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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Related Experiment Video

Updated: Aug 2, 2025

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
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Adaptive data collection for intraindividual studies affected by adherence.

Greta Monacelli1,2, Lili Zhang1,2, Winfried Schlee3

  • 1School of Computing, Dublin City University, Dublin, Ireland.

Biometrical Journal. Biometrische Zeitschrift
|April 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic algorithm for ecological momentary assessments (EMAs) to optimize data collection. The method balances participant burden and data quality, outperforming current approaches in a tinnitus study.

Keywords:
control chartsdesign optimizationecological momentary assessmentsintraindividual studies

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Area of Science:

  • Digital Health
  • Psychometrics
  • Medical Informatics

Background:

  • Mobile technologies facilitate ecological momentary assessments (EMAs) for intraindividual variability studies.
  • Collecting self-reported data in EMAs presents challenges in balancing data quality with participant burden.
  • Existing methods often use static thresholds, which may not adapt to individual variability or adherence.

Purpose of the Study:

  • To develop a dynamic EMA triggering mechanism for data collection.
  • To optimize the balance between data quality and participant burden in EMA studies.
  • To address the specific challenge of demanding tasks at high/low self-reported variable values.

Main Methods:

  • A dynamic approach inspired by control chart methods and design optimization was employed.
  • The EMA triggering mechanism considers individual variability in self-reported variables and adherence.
  • The algorithm was tested using simulations and real-world data from a tinnitus longitudinal study.

Main Results:

  • The proposed dynamic algorithm demonstrated a higher F1 score and utility compared to random and static rule-based schedules.
  • Statistical analysis (Wilcoxon signed rank test) confirmed the algorithm's superiority.
  • The method effectively manages data collection impacted by participant adherence.

Conclusions:

  • The developed dynamic EMA triggering algorithm is effective in balancing data quality and participant burden.
  • This approach offers an improved strategy for EMA data collection, particularly in studies with adherence considerations.
  • The findings suggest a more efficient and participant-friendly method for collecting intensive longitudinal data.