What sample size would be most suitable for a structural equation modeling study to achieve reliable and generalizable results?
It depends on many factors like the comlexity of the models. It may be decided by the rule of thumb which
is to have at least 10 participants per parameter estimated. Some suggest a range of 5-10 participants per parameter, but more recent recommendations often lean towards 10-20 participants per parameter to ensure stability and reliability of estimates.
Also, from previous studies the minimum number for simple models 200-300 participants, moderate models 300-500, complex models which have more latent variables 400-500 or more.
Madam it depends on the case and requirements , if the bias is high then large sample if small then small sample will be sufficient but questionnaire should be efficient
Heba Ramadan, Can you provide more information on what constitutes a 'simple model' versus a 'moderate model' versus a 'complex model' in terms of participant requirements?
Abhishek Pandey I understand your point that the sample size depends on the case and requirements. I'd like to explore this further. Can you provide more examples of when you would consider a large sample size necessary?
@Hachemi Inas In structural equation modeling (SEM), the complexity of a model is described based on the number of variables, paths, and latent constructs involved.
Simple Model: number of Variables: Few observed variables (usually fewer than 5). Latent Constructs: none or very few latent variables. Paths: Few paths (direct effects) between variables, often one or two.
Moderate Model: number of Variables: Moderate number of observed variables (between 5 and 10). Latent Constructs: Includes a few latent variables. Paths: More paths, possibly including direct and indirect effects.
Complex Model: Number of Variables: Large number of observed variables (more than 10). Latent Constructs: Several latent variables. Paths: Many paths, including direct, indirect, and possibly higher-order interactions.
Practical Application
When describing the complexity of a model, consider not only the number of variables and paths but also the theoretical framework and the research questions addressed. Simpler models are often easier to interpret and validate, while complex models can provide richer insights but require more rigorous testing and larger datasets.
Heba Ramadan Thank you so much for sharing this insightful summary of the SEM models! This perspective will undoubtedly help me better understand and interpret the results of my own SEM analyses.
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