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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Introduction to Statistics01:17

Introduction to Statistics

The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

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

Sort by
Same author

Missing Data Sensitivity Analyses for Alcohol Research.

Alcohol, clinical & experimental research·2026
Same author

A Self-Guided App-Based Mindfulness Intervention for Racially and Ethnically Minoritized Individuals Who Experience Discrimination-Related Mental Health Symptoms: Randomized Controlled Trial.

Journal of medical Internet research·2026
Same author

Factored structural equation modeling in blimp.

Psychological methods·2026
Same author

To Disaggregate or Not to Disaggregate: A Focus on Covariates in Multilevel Models.

Multivariate behavioral research·2026
Same author

Variations in alcohol craving and negative mood during a clinical trial of ibudilast for alcohol use disorder.

Alcohol, clinical & experimental research·2025
Same author

College students' socioeconomic background and sleep during the first year of college.

Sleep health·2025
Same journal

The overlooked role of internalizing symptoms in adolescent executive function: Insights from self- and teacher ratings.

Journal of school psychology·2026
Same journal

How do schools support students after a behavioral threat assessment?

Journal of school psychology·2026
Same journal

The roles of parent and teacher support in students' academic development: Do associations change over the course of adolescence or is support always important for all students?

Journal of school psychology·2026
Same journal

Pragmatic measures for school-based research and practice: Best practices and future directions.

Journal of school psychology·2026
Same journal

Pragmatic measurement of mechanisms: Does use of coping strategies mediate the effects of a teacher stress intervention?

Journal of school psychology·2026
Same journal

Beyond the individual: Latent growth patterns of schoolwide bullying perpetration, traditional victimization, and cyber victimization in relation to social and emotional learning.

Journal of school psychology·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2026

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
08:12

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Published on: February 16, 2024

An introduction to modern missing data analyses.

Amanda N Baraldi1, Craig K Enders

  • 1Arizona State University, USA. Amanda.Baraldi@asu.edu

Journal of School Psychology
|December 17, 2009
PubMed
Summary
This summary is machine-generated.

Modern missing data analysis methods like maximum likelihood and multiple imputation offer advantages over traditional techniques. This research explains these methods and demonstrates their application, including planned missing data strategies.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: Jun 17, 2026

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
08:12

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Published on: February 16, 2024

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Statistics
  • Methodology
  • Data Analysis

Background:

  • Traditional missing data techniques (deletion, mean imputation) have limitations and require strict assumptions.
  • Recent research highlights advanced methods: maximum likelihood and multiple imputation.
  • These modern approaches offer greater flexibility and robustness in handling missing data.

Purpose of the Study:

  • To explain the theoretical foundations of missing data analysis.
  • To provide accessible descriptions of maximum likelihood and multiple imputation.
  • To illustrate the application of these methods with real-world data and discuss planned missing data designs.

Main Methods:

  • Overview of traditional missing data techniques.
  • Detailed explanation of maximum likelihood estimation.
  • Description of multiple imputation techniques.
  • Application examples using the Longitudinal Study of American Youth data.

Main Results:

  • Maximum likelihood and multiple imputation are presented as superior alternatives to traditional methods.
  • The utility of auxiliary variables in maximum likelihood estimation is demonstrated.
  • Examples illustrate practical implementation of advanced missing data techniques.

Conclusions:

  • Maximum likelihood and multiple imputation provide powerful tools for analyzing data with missing values.
  • Researchers can enhance study designs by incorporating planned missing data.
  • Adoption of these advanced methods can lead to more accurate and reliable research findings.