Contents
How does CFA calculate sample size?
Simulation studies show that with normally distributed indicator variables and no missing data, a reasonable sample size for a simple CFA model is about N = 150 (Muthén and Muthén, 2002). For multi-group modeling, the rule of thumb is 100 cases/observations per group (Kline, 2005).
What is the minimum sample size for CFA?
Most researcher would recommend using sample sizes of at least 200/ 5 or 10 cases per parameters (see for an overview Kline, 2011, pp: 11-12). They found sample size requirements ranging from 30 (Simple CFA with four indicators and loadings around . 80) up to 450 cases (mediation models).
How many participants do you need for structural equation modelling?
Specifically, Kline (2015) recommended that the N:q ratio should be 20 to 1, or 20 observations (participants) for each estimated parameter in the model.
How does multilevel confirmatory factor analysis ( MCFA ) work?
Multilevel Confirmatory Factor Analysis (MCFA) extends the power of Confirmatory Factor Analysis (CFA) to accommodate the complex survey data with the estimation of the level-specific variance components and the respective measurement models.
Why is sample size important in factor analysis?
They are prerequisites for a priori sample size determination. Scale development in general and Factor Analysis (EFA, CFA) and SEM are large sample size methods because sample affects precision and replicability of the results. However, the existing literature provides limited and sometimes conflicting guidance on this issue.
Which is true about sample size and sample power?
Adequate statistical power contributes to observing true relationships in a dataset. With a thoughtful power analysis, the adequate but not excessive sample could be detected. Therefore, this paper reviews the issue of what sample size and sample power the researcher should have in the EFA, CFA, and SEM study.
Are there any problems with a large sample size?
SEM is also a large sample approach (Kline, 2016). It is generally accepted that problems may arise due to a small sample size. Some of them include―but they are not limited―to estimation convergence failure, improper solutions (e.g., Heywood cases), inaccurate parameter estimates and model fit statistics (Wang & Wang, 2012).