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Cost-effective experience sampling method studies: Integrating budget constraints into sample size decisions.

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  • 1Research Group of Quantitative Psychology and Individual Differences, KU Leuven.

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|October 23, 2025
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Summary
This summary is machine-generated.

Planning experience sampling method (ESM) studies requires balancing sample size (N) and measurement occasions (T) with budget constraints. This research introduces a cost-function approach for optimal sample size decisions in ESM research.

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Area of Science:

  • Psychology
  • Psychiatry
  • Research Methodology

Background:

  • The experience sampling method (ESM) is vital for studying daily psychopathological processes.
  • Determining optimal sample size (N participants, T measurement occasions) is critical but complex.
  • Existing sample size planning methods often neglect budget and feasibility constraints.

Purpose of the Study:

  • To extend traditional power analysis by integrating budget constraints into ESM sample size planning.
  • To demonstrate formalizing budget considerations into cost functions for ESM studies.
  • To provide a method for optimally selecting N and T values within budget limitations.

Main Methods:

  • Developed a framework integrating budget constraints into power analysis for ESM studies.
  • Formalized budget considerations into study-specific cost functions.
  • Applied the framework to optimally determine participant (N) and measurement occasion (T) numbers.

Main Results:

  • Optimal sample size decisions (N and T) are significantly influenced by study-specific cost functions.
  • Illustrative examples show substantial differences in optimal N and T across various ESM designs.
  • The proposed method allows for budget-conscious, realistic sample size planning in ESM research.

Conclusions:

  • Integrating budget constraints into sample size planning is essential for realistic ESM study design.
  • The formalized cost-function approach enables optimal selection of N and T, balancing power and cost.
  • This methodology supports more efficient and feasible ESM research across diverse psychopathological investigations.