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

Longitudinal Research02:20

Longitudinal Research

13.6K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.6K
Cross-Sectional Research01:50

Cross-Sectional Research

12.9K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
12.9K

You might also read

Related Articles

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

Sort by
Same author

Panel Conditioning Biases in the Current Population Survey's Food Security Supplement.

Public opinion quarterly·2025
Same author

Invisible disabilities and health among U.S. postsecondary students.

Journal of American college health : J of ACH·2025
Same author

Invisible disabilities and college academic success: New evidence from a mediation analysis.

Social science research·2024
Same author

If Residential Segregation Persists, What Explains Widespread Increases in Residential Diversity?

Demography·2023
Same author

Does participating in a long-term cohort study impact research subjects' longevity? Experimental evidence from the Wisconsin Longitudinal Study.

SSM - population health·2022
Same author

COVID-19 and changes in college student educational expectations and health by disability status.

SSM - population health·2022
Same journal

Elementary school discipline lowers students' sense of belonging.

Social science research·2026
Same journal

Virtual charter students have worse labor market outcomes as young adults.

Social science research·2026
Same journal

What are we modeling? An evaluation of depressive symptom trajectory models from adolescence to early midlife in the Add Health cohort.

Social science research·2026
Same journal

Flexible work arrangements, gender ideology, and housework time among dual-earner couples.

Social science research·2026
Same journal

Mobility patterns predict increasing polarization between neighborhoods.

Social science research·2026
Same journal

State-level Gender Inequality and Couples' Relative Earnings Following Parenthood over Four Decades.

Social science research·2026
See all related articles

Related Experiment Video

Updated: Mar 21, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

Measuring students' school context exposures: A trajectory-based approach.

Andrew Halpern-Manners1

  • 1Department of Sociology, Indiana University, Bloomington, IN, USA.

Social Science Research
|May 20, 2016
PubMed
Summary
This summary is machine-generated.

Longitudinal studies reveal that children

Keywords:
AchievementLongitudinal latent class analysisSchool effects

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Related Experiment Videos

Last Updated: Mar 21, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Area of Science:

  • Educational Psychology
  • Developmental Psychology
  • Sociology of Education

Background:

  • Traditional studies of school effects often rely on single time-point measures, potentially overlooking crucial developmental and contextual dynamics.
  • Existing research may not adequately capture age-related variations in children's responsiveness to their educational environments.
  • The impact of changing school contexts and varying lengths of exposure on student outcomes remains underexplored.

Purpose of the Study:

  • To propose and validate a longitudinal model for assessing school effects on academic performance.
  • To address limitations of single time-point measures in understanding the complex relationship between school environments and student achievement.
  • To investigate how distinct trajectories of school context influence long-term academic outcomes.

Main Methods:

  • Development and application of a longitudinal model incorporating finite mixture modeling techniques.
  • Identification of distinct trajectories of school contexts experienced by students throughout their academic careers.
  • Statistical analysis to determine the relationship between identified school context trajectories and 8th-grade academic achievement.

Main Results:

  • Distinct school context trajectories were identified, varying significantly in shape and indicating changes in students' environments over time.
  • Students' trajectories of exposure to different school contexts were found to be significantly related to their 8th-grade academic performance.
  • These longitudinal effects remained significant even when controlling for static, point-in-time measures of school context.

Conclusions:

  • A longitudinal perspective is crucial for accurately understanding school effects on academic achievement.
  • Student academic performance is influenced not only by the static nature of schools but also by the dynamic trajectories of their school experiences.
  • Future research should adopt longitudinal methodologies to capture the nuanced impact of evolving school environments on child development.