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Observational Research – Methods and Guide

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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

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Observation

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

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

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

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

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

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

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

John Dudovskiy

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.

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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.

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

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Non-Experimental Research

32 Observational Research

Learning objectives.

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

What Is Observational Research?

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

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation .  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

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

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that are collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation , the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

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

In contrast with undisguised participant observation ,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [2]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

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

Structured Observation

Another observational method is structured observation . Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation. Often the setting in which the observations are made is not the natural setting. Instead, the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation.

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

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [4] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

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

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [5] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

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

When the observations require a judgment on the part of the observers—as in the studies by Kraut and Johnston and Cohen and his colleagues—a process referred to as   coding is typically required . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that guides different observers to code them in the same way. This difficulty with coding illustrates the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

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

Case Studies

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

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

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

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [8] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

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

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

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

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

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

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample of individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation.

However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods. The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with both internal and external validity. Case studies lack the proper controls that true experiments contain. As such, they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (a possibility suggested by the dissection of HM’s brain following his death) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So, as with all observational methods, case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically an abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity. With case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research involves analyzing archival data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [9] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [10] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

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

Media Attributions

• What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
• Pappenheim 1882  by unknown is in the  Public Domain .
• Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

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

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

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

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

Refers to when a measure changes participants’ behavior.

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

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

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

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

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

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

An in-depth examination of an individual.

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

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

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15 Observational Methods

Jamie M. Ostrov, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY

Emily J. Hart, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY

• Published: 01 October 2013
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Systematic observational methods require clearly defined codes, structured sampling and recording procedures, and are subject to rigorous psychometric analysis. We review best practices in each of these areas with attention to the application of these methods for addressing empirical questions that quantitative researchers may posit. Special focus is placed on the selection of appropriate observational methods and coding systems as well as on the analysis of reliability and validity. The use of technology to facilitate the collection and analysis of observational data is discussed. Ethical considerations and future directions are raised.

Introduction

Systematic observational methods have been a common technique employed by psychologists studying human and animal behavior since the inception of our field, and yet best practices for the use of observational instruments ( see Table 15.1 ) are often not known or adopted by researchers in our field. As such, the quality of observational research varies widely, and thus, it is our goal in the present chapter to review and explicitly define the standards of practice for this important methodological tool in the psychological sciences. Bakeman and Gottman (1987) have previously defined observational methods to include the a priori use of operationally defined behavioral codes by observers who have achieved interobserver reliability. Importantly, the setting or context is not what defines a method as

being systematic ( Pellegrini, 2004 ). That is, systematic observations may be conducted in the laboratory, schools, workplace, public spaces and coded

live or via recordings/transcripts. Therefore, having clear definitions and sampling/recording rules as well as reliable codes delineates informal, unsystematic observation from systematic observation. We also distinguish between the use of nonsystematic field notes and other data collection techniques that are often used in qualitative studies by ethologists and educational practitioners in naturalistic contexts and only include a review and analysis of systematic observational methods (Pellegrini, Ostrov, Roseth, Solberg, & Dupuis, in press).

Nonsystematic sampling techniques such as Ad libitum (i.e., ad lib) in which there are no a priori systematic sampling or recording rules are often used by researchers as a part of pilot testing and help to inform the development of systematic observational coding systems ( Pellegrini, 2004 ). Thus, ad lib sampling approaches are important to understand the context and nature of the behaviors under study, but they will not be discussed further in this review. Observational methods may be used in a variety of designs from correlational and quasi-experimental to experimental and even randomized trial designs ( Bakeman & Gnisci, 2006 ). However, it is more typical to find systematic observational methods used outside the laboratory to maximize ecological validity and, thus, less likely as part of experimental manipulations ( Bakeman & Gnisci, 2006 ). The current review will be relevant to all research designs with a focus on those methods that are well designed for quantitative data analysis.

History of Observational Methods

The use of systematic observational methods has been used extensively by psychologists throughout the history of our field to examine various empirical questions ( see   Langfeld, 1913 ). One of the first documented cases of systematic observational methods in the extant literature was from a study by Goodenough (1930) and was part of an increasing trend in the systematic study of young children as part of the Child Welfare Movement in the United States, which was supported by the National Research Council (for review, see   Arrington, 1943 ). In fact, her seminal work was also one of the first studies in psychology to be published using time sampling ( see Sampling section below) observational procedures ( Arrington, 1943 ). In her classic work (appearing in the first issue of Child Development ), Florence L. Goodenough reported on several observational studies conducted in her laboratory at the Institute of Child Welfare (now Institute of Child Development) of the University of Minnesota. This study highlights several best practices that are still endorsed today. For example, careful pilot testing of the observational codes was conducted, and revisions were made to generate mutually exclusive codes ( see Coding section below) and reliable distinctions between the categories. In addition, observations of each child’s physical activity were conducted only once per day and only by one observer at a time so that observations of behavior were conducted independent of one another. Goodenough (1930) carefully defined the a priori categories or observational codes and demonstrated interobserver reliability for each of these codes. Finally, Goodenough (1930) described the justification for her observational procedures and discussed alternative techniques (e.g., the optimum duration for an interval within a time-sampling procedure). There are other well-known examples of systematic observation conducted by contemporaries of Goodenough, including Parten’s (1932) study of young children’s play behavior, which also illustrate best practices (e.g., clearly defined, mutually exclusive observational codes; rules designed to maintain independence of sampling and decrease observer error). Some of the earliest observational studies focused on either children or non-human animals (e.g., Crawford, 1942 ), as other techniques for studying behavior (and often social domains of study) were either not as well suited for the research questions or not available at the time. Today, systematic observational methods are used in research and applied settings ( Pellegrini, 2001 ) and relevant for training in all domains and subdisciplines of the social and behavioral sciences ( Krehbiel & Lewis, 1994 ).

Sampling and Recording Rules

Systematic observational systems follow various sampling and recording rules that are designed for different contexts and research questions. The following section includes a review of the central sampling and recording rules that quantitative scholars would use for conducting systematic observations ( see Table 15.2 for a summary of the strengths and weaknesses of each approach). Recently adopted best practices for direct systematic observation are relevant for each of these types of observational methods, and they are briefly reviewed here. These practices, which were first introduced by Hintze, Volpe, and Shapiro (2002) , include (1) the observational system is designed to measure well-defined behaviors; (2) the behaviors are operationally defined a priori ; (3) observations are recorded using objective, standardized (i.e., manualized training protocols) sampling procedures and recording rules; (4) the context and timing of sampling is explicitly determined; and (5) scoring and coding of data are conducted in a standardized fashion ( see Leff & Lakin, 2005 , p. 476).

Time Sampling

A time-dependent observational procedure in which the researcher a priori divides the behavior stream into discrete intervals and each time interval is scored for the presence or absence of the behavior in question is defined as a time sampling observational approach. That is, the time interval is the unit coded ( Bakeman & Gottman, 1987 ). Time sampling procedures may be conceptualized as either 0/1 (i.e., absent/present or nonoccurrence/occurrence) or continuous in nature. A time sampling procedure is an efficient method of sampling, as multiple data points may be collected from a single participant in a short period of time. Time sampling is well suited for measuring rather discrete behaviors, such as overt behaviors (e.g., on task and off task behavior in classrooms), or with behaviors that are frequently occurring. For example, a recent study of the frequency of various behaviors (e.g., off task behavior, noncompliance) during several naturalistic activities in 30 children with various psychiatric diagnoses used a reliable 0/1 time sampling approach with a 15-second interval ( Quake-Rapp, Miller, Ananthan, & Chiu, 2008 ). Alternatively, time sampling is not well designed for infrequently occurring events or events that are long in duration ( Slee, 1987 ). A clear advantage is that time sampling is relatively inexpensive because it is an efficient use of the research assistant ( Bakeman & Gottman, 1987 ). Further, 0/1 sampling is also easier for the observer than alternatives such as instantaneous sampling, in which the research assistant notes if the behavior is present at a precise moment in time rather than it occurring during a larger interval of time. A major disadvantage of the time sampling approach is that the researcher delineates the particular time interval and therefore arbitrarily categorizes the behavior into discrete artificial units of time that may or may not be meaningful ( Slee, 1987 ). Moreover, some behaviors may exceed the often brief interval of time that is selected for the sampling. Thus, it is crucial to carefully justify the interval that is selected. The intervals are often brief and the behaviors in question should be readily apparent and easily observable by trained research assistants. If frequency estimates are to be obtained, then the interval in question needs to be sufficiently brief so that an accurate assessment can be made. That is, typically with an interval approach, a maximum of one behavior is recorded during an interval even if the behavior independently occurs more frequently during this interval ( Slee, 1987 ). Thus, special attention needs to be given to the pilot testing of the observational scheme and various durations of the interval if frequency assessments are desired.

Time sampling procedures are used in a range of settings and studies to test various empirical questions that often have applied significance. For example, Macintosh and Dissanayake (2006) adopted a 0/1 time sampling technique to assess spontaneous social interactions in school-aged children with high-functioning autism or Asperger’s disorder as well as typically developing children. Observations were conducted in the schoolyard. For each timed interval of 30 seconds, one type of behavior (e.g., parallel play) from a particular behavioral domain (e.g., social participation) was coded. For reliability purposes, a second observer made independent ratings for 20% of the entire sample. Intraclass correlation reliability coefficients were all acceptable for each type of behavior (0.78–0.99) with the exception of nonverbal interaction (i.e., gestures; 0.58), which are often difficult to reliably assess in live settings ( see also   Ostrov & Keating, 2004 ). Results meaningfully distinguished between the typically developing children and the clinical groups and revealed few differences between the two clinical groups, supporting the use of time sampling as a means to discriminate between clinical and nonclinical groups ( Macintosh & Dissanayake, 2006 ). Time sampling procedures have several other applications and clinical considerations. For example, time sampling methods may differentially affect how treatment effects are interpreted ( Meany-Daboul, Roscoe, Bourret, & Ahearn, 2007 ) and may be appropriate for classroom-based research that tests adherence to educational policies intended to aid students with special needs ( Jackson & Neel, 2006 ; Soukup, Wehmeyer, Bashinski, & Boyaird, 2007 ).

Event Sampling

Event-based sampling is also known as behavior sampling and permits a researcher to study the frequency, duration, latency, and intensity of the behavior under study ( Pellegrini, 2004 ). Essentially, unlike time sampling, event sampling is a type of observational sampling in which the events are time-independent and the behavior is the unit of analysis ( Bakeman & Gottman, 1987 ). Event sampling allows the behavior to remain as part of the naturally occurring phenomenon and may unfold in a manner generally consistent with the timing of the behavior in the natural setting. This type of sampling also can be efficient in terms of the total amount of time needed for observations. Unlike other sampling techniques (e.g., time sampling), a third advantage is that event sampling may be used when the construct under study is either frequently or infrequently occurring ( Slee, 1987 ). There are some clear disadvantages to event-based sampling procedures, and this may be a reason that it is less commonly seen in the literature. First, it is sometimes challenging to delineate the independence of events—that is, the researcher must specify when one event ends and the next event begins. Second, event sampling does not lend itself well to coding of dyadic interactions such as parent–child or romantic partner relations in which there is a fair amount of interdependence between the participants ( Slee, 1987 ).

Event sampling also has wide applicability and has even been used to understand the propensity to violence at sporting events. For example, Bowker et al. (2009) used an event-sampling approach to examine spectator comments at youth hockey games in a large Canadian city. A group of five observers attended 69 hockey games played by youth in two age groups: 11–12 years and 13–14 years. Verbal comments were coded as positive, negative, corrective, or neutral and rated for intensity. Most of the comments elicited by spectators were positively toned. The valence of spectator comments was influenced by gender (i.e., the gender of the children playing) and the purpose for which the game was being played (i.e., competitive or recreational). These results support the utility of event sampling at social and athletic events, where particular behaviors are likely to occur during a finite period of time. Time sampling may not be appropriate in such circumstances because of the presence of a high concentration of individuals in a single setting and many potential interruptions arising from the nature of the activity.

Participant Observation

Although participant observation has been more frequently used with nonsystematic field observation and in disciplines that focus on qualitative methods, it is possible to conduct systematic participant observation as part of quantitative studies. Systematic participant observation has been the method of choice for behaviors of interest that require “an insider’s perspective” ( Pellegrini, 2004 , p. 288) or for contexts in which the sampling period may be long and informal. Moreover, this method is well suited for the use of more global observational ratings that sample events. This procedure has wide applicability, and participant observation has an extensive history of successful use from studies of children with behavioral problems at summer camps in clinical psychology (e.g., Newcomb, 1931 ; Pelham et al., 2000 ) to worker stress in organizational psychology (e.g., Länsisalmi, Peiró, & Kivimäki, 2000 ). For example, a recent study of children diagnosed with disruptive behavioral disorders and enrolled in a summer treatment program used staff counselors to complete daily participant observations of social behaviors of the children while they engaged in various camp activities ( Lopez-Williams et al., 2005 ). A second study of social competence among reunited adolescents ( M a g e = 1 5 . 5 years) who had attended a research-based summer camp when they were 10 years old revealed the predictive validity of participant observer (i.e., camp counselor) ratings of social skills ( Englund, Levy, Hyson, & Sroufe, 2000 ). The validity of the participant observations of social competence when the participants were 10 years old was determined by revealing significant prospective correlations with a group-problem solving task that was videotaped and coded by two independent raters along several dimensions (e.g., self-confidence, agency, overall social competence) when the participants were 15 years old. The results support the use of participant observations in studying the development and stability of complex, multifaceted constructs like social competence.

Focal Sampling

Focal person sampling involves selecting (typically at random from a roster of participants) one participant and observing the individual for a defined time period. For each sampling interval (ranges vary depending on the question of interest), the observer records all relevant behaviors of the focal person. As we have previously discussed ( see Pellegrini et al., in press), for studies of dyads or small groups, the sampling interval should be as long as the typical interaction or displayed behavior of interest. For example, in our work, we study the display of relational aggression (i.e., the use of the relationship as the means of harm via social exclusion, withdrawing friendship, spreading malicious rumors), and given the nature of these behaviors, we have found that an interval of 10 minutes is a reasonable interval for assessing the intent for harm as well as the subtle nature of these peer interactions ( Ostrov, 2008 ; Ostrov & Keating, 2004 ).

Focal sampling may technically use continuous (e.g., Fagot & Hagan, 1985 ; Laursen & Hartup, 1989 ), 0/1 (e.g., Hall & McGregor, 2000 ; Harrist & Bradley, 2003 ), or instantaneous recording rules ( see   Pellegrini, 2004 ). However, focal sampling often uses continuous recording procedures because it permits the simultaneous coding of various behaviors, sequences of behaviors, and interactions with multiple partners in a live setting (e.g., Arsenio & Lover, 1997 ; Keating & Heltman, 1994 ). For example, in our observational studies of relational aggression among young children, we always have used focal sampling with continuous recording given the somewhat covert nature of the behaviors we have targeted for observation, which require a longer period of direct assessment to decipher and appropriately record the behaviors ( Ostrov & Keating, 2004 ). Focal participant sampling is often conducted across multiple days and contexts to better capture the true nature of the behavior rather than any state-dependent artifacts. Given the amount of time and the continuous nature of the recordings, this technique permits the recording of behavior that is a close approximation to real-time recording, and a researcher may recreate the behavior of the focal participants with a high degree of accuracy (Pellegrini et al., in press). For example, we observe children in their naturally occurring play contexts on 8 separate days, and they are only ever observed once per day to maintain independence of the data. Thus, in our work, each participant is observed for 80 minutes (8 sessions at 10 minutes each session). More specifically, a study of 120 children resulted in more than 370 hours of observation across the two time-points of the short-term longitudinal study ( Ostrov, 2008 ). Therefore, time is a major cost of focal sampling because of the large number of independent observations typically conducted with this approach. Focal sampling may also be used with 0/1 or instantaneous sampling as recording procedures, but this is rarely done. As previously mentioned, both of these recording procedures require an a priori specified time interval, which is usually relatively brief (i.e., 1–10 seconds). Instantaneous recording is typically used only with scan sampling procedures ( see Scan Sampling section below). 0/1 time sampling is not usually used with focal sampling because we are often interested in assessing the true frequency of behaviors that may not be obtained with this procedure (i.e., an independent behavior could occur once or more than once during a set interval, but with 0/1 coding only one point is scored).

Despite the emphasis on the use of these methods for studying basic social behavior, focal sampling procedures may be used in a wide range of studies. It is common in the literature to find focal participant sampling studies on a range of social behavior topics: social dominance in children ( Keating & Heltman, 1994 ) and adults ( Ostrov & Collins, 2007 ), play behavior ( Pellegrini, 1989 ), emotion and aggression ( Arsenio & Lover, 1997 ), conflict ( Laursen & Hartup, 1989 ), and peer relations with young children and non-human primates (e.g., Hinde, Easton, & Meller, 1984 ; Silk, Cheney, & Seyfarth, 1996 ). However, there are many practical applications of focal participant sampling ( see   Leff & Lakin, 2005 ; Pellegrini, 2001 ). For example, applied studies have been conducted that have used these observational techniques for examining the adjustment of children with special needs in elementary schools ( Hall & McGregor, 2000 ), peer victimization in early adolescence ( Pellegrini & Bartini, 2000 ), and for testing the efficacy of randomized behavioral interventions (e.g., Harrist & Bradley, 2003 ; Ostrov et al., 2009 ).

Scan Sampling

Instantaneous or scan sampling is a more efficient observational procedure than focal sampling. Scan sampling exclusively relies on instantaneous recording rules ( Pellegrini, 2001 ). With this procedure the observer scans the entire observation field for a possible behavior or event for a particular period of time. If an event is noted during that scan, then it is recorded. Typically, a number of discrete scans occur across a number of days to maximize the independence of the data. A participant’s data is usually summed across the scans to yield a behavioral score for the construct of interest. A concern with this approach is that it may not accurately assess the true frequency of behaviors if spacing is not adequate between the scans ( Pellegrini, 2004 ). Moreover, given the typical approach in which scans are conducted on an entire reference group in their natural context, behaviors that are selected for this approach must be readily apparent, discrete, and overt behaviors that require typically only a few seconds to observe. In our own field, McNeilly-Choque, Hart, Robinson, Nelson, and Olsen (1996) conducted a study of young children’s aggressive behavior in which they used a random scan sampling method that yielded 100 five-second scans during a 5- to 7-week period, resulting in 8 minutes of total observation per participant ( McNeilly-Choque, Hart, Robinson, Nelson, & Olsen, 1996 ). Thus, this study demonstrated the feasibility and efficiency of systematic scan sampling observations of aggressive behavior on the playground.

Semi-Structured Observations

Analog tasks or semi-structured observations, involving controlled simulations or analog situations, are observational tasks designed to mimic naturalistic conditions. Semi-structured observational procedures are another observational paradigm well suited for low base rate events. The recording and coding procedures are often identical to the procedures an observer would use in a naturalistic setting; however, the context in which the behaviors emerge is different. Often analog tasks are completed in a laboratory or similarly controlled setting and are videotaped for subsequent coding by unaware observers. Thus, analog observational paradigms permit a great deal of experimental control/standardization of procedures, and with the use of videotapes, observers are able to objectively code the session using the same recording rules as permitted in other contexts. A clear advantage of these procedures is that they are efficient and require less cost and time spent observing participants. If the study is not designed well, then a major disadvantage is a lack of ecological validity (i.e., degree to which the context in which the research is conducted parallels the real-life experience of the participants), and poor generalizability of the findings is possible. Moreover, a relatively small sampling of behavior does not provide for a true frequency of behavior or for a representative sample of behavior with many interaction partners (i.e., the researcher is not able to examine individual–partner interactions). Other researchers have addressed this concern by using a “round robin” approach in which each participant completes an analog session with several (or all) other member of the reference group, which may improve the validity of the approach but, of course, adds a great deal of time and expense ( see   Hawley & Little, 1999 ).

In our own research we have used a semi-structured observational paradigm to provide an efficient estimate of young children’s aggressive behavior. To this end, we created a brief (9-minute) analog situation to observe various aggressive and prosocial behaviors (i.e., within dyads or triads) in early childhood ( Ostrov & Keating, 2004 ; Ostrov, Woods, Jansen, Casas, & Crick, 2004 ). The procedures and a review of the psychometric findings are described extensively elsewhere (e.g., Ostrov & Godleski, 2007 ), but essentially, each assessment includes three trials of 3 minutes each. For each trial, the children are given the same developmentally appropriate picture to color (e.g., Winnie the Pooh). For triads, three crayons are placed on the table equidistant from all participants, and only one crayon is the functional instrument (e.g., orange crayon for Winnie the Pooh) and two are functionally useless white crayons. At the end of the trial, a new picture and new crayons are placed on the table. This procedure is designed to produce mild conflict among the children and was developed to permit the children to engage in a variety of behaviors: prosocial behavior (e.g., sharing the one functional crayon or breaking into pieces to share), relational aggression (e.g., telling the child they will not be their friend anymore unless they give them the crayon), and physical aggression (e.g., taking the crayon away from someone else). The analog task was designed to be developmentally appropriate and resemble everyday conflict interactions concerning limited resources that young children experience in their typical preschool classroom. Highly trained research assistants monitored the entire session and intervened if needed to guarantee the safety of all participants and reduce the likelihood of participant distress. Moreover, at the end of the session, the children were each individually given access to a full box of crayons to diminish any distress and they were praised for their performance ( see   Ostrov et al., 2004 ). This paradigm is thus designed to elicit the behavioral constructs of interest in a more controlled environment than free play yet ensures the ethical treatment of participants.

One way to demonstrate the ecological validity of semi-structured observations is to correlate behaviors observed in a semi-structured context with behaviors observed in a more naturalistic context. For example, Coie and Kupersmidt (1983) found that social status in experimentally contrived playgroups comprised of unfamiliar peers matched social status in the classroom, supporting the validity of a contrived playgroup paradigm for studying social development ( see also   Dodge, 1983 ). Similarly, our own brief semi-structured observational paradigm (i.e., coloring task) has been shown to significantly predict observational scores collected from concurrently assessed naturalistic (i.e., classroom and playground free play) focal child observations with continuous recording ( r = 0 . 4 8 ) and to predict future (i.e., 12 months later) behavior in naturalistic contexts at moderate levels ( see   Ostrov et al., 2004 ).

Methods of Recording

Various methods of recording (i.e., checklist, detailed records, or observation forms) vary widely and should be based on the type of recording procedures that a researcher adopts. For example, time sampling (i.e., 0/1) and instantaneous or scan sampling procedures are well suited for checklist forms in which the prescribed intervals simply receive a check or a precise code indicating the occurrence or absence of the behavior in question. However, focal participant sampling often requires observation forms that permit greater detail and several codes that are recorded either simultaneously or in close temporal proximity, and, as such, a form that includes the behaviors or events of interest with space for recording the behavior in detail may be needed (for example forms and templates, see Pellegrini et al., in press). A general concern here is that the more time spent writing details about the behavior/event removes the observer’s attention from the participants and important details may be lost. Some observational procedures like time sampling provide the observer with a set period of time after the interval for recording behavior. In general, the easier the observation form is to complete, the less room there is for error. With that said, checklists often do not permit systematic reviews for accuracy of codes by the master trainer. For example, observers that are observing the same participant as part of a reliability check could both code a behavior as “PA” for physical aggression when in fact one research assistant observed a “hit” and the other observed a “kick,” which, depending on the observational system, may be different and might not warrant a positive match or agreement. Thus, depending on the coding scheme and intentions of the researcher, these may artificially match for reliability purposes when in fact they were closely related but discrete behaviors. Finally, if observers record some written details about the event, they may inform subsequent decision rules concerning whether a recorded behavior from observer 1 matches or does not match observer 2 for reliability assessments.

Coding Considerations

The development of a reliable coding scheme is crucial for appropriately capturing the behaviors in question and testing the experimenter’s a priori hypotheses ( Bakeman & Gottman, 1987 ). There are three types of coding categories that are often included in observational systems: physical description codes, consequence codes, and relational or environmental relations codes ( Pellegrini, 2004 ). Physical description is believed to be the most “objective” type of codes because these describe “muscle contraction” ( Pellegrini, 2004 , p. 108) and might, for example, be involved in recording a participant’s social dominance or submissiveness (e.g., direct eye contact, rigid posture, arms akimbo; see   Ostrov & Collins, 2007 ). The second type of codes is for those of consequence in which a constellation of behaviors are part of a single code if they lead to the same outcome ( Pellegrini, 2004 ). For example, if we were interested in studying social dominance, then we might code taking objects away from others that result in a submissive posture on the part of the nonfocal participant to be an indicator of social dominance ( Ostrov & Collins, 2007 ). The third type of codes includes categories in which participants are described in relation to the context in which they are observed ( Pellegrini, 2004 ). An example of a relational observational category would be a coding scheme that accounted for where and with whom an individual was socially dominant. In terms of costs and benefits, it is clear that physical description codes are often easier to train and therefore potentially more reliable. It is possible that consequence codes may be unreliable given a misunderstanding of the sequence of events ( Pellegrini, 2004 ). Relational codes involve the appropriate documentation of multiple factors and therefore create more possibilities of error (for discussion, see   Pellegrini, 2004 ; Bakeman & Gottman, 1987 ). Overall, the level of analysis from micro- to macro-coding schemes is important to consider and the most objective and reliable system for addressing a researcher’s particular research question should be adopted.

A second consideration is the determination of whether to use mutually exclusive and exhaustive codes. Mutually exclusive codes are used when a single behavior may be recorded under one and only one code. In our observational studies, our coding scheme includes mutually exclusive codes such that a single behavior may be coded as either physical aggression or relational aggression, but not both. Exhaustive coding schemes are designed such that for any given behavior of a theoretical construct, there is an appropriate code for that behavior. For example, in our work we have codes for physical, relational, verbal, or nonverbal aggression as well as aggression not otherwise specified. Thus, if we determine a behavior is an act of aggression, then it may be coded as one of our behaviors in our scheme. Often schemes include mutually exclusive and exhaustive codes because there are several benefits to this approach ( see   Bakeman & Gottman, 1987 ). Having mutually exclusive codes means that researchers are not violating assumptions of independence, which are often needed for parametric statistics. For example, if a single behavior may be coded as both physical and relational aggression, then that may violate our assumption that the data are independent and come from independent behavioral interactions ( Pellegrini, 2004 ). Having exhaustive codes also speaks to the content validity of a coding scheme. That is, if the overall construct appropriately measures all facets of that construct, then the behavior in question should be included in the observational system, and exhaustive schemes guarantee this occurrence. It is important to recall that the larger the coding scheme, the more taxing the observational procedures will be for observers and the greater the possibility of observer error.

Scoring of observational data is similar to the scoring of any quantitative data within the social and behavioral sciences, and it often depends on the convention within a particular field and the type of observational sampling and recording techniques that are adopted. For example, for focal participant sampling with continuous recording, frequency counts are often generated by summing each independently recorded behavior across the various sessions. In our own research, that would mean that an individual participant would get a score for each of the constructs (i.e., physical aggression, relational aggression, verbal aggression, etc.) by summing all the behaviors within a construct (e.g., all physical aggression behaviors) across all eight sessions ( Ostrov & Keating, 2004 ). If the number of sessions is different for each participant because of missing data, then it is often common practice to divide by the number of sessions completed to generate an average rate of behavior per session ( see   Crick, Ostrov, Burr et al., 2006 ). Occasionally it is apparent that an error was made in the original coding of behaviors. Best practices have not been established for addressing these concerns, but as long as these errors are not systematic, the adopted solutions are often not a concern. To avoid problems with potential scoring biases, the observers and coders should always be unaware of the participant’s condition and/or past history. In addition, whenever possible, observers and coders should be unaware of the study hypotheses.

Psychometric Properties

Reliability.

Reliability is often conceptualized as consistency within or between individuals (i.e., intra-observer or inter-observer), within measures (internal consistency), or across time (i.e., test–retest). Arguably, for observational methods, the most important measure of consistency is inter-observer reliability, or the degree to which two sets of observations from two independent observers agree ( Stangor, 2011 ). In the present review, we will first address intra-observer reliability and then focus on the assessment of inter-observer reliability.

Intra-observer, or within-observer, reliability is defined as a situation in which two sets of observations by the same research assistant agree or are consistent. Essentially, intra-observer reliability is assessing how consistent a particular observer is when coding specific behaviors either between sessions (i.e., across time) or within a single session. As Pellegrini (2004) has discussed in more detail, we may conceptualize and test (e.g., Pearson’s Product-Moment Correlation Coefficient) intra-observer reliability in ways similar to test–retest reliability, and thus, intra-observer reliability is essentially the temporal stability of the observational measure for a given observer between testing sessions. We might desire to know the degree to which the observational score on a given behavioral construct for the same observer is stable across time to test for observer drift (a threat to the validity of the observational data), or the likelihood that observers are deviating from initial training procedures over time and modifying the definitions of the constructs under study ( Smith, 1986 ). Intra-observer reliability or consistency within an observer may also be conceptualized as the reliability of an observer’s scores within a single session, and in this case the test is analogous to assessments of internal consistency (e.g., Cronbach’s α ). As Pellegrini (2004) has stated, we assume an observer is first reliable or consistent in their scoring/recording by themselves prior to testing if they agree with an independent observer (i.e., inter-observer reliability).

As mentioned, inter-observer reliability or consistency between observers is the gold standard for observational research. Essentially, inter-observer reliability involves comparing the independent codes of the observers with other trained observers. There are several ways to assess this psychometric property ( see   Pellegrini, 2004 ), but the key task is comparing agreement across all of the observers. An important best practice for inter-observer reliability procedures is to ensure that observers are sampling/recording the same behaviors independently. Independent coding may be conducted with the use of video and private coding sessions without discussion until all codes have been completed. Inter-observer reliability may be assessed live in the field if the observers take precautions to avoid conveying to their partner how (and, in some cases, when) they are recording the behavior in question. A second best practice is to assess for reliability across the study to help avoid various biases (e.g., observer drift) and coding/recording errors from corrupting the integrity of the data. That is, observers should be checked against a master coder at the start of the study just after training ends, and each observer should pass an a priori reliability threshold (e.g., Cohen’s κ 〉 0 . 7 0 ). Next, their observations should be compared against other independent reliable observers throughout the duration of the study, and the trainer should provide constructive feedback for any deviations from the training protocol. Finally, an important consideration is for what percentage of time inter-observer reliability will be checked. This percentage should be a function of the number of cases or possible events that will be recorded, but typically 15% to 30% of a randomly selected sample of the possible sessions is coded by more than one observer for assessing inter-observer reliability. To avoid potential biases, a best practice is for each observer to conduct reliability observations with all other observers in a round-robin format.

There are several ways to statistically measure inter-observer reliability. In the past, authors relied on zero-order correlations (Pearson’s r ) but that problematic practice is not seen as often in the recent literature. A second statistical method that is still reported in peer-reviewed journals is percent agreement. Percent agreement may be expressed in Equation 1 :

where P o b s is the proportion of agreement observed, N A is the total number of agreements, and N D is the total number of disagreements. Percent agreement is not currently best practice, as it is influenced by the number of cases (i.e., it may be biased by relatively few cases) and because it is not compared against a standard threshold ( Bakeman & Gottman, 1987 ). Finally, one of the central concerns with percent agreement (as well as Pearson’s r ) as a measure of inter-observer reliability is that it does not control for chance agreement ( Bakeman & Gottman, 1987 ).

Cohen’s (1960)   κ is a preferred statistic for inter-observer reliability because it does control for chance agreements and is a more “stringent statistic,” allowing greater precision in assessing reliability at a specific moment in time or for particular events rather than overall summaries of association ( Bakeman & Gottman, 1987 , p. 836). Importantly, κ may only be used when coders use a categorical scale ( Bakeman & Gottman, 1987 ) and when a 2 x 2 matrix may be created to depict the proportion of agreements/disagreements for occurrences/nonoccurrences of behavior for any two observers ( Pellegrini, 2004 ). When calculating the rate of agreement, it is important to a priori indicate any time parameters (i.e., within what period of time must both observers note the occurrence of a behavior, also known as the tolerance interval). Some experts caution that extremely short tolerance intervals (e.g., 1 sec) may be overly stringent and artificially reduce the degree of agreement given typical reaction times of observers ( see   Bakeman & Gnisci, 2006 ). If time sampling is being used, then observers should be signaled by an external source (e.g., audible tone from an electronic device) to indicate when they should record the behavior ( see   Pellegrini, 2004 ). κ may be expressed in Equation 2 :

where P o b s is the proportion of agreement observed, and P e x p is the expected proportion of agreement by chance ( Bakeman & Gnisci, 2006 ). Equation 2 indicates that agreement anticipated as a result of chance is subtracted from both the numerator and denominator, thus κ provides the proportion of agreement corrected for chance agreements ( Bakeman & Gnisci, 2006 ). The range for κ is from - 1 . 0 0 to + 1 . 0 0 , with a value of “0” indicating that obtained agreement is equivalent to agreement anticipated by chance, and greater than chance agreement would yield positive values with +1.00 equal to perfect agreement between the observers ( Cohen, 1960 ). Interestingly, Cohen (1960) revealed that negative values (less than 0) were rare and suggested agreement at less than chance levels. It is possible to test if κ is significantly different from 0, but statistical significance is often not used as a threshold for determining an “adequate” or “good” criterion ( Bakeman & Gottman, 1987 ). Initially, Landis and Koch (1977) provided an index of the strength of agreement or “benchmarks” and reported the following standards: κ of < 0 . 0 0 was “poor,” 0 . 0 0 - 0 . 2 0 was “slight,” 0 . 2 1 - 0 . 4 0 was “fair,” 0 . 4 1 - 0 . 6 0 was “moderate,” 0 . 6 1 - 0 . 8 0 was “substantial,” and 〉 0 . 8 1 was “almost perfect” (p. 165). However, Bakeman and Gottman (1987) reported that a significant κ of less than 0.70 may be a reason for concern. Other scholars have noted that the conservative nature of κ permits one to use a slightly lower threshold for adequate levels of reliability than the typical convention of 0.70 and suggest that a κ coefficient of 0.60 or higher is “acceptable” and 0.80 or above is considered “good” ( Pellegrini, 2001 ).

Under circumstances when a κ coefficient may not be calculated (e.g., when noncategorical data is used or quadrants of the aforementioned occurrence matrix may not be available given the recording rules of the adopted observational procedure), scholars have suggested that an intraclass correlation coefficient (ICC) be computed between independent raters on the continuous data ( Bartko, 1976 ; McGraw & Wong, 1996 ; Shrout & Fleiss, 1979 ). There are several possible ICC formulas that could be depicted that are beyond the scope of the present review, and as such the interested reader is referred to the prior literature on this topic ( Shrout & Fleiss, 1979 ; McGraw & Wong, 1996 ). Intra-class correlation coefficients may be expressed as a function of either the reliability for a single rating (i.e., the reliability of a typical, single observer compared to another observer) or the average rating of the observations across all the raters ( McGraw & Wong, 1996 ). The average rating ICC uses the Spearman-Brown correction to indicate the reliability for all the observers averaged together ( Bartko, 1976 ). The absolute value of an ICC assessing average ratings will be greater or equal to the ICC for a single rater ( Bartko, 1976 ). Intra-class correlation coefficients may also be calculated as an index of “consistency” or as a measure of “absolute agreement.” Essentially, if systematic differences among observers are of interest, then the “absolute agreement” formula accounts for observer variability in the denominator of the ICC estimate, and this is not included for ICCs that measure “consistency” (for further detail, see   McGraw & Wong, 1996 ). Intra-class correlation coefficients range from –1.00 to +1.00, where negative values indicate a lack of reliability and +1.00 would indicate perfect agreement ( Bartko, 1976 ). An advantage to ICCs is that confidence intervals may be calculated ( see   McGraw & Wong, 1996 ). Typically, acceptable levels of reliability for ICCs are similar to other criteria in the field, and as such, levels greater than or equal to 0.70 are considered “acceptable” (e.g., Ostrov, 2008 ; NICHD Early Child Care Research Network, 2004 ).

In using observational research methods, an assessment of validity is equally as important as an assessment of reliability. Different types of validity should be considered to strengthen the inferences drawn from a particular method, with construct validity being most fundamental to any empirical inquiry. Construct validity is the degree to which the construct being studied actually measures the concept that a researcher intends to study ( Stangor, 2011 ). Construct validity is often established through assessments designed to measure convergent and discriminant validity. Convergent validity rests on the assumption that if a construct is truly being measured, then alternative assessments of the same construct should be correlated with each other ( Stangor, 2011 ). For example, an observational method intended to measure disruptive behaviors in the classroom should be correlated with teacher reports of disruptive behaviors. Alternatively, discriminant validity suggests that the construct being studied should not be correlated with other variables unrelated to the construct ( Stangor, 2011 ). Should the expected convergent and discriminant associations not be observed, then it is unclear what an instrument or observational system is measuring.

Other types of validity that are secondary yet still important to the establishment of a psychometrically sound observational system include content validity and criterion validity. Content validity refers to the extent to which a measure adequately assesses the full breadth of the construct being studied ( Stangor, 2011 ). For example, an observational study of children’s play behavior should code for different types of play, given that it is a diverse construct. To ensure that all facets of a construct are included in an observational system, correspondence with experts and focus groups/review panels may be used. Criterion validity involves an assessment of whether a study variable is associated with a theoretically relevant outcome measure. If observations are associated with an outcome that is measured at the same point in time at which observations are conducted, then concurrent validity is demonstrated. If observations are associated with an outcome that is measured at a future point in time, then predictive validity is demonstrated. For example, concurrent validity would be confirmed by associations between classroom observations of disruptive behavior and teacher report of rejection by peers, and predictive validity would be confirmed by associations between classroom observations of disruptive behavior and future parent -report of academic performance.

threats to validity: sources of bias and error

There are numerous biases for which observational methods are susceptible. A key bias is the aforementioned observer drift, and it is paramount that investigators monitor for this threat to the validity of the data by carefully assessing observational records and calculating reliability coefficients for the duration of the study. Importantly, in addition to the aforementioned discussion about intra-observer reliability, observer drift may also be indicated if there is a drop in inter-observer reliability among the phases of training and data collection ( Smith, 1986 ). A second strategy to mitigate observer drift is to regularly retrain observers. In instances where particular observers demonstrate problematic coding patterns, retraining should be individualized and should target the particular area of concern. In general, retraining is a practice that is beneficial for every observer because it reinforces proper coding procedures and observer behavior, thereby ensuring the integrity of the study.

A second type of distortion that must be considered results from participant reactivity, which is also a threat to the validity of the observational data. Reactivity occurs when the individuals under study alter their behavior because of the presence or influence of an observer. Consequently, the behavior observed does not provide a true representation of the construct being measured. If participants avoid a particular location within a setting or modify their behavior because they know they are being recorded, this is a major concern for the validity of the data ( Stangor, 2011 ). Depending on the nature of the study, reactivity may be more probable. For example, when observers need to remain within earshot of a focal participant to hear and see the behavioral interactions, it is crucial that the observers remain unobtrusive (e.g., Pellegrini, 1989 ). Researchers should explicitly address reactivity by training observers in the field to have a minimally responsive manner ( Pellegrini, 2004 ). Essentially, observers should use neutral facial expressions and control their nonverbal behavior, posture, movement, and reactions to events during live coding. It is also possible that participants may be reactive to cameras and other recording devices, and efforts should be made to habituate participants to this equipment ( see Use of Technology and Software section below) and monitor for this occurrence. Thus, this habituation process should occur prior to the actual collection of data ( Pellegrini, 2004 ). In our studies, we spend a minimum of several days in the observational environment (and will do so for as long as needed) simulating our observations, which provide the participants an opportunity to habituate to our presence and reduce reactivity prior to actual data collection. Therefore, regardless of live or videotaped coding, researchers should observe for participant reactivity and report the degree of reactivity in their studies (e.g., Atlas & Pepler, 1998 ). We define participant reactivity as any direct eye contact between the focal participant and observer, comments from the focal participant to the observer about our presence, or comments about our presence to others in the environment ( Ostrov, 2008 ). Our training procedures and careful monitoring has resulted in relatively low levels of reactivity in several studies (e.g., 1.5–2.5 times per focal participant during 80 min of observation; Crick, Ostrov, Burr et al., 2006 ).

Observer expectancy effects are a third bias ( Hartmann & Pelzel, 2005 ), which is essentially when observers form expectations about the nature of the data based on their knowledge or assumptions about the study goals and hypotheses, which is why best practice is to use unaware observers, when possible, and to use unaware observers for reliability purposes, at a minimum.

A final source of bias that we will discuss is gender bias as this is a well-documented concern with observational methods ( Ostrov, Crick, & Keating, 2005 ). Past research has documented that untrained observers maintain gender biases when observing, for example, physical aggression ( Lyons & Serbin, 1986 ; see also   Condry & Ross; 1985 ; Susser & Keating, 1990 ). That is, men tend to rate boys as more physically aggressive than girls, even when boys and girls are displaying comparable levels of aggression ( Lyons & Serbin, 1986 ). Moreover, male and female college students have shown documented gender biases based on knowledge about gender of young children in past experimental studies ( Gurwtiz & Dodge, 1975 ). Finally, in our own research, we have documented that male college students are less likely to correctly identify relational aggression or prosocial behavior than their female peers ( Ostrov et al., 2005 ). Please note that although the examples were related to our field of study (i.e., aggression), gender biases may be present for a variety of topics of study. Importantly, it may be that when individuals are trained to recognize potential biases, they are more likely to be objective in their coding of behavior ( Lyons & Serbin, 1986 ).

Use of Technology and Software

Excellent detailed reviews of computer-assisted recording devices and observational software programs are available ( see   Hoch & Symons, 2004 ), and thus, the present goal of this section is to briefly review the current state of technology and software for assisting in systematic observations in the laboratory and field. The following will include a review of the three most common observational software programs as well as the use of handheld devices and remote audiovisual equipment. The commercially available programs vary widely in function and cost, but most permit the observer to define a coding scheme and corresponding letter or number codes that observers can quickly use when making observations live or when coding digital media in the laboratory. Overall, advances in technology have made observational methods more efficient (e.g., flexible data reduction procedures and automatic statistical analyses), accurate (i.e., automatic rewind and playback functions reduce errors in coding), and applicable to a wider range of settings and topics of study ( Bakeman & Gnisci, 2006 , p. 140).

The first software program and associated computer-assisted recording devices that we will discuss is the Observer ® ; system by Noldus Inc. ( Noldus, Trienes, Hendriksen, Jansen, & Jansen, 2000 ). The current version is Observer XT, which permits both time sampling as well as continuous event-based observational systems and has been used in both human and animal research ( see   http://www.noldus.com/the-observer-xt/observer-xt-research ). A notable feature is that this software permits an assessment of response latency of the time between the onset of a stimulus and the initiation of the response, which facilitates consequence coding ( see Coding Considerations section above). The software also permits the linking of data from multiple modalities (e.g., observational reports, physiological responses) with a continuous time synch. The software may be used in the field with durable handheld devices or in the laboratory with live streaming video linked directly with the coding program ( Noldus et al., 2000 ). Finally, the new version of the software permits searches of the data for particular comments, events, or behaviors, and data may be exported to various statistical software packages ( Noldus et al., 2000 ). Jonge, Kemner, Naber, and van Engeland (2009) used an earlier version of the Observer software to code data from a study on block design reconstruction in children with autism spectrum disorders and a group of comparison participants. The use of the videotaped sessions and later coding by unaware observers meant that the coders using the software were unaware of the child’s group status. The software permitted the coders to record the amount of time the children took to reconstruct the block design pattern as well as a range of errors ( Jonge et al., 2009 ). The program was used to calculate Cohen’s κ based on two independent coders ( Jonge et al., 2009 ), who could make independent evaluations of the behavior without biasing their coding partner.

The second observational software program that we examine is the Multi-Option Observation System for Experimental Studies (MOOSES; Tapp, Wehby, & Ellis, 1995 ) and the associated Procoder for Digital Video (PCDV; Tapp& Walden, 1993 ), which permits viewing and coding of digital media ( see   http://mooses.vueinnovations.com/overview ). The MOOSES and PCDV programs also permit event and time sampling and for the coding of real-time digital media files or verbatim transcripts of observational sessions ( Tapp & Walden, 1993 ; Tapp et al., 1995 ). In fact, data files may be exported to MOOSES for event coding or to another format known as the Systematic Analysis of Language Transcripts (SALT) for transcription data coding. MOOSES automatically timestamps events and may provide frequency and duration codes as well as basic reliability statistics (e.g., Cohen’s κ ), and MOOSES is designed for sequential analysis ( Tapp et al., 1995 ). A handheld version of MOOSES is available. MOOSES/PCDV has been described as a lower cost alternative to The Observer ( Hoch & Symons, 2004 ).

The third system we review is the Behavior Evaluation Strategies and Taxonomies (BEST; Sharpe & Koperwas, 2003 ). This computer system includes both the BEST Collection for capturing digital media files and the BEST Analysis program for both qualitative and quantitative analysis of the observational data ( Sidener, Shabani, & Carr, 2004 ). The BEST program may be used for examining the frequency or duration of events, and sophisticated sequential analysis may be conducted. Much like the more expensive alternatives, this program will calculate reliability statistics (e.g., Cohen’s κ ) and will summarize data in table or various graph formats. A review of this program suggests that BEST does not handle the collection of interval-based data well, but the BEST Analysis program will allow a researcher to analyze this type of observational data ( Sidener et al., 2004 ). A new platform permits video display for captured data from video files, and although the program was initially written for Windows ® ; , there are inexpensive Apple ® ; iPhone ® ; and iPod Touch ® ; applications available for data collection ( see   http://www.skware.com ).

Various types of technology (e.g., audio and video recordings) have an extensive history in the field and laboratory to assist researchers in better capturing verbal and nonverbal interactions (e.g., Abramovitch, Corter, Pepler, & Stanhope, 1986 ; Stauffacher & DeHart, 2005 ). Remote audiovisual recordings provided an opportunity to combine the benefits of both audio and video recording while also reducing reactivity to typical recording devices when participants were observed in naturally occurring settings ( Asher & Gabriel, 1993 ; Atlas & Pepler 1998 ; Pellegrini, 2004 ; Pepler & Craig, 1995 ; Pepler, Craig, & Roberts, 1998 ). That is, videotaping with a telephoto zoom lens from an unobtrusive location in the natural setting and recording audio via a system of wireless microphones provides an externally valid way to record behavior and a time-synched verbal record of the interaction ( Pepler & Craig, 1995 ). Thus, remote audiovisual observational recordings provide all the benefits of having a video for subsequent coding by unaware observers (i.e., the ability to pause, rewind, and analyze subtle nonverbal behaviors) as well as a complete verbal transcript, which helps to put the video data in proper context ( Asher & Gabriel, 1993 ; Pepler & Craig, 1995 ). Wireless microphones typically are housed within small vests or waist pouches that participants wear, and often only the focal participant has an active or live microphone, and others in the reference group have “dummy” microphones that resemble the weight and look of the real microphone. Importantly, observational codes made with the remote audiovisual equipment have demonstrated acceptable inter-observer reliability coefficients (e.g., κ = 0 . 7 6 ; Pepler & Craig, 1995 ). Moreover, this procedure as well as sufficient exposure to the equipment by the participants has been found to produce low levels of participant reactivity (e.g., <5%, Atlas & Pepler, 1998 ; see also   Asher & Gabriel, 1993 ). The benefits of a rich observational record with low levels of reactivity within settings of high ecological validity seem to outweigh the costs, which include additional training, equipment costs, and some ethical considerations. A central ethical consideration is that individuals without consent may be recorded indirectly. A possible solution is to temporarily store and then, after processing, discard film clips of individuals without consent ( Pepler & Craig, 1995 ), but this solution may violate the rights of nonparticipants. Alternatively, a researcher could restrict access to the observational setting to only those with consent, but this second approach is a threat to the ecological validity of the procedures ( Pepler & Craig, 1995 ). An additional concern is that third parties may wish to use the data as surveillance, which might limit the rights of participants being recorded. As such, policies related to confidentiality and any possible limits of confidentiality should be discussed with the participants and any other possible party that may desire access to the data ( see   Pepler & Craig, 1995 ). Importantly, to our knowledge, remote audiovisual observational methodology has only been used with school-aged children in the classroom ( Atlas & Pepler, 1998 ) and typically on the playground (e.g., Asher & Gabriel, 1993 ; Pepler, Craig, & Roberts, 1998 ); thus, it is not clear if older individuals would be more aware and reactive to the procedure and equipment ( Pepler & Craig, 1995 ).

Ethical Considerations

There are several ethical considerations with observational research. With naturally occurring phenomena, there may be a temptation to observe social interactions and behavior without obtaining informed consent. Although this practice may technically be exempt from most Institutional Review Board (IRB) review (i.e., if identifying information is not collected and video or audio recordings of the public behavior are not made), we strongly encourage researchers to obtain informed consent from participants and assent from legal minors to support their right for autonomy but also so that all risks (e.g., breaches of confidentiality) may be appropriately conveyed. To avoid these breaches of confidentiality, researchers conducting live observations typically use identification codes rather than identifying information about the participants on all observation forms and in data files. Access to video or audio recordings of observational sessions is typically restricted to only those individuals (e.g., coders) who must have access as part of the research study. Participants should be fully informed for how long the observational recordings will be maintained and when they will be destroyed. A final ethical consideration concerns intervention efforts or at what point the researcher or observers will intervene (for a discussion of duty to warn with observational methods, see   Pepler & Craig, 1995 ) and directly or indirectly act on the behalf of the participants. For example, in our observational studies, we have clearly established procedures for when we will notify a teacher that a child in the observation setting is in danger or in need of help (e.g., leaving the controlled area, serious injury). These procedures are discussed at the start of the study with school officials and are part of our consent process, which we believe are best practices.

An Overview of Procedures for a High-Quality Systematic Observational Study

The researcher begins by a priori selecting and operationally defining behaviors of interest. Next, the researcher adopts a coding scheme by selecting the most appropriate sampling and recording procedures given the nature of the behavior under study and the observational context ( see Table 15.2 ). Ethical considerations should be addressed during this development stage of the observational method and should be evaluated for the duration of the study. If the observational scheme is newly developed for the study, then it is imperative that pilot testing occur within a similar context and with a sample representing the target population. If it is not a new scheme or if pilot testing does not indicate any problems, then the investigator may begin training observers. If there are problems noted, then it is important to rectify these issues as quickly as possible to avoid further errors in the study. It is possible that modifications will be needed regarding the operational definition of the observed constructs or changes may be needed to the procedures and coding scheme given the nature of the context or sample under study. Once these changes are adopted, additional checks should be made to verify the solution has worked to ameliorate the original concerns. Training involves the use of a standardized manual, and initial reliability training assessments are conducted prior to the collection of data. Behavior is sampled in the lab or in the field in accordance with the adopted sampling and recording rules, and inter-observer reliability is collected for the duration of the study. Validity assessments are also conducted using alternative informants and methods. If reliability or validity problems are detected, then this may also yield further modifications to the coding scheme to address the problems. If no psychometric problems are noted, then coding and scoring of the observational data occurs using standardized procedures. Finally, the data are analyzed and reported, which concludes the systematic observational study ( see Fig. 15.1 ).

Systematic observational methods provide an opportunity to record the behavior of humans and animals in a relatively objective manner, without sacrificing ecological validity. In the present chapter, we have attempted to identify best practices as well as benefits and costs of various sampling and recording techniques. Quantitative researchers should be guided by a priori research questions and hypotheses when selecting the most appropriate sampling and recording procedure for the specific research setting. Systematic observations require careful attention to coding and scoring decisions and a focus on achieving acceptable levels of reliability and validity. As a field, we must work to establish more stringent standards of reliability (i.e., inter-observer) and validity (i.e., construct) for observational methods. Moreover, we must continue to address and reduce various sources of bias and error. The use of computer-assisted software and digital analysis technology provide some promising options for increasing the efficiency and appeal of systematic observations in the field. Attention must also be given to key ethical considerations to guide appropriate conduct as an observational researcher. Careful consideration of these issues may inform quality research in a wide variety of basic, clinical, and educational contexts.

Procedures for a high-quality systematic observational study.

Future Directions

Observational methods have been a part of the social and behavioral sciences since the early years of our field, and we anticipate that there is a bright future for observational methods within the quantitative scholar’s toolbox. We have defined seven questions and two remaining issues that we believe the field should work to address. This list is not exhaustive, but we hope these questions will generate future work using systematic observational methods.

1. What is the utility of observational methods above and beyond additional informants? Given the time and cost of observational methods, it is necessary to continue to demonstrate that observational methods have incremental predictive utility or may explain unique amounts of variance in relevant outcomes, above and beyond other informants and measures ( Doctoroff & Arnold, 2004 ; Shaw et al., 1998 ). For example, we have demonstrated that observations of relational and physical aggression account for a significant amount of unique variance above and beyond teacher reports of relational and physical aggression in the prediction of teacher-reported deceptive and lying behaviors ( Ostrov, Ries, Stauffacher, Godleski, & Mullins, 2008 ).

2. How does one best examine the construct validity of observational methods? To date, there is not wide consensus on the best approach for demonstrating the construct validity of observational systems. The typical approach is to compare observational data to other “gold standard” methods. For example, convergent evidence is achieved when high levels of association are found across methods such as between observations of aggression subtypes in classrooms, observations of aggression subtypes via semi-structured observations, and with various informants including teacher reports and parent reports of aggression subtypes (e.g., Crick, Ostrov, Burr, et al., 2006 ; Hinde et al., 1984 ; Ostrov & Bishop, 2008 ; Ostrov & Keating, 2004 ; Pellegrini & Bartini, 2000 ).

3. How do we detect observer biases? We believe the field has only begun to address the important issue of how to assess and identify observer biases. Much further work is needed to examine a host of possible biases from observer drift and observer expectancy effects to gender biases as well as other possible sources of distortion such as halo effects and potential expectancy biases derived from prior knowledge of participants in longitudinal studies ( Hartmann & Pelzel, 2005 ). In addition, more focus should be placed on assessing participant reactivity. Few studies report this source of error and threat to validity, and we encourage observational researchers to quantify the degree to which their participants are reactive to the observational procedures.

4. How do we eliminate observer biases and other sources of error? Once we identify observer biases, we need more evidence-based information on how to appropriately eliminate these biases and sources of error. The literature has indicated few possible solutions (e.g., increased training for individuals with identified biases). In addition, more emphasis should be placed on identifying best practices for reducing reactivity. It is clear that minimally responsive procedures and habituation practices have worked effectively to reduce reactivity to low levels (e.g., <5% of time), but our goal should be to eliminate this source of error from our data.

5. What is the sufficient amount of time for observational sampling? Too often the time interval for time sampling as well as the total duration of observed time for event-based coding systems is decided without sufficient justification, and greater work is needed to establish parameters and strategies for determining the most efficient and useful time intervals for various behaviors and settings.

6. How do we reduce the cost of observational methods? One of the biggest obstacles to greater adoption of systematic observational methods is the cost of observational procedures. Typically, large staffs of highly trained individuals are needed for observational work, and although volunteer research assistants may be used to address this concern, this is still a significant barrier to further work in this area. Moreover, the overall amount of time to conduct an observational study is potentially longer than comparable studies with other methods, and thus we must work to make training procedures, data collection, and coding processes more efficient. The use of computer-assisted software and coding technology will continue to greatly help in this regard.

7. How do we refine and create observational software so that it is compatible with all types of observational systems and more flexible as well as affordable? Although observational software and recording devices have advanced a great deal in recent years ( see   Hoch & Symons, 2004 ), the software must become more flexible to accommodate a greater range of observational sampling and recording procedures. Moreover, the financial cost of these programs and licenses are often prohibitive, and efforts must be made to develop high-quality, affordable, and flexible computer-assisted observational software programs.

8. A key remaining issue is that as a field we need to move away from the use of Pearson product moment correlations and percent agreement as a standard measure of assessing inter-observer reliability. Given what we know about the role of chance agreement from classic (e.g., Cohen, 1960 ) and modern sources ( Bakeman & Gottman, 1987 ; Pellegrini, 2004 ), it is not clear why some peer-reviewed manuscripts continue to only present either Pearson product moment correlations or percent agreement as strong evidence of inter-observer reliability.

9. A second remaining concern is that greater discussion of the ethical issues involved in observational methods is needed. For example, as we have discussed, it is not always clear when intervention is needed by observers in the field. Further, greater work needs to be conducted to examine how we may best ensure confidentiality of data with detailed observational records. Finally, we must focus on how we ensure confidentiality with the transfer of electronic observational data via handheld devices and other electronic technology.

Author Note

We wish to thank Jennifer Kane and members of the UB Social Development Laboratory for their assistance with the preparation of this chapter. Thanks to Dr. Leonard J. Simms for comments on an earlier draft. Special thanks to Dr. Anthony D. Pellegrini, who has greatly influenced the way we conceptualize systematic observational methods. The authors are affiliated with the Department of Psychology, University at Buffalo, The State University of New York. Please direct correspondence to the first author at [email protected] or 716-645-3680.

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Observation

Observation Methods

• First Online: 14 December 2017

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• Malgorzata Ciesielska 4 ,
• Katarzyna W. Boström 5 &
• Magnus Öhlander 6  

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Observation may be seen as the very foundation of everyday social interaction: as people participate in social life, they are diligent observers and commentators of others’ behavior. Observation is also one of the most important research methods in social sciences and at the same time one of the most complex. It may be the main method in the project or one of several complementary qualitative methods. As a scientific method it is has to be carried out systematically, with a focus on specific research questions. Therefore, we start with practical guide on clarifying research objectives, accessing the research field, selecting subjects, observer’s roles, and tips on documenting the data collected. The observation comprises several techniques and approaches that can be combined in a variety of ways. Observation can be either participant or not, direct or indirect. Further in this chapter, the main characteristics of three types of observations are outlined (the fourth type—direct non-participant—is discussed in the chapter on shadowing). While participant observation follows the ideal of a long-time immersion in a specific culture as a marginal member, researcher conducting non-participant observation takes position of an outsider and tries to distance him/herself from the taken-for-granted categorizations and evaluations. In the case of indirect observation, the researcher relies on observations of others (e.g. other researchers), various types of documentation, or self-observation. The chapter discusses the differences between those types of observation, shows inspirational examples from previous studies, and summarizes the method.

• Participant observation
• Non-participant observation
• Direct observation
• Indirect observation
• Observer’s role

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Ciesielska, M., Boström, K.W., Öhlander, M. (2018). Observation Methods. In: Ciesielska, M., Jemielniak, D. (eds) Qualitative Methodologies in Organization Studies. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-65442-3_2

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Observation Method in Research (Definition & Types)

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

Definition The observation method is a process that involves human or mechanical observation to observe and describe the behavior of a subject. As the name suggests observational research is a way of collecting relevant information and data by involves observing people’s behavior. The observational Research method is also referred to as a participatory study because the researcher has to establish a link with the respondent and for this has to immerse himself in the same setting as theirs. Only then can he use the observational research method to record and take notes.

Table of Contents

Meaning and examples

Observational research  is used in cases where you want to avoid an error that can be a result of bias during evaluation and interpretation processes. Observational research is one of the many other research methods to obtain objective data by watching a participant and recording it for analysis at a later stage.

A researcher can use observational research, for example in a school, and record the behavior of the children at a young age. Are the children comfortable sharing their tiffin at such an early age will make a good study for the researcher? In this example, the researcher can observe and record the details objectively. The observational research data collection method is associated with observational research methods but with a few ethical issues as it needs the full consent of a research participant.

The observation method in data collection can be

• Structured observation method – Structured Observation is a systematic observation method where data is collected as per a pre-defined schedule. The specific variable is used in this observation data collection method.
• Unstructured observation method – The unstructured observation method is different from structured observation because it is conducted in a free and open manner without using any pre-determined objectives, schedules, or variables.

Sampling Methods for Observational Data

While conducting observational research, the researcher has a vital role to play in collecting data, as he will have to collect, record and classify the data appropriately. The research methods primary sampling methods are

• Event sampling – In the event sampling observation method the researcher decides beforehand what events or behavior he will record and which ones he is going to ignore
• Time sampling – In the time sampling observation method, the researcher chooses the time when he will observe. He makes a record of the occurrence only in the specified and pre-determined period
• Target -time or instantaneous sampling –   In the target-time or instantaneous sampling observation method, the researcher decides beforehand the moments when the observation will happen and will be recorded at that moment. Everything that happens before or after that moment is of no consequence and hence is typically ignored

Types of Observational Research Methods

The different types of observation methods in observational research are as follows-

1) Controlled observations

The controlled observation is carried out in a closed space. It is the researcher who has the authority to decide the place and the time where and when the observation will take place. He also decides who the participants will be and in what circumstances will he use the standardized process.

The participants are chosen for a variable group randomly. The researcher observes and records a detailed and descriptive quantitative data of behavior and divides it into a distinct category. Sometimes the researcher codes the action as per an agreed scale by using a behavior schedule. The coding can include letters or numbers or a range to measure behavior intensity and describe its characteristics.

The collected data from observational research methods is often turned into statistics for laboratory research. In a controlled observation method, the participants are informed by the researcher about the aim of the research. This makes them aware of being observed. The researcher avoids direct contact during the observation method and generally uses a two-way mirror to observe and record details.

Advantages of controlled observation method

• The data and information received from a controlled observation method are structured and analytical. It is thus easy to analyze it quickly and is considered less time-consuming than the other observation methods
• Other researchers can easily replicate the report that has been created through the controlled observation method. They use a similar observation schedule, and this makes it easy to test for reliability.
• As the controlled observation method is several quick observations can be conducted within a short time frame. Thus the researcher can collect large samples which makes it easier for him to generalize a large population

Limitations of controlled observation method 

• The controlled observation method lacks validity because when the participants are aware of being observed their behavior will automatically change

2) Naturalistic observations

Social scientists and psychologists generally use the naturalistic observation method in observational research studies. The process of naturalistic observation involves observing and studying the spontaneous behavior of the participants in open or natural surroundings. The role of the researcher is to find and record whatever he can see and observe in natural habitat.

Advantages of naturalistic observation method 

• When a participant is in a natural habitat, his flow of behavior is natural and not forced.
• Naturalistic observation studies have gained better ecological validity than the controlled observation method
• The naturalistic observation method is used by the researchers to create new ideas . The researcher has the chance to observe the total situation and can find avenues that other people have not thought about

Limitations of naturalistic observation methods

• The naturalistic observation method facilitates observations on a micro-scale. It often lacks a representative sample and thus cannot help the researcher in making a generalization that relates to a broader society
• In naturalistic observation, the researcher needs proper training to recognize aspects that are significant and worth attention.
• The observations through naturalistic observation method are not as reliable as the researcher wants them to be because it is not possible to control some variables. This is why other researchers cannot similarly repeat the study or research.
• Establishing the cause and effect relationship is not possible because the researcher cannot manipulate the variables

3) Participant observation

The participant observation method is often considered a variant of the naturalistic observation method because it has some similarities with it. The point of difference called disguised naturalistic observation, is that the researcher is not a distant observer anymore because he has joined the participants and become a part of their group. He does this to get a more in-depth and greater insight into their lives.

In participant observation, The researcher interacts with other members of the group freely, participates in their activities, studies their behavior and acquires a different way of life. Participant observation can be overt or covert.

• Overt participant observation: When the researcher asks permission from a group to mingle the observation method is known as overt. He does so by revealing his true purpose and real identity to the group with whom he wants to mingle
• Covert participant observation: When the researcher does not show either his true identity or real meaning to the group he wants to join then the observation is known as covert. He keeps both concealed and takes on a false role and identity to enter and mingle in the group. He generally acts as if he is a genuine member of that group

Advantages of Participant Observation methods

• It is easy to study and observe the natural behavior of the participants in the group by becoming a part of that group. The respondents generally do not know that they are being observed and behavior recorded, so they are not restrained or constrained in their activities and behavior
• The researcher becomes understanding by following the events of the respondents from such a close angle.
• During the participant observation method, the researcher develops a good and healthy relationship with the respondents. This rapport helps him to participate in all the activities and make observations with a detached mind
• The participant observation method helps the research to observe the actual behavior of the respondents and create an inclusive and intensive case study of that group
• Actual participation in the activities provides the researcher with an opportunity to converse freely with other members about various events, their meaning and their importance to them. He gains an in-depth knowledge which would not have been possible only by observation.

Limitations of Participant Observation Methods

• It is challenging to work undercover. For example, the researcher will have only to observe and not record in front of others because he will not want to blow his cover. He relies heavily on his memory which can be faulty at times
• Sometimes the researcher becomes too involved in the intricacies of that group. There is a higher chance of losing his objectivity because his reporting will be selective and dependent on his memory
• The emotional participation of the researcher can result in bias interpretation. He will be influenced to some degree and a time might come when he would start supporting them unconditionally because their views and behavior will ultimately become his. This will result in a personal viewpoint of the scenario and not an objective or scientific report
• In the participant observation method, the researcher’s experience becomes intense because of his proximity to the group members but the range becomes limited
• The researcher misses many vital points because of his familiarity
• Proximity with the group will involve him in group factionalism, and he will have to take sides. He then loses his objectivity as an impartial observer with whom everyone is ready to cooperate.

Advantages of the Observation Method

• Provides direct access to research phenomena
• By observing firsthand, the researcher can collect, check and record accurate data
• Greater flexibility in terms of application
• Generate a permanent record of phenomena and the researcher or others can refer with it later
• The organization method is one of the simplest methods of data collection. It does not require too much technical knowledge
• The observation method is one of the best ways to formulate a hypothesis. The researcher can observe and come to know about the activities, perceptions , likes, and dislikes to form a theory on his subject
• Observational methods are one of the most common methods used in all sciences and is very easy to follow and accept
• In some instances observation is the only available tool to collect essential data and information
• The observation method does not require the willingness of the participant to record. The researcher can observe from a distance and record his findings

Disadvantages of the Observation Method

• Observational methods Face a severe disadvantage because it takes a longer time frame compared to other data collection methods
• There is a chance of higher observer bias in the observation method
• Several personal behaviors are not open for observation and this proves a limitation in case of observation method
• There is a higher chance of the observer influencing the behavior of a sample group elements
• Uncertainties of the event cannot determine the actual time when the event will take place, and this is why every occurrence that is open to observation cannot be observed
• Many of the incidents are abstract like love, affection and the researcher can’t gain an exact and correct account of those
• The social phenomena generalization made by observation are not considered reliable as it cannot be used for lab experiments
• In some cases, it is seen that two persons observing the same phenomena come at different results and this can lead to faulty perceptions
• Observation method is considered an expensive affair as it requires hard effort, plenty of time and high cost

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Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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Participant Observation

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Field Guide

• Participant Observation Field Guide

What is an observation?

A way to gather data by watching people, events, or noting physical characteristics in their natural setting. Observations can be overt (subjects know they are being observed) or covert (do not know they are being watched).

• Researcher becomes a participant in the culture or context being observed.
• Requires researcher to be accepted as part of culture being observed in order for success

Direct Observation

• Researcher strives to be as unobtrusive as possible so as not to bias the observations; more detached.
• Technology can be useful (i.e video, audiorecording).

Indirect Observation

• Results of an interaction, process or behavior are observed (for example, measuring the amount of plate waste left by students in a school cafeteria to determine whether a new food is acceptable to them).

Suggested Readings and Film

• Born into Brothels . (2004) Oscar winning documentary, an example of participatory observation, portrays the life of children born to prostitutes in Calcutta. New York-based photographer Zana Briski gave cameras to the children of prostitutes and taught them photography
• Davies, J. P., & Spencer, D. (2010).  Emotions in the field: The psychology and anthropology of fieldwork experience . Stanford, CA: Stanford University Press.
• DeWalt, K. M., & DeWalt, B. R. (2011).  Participant observation : A guide for fieldworkers .   Lanham, Md: Rowman & Littlefield.
• Reinharz, S. (2011).  Observing the observer: Understanding our selves in field research . NY: Oxford University Press.
• Schensul, J. J., & LeCompte, M. D. (2013).  Essential ethnographic methods: A mixed methods approach . Lanham, MD: AltaMira Press.
• Skinner, J. (2012).  The interview: An ethnographic approach . NY: Berg.
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Qualitative research method-interviewing and observation

Shazia jamshed.

Department of Pharmacy Practice, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan Campus, Pahang, Malaysia

Buckley and Chiang define research methodology as “a strategy or architectural design by which the researcher maps out an approach to problem-finding or problem-solving.”[ 1 ] According to Crotty, research methodology is a comprehensive strategy ‘that silhouettes our choice and use of specific methods relating them to the anticipated outcomes,[ 2 ] but the choice of research methodology is based upon the type and features of the research problem.[ 3 ] According to Johnson et al . mixed method research is “a class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, theories and or language into a single study.[ 4 ] In order to have diverse opinions and views, qualitative findings need to be supplemented with quantitative results.[ 5 ] Therefore, these research methodologies are considered to be complementary to each other rather than incompatible to each other.[ 6 ]

Qualitative research methodology is considered to be suitable when the researcher or the investigator either investigates new field of study or intends to ascertain and theorize prominent issues.[ 6 , 7 ] There are many qualitative methods which are developed to have an in depth and extensive understanding of the issues by means of their textual interpretation and the most common types are interviewing and observation.[ 7 ]

Interviewing

This is the most common format of data collection in qualitative research. According to Oakley, qualitative interview is a type of framework in which the practices and standards be not only recorded, but also achieved, challenged and as well as reinforced.[ 8 ] As no research interview lacks structure[ 9 ] most of the qualitative research interviews are either semi-structured, lightly structured or in-depth.[ 9 ] Unstructured interviews are generally suggested in conducting long-term field work and allow respondents to let them express in their own ways and pace, with minimal hold on respondents’ responses.[ 10 ]

Pioneers of ethnography developed the use of unstructured interviews with local key informants that is., by collecting the data through observation and record field notes as well as to involve themselves with study participants. To be precise, unstructured interview resembles a conversation more than an interview and is always thought to be a “controlled conversation,” which is skewed towards the interests of the interviewer.[ 11 ] Non-directive interviews, form of unstructured interviews are aimed to gather in-depth information and usually do not have pre-planned set of questions.[ 11 ] Another type of the unstructured interview is the focused interview in which the interviewer is well aware of the respondent and in times of deviating away from the main issue the interviewer generally refocuses the respondent towards key subject.[ 11 ] Another type of the unstructured interview is an informal, conversational interview, based on unplanned set of questions that are generated instantaneously during the interview.[ 11 ]

In contrast, semi-structured interviews are those in-depth interviews where the respondents have to answer preset open-ended questions and thus are widely employed by different healthcare professionals in their research. Semi-structured, in-depth interviews are utilized extensively as interviewing format possibly with an individual or sometimes even with a group.[ 6 ] These types of interviews are conducted once only, with an individual or with a group and generally cover the duration of 30 min to more than an hour.[ 12 ] Semi-structured interviews are based on semi-structured interview guide, which is a schematic presentation of questions or topics and need to be explored by the interviewer.[ 12 ] To achieve optimum use of interview time, interview guides serve the useful purpose of exploring many respondents more systematically and comprehensively as well as to keep the interview focused on the desired line of action.[ 12 ] The questions in the interview guide comprise of the core question and many associated questions related to the central question, which in turn, improve further through pilot testing of the interview guide.[ 7 ] In order to have the interview data captured more effectively, recording of the interviews is considered an appropriate choice but sometimes a matter of controversy among the researcher and the respondent. Hand written notes during the interview are relatively unreliable, and the researcher might miss some key points. The recording of the interview makes it easier for the researcher to focus on the interview content and the verbal prompts and thus enables the transcriptionist to generate “verbatim transcript” of the interview.

Similarly, in focus groups, invited groups of people are interviewed in a discussion setting in the presence of the session moderator and generally these discussions last for 90 min.[ 7 ] Like every research technique having its own merits and demerits, group discussions have some intrinsic worth of expressing the opinions openly by the participants. On the contrary in these types of discussion settings, limited issues can be focused, and this may lead to the generation of fewer initiatives and suggestions about research topic.

Observation

Observation is a type of qualitative research method which not only included participant's observation, but also covered ethnography and research work in the field. In the observational research design, multiple study sites are involved. Observational data can be integrated as auxiliary or confirmatory research.[ 11 ]

Research can be visualized and perceived as painstaking methodical efforts to examine, investigate as well as restructure the realities, theories and applications. Research methods reflect the approach to tackling the research problem. Depending upon the need, research method could be either an amalgam of both qualitative and quantitative or qualitative or quantitative independently. By adopting qualitative methodology, a prospective researcher is going to fine-tune the pre-conceived notions as well as extrapolate the thought process, analyzing and estimating the issues from an in-depth perspective. This could be carried out by one-to-one interviews or as issue-directed discussions. Observational methods are, sometimes, supplemental means for corroborating research findings.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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