omegasCFA {Bayesrel} | R Documentation |

When supplying a data set that is multidimensional the function estimates the reliability of the set by means of omega_total and the general factor saturation of the set by means of omega_hierarchical The procedure entails fitting a hierarchical factor model using a CFA. The second-order (hierarchical, higher-order), the bi-factor, and the correlated factor model can be used in the CFA. The CFA is performed using lavaan 'Yves Rosseel', <https://CRAN.R-project.org/package=lavaan>. Coefficients omega_t and omega_h (only for second-order and bi-factor model) can be computed from the factor model parameters.

```
omegasCFA(
data,
n.factors = NULL,
model = NULL,
model.type = "second-order",
interval = 0.95,
missing = "fiml",
fit.measures = FALSE
)
```

`data` |
A matrix or data.frame containing multivariate observations, rows = observations, columns = variables/items |

`n.factors` |
A number specifying the number of group factors that the items load on |

`model` |
A string that by default NULL (=balanced) distributes the items evenly among the number of group factors. This only works if the items are a multiple of the number of group factors and the items are already grouped in the data set, meaning, e.g., items 1-5 load on one factor, 6-10 on another, and so on. A model file can be specified in lavaan syntax style (f1=~.+.+.) to relate the items to the group factors. The items' names need to equal the column names in the data set, aka the variable names |

`model.type` |
A string denoting if the model that should be fit is the second-order or bi-factor model or the correlated factor model. This comes down to the researcher's theory about the measurement and the model fit. |

`interval` |
A number specifying the confidence interval, which is Wald-type |

`missing` |
A string denoting the missing data handling, can be "fiml" (full information ML) or "listwise". Specifying "pairwise" will defulat to "fiml" |

`fit.measures` |
A logical denoting if fit.measures from the CFA should be computed, the output then contains the chisq statistic, chisq df, chisq p-value, cfi, tli, rmsea, rmsea 90% ci lower, rmsea 90% ci upper, rmsea<.05 p-value, aic, bic, unbiased srmr, unbiased srmr 90% ci lower, unbiased srmr 90% ci upper, unbiased srmr<.05 p-value |

The point estimates and the Wald-type confidence intervals for omega_t and omega_h (for the second-order and bi-factor model)

```
res <- omegasCFA(upps, n.factors = 5, model = NULL, model.type = "bi-factor",
missing = "listwise")
# or with specified model syntax relating the group factors to the items:
model <- "f1 =~ U17_r + U22_r + U29_r + U34_r
f2 =~ U4 + U14 + U19 + U27
f3 =~ U6 + U16 + U28 + U48
f4 =~ U23_r + U31_r + U36_r + U46_r
f5 =~ U10_r + U20_r + U35_r + U52_r"
res <- omegasCFA(upps, n.factors = 5, model = model, model.type = "second-order",
missing = "listwise")
```

[Package *Bayesrel* version 0.7.7 Index]