How Can We Study Change Within People?

From repeated observations to individual processes and intervention comparisons.

Level: Intermediate
Reading load: 8 core readings in 6 units
Prerequisites: Familiarity with constructs, measurement validity and the distinction between association and causation. Basic understanding of regression, effect estimates and uncertainty. No experience fitting longitudinal models is assumed.
Last reviewed: September 2026

Introduction

A difference between lives need not explain a change within a life. People who usually sleep less may report more stress; whether one person feels more stressed after an unusually short night is another question. Repeated observations bring individual processes within reach, provided the design, measures, and analysis respect the particular comparison we wish to make.

This path follows eight readings from intensive observation through developmental change and models of observations gathered over time to individual intervention studies. Conceptual papers supply distinctions; repeated reports collected during people's everyday lives across adulthood and four treatment cases make the inferential choices concrete. The central task is to say what was compared, over which time scale, and with which remaining alternatives.

The later methods papers use equations and model diagrams. Translate each component into an observation or comparison: a usual level, a deviation, a trajectory, or an association across occasions. The goal is to judge what a model estimates, rather than treating its name as a guarantee of explanation.

Unit 1 of 6

Whose process are we studying?

An average, a pattern across people and a process within one person can answer different questions. Begin with ways to describe temporal dynamics and variation between individuals without assuming either level automatically explains the other.

Reading
1 of 8
No Time Like the Present: Discovering the Hidden Dynamics in Intensive Longitudinal Data
Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26(1), 10–15. https://doi.org/10.1177/0963721416666518
Foundational argument · Methodological contribution · Moderate · Full work: 6 pages
Track the distinction between an individual time series and a model that allows dynamics to vary across people.
Why you’re reading it. Develop a vocabulary for persistence, change and heterogeneity before interpreting a temporal model.
A question to consider. For sleep and stress, write one between-person question and one within-person question. What observations would each require?

Unit 2 of 6

What do our observations capture?

A repeated-measure design samples moments as well as people. Timing, questions, missed responses and participant burden influence which experiences become visible and which remain absent.

Reading
2 of 8
Experience Sampling Methodology in Mental Health Research: New Insights and Technical Developments
Myin‐Germeys, I., Kasanova, Z., Vaessen, T., Vachon, H., Kirtley, O., Viechtbauer, W., & Reininghaus, U. (2018). Experience sampling methodology in mental health research: New insights and technical developments. World Psychiatry, 17(2), 123–132. https://doi.org/10.1002/wps.20513
Methodological contribution · Foundational argument · Moderate · Full work: 10 pages
Connect the proposed research question to the sampling schedule, momentary items and handling of missed observations.
Why you’re reading it. Treat experience sampling as a set of measurement and design choices.
A question to consider. A participant ignores prompts during stressful meetings. How could that pattern distort an estimated stress–context association, and what could the study record to assess it?

Unit 3 of 6

How do short-term fluctuations relate to development?

A week’s fluctuations and change over a decade occur on different time scales. Measurement bursts connect them, but the connection depends on what is summarized and who remains under observation.

Reading
3 of 8
Time-Structured and Net Intraindividual Variability
Ram, N., & Gerstorf, D. (2009). Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes. Psychology and Aging, 24(4), 778–791. https://doi.org/10.1037/a0017915
Foundational argument · Methodological contribution · Challenging · Full work: 14 pages
Compare summaries that discard temporal order with models that retain it. Then consider what repeated bursts add.
Why you’re reading it. Separate an amount of variability from a process and from a change in that process.
A question to consider. Two people have the same mean and spread of emotion ratings. What patterns could still distinguish their weeks, and how would you measure whether those patterns change over years?
Reading
4 of 8
Emotional Experience Improves With Age: Evidence Based on Over 10 Years of Experience Sampling
Carstensen, L. L., Turan, B., Scheibe, S., Ram, N., Ersner-Hershfield, H., Samanez-Larkin, G. R., Brooks, K. P., & Nesselroade, J. R. (2011). Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychology and Aging, 26(1), 21–33. https://doi.org/10.1037/a0021285
Landmark empirical study · Moderate · Full work: 13 pages
Apply the measurement-burst framework to daily emotion reports collected at successive waves. Track refreshment samples, attrition and the difference between modelled age patterns and every individual’s path.
Why you’re reading it. Examine a study that links intensive observations to long-term development.
A question to consider. Which conclusion requires the repeated waves rather than a single week of reports, and how could selective retention change it?

Unit 4 of 6

What does a within-person association mean?

Repeated data contain several sources of variation. A model must distinguish the reference level and trend from deviations around them before a coefficient can answer the intended question.

Reading
5 of 8
The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change
Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619. https://doi.org/10.1146/annurev.psych.093008.100356
Methodological contribution · Foundational argument · Challenging · Full work: 37 pages
Use the simulated examples to distinguish person means, temporal deviations and trends. Follow the correction notice when interpreting notation.
Why you’re reading it. Make the comparison represented by a longitudinal coefficient explicit.
A question to consider. Students are observed across different school years while workload rises. What would change if stress were compared with each student’s overall mean versus their expected level at that time?

Unit 5 of 6

Does one change precede another?

A lagged association depends on which differences the model separates and on the interval between observations. Neither repeated measurement nor a directional arrow supplies all the assumptions needed for a causal claim.

Reading
6 of 8
A Critique of the Cross-Lagged Panel Model
Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
Methodological contribution · Critical reappraisal · Challenging · Full work: 15 pages
Compare the traditional and random-intercept models in the parent–adolescent illustration, keeping their assumptions visible.
Why you’re reading it. Evaluate what a temporal association describes before interpreting it as a mechanism.
A question to consider. If a lagged association disappears after separating stable differences, what conclusion is warranted, and why would “there can be no causal effect” go beyond the result?

Unit 6 of 6

Can we evaluate change for an individual?

An intervention comparison requires more than observations before and after treatment. First examine ways to build experimental control, then assess a study whose fixed baseline and weekly measurements offer a more limited contrast.

Reading
7 of 8
Randomized Single-Case Experimental Designs in Healthcare Research: What, Why, and How?
Tanious, R., & Onghena, P. (2019). Randomized single-case experimental designs in healthcare research: What, why, and how? Healthcare, 7(4), Article 143. https://doi.org/10.3390/healthcare7040143
Methodological contribution · Challenging · Full work: 19 pages
Identify the planned assignment mechanism, permitted phase changes and ethical constraints behind each design. Keep hypothetical randomization separate from what the original studies did.
Why you’re reading it. Connect a single-case analysis to the comparison the design actually supplies.
A question to consider. For an intervention with lasting effects, how could staggered onset strengthen inference without requiring withdrawal, and which timing decisions would need to be made in advance?
Reading
8 of 8
How Does Change Unfold?
Caneiro, J. P., Smith, A., Linton, S. J., Moseley, G. L., & O'Sullivan, P. (2019). How does change unfold? An evaluation of the process of change in four people with chronic low back pain and high pain-related fear managed with Cognitive Functional Therapy: A replicated single-case experimental design study. Behaviour Research and Therapy, 117, 28–39. https://doi.org/10.1016/j.brat.2019.02.007
Methodological contribution · Challenging · Full work: 12 pages
Compare the four trajectories with the design principles just introduced. The treatment began after a fixed baseline; distinguish this from randomized onset.
Why you’re reading it. Assess observed individual improvement and proposed mechanisms as separate claims.
A question to consider. What one design change would most strengthen the intervention comparison, and what one measurement change would help investigate the order of fear and disability changes?

Scope and omissions

This is an intermediate introduction to the reasoning behind repeated-observation research. It covers levels of inference, sampling, variability, measurement bursts, longitudinal models and single-case comparisons. It is not a software course or exhaustive treatment of growth models, dynamic structural equation models, continuous-time analysis or causal time-series identification.

Several papers draw examples from mental health and treatment. Their inclusion concerns design and interpretation; it does not establish a current standard of care. Questions of generalizing to other populations and settings, qualitative accounts of experience, and combining studies receive separate Methods paths.

Synthesis

Repeated observations open several views of change: a usual level, a deviation from it, a longer trajectory, and relations across occasions. These views must be distinguished before they can be connected. Sampling, measurement, and modelling determine which comparison is available; an association over time still needs further assumptions to support a causal claim. Evidence about an individual also leaves the question of its reach to other people open.

Imagine a study of sleep and stress among students: short daily reports for two weeks, repeated each term, followed by an intervention introduced at different times. This is an invented design exercise, not a report of an existing study.

Write a one-page argument that states the within-person question, the between-person question and the time scale each concerns. Explain how you would distinguish a term-long trend from day-to-day deviations, what a lagged association would estimate, and which assumptions would still stand between that association and a causal claim.

Then specify a feasible intervention comparison. Describe what could be randomized, how carryover or lasting effects would matter, and what pattern would challenge your explanation. Include a plan for missed reports, measurement burden and privacy. End by separating what you could learn about an observed individual from what would require evidence about other people or settings.

For claims that extend beyond the observed people, materials and settings, continue with When Do Psychological Findings Generalize?.