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Related Concept Videos

Sampling Methods: Overview01:06

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Convenience Sampling Method00:55

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Cluster Sampling Method01:20

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Systematic Sampling Method01:17

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Review and Preview01:13

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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VisualEyes: A Modular Software System for Oculomotor Experimentation
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ESMvis: a tool for visualizing individual Experience Sampling Method (ESM) data.

Laura F Bringmann1, Date C van der Veen2, Marieke Wichers3

  • 1Department of Psychometrics and Statistics, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, The Netherlands. l.f.bringmann@rug.nl.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|November 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces ESMvis, a dynamic visualization tool for experience sampling method (ESM) data. ESMvis offers insightful, person-specific feedback by visualizing raw data, aiding in understanding mental health fluctuations.

Keywords:
Clinical practiceExperience sampling methodIntensive longitudinal dataPersonalized feedbackVisualization

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Area of Science:

  • Psychological assessment
  • Data visualization
  • Digital health

Background:

  • Experience Sampling Method (ESM) collects intensive longitudinal data on affect and context during daily life.
  • Current ESM feedback reports often lack dynamic insights, relying on summary statistics.
  • Personalized feedback is crucial for understanding mental health fluctuations.

Purpose of the Study:

  • To develop a tool for dynamically visualizing ESM data for improved person-specific feedback.
  • To address the limitations of static, summary-based feedback in current ESM applications.

Main Methods:

  • Introduced ESMvis, a novel framework for visualizing raw ESM data.
  • Utilized R software for data visualization.
  • Applied ESMvis to longitudinal ESM data from a patient with obsessive-compulsive disorder and comorbid depression.

Main Results:

  • Developed a movie-format visualization capturing overall data trajectory and specific time moments.
  • Successfully identified two patient relapses through visual analysis of the dynamic data.
  • Therapist and patient found ESMvis to be an insightful addition to standard care.

Conclusions:

  • ESMvis effectively provides personalized feedback through dynamic visualization of ESM time-series data.
  • The tool is freely available, adjustable, and widely applicable in clinical practice and research.
  • ESMvis can serve as an exploratory tool, generating hypotheses and informing advanced statistical analyses.