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 Experiment Videos

Analysis with missing data in drug prevention research

J W Graham1, S M Hofer, A M Piccinin

  • 1College of Health and Human Development, Pennsylvania State University, University Park 16802-6504, USA.

NIDA Research Monograph
|January 1, 1994
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Protocol for the Brain Health Support Program Study of the Canadian Therapeutic Platform Trial for Multidomain Interventions to Prevent Dementia (CAN-THUMBS UP): A Prospective 12-Month Intervention Study.

The journal of prevention of Alzheimer's disease·2023
Same author

The American Medical Association, Denver Meeting, June 7-10, 1898.

Texas medical journal (Austin, Tex.)·2023
Same author

American Medical Association-The Preliminary Announcement of the Committee of Arrangements: Denver Meeting, June 7-10, 1898.

Texas medical journal (Austin, Tex.)·2023
Same author

Decline in Memory, Visuospatial Ability, and Crystalized Cognitive Abilities in Older Adults: Normative Aging or Terminal Decline?

Journal of aging research·2017
Same author

Maximizing the Usefulness of Data Obtained with Planned Missing Value Patterns: An Application of Maximum Likelihood Procedures.

Multivariate behavioral research·2016
Same author

Controlling Correlational Bias via Confirmatory Factor Analysis of MTMM Data.

Multivariate behavioral research·2016
Same journal

EVALUATION OF DRUG ABUSE TREATMENT MEDICATIONS: CONCORDANCE BETWEEN CLINICAL AND PRECLINICAL STUDIES.

NIDA research monograph·2006
Same journal

Problems of drug dependence 1999: Proceedings of the 61st annual scientific meeting, the College on Problems of Drug Dependence, Inc.

NIDA research monograph·2001
Same journal

Problems of drug dependence 1997: proceedings of the 59th annual scientific meeting.

NIDA research monograph·1998
Same journal

Laboratory Behavioral Studies of Vulnerability to Drug Abuse. Proceedings of a meeting. August 2-3, 1994.

NIDA research monograph·1998
Same journal

Disaggregating the liability for drug abuse.

NIDA research monograph·1998
Same journal

Integrating genetic and behavioral models in the study of substance abuse mechanisms.

NIDA research monograph·1998
See all related articles

This study introduces statistical solutions for missing data in prevention research. Applying methods like the Expectation-Maximization (EM) algorithm can yield unbiased results from available data.

Area of Science:

  • Statistics
  • Prevention Research
  • Data Analysis

Background:

  • Missing data is a persistent challenge in prevention research.
  • Statistical solutions exist but are underutilized in practice.
  • This chapter bridges the gap between statistical theory and applied prevention research.

Purpose of the Study:

  • To introduce systematic application of modern missing data analysis techniques.
  • To provide practical guidance for handling missing data in prevention studies.
  • To address specific issues like respondent burden, attrition, and costly measurement.

Main Methods:

  • Focuses on missing data analysis for continuous, normally distributed data.
  • Applicable to analyses using covariance matrices, particularly within the general linear model.

Related Experiment Videos

  • Illustrates methods with examples from drug prevention research.

    • Main Results:

    • Recommends Expectation-Maximization (EM) algorithm or other maximum likelihood procedures (e.g., multiple imputation).
    • Highlights that appropriate methods maximize use of available data without generating new information.
    • Emphasizes that alternative analyses may produce biased results.

    Conclusions:

    • Researchers should prioritize EM algorithm or multiple imputation for missing data.
    • Alternative analyses should be used cautiously due to potential bias.
    • Understanding and modeling the cause of missingness is crucial.
    • Adjusting estimates for originally missing sampled cases can improve accuracy.