus includes some discussion of preliminary analysis and how it may be recorded.

It is always tricky to lay out methodological procedure when, in reality, the process is layered, cyclical, or non-linear (Spradley, 1980 ). For the researcher interested in observation, it is important to keep in mind the idea of “movement between” as stated by Denzin ( 2017 ). A vital skill for expert qualitative observation is to actually exist and think “between.” This allows for subjectivity and emic or insider understandings to inform, but not supersede, concrete thick descriptions (Geertz, 1973 ) of interaction in the field. This skill takes significant practice and mentorship. The included examples describe the process of a novice researcher, to show how one might begin to build capacity for observation and subsequent interpretation. Following the discussion of methodological procedure, there is a brief discussion of implications and encouragements for the use of ethnographic observation in educational settings.

Methodological Cycles of Observation

This section breaks the methodological process of observation in school settings into three domains: Prior-to-field , in-field , and post- or inter-field . These domains can be viewed as somewhat cyclical in nature and, realistically speaking, are not always discrete. As the researcher becomes more embedded in the research setting, more familiar with the context, and more adept at the “move between” description and analysis, the lines between the domains become blurry. So while one may separate these domains for the sake of explanation, they should be taken not as singular, but rather as guiding moments in the process of qualitative observation.

In the prior-to-field domain, the researcher examines or states their own epistemological stance toward the work, as well as their own biases toward the setting or subject matter. This reflexive work not only sets the tone for the in-field domain, but also allows the researcher to consider appropriate research questions. In the post- or inter-field domain, the researcher revisits their in-field observations to again navigate between the concrete field notes taken and their own subjective interpretations. This domain also provides opportunity to further focus observation and refine the research questions. Additionally, researchers may consider this an apt moment to check with participants for their own interpretations of interactions observed.

Prior-to-Field

Observation is more than simple data collection and, despite differing epistemological orientations, nearly all sources agree that observation-based research should be rigorously conducted. In other words, data gathered through observation or ethnography is “more than casually observed opinion” (Angrosino & Rosenberg, 2011 , p. 468). In more recent iterations of ethnographic methodology, observation is highlighted as a site of interaction. In this postmodern context, researcher subjectivity is acknowledged—rendering the researcher a participant, co-constructor, and co-negotiator of meaning at the study site. Angrosino and Rosenberg ( 2011 ) stated, “our social scientific powers of observation must, however, be turned on ourselves and the ways in which our experiences interface with those of others in the same context if we are to come to an understanding of sociocultural processes” (p. 470). This discussion of the nature of observation-based research is a vital starting point since it orients the researcher to the cultural meanings of the study site and encourages them to acknowledge their own subjectivity. As in post-critical ethnography (Noblit, Flores, & Murillo, 2004 ), this orientation serves to situate the project as theory and methodology that are inextricably intertwined. This means that the researcher needs to be aware of the experiences, meanings, and biases they bring to the field.

From a sociological standpoint, each of us moves in the world based on a number of more or less abstract identity markers that influence how others interact with us. A particular caution for educational researchers exists in the vast differences we know that students have in their schooling experiences. These differences are often based on social markers such as race, ethnicity, socioeconomic status, gender, sexuality, and religion. Schooling, as an institution, mirrors and even amplifies the social hierarchies of society such that some are distinctly privileged in educational settings, while others experience oppression and disadvantage. So, to build on the assertion that nearly all education researchers have “insider” experience with schooling, it is important to note that these experiences can differ greatly. Sometimes parallel or similar experiences may limit the view of the researcher in that they may see only their own experiences, and may not look beyond that feeling to truly engage what others might experience. Additionally, differing experiences or social positioning may result in misinterpretation of cultural meaning. Thus, educational researchers must prioritize the move between social meanings of their own and those of participants observed. This is one reason, in particular, why it is so important to record concrete sensory detail in the field.

When a researcher records concrete details, they are recording what is seen . If a researcher were to record only what they think about the events taking place in the field, this judgement (for that is what it is) may supplant other potential meanings that may be discovered. Recording concrete sensory details allows the researcher the space to later move between their own subjectivity and those of the participants—particularly during the process of writing expanded field notes. This process takes time and practice. Indeed, it takes a vigilant researcher to parse out the expectations overlaid on educational research settings by their own experiences from the experiences of others. In consideration of the ways that a researcher might begin to identify and examine their own biases, a good starting point is Sensoy and DiAngelo ( 2017 ). In their book Is Everyone Really Equal: An Introduction to Key Concepts in Social Justice Education , the authors guide the reader through an approachable exploration of concepts such as power, oppression, prejudice, discrimination, privilege, and social construction. Each of these concepts is vital for understanding researcher biases and how they influence interpretations in the field. In general, this examination process is referred to in the field as reflexivity, or “critical reflection on how researcher, research participants, setting, and research procedures interact with and influence each other” (Glesne, 2016 , p. 145). Pillow ( 2003 ) pointed out that this reflective process does not absolve the researcher of their own biases, yet has important ramifications for the analysis and findings.

Those who have trained and served as educators may have particular insight to offer in the field of educational research. They may understand the field in more depth, having recently experienced the nuance and pressures of policy. To those who say that prior experience in the field may bias the investigation—it does. However, all researchers are biased in that they experience the world in a particular manner and ascribe specific cultural and social meanings to settings and events. It is also necessary to acknowledge here that effective use of this depth of understanding for qualitative observation does not come without caution.

Prior to entering the field, researchers may make preliminary decisions about their level of involvement, participation, and immersion. While older iterations of ethnographic methodology encouraged the observer to participate as little as possible, this can hinder the researcher’s ability to truly understand indigenous meanings of the social situation being observed. Certainly, the lesser-involved researcher will have greater opportunity to record copious notes. However, simply being present in the setting does have an effect on participants and may alter the way that they act or interact. Furthermore, researchers need not see the roles of participant and researcher as two poles. Rather, it is useful to think of these as two ends of a continuum, where the researcher(s’) role is never static.

While research ethics are not the primary focus of this article, it would not be appropriate to advocate for observation without mentioning that participants’ rights and confidentiality should be considered at every step of the process. Prior to entering the observation setting, the researcher must obtain approval from an Institutional Review Board (IRB). This is particularly important for research in schools, where participants may be minors and parental consent for participation may be required. Once approval is granted, the researcher should obtain consent from participants and provide a disclosure of nature of the study and time requirements for engaging in the study. Additionally, participants should be reminded that they can opt out of the study at any time. The IRB will also provide explicit guidelines on how all sensitive or identifiable data should be stored to protect participants’ identity.

Another key decision to make prior to entering the field is how field notes will be recorded. While notes can certainly be recorded on paper, or using a word-processing program on a laptop, pervasive use of personal digital technology (smartphones, tablets, etc.) has transformed the available options for documenting the field. As long as one has received approval for photo or video documentation from IRB, digital photography is instantaneous and can help document the research setting in greater detail. Digital videos can record activities and interactions such that the researcher can return to these when expanding field notes for further verification or perspective. Aside from simple dialogue, voice recorders can also record soundscapes , a growing area of qualitative research analysis (Gershon, 2013 ). There are also a number of app-based note-taking and qualitative-analysis programs helpful for observational research, including: Atlas.ti Mobile, Evernote, EverClip, MAXApp (corollary to MAXQDA), and Indeemo. Additionally, Google Could now offers a free speech-to-text function that can capture dialogue in more detail than one might be able to do on paper or by typing.

The choice of note-taking platform should take into account participants’ wishes, as well as the needs inherent to the setting. This decision is not just a simple question of what will work best for the researcher and their research product. Returning to the prior discussion of educator evaluation, teachers may associate note-taking—on paper or electronically—with recording judgement. When I have mentored student teachers, they have expressed that the tapping sound produced by typing on a laptop can increase their anxiety exponentially. While these considerations may sound superficial, the comfort level of participants is of utmost importance for the researcher in establishing themselves as collegial, and not intrusive. In fact, I have found it to be useful to ask a classroom teacher how they would prefer for me to record my observations. Regardless of their choice, I always assure them that I am “documenting” the events taking place, and not recording judgement.

Before moving on, it is worth noting that any prior-to-field decision-making may shift and evolve throughout the process of the research engagement. Qualitative research, by nature, seeks to understand meaning from the perspective of the actors in a particular context. Thus, the researcher must be willing to follow threads of understanding or thought, even if they are unexpected. For example, one may plan for low participation (Spradley, 1980 ) in the setting, but one day during the field visit the teacher may invite the researcher to lead a group of students through a math activity. In the interest of building rapport and trust with the participants, it may be necessary to move to a higher level of participation in response to this invitation. This will be discussed in further detail relating to the in-field domain. Emerson et al. ( 2011 ) stated that a good participant observer must be both “sensitive and perceptive about how they are seen by others” (p. 4). If the participants see the researcher as detached, unhelpful, or otherwise standoffish, this can affect their level of comfort and shift the insights they choose to share. Changes in the researcher’s level of participation should be recorded in field notes, and do not negate the reliability of eventual findings. In fact, participants may share additional insights with researchers who show interest in their perspectives, actions, and thoughts.

This section details two major considerations for researcher(s) embarking upon in-field observations: What to look for, and how to record what is seen. This is obviously oversimplified, but these two considerations will help to organize the process of collecting qualitative data via observation. These decisions can be made by an individual researcher or by research teams working together to investigate a particular setting or phenomenon.

What Should the Researcher Look For?

The first thing a novice researcher often asks about observation in the field is “What should I be looking for?” This question is loaded, and takes some time to unpack. While there may be something that the researcher hopes will happen, it is important to focus explicitly on what does happen, and how it happens. One of the first skills that a participant observer must begin to hone is explicit awareness of a situation (Spradley, 1980 ). This awareness can be compared to that of a wide-angle camera lens that takes in as much as possible. The goal, Spradley stated, is to overcome the “selective inattention” most people employ to conduct daily tasks and interactions (p. 55). This explicit awareness is not solely directed outward. Spradley also noted that the researcher must increase their introspectiveness so that they are better able to see and reflect upon the cultural frames and meanings associated with that which is observed.

Using the metaphor of a wide-angle lens, one common way to begin observation is through descriptive observation . In this case, the researcher approaches the observation with very general questions in mind. For example: “What is happening here?” or “What is going on?” These broad, open questions allow for the researcher to see and feel the setting as it is, without overlaying a priori meanings or assumptions.

Table 1. Spradley’s Descriptive Question Matrix

Source: . Spradley ( 1980 , pp. 82–83).

Spradley ( 1980 ) outlined a “Grand Tour” as a procedure for descriptive observation. In this overview, the researcher would take note of various facets of the setting and participants including:

The first three facets are presented in bold (author’s emphasis) because these three form a meaningful starting point for any observation, and the remaining six provide additional nuance. A diagram can be useful for illustrating the set-up of the space, mapping objects as well as actors. After examining each of these facets of the setting, Spradley recommended creating a descriptive question matrix wherein the researcher integrates observations from two or more of the facets to examine how they might interact. For example, consider how a student who is disabled might interact with a space that is not accessible for mobility. More detail is provided in Table 1 .

Emerson et al. ( 2011 ) also advocated for a wide-angle lens and prioritized the senses in helping to establish initial impressions. They expanded on the facets listed by Spradley, encouraging the researcher to consider physical space and environment in terms of characteristics such as size, space, noise, and layout. It terms of actors in a setting, they also suggested observing such characteristics as perceived race and gender, dress, comportment, and proximity to other actors. Moving beyond these facets, Emerson et al. also advocate that the researcher ask the question “What is significant or unexpected?” in the field. In other words, what seems out of place or out of the expected flow? Such unexpected moments are often of the most interest, and also represent some of the most significant cultural learning for the researcher. For instance, do the actors in the field react as though the same event is unexpected? If not, the researcher will need to examine the event, activities preceding the event, and those following the event to work to understand the significance. It is also important to register one’s own feelings, as the researcher, when observing in the field. Then, in working to understand one’s own reactions, feelings, and biases in comparison to those in the field, one may reveal cultural meanings unique to the context. It is important to note that the researcher should not take their own feelings as findings. Rather, they should move beyond their own reactions toward an analysis of what those in the setting may find significant (Emerson et al., 2011 ).

Focused observation takes place after the researcher has been in the field for some time, and serves to limit the inquiry in a meaningful manner. Whereas in descriptive observation, the research questions were general, in focused observation the researcher engages more structural questions (Spradley, 1980 ). For example: What are all the ways that a teacher asks a student to focus on their work? Focused observations may be conducted as surface or in-depth investigations. According to Spradley, surface investigations examine a number of cultural domains in some depth. In-depth observations are just that, observations where the researcher selects one domain and examines it thoroughly. These cultural domains may be selected based on personal interest, suggestion by informant, theoretical interest, or other strategic reasoning (Spradley, 1980 ). Additionally, this can lead the researcher to a potential taxonomy of events or codes occurring at the site ( selective observation ).

While Spradley’s approach can be useful and meaningful, there is also room to hone the initial general research question of “What is happening here?” to a more structured prompt that does not demand taxonomic reduction. An example of such a prompt engages the significant or unexpected events described by Emerson et al. ( 2011 ). In this case, the researcher might choose to further examine a particular event or occurrence, asking the questions: When this event happens, how does it happen? What else is happening? What changes? This way, the researcher is not limited to types of interaction, but can also consider the means by which these interactions take place and the dynamics that are set into motion.

Recording Field Notes

Field notes are the first phase of documenting happenings as data via observation—a method of inscription or textualization which later serves as a basis for iterative analysis. Further, according to Emerson et al., “Field notes are distinctively a method for capturing and preserving insights and understandings” ( 2011 , p. 14). There is no best way to record field notes, and none approaches a truly objective accounting of the events that occurred. One observer may choose to record significant events or key phrases that another observer does not choose to record. Thus, when conducting research in teams, it is useful to cross-check notes with others who observed the same events. This can be done in formal calibration meetings or informal conversations post-observation. Cross-checking can also be performed as a type of member check with participants, where the researcher might ask if anything was missed. Subjectivity is always implicated, since each observer filters events through their own cultural meanings and understanding of the social world. Yet, researchers observing in social settings are still encouraged to record what they see as concretely as possible. Taking a step back, researchers must decide the appropriate method for recording notes in the field. In the moment, researchers will need some method to record jottings, which are “a brief written record of events and impressions captured in key words and phrases” (Emerson et al., 2011 , p. 29). These quickly written or typed fragments are used to help the researcher as they later create detailed expanded field notes.

A researcher may choose to take notes on paper or another electronic device. When permission is appropriately obtained, the researcher may also create video or audio recordings of the setting. Even when a recording is made, the researcher should still take jottings when possible as a source for both back up and further detail. The choice of paper or electronic device should be made based on the setting and the researcher’s level of participation in the field. In any case, the method used should be as unobtrusive as possible and should not disturb the events taking place. The researcher may choose to take jottings down openly—so that participants can see them writing or typing—or in a hidden manner (Emerson et al., 2011 ). The decision of how to record jottings in the field is also dependent on a number of other factors, including the nature of the research questions, the skill of the researcher, the mobility required by the setting, availability of power or Internet, and the language of the researcher as compared to the participants.

As events in a research setting unfold, the researcher should take down short notes in order to later remember the events when assembling expanded field notes. These jottings may be fragments of interactions, keywords, phrases, or verbatim quotes (when possible). For example:

Music Education Class Participants: 1 Instructor, 8 Students (college-aged), 1 researcher 2:15 p.m . Instructor (Dr. Hart) tells class they are making a chart about assumptions Hope: Learning takes place in a building Hart: So, learning should look a certain way Hope: No! Not what I meant Hart says translating to fit in chart Hope: No, no! (shakes head and looks at me) Me: I think she is saying that learning could happen outdoors, or at home . Hope: Yes!! Hart writes “Learning should look a certain way” on chart, ignoring our protestations Hope frowns scrunches eyebrows together. Looks down at phone . 1

Jottings may also consist of drawings and diagrams that document the space. Jottings should always show time and date, and it is useful to check the clock and record the time every 5–10 minutes or so throughout the observation. This will help later, when considering and analyzing the pace of events. The question of when a researcher should take down jottings is also worth consideration. If the researcher is involved in a conversation, or is an otherwise active participant in the situation or events, they should prioritize this interaction over note-taking. Tact and rapport are vitally important to qualitative observation, and sometimes note-taking may come across as if the researcher is rude or not listening. Wait for breaks or lulls in the conversation to record jottings. If your participation requires that you move around a room or other space, it may be best to use a small notebook or electronic tablet that is easily carried.

Our inclination as educational researchers is often to provide evaluative feedback on the performance of the educator being observed. When recording field notes, it is important to resist this urge. Jottings should include as much detail as possible, using descriptive and concrete language. Emerson et al. ( 2011 ) suggest the following recommendations on how one might document what is observed. First, one should describe all key components of the setting, using concrete sensory details that would help a third-party reader gain a reasonable vision of the actors and events. Rather than stating that a participant looked defeated, for instance, it would be more appropriate to record the details of their bearing that lead you to believe this is the case. In this example, the researcher might record: The participant’s eyes were cast down toward the ground and their shoulders were hunched forward . Additionally, researchers should avoid characterizing events through generalization or summary in field notes, since these represent a form of analysis or judgement. The purpose in avoiding generalization at this phase is to leave the possibility open for alternative interpretation once the full data set is established. It is possible that later events may clarify or alter the meaning of a particular social act.

Feelings and emotions will always be present in a research setting, and should be acknowledged and recorded. Emerson et al. ( 2011 ) noted that it can be informative to describe actors’ emotional expressions and responses to the events occurring throughout the observation. They also recommend that the researcher record their own impressions and feelings about the events. Having recorded these feelings and responses, the researcher can compare their own reactions to those of the participants in order better to understand the cultural and social meanings unique to that setting and those actors. However, the impressions and feelings of the researcher do represent a form of analysis, and should be specifically recorded as such.

In field notes, the researcher should differentiate between the types of information they record so that it will be recognizable when they return to the jottings to expand them into completed field notes. Concrete descriptions of sensory details and verbatim interactions should be recorded in one manner or place, and impressions or personal feelings should be recorded differently. For example, some researchers choose to separate these types of jottings into two columns in their notebook before entering the field. Others use the comment function in word-processing software to separate analytic commentary from notes. These parallel notes can also be recorded using the advanced functionality of apps such as Evernote and MAXApp.

Both types of recording are important, and serve to help the researcher remember what they were seeing and feeling while in the field. These reminders will serve as recall prompts when the researcher goes to expand their field notes into full notes, and later when they use those notes to create analytic memos.

Post-Field or Inter-Field

This domain is dually named to highlight the fact that qualitative participant observers should complete multiple observations over a significant length of time. A single observation is not sufficient for allowing the researcher to understand contextual cultural meanings, and most qualitative methodologists encourage in-depth, long-term engagement in the field. Thus, the inter-field domain name refers to the idea that researchers will likely need to enter and exit the field a number of times. Expanded field notes, notes-on-notes, and memos should be created in between visits to help focus the study. At some point, examination of field notes and other qualitative data (i.e., interviews, documents) will start to seem redundant. In other words, the researcher(s) will begin to see the same phenomena occurring, with nothing new arising in successive observations. In other words, they have reached the point of data saturation (Glesne, 2016 ). There is not a set number of observations, or a pre determined length of field observation, necessary for rigorous qualitative observation. Rather, the researcher(s) must determine this point of theoretical saturation for themselves.

Expanded Field Notes

The process of observation does not stop once the researcher leaves the field. One cannot possibly record every detail of the observation in the moment, so jottings should be re-read and expanded after the fact. In order to preserve detail with the freshest memory, a number of sources recommend that the researcher read over jottings and expand them into fully realized field notes within 24 to 48 hours. This expansion process involves recreating a record of the events and interactions observed in full, rich detail (Geertz, 1973 ). In the field, the researcher may not have had time to record these happenings fully, but the jottings serve to jog the memory so that the researcher can later recall the field more fully. Expanded field notes may take the form of prose (paragraphs), a script of dialogue, figures, or diagrams. Time notations from jottings should be preserved in expanded field notes, and researcher asides or commentaries should also be kept separate from concrete sensory observations. Here is an example of field notes expanded from the jottings provided in the section “ Recording Field Notes ”:

Music Education Class Participants: 1 Instructor, 8 Students (college-aged), 1 researcher 2:15 p.m . The instructor, Dr. Hart asks the students what assumptions we make about learning. Hope, a white woman, raises her hand and says, “We assume that learning takes place in a building.” I feel that I understand what she’s saying and nod in agreement. Though I’ve nodded my head somewhat unconsciously, I notice that Hope has seen me agreeing with her. Dr. Hart says: “Yes, we assume that a school should look a certain way.” She says “No, that’s not what I mean!” and looks at me. Dr. Hart says that he’s going to translate her meaning a bit so that it will fit the chart they’ve been creating, and that, basically, it’s the same meaning anyway [paraphrased]. Hope looks disconcerted, with her eyebrows scrunched together. She is also shaking her head to left and right (as if to disagree) and frowning. She tries to reiterate her point, [paraphrase] “I am saying that learning experiences don’t need to happen in a building.” She again looks at me and I feel compelled to speak up. I say, “I think I know what you’re saying, you mean that you don’t have to be inside a school to learn, that you can learn outdoors, and at home with your family.” She says, “Yes! That’s what I mean!” Dr. Hart says “Oh, Ok!” but then asks John to write-up his original statement of “Schools look a certain way.” Hope slouches in her chair and rounds her shoulders, picks up her phone and begins to type .

In a first visit to a setting, it may be useful to assign pseudonyms or codes to participants to help with de-identifying participant data throughout the field notes. In addition to assigning such codes, the researcher should keep a code book or identifying document, preferably stored separately.

Expanded field notes should include as much detail as possible. Emerson et al. ( 2011 ) elaborated on this descriptive writing strategy that “calls for concrete details rather than abstract generalizations, for sensory imagery rather than evaluative labels, and for immediacy through details presented at close range” (p. 58). By necessity, this means that field notes will be long and labor intensive, with the added pressure that the researcher should record them as soon as possible to avoid losing detail. It is important not to skip this step of the process. It is easy to forget the particularities of the social field over time, and expanded field notes preserve complexity and richness of the data. Additionally, expanded field notes are vital when collaborating with other researchers, as they allow the others to experience a full description of events even if they were not present.

Notes-on-Notes

While writing expanded field notes, the researcher will inevitably begin to develop preliminary commentary and impressions. These impressions should not be considered findings when they arise from a single observation. Rather, they should be noted clearly so that the researcher may confirm or disconfirm their impressions in subsequent observations, interviews, or document analysis. To do this, researchers should create a short memo containing notes-on-notes for each field observation. Such a memo should move beyond impressions and begin to comment or theorize on what is observed. That said, notes-on-notes should not be considered findings until they have been compared to observations and triangulated with other types of data. Notes-on-notes can help to focus and narrow the research questions, and aid in moving the research project from descriptive to focused observation. Additionally, they may help in generating interview guides for focus groups or individual interviews where preliminary findings can be confirmed or ruled out. This is also a place for the researcher to record their own feelings in more detail. For example, if the researcher is experiencing frustration because they are not able to observe interactions between particular participants, they may note this frustration in the notes-on-notes memo. Notes-on-notes need not be lengthy; sometimes a paragraph or two is enough to express whatever should be noted for follow-up or later confirmation.

The process of qualitative observation is cyclical. Expanded field notes, along with the corresponding notes-on-notes, will most often direct the researcher back to the field to gather further information. The requisite information may represent a broadening of perspective, or a narrowing, depending on the setting and participants. Experienced researchers often begin the analytic process immediately upon entering a field of study, parsing out codes and themes in the data that they can further clarify (and sometimes quantify) as the study progresses. Analysis and coding are not included in this article, though the authors cited herein offer great insight on that topic.

Encouragements

One of the most encouraging aspects of observational research in educational settings is the opportunity to build partnerships and rapport with those who are currently working in the field. Very often there is a perceived divide between academics and P–12 teachers who work in classrooms. Again, the importance of developing rapport, basic trust, as well as collegiality cannot be overstated. Meaningful partnerships across these perceived divides are one of the most productive potential sites for educational change and reform to occur. These are the sites where, together, we might exert the most influence over policy, equity, and curriculum.

Rapport building should be genuine. It is not advisable to fake an interest in a site of study or associated stakeholders simply to benefits one’s own research agenda. Such an approach echoes the exploitative measures of early ethnographers, and is considered highly unethical. Thus, a skill that we have not yet explored regarding qualitative observation in educational settings is the ability of the researcher to seek and build meaningful, ethical relationships with those they study. The conundrum here then becomes that when we establish real relationships with participants, our subjectivity is engaged on yet another level. However, the benefits largely outweigh any potential pitfalls.

Moving beyond the stereotypical idea of one observer recording the events of a classroom, another opportunity is that of participatory action research. By engaging stakeholders in the design and execution of the research, the research may address issues that are pressing or of great importance to participants. This serves to generate educational change regarding issues that are of urgent concern to those engaged in the field on a day-to-day basis. A particular arena of possibility here involves engaging students in research.

Final Thoughts

To summarize, observation in educational settings is a detailed and rigorous process. This process involves self-reflection, attention to concrete and sensory details, and, most important, the ability to build rapport with participants. This article has detailed one methodological perspective and approach toward qualitative observation in educational settings. This approach can be used in both traditional and nontraditional educational settings, provided that the researcher maintains flexibility and an introspective approach to observation and, later, analysis. Cornerstone observational studies such as Ladson-Billings’s ( 2009 ) The Dreamkeepers , Lareau’s ( 2011 ) Unequal Childhoods , and Willis’s ( 2017 ) Learning to Labour provide useful examples of the insights that can be gleaned from observation.

The reflective “move between” one’s own subjectivity and that of participants is truly the generative site of observational research (Denzin, 2017 ). When done well, this moving in between can reveal similarities and differences, and can help people to take the time to understand diverse experiences, rather than approaching them from a stance of judgement and evaluation. Truly, observational research is a place where we have the opportunity to focus deeply on the experience of others. This is not just to walk in their shoes, but to understand the forces and meanings that influence their daily lives. These are some of the most exciting moments of potential change that qualitative research has to offer.

Methodological Texts

• Emerson, R. M. , Fretz, R. I. , & Shaw, L. L. (2011). Writing ethnographic fieldnotes (2nd ed.). Chicago, IL: University of Chicago Press.
• Spradley, J. P. (1980). Participant observation . New York, NY: Holt, Rhinehart, and Winston.

Representative Studies

• Ladson-Billings, G. (2009). The dreamkeepers: Successful teachers of African American children . San Francisco, CA: John Wiley & Sons.
• Lareau, A. (2011). Unequal childhoods: Class, race, and family life . Berkeley: University of California Press.
• Willis, P. (2017). Learning to labour: How working class kids get working class jobs . New York, NY: Routledge.
• Angrosino, M. , & Rosenberg, J. (2011). Observations on observation: Continuities and challenges. In N. K. Denzin & Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (4th ed., pp. 467–478). Thousand Oaks, CA: SAGE.
• Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods . New York, NY: Routledge.
• Dewey, J. (1938). Experience and education . Indianapolis, IN: Kappa Delta Pi.
• Geertz, C. (1973). Thick description: Toward an interpretive theory of culture. The interpretation of cultures (pp. 3–30). New York, NY: Basic Books.
• Gershon, W. S. (2013). Vibrational affect: Sound theory and practice in qualitative research. Cultural Studies?↔Critical Methodologies, 13 (4), 257–262.
• Glesne, C. (2016). Becoming qualitative researchers: An introduction (5th ed.) New York, NY: Pearson.
• Lave, J. , & Wenger, E. (1991). Situated learning: Legitimate peripheral participation . Cambridge, U.K.: Cambridge University Press.
• Noblit, G. W. , Flores, S. Y. , & Murillo, E. G. (2004). Postcritical ethnography: Reinscribing critique . Cresskill, NJ: Hampton Press.
• Pillow, W. (2003). Confession, catharsis, or cure? Rethinking the uses of reflexivity as methodological power in qualitative research. International Journal of Qualitative Studies in Education , 16 (2), 175–196.
• Sensoy, O. , & DiAngelo, R. (2017). Is everyone really equal? An introduction to key concepts in social justice education . New York, NY: Teachers College Press.

1. Expanded field notes from these jottings are included in the section “ Expanded Field Notes .”

Related Articles

• Ethnography and Education
• Qualitative Design Research Methods
• Interviews and Interviewing in the Ethnography of Education
• Writing and Managing Multimodal Field Notes
• Ethnography Across Borders
• Mixed Methods Approaches and Qualitative Methodology for Higher Education Policy Research
• Qualitative Data Analysis and the Use of Theory

Printed from Oxford Research Encyclopedias, Education. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 21 April 2024

• Cookie Policy
• Privacy Policy
• Legal Notice
• Accessibility
• [66.249.64.20|185.80.149.115]
• 185.80.149.115

Character limit 500 /500

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.

John Dudovskiy

by Burk Krohe | Apr 19, 2024

Mohi Saki, an assistant professor of physics and astronomy, has been granted 13.1 hours of observation time on the James Webb Space Telescope to study six Halley-type comets. Over the course of about a year, Saki and his six co-principal investigators will study the composition of the comets with the Webb Telescope’s high-resolution spectroscopy. (Photo by Derik Holtmann)

When Mohi Saki submitted a proposal for research time on NASA’s James Webb Space Telescope , he estimated there was about a 5% chance it would be accepted.

Securing time on the telescope is a highly competitive process, so it felt like winning the cosmic lottery when Saki received an email in March notifying him that his study had been approved. Saki, an assistant professor of physics and astronomy at the University of Missouri–St. Louis , was walking to class when he got the news.

“I was just screaming,” he said, recalling the moment. “It’s so amazing. I couldn’t believe it.”

Saki, who is also an UMSL graduate, has been granted 13.1 hours of observation time on the Webb Telescope beginning in this July to study six Halley-type comets – those with orbital periods less than 200 years. Due to their lengthy orbits, they only appear once, maybe twice, in one’s lifetime.

“Last time most of them were around, we didn’t have the technology to study them back in the ’80s, ’90s,” Saki explained. “We didn’t have the sensitivity that we have today with our instruments. Right now, we do, and we have a bunch of them coming in to the inner parts of the solar system. So, we really put together a clever proposal going after these comets with the technology that we have. By the time our proposed targets come back, it will be another 70 years.”

Over the course of about a year, Saki and his six co-principal investigators will study the composition of the six Halley-type comets with the Webb Telescope’s high-resolution spectroscopy. They will then compare their results to data on comets with shorter orbital periods and comets with even longer orbital periods.

His partners on the project include Erika Gibb , professor and chair of the Department of Mathematics, Physics, Astronomy and Statistics at UMSL; Dennis Bodewits , professor of astrophysics at Auburn University; Youssef Moulane, research scientist at Mohammed VI Polytechnic University; John W. Noonan, postdoctoral research fellow at Auburn University; Nathan X. Roth, research assistant professor of physics at American University; and Stefanie N. Milam, deputy project scientist for planetary science at the NASA Goddard Space Flight Center.

Saki’s interest in space began with a desire to investigate the unknown, which led him to earn a bachelor’s degree in physics from Kharazmi University and a master’s degree in computational physics from Amirkabir University of Technology in his native Iran. In 2015, Saki came to UMSL to pursue a second master’s degree in physics and astronomy.

He went on earn his PhD in physics from Missouri University of Science and Technology, though he was able to continue his research at UMSL. He then served as a postdoctoral researcher at Auburn for two years before returning to UMSL last year.

The programs at UMSL and Missouri S&T were rigorous but also rewarding.

“It’s very competitive,” he said. “The subjects are very difficult. There are lots of things to learn, a lot of new things that you have to teach to yourself. So, it was very challenging, but at the same time, it’s pretty fascinating because you’re learning things that are building blocks of nature. We learn the rules that govern the universe.”

Saki added that UMSL is one of the few institutions in the country – Johns Hopkins University and NASA being the others – where one can be trained to conduct research on comets in the near infrared spectrum. His research concentration makes the opportunity to log time on the Webb Telescope all the more exciting.

The Webb Telescope is the most advanced telescope in the world available to scientists like Saki. With it, researchers are able to observe the infrared spectrum – the 1 to 30 micron wavelength.

“The reason this is significant is because many of these celestial bodies are moving so rapidly away from us that their light features, or photons, shift completely from the visible part of the wavelength spectrum, which is what the Hubble Space Telescope excels at,” Saki explained. “They have shifted enough that they are no longer visible. The light moves to the infrared spectrum, so if you wanted to study them using the Hubble Space Telescope, you would not see anything because they lack visible light features. The shift extends all the way to infrared, and that is where the strength of the Webb Telescope comes into play.”

The high sensitivity of the Webb Telescope makes it possible to distinguish individual lines of photons from one another, which is key to observing a comet’s coma, the nebulous atmosphere of sublimated gas and dust surrounding the nucleus.

Saki and his partners at American, Auburn, Mohammed VI Polytechnic, NASA and UMSL plan to study the coma composition of the six Halley-type comets with the Webb Telescope’s high-resolution spectroscopy. Based on the composition of the coma, they can then extrapolate the composition of the solid nucleus.

“Why is it important?” Saki said. “Because these comets are building blocks of the entire solar system.”

The Halley-type comets Saki and his colleagues have identified were formed around the time Jupiter, Saturn, Uranus and Neptune moved into their orbits, scattering the pieces left behind during planet formation, e.g. comets, throughout the entire solar system. Because of their long orbital periods, much of that time being outside of the inner solar system, the comets should still contain their initial composition.

“We can learn about the composition of the nucleus, which in turn gives us information about the composition of the planetary-formation region,” Saki said. “That’s the whole purpose of it, to understand what our solar system was like 4.5 billion years ago when it was formed.”

The team’s observations will be spread out over the course of a year because the six comets will pass through the inner solar system at different times. Saki said the project will start with three observations from July to August, then one from January to February next year, another from March to May and the final observation will be from May to June 2025.

The researchers will then compare their findings to available data on Jupiter-family comets, those with short orbital periods, which frequently travel through the inner solar system and are thought to suffer the most from thermal processing. They will also compare the findings to available data on Oort-Cloud comets, which have orbital periods of thousands of years.

“What we’re hoping to see is if these two different classes can be connected with our measurements,” Saki said.

Saki anticipates publishing at least four academic papers related to the project and hopes to submit at least one to Nature, a leading weekly scientific journal. Whatever the comets reveal, Saki is excited to be on the verge of discovery.

“It’s just amazing,” he said. “I feel like we are on to something very special. Once we have these comets – this category of comets that is well under-represented in the whole class of comets – I think we could probably say that we have added a few pieces to the puzzle. Now we can have a better picture of what the early solar system was like.”

LATEST NEWS

YOU MIGHT ALSO LIKE

CEO Magazine recognizes UMSL’s DBA program among world’s best for seventh straight year

UMSL was one of only 110 schools worldwide to make the publication’s list, which considered factors such as accreditation and international standing.

Apr 19, 2024

Eye on UMSL: ‘The Impresario’

University of Missouri–St. Louis students Rachel Anthonis, Rita Schien, and Vanessa Tessereau rehearsed for the UMSL Opera Workshop’s production of “The Impresario,” Mozart’s one-act comic opera.

Yuan Chen, Candace-Rae Davis and Bob Ross receive UMSL Hero Awards

The award is presented to up to three staff or faculty members each month in recognition of their efforts to transform the lives of UMSL students and the wider community.

Greater St. Louis Jazz Festival marks 20 years helping inspire, teach student musicians from schools across the region

Musicians from more than 40 high schools and middle schools in Missouri and Illinois took part in the three-day festival held last week at the Blanche M. Touhill Performing Arts Center.

Apr 15, 2024

Sleeping giant surprises Gaia scientists

Wading through the wealth of data from ESA’s Gaia mission , scientists have uncovered a ‘sleeping giant’. A large black hole, with a mass of nearly 33 times the mass of the Sun, was hiding in the constellation Aquila, less than 2000 light-years from Earth. This is the first time a black hole of stellar origin this big has been spotted within the Milky Way. So far, black holes of this type have only been observed in very distant galaxies. The discovery challenges our understanding of how massive stars develop and evolve. 

Matter in a black hole is so densely packed that nothing can escape its immense gravitational pull, not even light. The great majority of stellar-mass black holes that we know of are gobbling up matter from a nearby star companion. The captured material falls onto the collapsed object at high speed, becoming extremely hot and releasing X-rays. These systems belong to a family of celestial objects named X-ray binaries.  

When a black hole does not have a companion close enough to steal matter from, it does not generate any light and is extremely difficult to spot. These black holes are called ‘dormant’.

To prepare for the release of the next Gaia catalogue, Data Release 4 (DR4), scientists are checking the motions of billions of stars and carrying out complex tests to ­see if anything is out of the ordinary. The motions of stars can be affected by companions: light ones, like exoplanets; heavier ones, like stars; or very heavy ones, like black holes. Dedicated teams are in place in the Gaia Collaboration to investigate any ‘odd’ cases.

One such team was deeply engaged in this work, when their attention fell on an old giant star in the constellation Aquila, at a distance of 1926 light-years from Earth. By analysing in detail the wobble in the star’s path, they found a big surprise. The star was locked in an orbital motion with a dormant black hole of exceptionally high mass, about 33 times that of the Sun.

This is the third dormant black hole found with Gaia and was aptly named ‘Gaia BH3’. Its discovery is very exciting because of the mass of the object. “This is the kind of discovery you make once in your research life,” exclaims Pasquale Panuzzo of CNRS, Observatoire de Paris, in France, who is the lead author of this finding. “So far, black holes this big have only ever been detected in distant galaxies by the LIGO–Virgo–KAGRA collaboration, thanks to observations of gravitational waves.”

The average mass of known black holes of stellar origin in our galaxy is around 10 times the mass of our Sun. Until now, the weight record was held by a black hole in an X-ray binary in the Cygnus constellation (Cyg X-1), whose mass is estimated to be around 20 times that of the Sun.

“It’s impressive to see the transformational impact Gaia is having on astronomy and astrophysics,” notes Prof. Carole Mundell, ESA Director of Science. “Its discoveries are reaching far beyond the original purpose of the mission, which is to create an extraordinarily precise multi-dimensional map of more than a billion stars throughout our Milky Way."

Unmatched accuracy

The exquisite quality of the Gaia data enabled scientists to pin down the mass of the black hole with unparalleled accuracy and provide the most direct evidence that black holes in this mass range exist.

Astronomers face the pressing question of explaining the origin of black holes as large as Gaia BH3. Our current understanding of how massive stars evolve and die does not immediately explain how these types of black holes came to be. 

Most theories predict that, as they age, massive stars shed a sizable part of their material through powerful winds; ultimately, they are partly blown into space when they explode as supernovas. What remains of their core further contracts to become either a neutron star or a black hole, depending on its mass. Cores large enough to end up as black holes of 30 times the mass of our Sun are very difficult to explain.

Yet, a clue to this puzzle may lie very close to Gaia BH3.

An intriguing companion

The star orbiting Gaia BH3 at about 16 times the Sun–Earth distance is rather uncommon: an ancient giant star, that formed in the first two billion years after the Big Bang, at the time our galaxy started to assemble. It belongs to the family of the Galactic stellar halo and is moving in the opposite direction to the stars of the Galactic disc. Its trajectory indicates that this star was probably part of a small galaxy, or a globular cluster, engulfed by our own galaxy more than eight billion years ago.

The companion star has very few elements heavier than hydrogen and helium, indicating that the massive star that became Gaia BH3 could also have been very poor in heavy elements. This is remarkable. It supports, for the first time, the theory that the high-mass black holes observed by gravitational wave experiments were produced by the collapse of primeval massive stars poor in heavy elements. These early stars might have evolved differently from the massive stars we currently see in our galaxy.

The composition of the companion star can also shed light on the formation mechanism of this astonishing binary system. "What strikes me is that the chemical composition of the companion is similar to what we find in old metal-poor stars in the galaxy,” explains Elisabetta Caffau of CNRS, Observatoire de Paris, also a member of the Gaia collaboration.

“There is no evidence that this star was contaminated by the material flung out by the supernova explosion of the massive star that became BH3.” This could suggest that the black hole acquired its companion only after its birth, capturing it from another system.

Tasty appetiser

The discovery of the Gaia BH3 is only the beginning and much remains to be investigated about its baffling nature. Now that the scientists’ curiosity has been piqued, this black hole and its companion will undoubtedly be the subject of many in-depth studies to come.

The Gaia collaboration stumbled upon this ‘sleeping giant’ while checking the preliminary data in preparation for the fourth release of the Gaia catalogue.  Because the finding is so exceptional they decided to announce it ahead of the official release. 

The next release of Gaia data promises to be a goldmine for the study of binary systems and the discovery of more dormant black holes in our galaxy.  “We have been working extremely hard to improve the way we process specific datasets compared to the previous data release (DR3), so we expect to uncover many more black holes in DR4,” concludes Berry Holl of the University of Geneva, in Switzerland, member of the Gaia collaboration.

Notes for editors

" Discovery of a dormant 33 solar-mass black hole in pre-release Gaia astrometry " by Gaia Collaboration, P. Panuzzo, et al. is published today the journal Astronomy & Astrophysics (A&A).

Gaia is a European mission, built and operated by ESA. It was approved in 2000 as a European Space Agency Cornerstone Mission within ESA’s Horizon 2000 Plus science programme, supported by all ESA Member States. Member States also have a key role in the science portion of the mission as part of the Data Processing and Analysis Consortium (DPAC) responsible to turn the raw data into scientific products for Gaia Data Releases, in collaboration with ESA. DPAC brings together more than 450 specialists from throughout the scientific community in Europe. Gaia was designed and built by Astrium (now Airbus Defence and Space), with a core team composed of Astrium France, Germany and UK. The industrial team included 50 companies from 15 European states, along with firms from the US. The spacecraft was launched by Arianespace on 19 December 2013.

A listing of the researchers involved and the role of ESA Member States is available for media here [PDF].

Further information about this discovery  

Thank you for liking

You have already liked this page, you can only like it once!

Related Articles

Gaia’s decade of discoveries: unravelling the intricacies o…

Gaia factsheet

How does Gaia study the Milky Way?

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

What the data says (and doesn’t say) about crime in the United States

From the first day of his presidency to his campaign for reelection, Donald Trump has sounded the alarm about crime in the United States. Trump vowed to end “ American carnage ” in his inaugural address in 2017. This year, he ran for reelection on a platform of “ law and order .”

As Trump’s presidency draws to a close, here is a look at what we know – and don’t know – about crime in the U.S., based on a Pew Research Center analysis of data from the federal government and other sources.

Crime is a regular topic of discussion in the United States. We conducted this analysis to learn more about U.S. crime patterns and how those patterns have changed over time.

The analysis relies on statistics published by the Federal Bureau of Investigation (FBI) and the Bureau of Justice Statistics (BJS), the statistical arm of the U.S. Department of Justice. FBI statistics were accessed through the Crime Data Explorer . BJS statistics were accessed through the National Crime Victimization Survey data analysis tool . Information about the federal government’s transition to the National Incident-Based Reporting System was drawn from the FBI and BJS, as well as from media reports.

To measure public attitudes about crime in the U.S., we relied on survey data from Gallup and Pew Research Center.

How much crime is there in the U.S.?

It’s difficult to say for certain. The two primary sources of government crime statistics – the Federal Bureau of Investigation (FBI) and the Bureau of Justice Statistics (BJS) – both paint an incomplete picture, though efforts at improvement are underway.

The FBI publishes annual data on crimes that have been reported to the police, but not those that haven’t been reported. The FBI also looks mainly at a handful of specific violent and property crimes, but not many other types of crime, such as drug crime. And while the FBI’s data is based on information it receives from thousands of federal, state, county, city and other police departments, not all agencies participate every year. In 2019, the most recent full year available, the FBI received data from around eight-in-ten agencies .

BJS, for its part, tracks crime by fielding a large annual survey of Americans ages 12 and older and asking them whether they were the victim of a crime in the past six months. One advantage of this approach is that it captures both reported and unreported crimes. But the BJS survey has limitations of its own. Like the FBI, it focuses mainly on a handful of violent and property crimes while excluding other kinds of crime. And since the BJS data is based on after-the-fact interviews with victims, it cannot provide information about one especially high-profile type of crime: murder.

All those caveats aside, looking at the FBI and BJS statistics side-by-side does give researchers a good picture of U.S. violent and property crime rates and how they have changed over time.

Which kinds of crime are most and least common?

Property crime in the U.S. is much more common than violent crime. In 2019, the FBI reported a total of 2,109.9 property crimes per 100,000 people, compared with 379.4 violent crimes per 100,000 people.  

By far the most common form of property crime in 2019 was larceny/theft, followed by burglary and motor vehicle theft. Among violent crimes, aggravated assault was the most common offense, followed by robbery, rape, and murder/non-negligent manslaughter.

BJS tracks a slightly different set of offenses from the FBI, but it finds the same overall patterns, with theft the most common form of property crime in 2019 and assault the most common form of violent crime.

How have crime rates in the U.S. changed over time?

Both the FBI and BJS data show dramatic declines in U.S. violent and property crime rates since the early 1990s, when crime spiked across much of the nation.

Using the FBI data, the violent crime rate fell 49% between 1993 and 2019, with large decreases in the rates of robbery (-68%), murder/non-negligent manslaughter (-47%) and aggravated assault (-43%). (It’s not possible to calculate the change in the rape rate during this period because the FBI revised its definition of the offense in 2013 .) Meanwhile, the property crime rate fell 55%, with big declines in the rates of burglary (-69%), motor vehicle theft (-64%) and larceny/theft (-49%).

Using the BJS statistics, the declines in the violent and property crime rates are even steeper than those reported by the FBI. Per BJS, the overall violent crime rate fell 74% between 1993 and 2019, while the property crime rate fell 71%.

How do Americans perceive crime in their country?

Americans tend to believe crime is up, even when the data shows it is down.

In 20 of 24 Gallup surveys conducted since 1993, at least 60% of U.S. adults have said there is more crime nationally than there was the year before, despite the generally downward trend in national violent and property crime rates during most of that period.

While perceptions of rising crime at the national level are common, fewer Americans believe crime is up in their own communities. In all 23 Gallup surveys that have included the question since 1993, no more than about half of Americans have said crime is up in their area compared with the year before.

This year, the gap between the share of Americans who say crime is up nationally and the share who say it is up locally (78% vs. 38%) is the widest Gallup has ever recorded .

Public attitudes about crime also differ by Americans’ partisan affiliation , race and ethnicity and other factors. For example, in a summer Pew Research Center survey , 74% of registered voters who support Trump said violent crime was “very important” to their vote in this year’s presidential election, compared with a far smaller share of Joe Biden supporters (46%).

How does crime in the U.S. differ by demographic characteristics?

There are some demographic differences in both victimization and offending rates, according to BJS.

In its 2019 survey of crime victims , BJS found wide differences by age and income when it comes to being the victim of a violent crime. Younger people and those with lower incomes were far more likely to report being victimized than older and higher-income people. For example, the victimization rate among those with annual incomes of less than $25,000 was more than twice the rate among those with incomes of $50,000 or more.

There were no major differences in victimization rates between male and female respondents or between those who identified as White, Black or Hispanic. But the victimization rate among Asian Americans was substantially lower than among other racial and ethnic groups.

When it comes to those who commit crimes, the same BJS survey asks victims about the perceived demographic characteristics of the offenders in the incidents they experienced. In 2019, those who are male, younger people and those who are Black accounted for considerably larger shares of perceived offenders in violent incidents than their respective shares of the U.S. population. As with all surveys, however, there are several potential sources of error, including the possibility that crime victims’ perceptions are incorrect.

How does crime in the U.S. differ geographically?

There are big differences in violent and property crime rates from state to state and city to city.

In 2019, there were more than 800 violent crimes per 100,000 residents in Alaska and New Mexico, compared with fewer than 200 per 100,000 people in Maine and New Hampshire, according to the FBI .

Even in similarly sized cities within the same state, crime rates can vary widely. Oakland and Long Beach, California, had comparable populations in 2019 (434,036 vs. 467,974), but Oakland’s violent crime rate was more than double the rate in Long Beach. The FBI notes that various factors might influence an area’s crime rate, including its population density and economic conditions.

See also: Despite recent violence, Chicago is far from the U.S. ‘murder capital’

What percentage of crimes are reported to police, and what percentage are solved?

Most violent and property crimes in the U.S. are not reported to police, and most of the crimes that are reported are not solved.

In its annual survey, BJS asks crime victims whether they reported their crime to police or not. In 2019, only 40.9% of violent crimes and 32.5% of household property crimes were reported to authorities. BJS notes that there are a variety of reasons why crime might not be reported, including fear of reprisal or “getting the offender in trouble,” a feeling that police “would not or could not do anything to help,” or a belief that the crime is “a personal issue or too trivial to report.”

Most of the crimes that are reported to police, meanwhile, are not solved , at least based on an FBI measure known as the clearance rate. That’s the share of cases each year that are closed, or “cleared,” through the arrest, charging and referral of a suspect for prosecution, or due to “exceptional” circumstances such as the death of a suspect or a victim’s refusal to cooperate with a prosecution. In 2019, police nationwide cleared 45.5% of violent crimes that were reported to them and 17.2% of the property crimes that came to their attention.

Both the percentage of crimes that are reported to police and the percentage that are solved have remained relatively stable for decades.

Which crimes are most likely to be reported to police, and which are most likely to be solved?

Around eight-in-ten motor vehicle thefts (79.5%) were reported to police in 2019, making it by far the most commonly reported property crime tracked by BJS. Around half (48.5%) of household burglary and trespassing offenses were reported, as were 30% of personal thefts/larcenies and 26.8% of household thefts.

Among violent crimes, aggravated assault was the most likely to be reported to law enforcement (52.1%). It was followed by robbery (46.6%), simple assault (37.9%) and rape/sexual assault (33.9%).

The list of crimes cleared by police in 2019 looks different from the list of crimes reported. Law enforcement officers were generally much more likely to solve violent crimes than property crimes, according to the FBI.

The most frequently solved violent crime tends to be homicide. Police cleared around six-in-ten murders and non-negligent manslaughters (61.4%) last year. The clearance rate was lower for aggravated assault (52.3%), rape (32.9%) and robbery (30.5%).

When it comes to property crime, law enforcement agencies cleared 18.4% of larcenies/thefts, 14.1% of burglaries and 13.8% of motor vehicle thefts.

Is the government doing anything to improve its crime statistics?

Yes. The FBI has long recognized the limitations of its current data collection system and is planning to fully transition to a more comprehensive system beginning in 2021.

The new system, known as the National Incident-Based Reporting System (NIBRS), will provide information on a much larger number of crimes , as well as details such as the time of day, location and types of weapons involved, if applicable. It will also provide demographic data, such as the age, sex, race and ethnicity of victims, known offenders and arrestees.

One key question looming over the transition is how many police departments will participate in the new system, which has been in development for decades. In 2019, the most recent year available, NIBRS received violent and property crime data from 46% of law enforcement agencies, covering 44% of the U.S. population that year . Some researchers have warned that the transition to a new system could leave important data gaps if more law enforcement agencies do not submit the requested information to the FBI.

• Criminal Justice

8 facts about Black Lives Matter

#blacklivesmatter turns 10, support for the black lives matter movement has dropped considerably from its peak in 2020, fewer than 1% of federal criminal defendants were acquitted in 2022, before release of video showing tyre nichols’ beating, public views of police conduct had improved modestly, most popular.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy