Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

193
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...
193
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.3K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.8K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

2.0K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
2.0K
Data Validation01:15

Data Validation

211
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
211
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

174
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
174

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same authorSame journal

Should We Keep or Remove Outliers?

The Journal of nursing education·2026
Same author

Check for Outliers.

The Journal of nursing education·2026
Same author

Sub-lethal effects of natural cyanobacterial blooms on fish: Enzymatic activity and swimming performance in Gasterosteus aculeatus.

Harmful algae·2025
Same author

Questionable Research Practices: HARKing.

The Journal of nursing education·2025
Same author

Questionable Research Practices: Cherry Picking.

The Journal of nursing education·2025
Same author

Questionable Research Practices: <i>p</i>-Hacking.

The Journal of nursing education·2025
Same journal

Truth.

The Journal of nursing education·2026
Same journal

AI Utilization and Clinical Judgment: Predictors of Caring Behavior Among Nursing Students.

The Journal of nursing education·2026
Same journal

Mental Health Nursing Simulation to Develop the Therapeutic Use of Self.

The Journal of nursing education·2026
Same journal

Cultivating Clinical Judgment Through Wound Building: A Teaching Innovation.

The Journal of nursing education·2026
Same journal

Pathophysiology as a Predictor of Success in a Prelicensure Undergraduate Nursing Program.

The Journal of nursing education·2026
See all related articles

Related Experiment Video

Updated: Aug 22, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K

Prevent Problematic Missing Data.

John M Taylor

    The Journal of Nursing Education
    |November 7, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study focuses on preventing missing data in nursing education research. It explores pre-data collection strategies to ensure scientific validity and reliable findings.

    More Related Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.6K
    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
    10:26

    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

    Published on: September 11, 2021

    4.0K

    Related Experiment Videos

    Last Updated: Aug 22, 2025

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
    06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    14.6K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.6K
    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
    10:26

    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

    Published on: September 11, 2021

    4.0K

    Area of Science:

    • Nursing Education Research
    • Data Science in Healthcare

    Background:

    • Missing data significantly compromises the validity of nursing education research.
    • Proactive strategies are crucial for maintaining data integrity.

    Purpose of the Study:

    • To explore methods for preventing problematic missing data before collection.
    • To enhance the reliability and validity of nursing science.

    Main Methods:

    • Review of pre-data collection strategies.
    • Discussion of preventative approaches to data missingness.

    Main Results:

    • Identified several effective methods for preventing missing data.
    • Highlighted the importance of proactive measures in data collection.

    Conclusions:

    • Implementing pre-collection strategies is key to mitigating missing data issues.
    • Adopting these methods strengthens the scientific rigor of nursing education studies.