Dr. Egon Dejonckheere
Egon does research in Emotion, Clinical Psychology and Abnormal Psychology. His most recent publication is 'The Bipolarity of Affect and Depressive Symptoms', featured in Journal of Personality and Social Psychology: Personality Processes and Individual Differences.
What is the Experience Sampling Method?
The Experience Sampling Method (ESM) is a data collection technique designed to measure people’s thoughts, emotions, behaviors, and experiences as they occur in daily life. In modern ESM research, participants are prompted multiple times per day to report on their current state using brief (structured) surveys, usually delivered through an ESM app on their smartphones or other digital devices.
ESM is best understood as a specific type of ambulatory assessment. While ambulatory assessment broadly captures real-life data collection through both active and passive methods, ESM focuses on active self-reports of subjective experiences. Its defining feature is the collection of subjective information in real time and natural environments through momentary structured surveys, rather than retrospectively. Conceptually, it closely aligns with Ecological Momentary Assessment (EMA), and as a result, many researchers use the two terms interchangeably to refer to the same type of methodology.
A useful way to think about ESM is that it allows researchers to move from studying an isolated, static “picture” of psychological functioning toward observing the “film” of everyday life. Rather than asking participants to summarize how they felt over the past week or month, ESM repeatedly samples their experiences in the moment, thereby providing more fine-grained and temporally sensitive insight in psychological processes.
ESM is best understood as a specific type of ambulatory assessment. While ambulatory assessment broadly captures real-life data collection through both active and passive methods, ESM focuses on active self-reports of subjective experiences. Its defining feature is the collection of subjective information in real time and natural environments through momentary structured surveys, rather than retrospectively. Conceptually, it closely aligns with Ecological Momentary Assessment (EMA), and as a result, many researchers use the two terms interchangeably to refer to the same type of methodology.
A useful way to think about ESM is that it allows researchers to move from studying an isolated, static “picture” of psychological functioning toward observing the “film” of everyday life. Rather than asking participants to summarize how they felt over the past week or month, ESM repeatedly samples their experiences in the moment, thereby providing more fine-grained and temporally sensitive insight in psychological processes.
Core strengths of ESM
Although studies based on Experience Sampling Methodology (ESM) can differ substantially in their specific design, several defining characteristics consistently shape how the method operates and why it is valuable for studying psychological processes.
No recall bias
A central feature of ESM is its focus on real-time or near real-time assessment. Participants are typically asked to report on their current experiences (e.g., right now) or on very recent moments (e.g., in the last hour), which minimizes reliance on long-term memory. This allows researchers to capture experiences almost immediately as they occur in daily life and avoids distortions introduced when individuals are required to aggregate experiences across extended timeframes.
High ecological validity
Second, ESM is conducted in the real world. Rather than studying participants in laboratory environments or experimental settings, ESM assesses individuals in their everyday contexts, such as at home, at work, or during leisure activities. This ensures that the data reflect naturally occurring experiences and behaviors, which increases the external or ecological validity of the collected data.
Within-person change
A third defining characteristic is the use of repeated measurements. Participants are typically prompted multiple times per day across several days or weeks, resulting in intensive longitudinal data. This repeated sampling enables researchers to examine how emotions, thoughts, and behaviors fluctuate within individuals over time.
Different ways of sampling
A fourth feature relates to how the timing and control of assessments are structured. In ESM, this depends on the specific research question and the temporal process of interest. ESM research typically distinguishes two approaches. In signal-contingent sampling, prompts are delivered at predefined or randomly selected moments, such as fixed schedules or semi-random (stratified random) time points. In event-contingent sampling, ESM surveys are triggered by specific events, for example changes in passive sensing data or participant-initiated reports (e.g., after a social interaction, a panic attack or a hospital visit).
Context-specific focus
Finally, ESM is inherently contextual. Participants report not only on their internal states, but also on the situations in which these states occur, including their activity, social environment, and location, as well as their subjective appraisal of these contexts. This integration is essential for understanding how psychological processes are embedded in daily life, and how contextual factors may shape them.
Taken together, these characteristics explain why ESM provides a powerful framework for studying psychological functioning. By combining real-time assessment, real-world measurement, repeated sampling in appropriate sampling scheme, and contextual information, ESM offers a detailed and ecologically grounded view of how experiences unfold in everyday life.
Taken together, these characteristics explain why ESM provides a powerful framework for studying psychological functioning. By combining real-time assessment, real-world measurement, repeated sampling in appropriate sampling scheme, and contextual information, ESM offers a detailed and ecologically grounded view of how experiences unfold in everyday life.
Potential limitations of ESM
Despite Experience Sampling Methodology (ESM) offering clear strengths in capturing experiences in real time and in natural contexts, these advantages may come with important downsides. Compared to retrospective questionnaires and laboratory-based methods, ESM introduces a range of methodological, practical, and analytical challenges that require careful consideration.
Burden, missingness and inattentive responding
A primary limitation of ESM is the burden it places on participants. Frequent prompts throughout the day can interrupt ongoing activities and demand repeated cognitive effort, particularly in intensive protocols with many assessments per day. This burden has consequences for both the amount and the quality of the data collected.
From a data quantity perspective, higher burden is typically associated with declining compliance over time, leading to poor response rates or participant attrition. While some missingness in ESM is inevitable (perhaps perfect compliance is even suspicious?), the question is to what extent missed assessments are purely random. Participants may be less likely to respond in particular situations, such as during social interactions, work tasks, or moments of heightened negative affect. Consequently, certain contexts or states may be systematically underrepresented.
From a data quality perspective, even completed assessments are not immune to burden-related effects. Participants may provide brief or inattentive responses to reduce disruption, increasing the likelihood of careless responding. This introduces additional measurement error, as responses are present but may not accurately reflect the underlying experience. Taken together, participant burden in ESM affects not only how much data is collected, but also how reliable those data are.
From a data quantity perspective, higher burden is typically associated with declining compliance over time, leading to poor response rates or participant attrition. While some missingness in ESM is inevitable (perhaps perfect compliance is even suspicious?), the question is to what extent missed assessments are purely random. Participants may be less likely to respond in particular situations, such as during social interactions, work tasks, or moments of heightened negative affect. Consequently, certain contexts or states may be systematically underrepresented.
From a data quality perspective, even completed assessments are not immune to burden-related effects. Participants may provide brief or inattentive responses to reduce disruption, increasing the likelihood of careless responding. This introduces additional measurement error, as responses are present but may not accurately reflect the underlying experience. Taken together, participant burden in ESM affects not only how much data is collected, but also how reliable those data are.
Measurement reactivity and interference
ESM is designed to capture experiences as they naturally occur, yet the act of repeated measurement can itself influence those experiences. Being prompted multiple times per day may increase participants’ awareness of their thoughts, emotions, or behaviors. This heightened awareness can alter the processes under study, for instance by encouraging self-reflection or behavioral adjustment.
Demonstrating such reactivity effects empirically is challenging, as it requires disentangling genuine change from measurement-induced change. Evidence across domains such as affect, substance use, pain, body image and even suicidal urges generally suggests that these effects are small or negligible at an aggregate level. Nonetheless, this does not eliminate meaningful effects for specific individuals.
Closely related is the concept of measurement interference. At the behavioral level, prompts may disrupt ongoing activities, such as meetings, commuting, or leisure time, potentially altering behavior in the moment. At the affective level, prompts can elicit emotional responses, such as irritation, pressure to respond, or concern about missing notifications. These forms of interference highlight that ESM not only observes daily life, but may also subtly influence it.
Demonstrating such reactivity effects empirically is challenging, as it requires disentangling genuine change from measurement-induced change. Evidence across domains such as affect, substance use, pain, body image and even suicidal urges generally suggests that these effects are small or negligible at an aggregate level. Nonetheless, this does not eliminate meaningful effects for specific individuals.
Closely related is the concept of measurement interference. At the behavioral level, prompts may disrupt ongoing activities, such as meetings, commuting, or leisure time, potentially altering behavior in the moment. At the affective level, prompts can elicit emotional responses, such as irritation, pressure to respond, or concern about missing notifications. These forms of interference highlight that ESM not only observes daily life, but may also subtly influence it.
Response shifts
Another challenge in ESM concerns response shifts over time. These include processes such as reconceptualization and recalibration. Reconceptualization occurs when participants change how they interpret a construct during the study. For example, their understanding of “fatigue” or “social engagement” may evolve as they repeatedly reflect on these experiences. Recalibration refers to changes in how participants use response scales, such as becoming more conservative or more liberal in their ratings over time.
As a result, observed changes in ESM data may not solely reflect true changes in the underlying construct, but also shifts in interpretation or scale usage. This complicates the interpretation of within-person change, as apparent trends may partly be driven by reporting processes rather than actual experience.
As a result, observed changes in ESM data may not solely reflect true changes in the underlying construct, but also shifts in interpretation or scale usage. This complicates the interpretation of within-person change, as apparent trends may partly be driven by reporting processes rather than actual experience.
Privacy and ethical considerations
Because ESM captures data in participants’ everyday environments, often multiple times per day and sometimes in combination with sensor-based information, it raises important privacy and ethical concerns. Participants may be asked to repeatedly report on sensitive topics (e.g., anxiety, interpersonal conflict, or substance use), and frequent prompting can increase perceived intrusiveness. Ensuring adequate informed consent, secure data handling, and transparency about data use is therefore critical. Compared to traditional one-time surveys, ESM studies require more extensive consideration of participant autonomy, burden, and data protection.
No causal claims
As opposed to controlled laboratory experiments, ESM prioritizes ecological validity over experimental control. Researchers have limited influence over participants’ environments and the timing or nature of events. This restricts the ability to draw strong causal conclusions, as potential confounding factors cannot befully controlled. Although the temporal structure of ESM data allows for stronger inferences than cross-sectional designs, such as examining lagged associations or temporal precedence (e.g., Granger causality), the approach remains fundamentally observational. As such, ESM complements rather than replaces experimental methods.
Analytical challenges
ESM produces intensive longitudinal data that are inherently nested, temporally linked, and often incomplete due to missed assessments. Analyzing these data requires advanced statistical techniques, including multilevel modeling, time series approaches, or dynamic structural equation models. These methods are more demanding than standard analyses used in cross-sectional research and require substantial methodological expertise. Moreover, interpreting results can be challenging, particularly when distinguishing within-person processes from between-person differences and when accounting for temporal dependencies.
What research questions can ESM answer?
A key driver of the increasing use of Experience Sampling Methods (ESM) is the breadth of research questions it enables. By generating intensive longitudinal data, ESM allows researchers to examine psychological processes as dynamic phenomena unfolding over time. It has been applied across diverse domains, including emotion, psychopathology, pain, social media use, diet and nutrition, and personality. Despite this diversity, the research questions it addresses can typically be grouped into three core categories.
Investigating psychological constructs over time
A first class of questions concerns how experiences fluctuate within individuals. For example, in affective science, researchers may be interested in how mood changes across the day, whether certain emotional states follow daily or weekly patterns, or how stable particular emotion regulation processes are over time. These questions focus on within-person variability and are central to understanding the dynamic nature of emotional experience. In the domain of personality, ESM research has repeatedly shown that even traits traditionally considered stable (e.g., conscientiousness) can exhibit substantial variation within individuals.
Investigating temporal interrelations between psychological constructs
A second class of questions concerns the relations between variables in daily life. ESM makes it possible to examine whether certain experiences co-occur at the same moment, such as whether stress and negative affect tend to be elevated together. In addition, it allows researchers to study how contextual features are linked to psychological functioning. For example, in craving research, momentary urges to smoke may be higher in specific contexts, such as being in a bar, or being around others who are smoking.
Relatedly, ESM lets researchers investigate temporal relationships, for instance whether stress at one moment predicts pain symptoms at a later time (and vice versa). These lagged relationships are particularly informative, as they provide insight into the temporal ordering of psychological processes and help identify potential predictive chains underlying behavior and experience (i.e., so-called micro-processes).
Relatedly, ESM lets researchers investigate temporal relationships, for instance whether stress at one moment predicts pain symptoms at a later time (and vice versa). These lagged relationships are particularly informative, as they provide insight into the temporal ordering of psychological processes and help identify potential predictive chains underlying behavior and experience (i.e., so-called micro-processes).
Investigating between-person differences in temporal variability patterns
A third class of questions focuses on individual differences in these dynamic patterns. Although ESM emphasizes within-person processes, it also reveals that individuals differ in how these processes unfold. For example, some individuals may show stronger emotional reactions to stress, while others may exhibit more stable emotional patterns. By combining within-person and between-person perspectives, ESM enables researchers to integrate a general and more personalized perspective on psychological functioning.
Such research questions are typically analyzed using multilevel models, which account for the hierarchical structure of repeated measurements nested within individuals and enable the simultaneous estimation of within-person processes and between-person variability.
Such research questions are typically analyzed using multilevel models, which account for the hierarchical structure of repeated measurements nested within individuals and enable the simultaneous estimation of within-person processes and between-person variability.
Roots and history of ESM
The Experience Sampling Method (ESM) has its roots in a broader shift within psychology toward studying behavior and experience in real-world contexts. In the 1970s, ecological psychology emphasized that psychological processes are fundamentally shaped by the environments in which they occur. This perspective challenged the dominance of laboratory-based research and highlighted the importance of studying individuals in their natural settings.
It was within this context that Mihaly Csikszentmihalyi and Reed Larson developed ESM as a research method. Their early work involved signaling participants at random moments during the day through programmable watches and asking them to report on their current experiences in a paper booklet. This approach was designed to capture the flow of everyday life, including how individuals think, feel, and act in different situations. Their results highlighted that ESM captures aspects of experience that are difficult or impossible to assess using retrospective methods, thereby establishing the value of ESM as a scientific tool.
The earliest implementations of ESM thus relied on paper-and-pencil methods. Participants carried diaries and were prompted by devices such as programmable watches or pagers to complete a questionnaire when signaled. While this approach already captured the core principles of ESM, it was limited by practical challenges, such as delayed responding (i.e., the backlogging problem) or incomplete data (e.g., lost diaries).
Over time, technological advances transformed how ESM studies are conducted. The transition from paper diaries to electronic devices, such as PDAs, improved the accuracy and timing of assessments. More recently, the widespread adoption of smartphones has made ESM more accessible and scalable. Modern ESM studies now typically use mobile ESM apps that automate prompting, ensure accurate time-stamping, and facilitate real-time data collection in different research domains.
It was within this context that Mihaly Csikszentmihalyi and Reed Larson developed ESM as a research method. Their early work involved signaling participants at random moments during the day through programmable watches and asking them to report on their current experiences in a paper booklet. This approach was designed to capture the flow of everyday life, including how individuals think, feel, and act in different situations. Their results highlighted that ESM captures aspects of experience that are difficult or impossible to assess using retrospective methods, thereby establishing the value of ESM as a scientific tool.
The earliest implementations of ESM thus relied on paper-and-pencil methods. Participants carried diaries and were prompted by devices such as programmable watches or pagers to complete a questionnaire when signaled. While this approach already captured the core principles of ESM, it was limited by practical challenges, such as delayed responding (i.e., the backlogging problem) or incomplete data (e.g., lost diaries).
Over time, technological advances transformed how ESM studies are conducted. The transition from paper diaries to electronic devices, such as PDAs, improved the accuracy and timing of assessments. More recently, the widespread adoption of smartphones has made ESM more accessible and scalable. Modern ESM studies now typically use mobile ESM apps that automate prompting, ensure accurate time-stamping, and facilitate real-time data collection in different research domains.
How to conduct an ESM study?
Conducting a study that relies on Experience Sampling Methodology (ESM) involves several key steps. First, formulate a clear research question and specify the processes you aim to capture in everyday life. Next, develop brief momentary self-report items and select an appropriate study duration, assessment frequency and sampling design (e.g., signal-contingent or event-contingent prompting). After configuring the study in a mobile data collection platform, participants are typically onboarded during an intake session and instructed on how to complete assessments trough the ESM app in real time.
A critical step is piloting the study. Pilots help determine whether the assessment frequency, survey length, and timing are manageable in participants’ everyday contexts, and whether items are interpreted as intended. They also allow you to detect technical issues early and minimize risks related to low compliance or compromised data quality.
Once data collection is underway, it is important to monitor response rates and data quality, and subsequently analyze the data using methods that account for its nested and time-dependent nature. If you are planning to run an ESM study, using a dedicated platform can facilitate study setup, data capture, and secure data management, enabling you to focus on the substantive research questions rather than technical implementation.
For a more detailed walkthrough of each stage and the associated considerations, access our step-by-step guide to running an ESM study.
A critical step is piloting the study. Pilots help determine whether the assessment frequency, survey length, and timing are manageable in participants’ everyday contexts, and whether items are interpreted as intended. They also allow you to detect technical issues early and minimize risks related to low compliance or compromised data quality.
Once data collection is underway, it is important to monitor response rates and data quality, and subsequently analyze the data using methods that account for its nested and time-dependent nature. If you are planning to run an ESM study, using a dedicated platform can facilitate study setup, data capture, and secure data management, enabling you to focus on the substantive research questions rather than technical implementation.
For a more detailed walkthrough of each stage and the associated considerations, access our step-by-step guide to running an ESM study.
Dr. Egon Dejonckheere
Egon Dejonckheere is an Assistant Professor at Tilburg University, affiliated with KU Leuven, specializing in ambulatory assessment and intensive longitudinal research. His work combines methodological innovation to improve these methods with applications in mental health and emotion dynamics, and includes dozens of smartphone-based studies in daily life.
FAQ
01.
What is the difference between ESM and EMA?
The Experience Sampling Method (ESM) and Ecological Momentary Assessment (EMA) refer to the same general approach: collecting repeated, real-time self-reports in daily life. Although the terms originate from different research traditions and were coined by different authors, they are now used interchangeably in most research contexts.
02.
Why is ESM preferred over cross-sectional surveys?
ESM captures experiences in real time or shortly after they occur, reducing reliance on memory. This minimizes recall bias, which can distort retrospective reports through reconstruction, current mood, or prior beliefs.
03.
Why is ESM preferred over laboratory studies?
ESM assesses individuals in their natural environments, allowing researchers to observe how psychological processes unfold in everyday life. This leads to higher ecological validity compared to controlled laboratory settings.
04.
What sampling strategies are used in ESM studies?
ESM typically relies on two main sampling strategies: time-based sampling, where participants are prompted at fixed or random moments, and event-based sampling, where assessments are triggered by specific experiences or behaviors. The choice depends on the research objective and involves trade-offs between precision, burden, and coverage.
05.
What are potential disadvantages of ESM?
ESM provides strong ecological validity and detailed insight into processes as they unfold over time, but these strengths come with important limitations. Key challenges include participant burden, incomplete data due to non-response, superficial or inattentive reporting, potential measurement reactivity, reduced experimental control, privacy considerations, and the need for advanced analytical approaches. As a result, ESM studies are typically more complex to design, execute, and interpret than traditional survey or laboratory-based methods.
06.
What is measurement reactivity in ESM?
Measurement reactivity in ESM refers to the possibility that repeatedly prompting participants about their experiences can influence those experiences. For example, frequent assessments of mood or behavior may heighten self-awareness and lead to shifts in how people feel or act. Although evidence typically shows minimal effects at the group level, such reactivity may still be meaningful for certain individuals.
07.
What are reconceptualization and recalibration in ESM?
Reconceptualization and recalibration represent forms of response shift in ESM. Reconceptualization occurs when participants’ understanding of a construct, such as “concentration difficulties” or “being alone,” evolves over time. Recalibration, in contrast, refers to changes in how participants use response scales, for instance becoming more conservative or more liberal in their ratings. As a result, it becomes more difficult to determine whether observed changes reflect true changes in the underlying experienceor shifts in how participants report it.
08.
What kind of research questions can ESM answer?
ESM is particularly suited to three types of questions in everyday life: how experiences fluctuate within individuals over time, how variables relate to each other over time (both concurrently and across time points), and how individuals differ in these dynamic patterns.
09.
Why are smartphones commonly used in ESM research?
As opposed to paper-and-pencil diaries, smartphones with an ESM app enable automated prompting, precise time-stamping, and real-time data collection, which improves data quality and reduces issues such as delayed responding. They also allow integration with sensors and wearable devices, expanding the scope of measurement.