Related Experiment Video
Updated: Dec 11, 2025
10:26
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
4.3K
A comparison of meta-methods for synthesizing indirect effects.
Camiel H J van Zundert1, Milica Miočević1
1Department of Psychology, McGill University, Montreal, Canada.
Research Synthesis Methods
|August 25, 2020
Summary
Synthesizing indirect effects is crucial for understanding mechanisms. Correlation-based MASEM demonstrated the best performance in a simulation study, showing lower bias and superior interval estimates compared to other methods.
Area of Science:
- Psychometrics
- Statistical Modeling
Background:
- Understanding the indirect (mediated) effects of variables is essential for elucidating causal mechanisms.
- Synthesizing findings on indirect effects across multiple studies is a complex statistical challenge.
Purpose of the Study:
- To compare the performance of six different methods for synthesizing indirect effects.
- To provide empirical examples and code for applying these synthesis methods.
- To offer guidelines for best practices in synthesizing indirect effects.
Main Methods:
- A simulation study was conducted to evaluate correlation-based meta-analysis of structural equation models (MASEM), parameter-based MASEM, marginal likelihood synthesis, adjusted marginal likelihood synthesis, univariate methods, and two-parameter sequential Bayesian methods.
- Methods were compared based on bias, precision, root mean square error (RMSE) of point estimates, and power, coverage, and type I error rates of interval estimates.
- Simulation factors included the synthesis method, indirect effect strength, independent variable measurement level, and number of studies.
Main Results:
- Correlation-based MASEM exhibited the lowest bias and the best statistical properties for interval estimates.
- Marginal likelihood synthesis showed high power but poor coverage and type I error rates.
- Adjusted marginal likelihood synthesis and two-parameter sequential Bayesian methods performed adequately, with adjusted marginal likelihood synthesis offering higher power.
- Overall, correlation-based MASEM was the top-performing method.
Conclusions:
- Correlation-based MASEM is recommended as the optimal method for synthesizing indirect effects due to its superior performance.
- Guidelines for optimal practices, including the number of studies and reporting standards, are provided.
- Suggestions for future methodological research in indirect effect synthesis are outlined.
Related Concept Videos
01:09Directing Effect of Substituents: meta-Directing Groups
5.6K
Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
5.6K
01:27Inductive Effects on Chemical Shift: Overview
1.9K
The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
1.9K
01:25Adrenergic Agonists: Indirect-Acting Agents
2.4K
Indirect-acting adrenergic agonists potentiate the effects of endogenous catecholamines through different mechanisms without directly binding to adrenoceptors.
One mechanism involves depleting stored catecholamines by displacing them from synaptic vesicles. These agents, known as "displacers," are transported into vesicles at the expense of noradrenaline. Examples include amphetamine and tyramine, which lack a catechol moiety, resulting in prolonged action, improved oral...
One mechanism involves depleting stored catecholamines by displacing them from synaptic vesicles. These agents, known as "displacers," are transported into vesicles at the expense of noradrenaline. Examples include amphetamine and tyramine, which lack a catechol moiety, resulting in prolonged action, improved oral...
2.4K
01:21Friedman Two-way Analysis of Variance by Ranks
414
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
414
01:15Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
95
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
95
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
373
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
373