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

Data Collection by Survey01:07

Data Collection by Survey

9.1K
The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
9.1K
Surveys02:16

Surveys

17.0K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
17.0K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

991
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:
991
Introduction to Surveying, Plane Surveying and Geodetic Surveys01:27

Introduction to Surveying, Plane Surveying and Geodetic Surveys

1.1K
Surveying is the art and science of mapping the earth's surface. It involves measuring distances, angles in horizontal or vertical directions, and levels to understand the shape and size of land features. Surveying techniques are essential for various tasks, such as identifying the levels of a land area with reference to a specific point, and mapping undulations and water bodies.There are two main types of surveying: plane surveys and geodetic surveys. Plane surveys assume the earth is flat,...
1.1K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K
Maximum Deflection01:13

Maximum Deflection

1.1K
When analyzing beams under unsymmetrical loads, such as a train moving on a bridge, it is crucial to accurately determine the points of maximum stress and deflection. The process involves identifying the maximum deflection of the beam, which may not always occur at its midpoint due to the uneven distribution of the load.
The maximum deflection occurs at a specific point, known as point O, where the tangent to the deflection curve is horizontal. To find point O, the slope of the tangent at any...
1.1K

You might also read

Related Articles

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

Sort by
Same author

The Use of Generative Artificial Intelligence (AI) in Academic Research: A Review of the Consensus App.

Cureus·2025
Same author

Brain computed tomography reading of stroke patients by resident doctors from different medical specialities: An eye-tracking study.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2023
Same author

The Development of a Literacy-Based Research Integrity Assessment Framework for Graduate Students in Taiwan.

Science and engineering ethics·2022
Same author

Investigating the performance of level-specific fit indices in multilevel confirmatory factor analysis with dichotomous indicators: A Monte Carlo study.

Behavior research methods·2022
Same author

Oral presentation assessment and image reading behaviour on brain computed tomography reading in novice clinical learners: an eye-tracking study.

BMC medical education·2022
Same author

Comparisons of Science Motivational Beliefs of Adolescents in Taiwan, Australia, and the United States: Assessing the Measurement Invariance Across Countries and Genders.

Frontiers in psychology·2021

Related Experiment Video

Updated: Feb 12, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.8K

Using iMCFA to Perform the CFA, Multilevel CFA, and Maximum Model for Analyzing Complex Survey Data.

Jiun-Yu Wu1, Yuan-Hsuan Lee2, John J H Lin3

  • 1Institute of Education & Center for Teacher Education, National Chiao Tung University, Hsinchu, Taiwan.

Frontiers in Psychology
|March 30, 2018
PubMed
Summary
This summary is machine-generated.

iMCFA is a new tool for analyzing complex survey data, helping researchers build multilevel factorial models. It simplifies examining between- and within-level structures for Confirmatory Factor Analysis (CFA) and Multilevel CFA (MCFA).

Keywords:
LisrelMpluscomplex survey dataconfirmatory factor analysismaximum modelmultilevel structural equation modeling

More Related Videos

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes
09:10

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes

Published on: March 1, 2022

3.0K
Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop
07:43

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Published on: July 2, 2018

10.1K

Related Experiment Videos

Last Updated: Feb 12, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.8K
A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes
09:10

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes

Published on: March 1, 2022

3.0K
Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop
07:43

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Published on: July 2, 2018

10.1K

Area of Science:

  • Statistics
  • Survey Methodology
  • Psychometrics

Background:

  • Analyzing complex survey data requires specialized methods for multilevel structures.
  • Traditional multilevel modeling can be challenging due to model misspecification.
  • Investigating potential multilevel structures necessitates robust analytical techniques.

Purpose of the Study:

  • To introduce iMCFA (integrated Multilevel Confirmatory Analysis) for examining multilevel factorial structures in complex survey data.
  • To provide a user-friendly interface for constructing Confirmatory Factor Analysis (CFA), Multilevel CFA (MCFA), and maximum MCFA models.
  • To facilitate the analysis of between- and within-level structures in complex survey data.

Main Methods:

  • iMCFA allows visual specification of between- and within-level factorial structures.
  • The procedure generates variance-covariance matrices, intraclass correlations, and model outputs.
  • LISREL syntax for various models is provided for future research.

Main Results:

  • iMCFA effectively analyzes complex survey data with multilevel structures.
  • The tool was validated using empirical and simulated datasets with varying complexities.
  • Comparison with Mplus demonstrated the effectiveness of iMCFA's estimation methods.

Conclusions:

  • iMCFA offers a practical and effective approach for multilevel factorial analysis of complex survey data.
  • The tool enhances the ability of researchers to model and interpret multilevel structures.
  • iMCFA supports model comparison and provides valuable LISREL syntax for advanced analysis.