On This Page:ToggleHow Experience Sampling WorksTime-Based SamplingEvent-Based SamplingHybrid Sampling DesignsEMA ProtocolsPotential PitfallsManaging Missing DataEcological Validity and ReactivityEthical Considerations
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Ecological momentary assessment (EMA) is a research approach that gathers repeated, real-time data on participants’ experiences and behaviors in their natural environments.
This method, also known as experience sampling method (ESM), ambulatory assessment, or real-time data capture, aims to minimize recall bias and capture the dynamic fluctuations in thoughts, feelings, and actions as they unfold in daily life.
EMA typically involves prompting individuals to answer brief surveys or record specific events throughout the day using electronic devices or paper diaries.
This real-time data collection minimizes recall bias and offers a more accurate representation of an individual’s experience.
The repeated assessments collected in experience sampling studies allow researchers to study microprocesses that unfold over time, such as the relationship between stress and mood or the factors that trigger smoking relapse.
This makes EMA a valuable tool for researchers who want to study how people behave and feel in their natural environments.
Here are some key features of ecological momentary assessment:
How Experience Sampling Works
Traditionally, EMA studies relied on preprogrammed digital wristwatches and paper assessment forms. Wristwatches could be pre-programmed to emit beeps at random or fixed intervals throughout the day, signaling participants to record their experiences.
Currently, smartphones are the dominant tool for both signaling and data collection in ESM studies.
Not all participants have equal access to or comfort with technology. Researchers need to consider the accessibility of mobile interfaces for participants with visual or hearing impairments, varying levels of technological literacy, and preferences for different input methods.
Consider the specific characteristics and needs of the target population when selecting devices and designing survey interfaces.
EMA studies utilize specific sampling designs to determine when and how often participants are prompted to provide data.
Two primary sampling designs are commonly employed:
Participants receive prompts throughout the day: These prompts, often referred to as “beeps,” signal participants to answer a short questionnaire on their device.
The survey questions are carefully designed to capture information relevant to the research question. They often use validated scales to measure various psychological constructs, such as mood, stress, social connectedness, or symptoms.
Researchers should consider how long it takes to complete surveys, the frequency of assessments, and the overall burden on participants’ time and attention. Adjustments to the protocol (e.g., reducing survey length or frequency) might be necessary based on pilot participant feedback.
Researchers should assess whether survey items are clear, relevant, and appropriate for the context of participants’ daily lives.
The format of the questions can be open-ended, close-ended, or use scales, depending on the study’s aims. The questionnaires typically include questions about:
Participants’ responses to these surveys are then aggregated and analyzed to identify patterns in their experiences over time.
In addition to self-reported questionnaires, some EMA studies utilize sensors embedded in smartphones or wearable devices to collect passive data about the participant’s environment and behavior.
This could include data from GPS sensors, accelerometers, microphones, and other sensors that capture information about location, movement, social interactions, and physiological responses.
This sensor data can help researchers gain a richer understanding of the context surrounding participants’ experiences and potentially identify objective correlates of self-reported experiences.
The richness of EMA data requires careful planning and specific analytic approaches to leverage its full potential.
EMA studies, particularly those using mobile devices, can generate large, complex datasets that require appropriate data management and analysis techniques.
Researchers need to plan for data cleaning, handling of missing data, and using statistical methods, such as multilevel modeling (also known as hierarchical linear modeling or mixed-effects modeling), to account for the hierarchical structure of EMA data.
Multilevel models explicitly account for this nested structure, allowing researchers to partition variance at both the within-person (Level 1) and between-person (Level 2) levels.
This enables accurate estimation of effects and avoids the misleading results that can occur when using traditional statistical methods that assume independence.
Various statistical software packages are available for multilevel modeling, including HLM, Mplus, R, and Stata.
Time-Based Sampling
Time-based sampling in Ecological Momentary Assessment (EMA) or the Experience Sampling Method (ESM) involves collecting data from participants at specific times throughout the day, as opposed to event-based sampling, which collects data when a particular event occurs.
The goal is to obtain a representative sample of a participant’s experiences over time.
There are three main types of time-based sampling schedules:
1.Fixed-interval schedules
Participants are prompted to report on their experiences at predetermined times. This could involve receiving a signal to complete a survey every hour, twice a day (e.g., morning and evening), or once a day.
Fixed-interval schedules allow researchers to study experiences that unfold predictably over time.
For instance, a study on mood changes throughout the workday might use a fixed-interval schedule to capture variations in mood at specific points during work hours.
2.Random-interval schedules
Participants are prompted to report their experiences at random intervals or based on a more complex time-based pattern.
For example, a study investigating the relationship between stress and eating habits might use a variable-interval schedule to prompt participants to report their stress levels and food intake at unpredictable times throughout the day, capturing a broader range of daily experiences.
3.Time-stratified sampling
This strategy offers a more structured approach to random sampling. It involves dividing the total sampling time frame into smaller, predefined time blocks or strata, and then randomly selecting assessment times within each time block.
This method ensures a more even distribution of assessments across different times of the day while still maintaining some unpredictability.
Here’s how time-stratified sampling works:
For example, a researcher studying the association between stress and alcohol cravings among college students might use a time-stratified sampling approach with the following parameters:
Considerations for Time-Based Sampling:
Event-Based Sampling
Event-based sampling, also known as event-contingent sampling, requires participants to complete an assessment each time a predefined event occurs.
This event could be an external event (e.g., a social interaction) or an internal event (e.g., a sudden surge of anxiety).
For example, instructing participants to record details about every cigarette they smoke, including time, location, mood, and social context.
Event-based protocols offer a valuable tool for researchers interested in gaining a deeper understanding of how specific events are experienced and the factors that influence them.
Research Questions
Event-based sampling designs are particularly well-suited for studying specific events or behaviors in people’s daily lives.
Hybrid Sampling Designs
Hybrid sampling in EMA research combines elements of different sampling designs, such as event-based sampling, fixed-interval sampling, and random-interval sampling, to leverage the strengths of each approach and address a wider range of research questions within a single study.
This approach is particularly valuable when researchers want to capture both the general flow of daily experiences and specific events that might be infrequent or easily missed with purely time-based sampling.
Here are some common ways researchers combine sampling designs in hybrid EMA studies:
Adding a daily diary component to an experience sampling study
Researchers often enhance experience sampling studies with a daily diary component, typically administered in the evening.
While the experience sampling portion provides insights into momentary experiences at random intervals, the daily diary can assess global aspects of the day, such as overall mood, sleep quality, significant events, or reflections on the day’s experiences.
For instance, a study could use experience sampling to assess momentary stress and coping strategies throughout the day and then use a daily diary to measure participants’ overall perceived stress for that day and their use of specific coping strategies across the entire day.
This combination allows researchers to understand how momentary experiences relate to more global daily perceptions. Some studies incorporate both morning and evening diaries to capture experiences surrounding sleep and the transition into and out of the study’s focus time frame.
Incorporating event-based surveys into time-based designs
One limitation of purely random-interval sampling is that it might not adequately capture specific events of interest, especially if they are infrequent or unpredictable.
To address this, researchers can augment time-based protocols with event-based surveys, prompting participants to complete additional assessments whenever a predefined event occurs.
For example, a study on social anxiety could use random-interval sampling to assess participants’ general mood and anxiety levels throughout the day and then trigger an event-based survey immediately after each social interaction exceeding a certain duration, allowing for a more detailed examination of anxiety experiences in social contexts.
This hybrid approach provides a more comprehensive understanding of both the general experience of anxiety and the specific factors that influence it in real-life situations.
Combining time-based designs at different time scales
Researchers can utilize different time-based sampling designs to examine phenomena across different time scales.
For example, a study investigating the long-term effects of a stress-reduction intervention could incorporate weekly assessments using fixed-interval sampling to track changes in overall stress levels.
Additionally, random-interval sampling with end-of-day diaries could be employed to capture daily fluctuations in stress and coping.
Finally, a more intensive experience sampling protocol could be implemented for a shorter period before and after the intervention to assess changes in momentary stress responses.
This multi-level approach allows researchers to gain a comprehensive understanding of how the intervention affects experiences across different time frames, from daily fluctuations to weekly trends.
EMA Protocols
A protocol outlines the procedures for collecting data using the ecological momentary assessment.
It acts as a blueprint, guiding researchers in gathering real-time, in-the-moment experiences from participants in their natural environments.
These protocols differ primarily in how and when they prompt participants to record their experiences.
The optimal choice depends on aligning the protocol with the research question, participant burden considerations, technological capabilities, and the intended data analysis approach.
Example of an EMA Protocol
A study investigating the relationship between daily stress and alcohol cravings might involve the following EMA protocol:
By analyzing the collected data, researchers could examine how stress levels fluctuate throughout the evening, whether being alone or with others influences craving intensity, and if certain locations are associated with higher cravings.
Considerations when choosing a protocol
Potential Pitfalls
By anticipating and addressing these potential pitfalls, EMA researchers can enhance the rigor, validity, and ethical soundness of their studies, contributing to a richer understanding of human experiences and behavior in everyday life.
Managing Missing Data
Missing data is an inherent challenge in experience sampling research. By understanding the nature and mechanisms of missingness, researchers can make informed decisions about study design, data cleaning, and statistical analysis.
Unlike cross-sectional studies, where missing data might involve a few skipped items or participant dropouts, daily life studies often grapple with substantial missingness across various dimensions.
Employing appropriate strategies to minimize, manage, and model missing data is crucial for enhancing the validity and reliability of EMA findings.
There are several strategies for handling missing data in EMA research, each with implications for data analysis and interpretation:
Implications for Data Analysis and Interpretation:
The Trade-off Between Ecological Validity and Reactivity
By understanding the factors that influence reactivity and strategically designing studies to mitigate it, researchers can harness the power of EMA to illuminate the nuances of human behavior and experience in the real world.
Ecological Validity: Capturing Life as It Happens
Reactivity: The Observer Effect
Navigating the Trade-off
Reactivity is not inevitable in EMA studies. Several factors can influence its likelihood:
Researchers can employ strategies to minimize reactivity:
Ethical Considerations
Using intensive, repeated assessments in daily life research, while valuable for understanding human behavior in context, raises important ethical considerations.
Mitigating Participant Burden:
Participant burden refers to the effort and demands placed on participants due to the repeated nature of data collection, potentially impacting compliance and data quality.
Several strategies can be used to minimize the potential burden associated with frequent assessments:
Ensuring Informed Consent:
The need for robust informed consent procedures that go beyond traditional approaches to address the unique ethical challenges of intensive, repeated assessments:
Reading List
Hektner, J. M. (2007).Experience sampling method: Measuring the quality of everyday life. Sage Publications.
Rintala, A., Wampers, M., Myin-Germeys, I., & Viechtbauer, W. (2019). Response compliance and predictors thereof in studies using the experience sampling method.Psychological Assessment, 31(2), 226–235.https://doi.org/10.1037/pas0000662
Trull, T. J., & Ebner-Priemer, U. (2013).Ambulatory assessment.Annual review of clinical psychology,9(1), 151-176.
Van Berkel, N., Ferreira, D., & Kostakos, V. (2017).The experience sampling method on mobile devices.ACM Computing Surveys (CSUR),50(6), 1-40.
Examples of ESM Studies
Bylsma, L. M., Taylor-Clift, A., & Rottenberg, J. (2011). Emotional reactivity to daily events in major and minor depression.Journal of Abnormal Psychology, 120(1), 155–167.https://doi.org/10.1037/a0021662
Geschwind, N., Peeters, F., Drukker, M., van Os, J., & Wichers, M. (2011). Mindfulness training increases momentary positive emotions and reward experience in adults vulnerable to depression: A randomized controlled trial.Journal of Consulting and Clinical Psychology, 79(5), 618–628.https://doi.org/10.1037/a0024595
Hoorelbeke, K., Koster, E. H. W., Demeyer, I., Loeys, T., & Vanderhasselt, M.-A. (2016). Effects of cognitive control training on the dynamics of (mal)adaptive emotion regulation in daily life.Emotion, 16(7), 945–956.https://doi.org/10.1037/emo0000169
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008).Ecological momentary assessment.Annu. Rev. Clin. Psychol.,4(1), 1-32.
Kim, S., Park, Y., & Headrick, L. (2018). Daily micro-breaks and job performance: General work engagement as a cross-level moderator.Journal of Applied Psychology, 103(7), 772–786.https://doi.org/10.1037/apl0000308
Shoham, A., Goldstein, P., Oren, R., Spivak, D., & Bernstein, A. (2017). Decentering in the process of cultivating mindfulness: An experience-sampling study in time and context.Journal of Consulting and Clinical Psychology, 85(2), 123–134.https://doi.org/10.1037/ccp0000154
Steger, M. F., & Frazier, P. (2005). Meaning in Life: One Link in the Chain From Religiousness to Well-Being.Journal of Counseling Psychology, 52(4), 574–582.https://doi.org/10.1037/0022-0167.52.4.574
Sun, J., Harris, K., & Vazire, S. (2020). Is well-being associated with the quantity and quality of social interactions?Journal of Personality and Social Psychology, 119(6), 1478–1496.https://doi.org/10.1037/pspp0000272
Sun, J., Schwartz, H. A., Son, Y., Kern, M. L., & Vazire, S. (2020). The language of well-being: Tracking fluctuations in emotion experience through everyday speech.Journal of Personality and Social Psychology, 118(2), 364–387.https://doi.org/10.1037/pspp0000244
Thewissen, V., Bentall, R. P., Lecomte, T., van Os, J., & Myin-Germeys, I. (2008). Fluctuations in self-esteem and paranoia in the context of daily life.Journal of Abnormal Psychology, 117(1), 143–153.https://doi.org/10.1037/0021-843X.117.1.143
Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Gotlib, I. H. (2012). The everyday emotional experience of adults with major depressive disorder: Examining emotional instability, inertia, and reactivity.Journal of Abnormal Psychology, 121(4), 819–829.https://doi.org/10.1037/a0027978
Van der Gucht, K., Dejonckheere, E., Erbas, Y., Takano, K., Vandemoortele, M., Maex, E., Raes, F., & Kuppens, P. (2019). An experience sampling study examining the potential impact of a mindfulness-based intervention on emotion differentiation.Emotion, 19(1), 123–131.https://doi.org/10.1037/emo0000406
Olivia Guy-Evans, MSc
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
Saul McLeod, PhD
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.
