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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Convenience Sampling Method00:55

Convenience Sampling Method

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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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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. Among the various sampling methods used by...
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Systematic Sampling Method01:17

Systematic Sampling Method

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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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Cluster Sampling Method01:20

Cluster Sampling Method

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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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Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Two-Method Measurement Planned Missing Data With Purposefully Selected Samples.

Menglin Xu1, Jessica A R Logan2

  • 1The Ohio State University, Columbus, OH, USA.

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Summary
This summary is machine-generated.

Planned missing data designs can now incorporate purposeful missingness based on student performance, not just missing completely at random (MCAR) mechanisms. This method maintains statistical power while focusing assessments on target samples.

Keywords:
missing at randomplanned missingtreatment effects

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Area of Science:

  • Educational research methodology
  • Statistical modeling
  • Psychometrics

Background:

  • Planned missing data designs are increasingly used in education research.
  • Traditional methods rely on missing completely at random (MCAR) data.
  • Existing designs may not fully leverage targeted assessment strategies.

Purpose of the Study:

  • To evaluate the feasibility of planned missingness designs based on student performance.
  • To introduce and test a purposeful missingness method.
  • To compare its performance against the MCAR mechanism.

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • The purposeful missingness method was implemented within a two-method measurement design.
  • Parameter recovery was assessed across various conditions.

Main Results:

  • The purposeful missingness method demonstrated comparable accuracy to the MCAR method in recovering parameter estimates.
  • Performance was consistent across multiple simulated conditions.
  • This approach allows for focused assessment efforts on a target sample.

Conclusions:

  • Purposeful missingness is a viable alternative to MCAR in planned missing data designs.
  • This method can maintain statistical power while optimizing assessment resources.
  • It offers a flexible approach for applied education research.