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

Stratified Sampling Method01:16

Stratified Sampling Method

14.0K
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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
14.0K
Group Design02:01

Group Design

9.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
9.9K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

392
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
392
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.3K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.3K
Sampling Plans01:23

Sampling Plans

618
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...
618
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

180
Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
180

You might also read

Related Articles

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

Sort by
Same author

Guessing During Testing is a Person Attribute Not an Instrument Parameter.

Educational and psychological measurement·2025
Same author

From Development to Validation: Exploring the Efficiency of Numetrive, a Computerized Adaptive Assessment of Numerical Reasoning.

Behavioral sciences (Basel, Switzerland)·2025
Same author

Development of a Forced-Choice Personality Inventory via Thurstonian Item Response Theory (TIRT).

Behavioral sciences (Basel, Switzerland)·2025
Same author

Coaching inexperienced clinicians before a high stakes medical procedure: randomized clinical trial.

BMJ (Clinical research ed.)·2024
Same author

Types and Predictors of Service use Among Young Children Recommended to Receive Intensive Services After Initial Autism Spectrum Disorder Diagnosis.

Journal of autism and developmental disorders·2024
Same author

Calibrating Items Using an Unfolding Model of Item Response Theory: The Case of the Trait Personality Questionnaire 5 (TPQue5).

Evaluation review·2023

Related Experiment Video

Updated: Nov 13, 2025

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

6.5K

Identifying Student Subgroups as a Function of School Level Attributes: A Multilevel Latent Class Analysis.

Georgios D Sideridis1,2, Ioannis Tsaousis3, Khaleel Al-Harbi4

  • 1Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.

Frontiers in Psychology
|March 15, 2021
PubMed
Summary

This study identified four student achievement profiles in Saudi Arabia, influenced by parental education and student absences. School environment significantly impacts these profiles, highlighting key factors for academic success.

Keywords:
cross sectional designmeasurement invariancemultilevel latent class analysismultilevel mixture modelingnational data

More Related Videos

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.9K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.4K

Related Experiment Videos

Last Updated: Nov 13, 2025

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

6.5K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.9K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.4K

Area of Science:

  • Educational Psychology
  • Sociology of Education
  • Quantitative Research Methods

Background:

  • Student achievement is influenced by a complex interplay of individual, familial, and institutional factors.
  • Understanding these influences is crucial for developing targeted educational interventions.
  • Previous research has often examined these factors in isolation, necessitating a holistic approach.

Purpose of the Study:

  • To profile high school students' academic achievement based on demographic characteristics, parental attributes, and school behaviors.
  • To investigate the structure of student achievement profiles over time using latent profile analysis.
  • To examine the influence of school-level factors on student achievement profiles.

Main Methods:

  • Multilevel latent profile analysis was employed to account for the nested structure of students within schools.
  • Data from three random samples of 2,000 students (2016-2018) from 50 high schools in Saudi Arabia were analyzed.
  • Model fit was assessed using Bayesian Information Criterion (BIC), Bayes factor, and other information criteria.

Main Results:

  • A four-profile solution emerged, indicating distinct student achievement patterns.
  • Parental education and student absences were key differentiators between profiles, with higher parental education and fewer absences predicting higher achievement.
  • Significant variability in achievement profiles was observed across different schools, suggesting a school-level effect.

Conclusions:

  • Student achievement is not monolithic but can be categorized into distinct profiles influenced by family background and individual behavior.
  • Interventions should consider both parental education levels and student attendance as critical factors.
  • The school environment plays a significant role in shaping student achievement trajectories, necessitating school-level considerations in educational policy.