Related Experiment Video
Updated: May 17, 2026
08:27
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
Should researchers use single indicators, best indicators, or multiple indicators in structural equation models?
Leslie A Hayduk1, Levente Littvay
1Department of Sociology, University of Alberta, Edmonton, Alberta T6G 2H4, Canada. LHayduk@ualberta.ca
BMC Medical Research Methodology
|October 24, 2012
Summary
Using fewer indicators in structural equation modeling (SEM) enhances theoretical development. This approach, focusing on the best indicators, allows for more sophisticated models and better control of confounding variables.
Area of Science:
- Statistics
- Psychometrics
- Social Sciences
Background:
- Structural equation modeling (SEM) integrates path analysis and factor analysis.
- This integration created a tension between factor analysis's preference for multiple indicators and path analysis's flexibility with fewer indicators.
Purpose of the Study:
- To address the tension in structural equation modeling regarding indicator selection.
- To advocate for a more parsimonious use of indicators in SEM.
Main Methods:
- Conceptual analysis of structural equation modeling principles.
- Review of theoretical implications of indicator selection in statistical modeling.
Main Results:
- Multiple indicators can restrict theoretical development in SEM.
- Fewer, carefully selected indicators facilitate more sophisticated and theoretically robust models.
Conclusions:
- Recommend using the fewest, most effective indicators (1-3) for each latent variable.
- Excessive indicators offer diminishing returns and can introduce scale-related issues.
- Employing fewer indicators allows for better control of confounders and deeper investigation of causal mechanisms.
Related Concept Videos
01:25Multiple Regression
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
02:39Indicators
Certain organic substances change color in dilute solution when the hydronium ion concentration reaches a particular value. For example, phenolphthalein is a colorless substance in any aqueous solution with a hydronium ion concentration greater than 5.0 × 10−9 M (pH < 8.3). In more basic solutions where the hydronium ion concentration is less than 5.0 × 10−9 M (pH > 8.3), it is red or pink. Substances such as phenolphthalein, which can be used to determine the pH of a solution, are called...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
02:01Factorial Design
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
02:01Group Design
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 the two are due to...
01:24Strategies of Self-Presentation III: Self-Monitoring
Self-monitoring is a central construct in understanding individual differences in self-presentation strategies across social contexts. It refers to how individuals observe, regulate, and control their expressive behavior and self-presentation following situational cues. Self-monitoring reflects a person's sensitivity to social appropriateness and willingness to adapt behavior to fit varying interpersonal demands.High vs. Low Self-Monitoring IndividualsIndividuals high in self-monitoring are...