What must be sample size?

How Much Should Be Sample Size Of Research Study?

Even though purpose of suitable sample size is a serious matter in SEM, unluckily, there is no agreement in the literature concerning what would be the fitting sample size for SEM. Some indication exists that simple SEM models could be importantly tested even if sample size is quite small (Hoyle, 1999; Hoyle and Kenny, 1999; Marsh and Hau, 1999), but usually, N = 100–150 is considered the smallest sample size for showing SEM (Tinsley and Tinsley, 1987; Anderson and Gerbing, 1988; Ding, Velicer, and Harlow, 1995; Tabachnick and Fidell, 2001). Some researchers consider an even larger sample size for SEM, for example, N = 200 (Hoogland and Boomsma 1998; Boomsma and Hoogland, 2001; Kline, 2005). 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).

Sample size is often considered in light of the number of observed variables. For normally distributed data, Bentler and Chou (1987) suggest a ratio as low as 5 cases per variable would be sufficient when latent variables have multiple indicators. A widely accepted rule of thumb is 10 cases/observations per indicator variable in setting a lower bound of an adequate sample size (Nunnally, 1967).

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).

When you want to measure a model and referring it to a society, you must to have a large sample size, but in some books noted that the min sample size is about 200, and 15 data for a variable. You can also check Tabanchik and Fidel`s (2013) point of view, Using Multivariate Statistics.

According to Kline (2011) a typical sample size in studies where SEM is used is about 200 cases.

Sample size is considered to be critical in achieving sufficient statistical power [McQuitty, 2004]. Schreiber et al. (2006) points out that normality of the data and estimation methods jointly require a minimum sample size. Nunnally (1967) and Schreiber et al. (2006) suggest a general rule of ten observations for every free parameter,(Muhammad Ilyas).