Sample size planning for structural equation modeling from the power analysis perspective

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Description

Calculate the necessary sample size for an SEM study, so as to have enough power to reject the null hypothesis that (a) the model has perfect fit, or (b) the difference in fit between two nested models equal some specified amount.

Usage

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ss.power.sem(F.ML = NULL, df = NULL, RMSEA.null = NULL, RMSEA.true = NULL, 
F.full = NULL, F.res = NULL, RMSEA.full = NULL, RMSEA.res = NULL, 
df.full = NULL, df.res = NULL, alpha = 0.05, power = 0.8)

Arguments

F.ML

The true maximum likelihood fit function value in the population for the model of interest. Leave this argument NULL if you are doing nested model significance tests.

df

The degrees of freedom of the model of interest. Leave this argument NULL if you are doing nested model significance tests.

RMSEA.null

The model's population RMSEA under the null hypothesis. Leave this argument NULL if you are doing nested model significance tests.

RMSEA.true

The model's population RMSEA under the alternative hypothesis. This should be the model's true population RMSEA value. Leave this argument NULL if you are doing nested model significance tests.

F.full

The maximum likelihood fit function value for the full model.

F.res

The maximum likelihood fit function value for the restricted model.

RMSEA.full

The population RMSEA value for the full model.

RMSEA.res

The population RMSEA value for the restricted model.

df.full

The degrees of freedom for the full model.

df.res

The degrees of freedom for the restricted model.

alpha

The Type I error rate.

power

The desired power.

Author(s)

Keke Lai (University of California - Merced)

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