Description Details Value Author(s) References Examples
Package for assessing the unidimensionality of a set of items using external validity information. It can be applied on linear or graded factor analytic models.
unival
is based on the procedure proposed by Ferrando & Lorenzo-Seva (2019). The authors proposed two group of procedures: A group of differential validity procedures to assess the extent to which the primary factor scores relate differentially to the external variables; and a group of incremental validity procedures to assess the extent to which the primary factor scores yield predictive validity increments with respect to the single general factor scores. Both groups of procedures are based on a second-order modelling schema for the general factor.
The factor scores have to be obtained externally, we suggest using FACTOR program (Lorenzo-Seva & Ferrando, 2013) or using the functions mirt
, fscores
and summary-method
included on the mirt
package (Chalmers, 2012).
More information can be found on the documentation page of the function unival
.
|
Assess essential unidimensionality using external validity information. |
Pere Joan Ferrando
David Navarro-Gonzalez
Urbano Lorenzo-Seva
Chalmers, R. P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi: 10.18637/jss.v048.i06
Lorenzo-Seva, U., & Ferrando, P. J. (2013). Factor 9.2: A comprehensive program for fitting exploratory and semiconfirmatory factor analysis and IRT models. Applied Psychological Measurement, 37(6), 497-498. doi: 10.1177/0146621613487794
Ferrando, P.J. & Lorenzo-Seva, U. (2019). An External Validity Approach for Assessing Essential Unidimensionality in Correlated-Factor Models. Educational and Psychological Measurement. doi: 10.1177/0013164418824755
1 2 3 4 5 6 7 8 9 10 11 | ## perform unidimensionality analysis using an example dataset. The dataset is composed by the
## criterion and the factor scores, already computed using FACTOR. The correlation between factors
## was also obtained using this program. An alternative could be using the functions included in
## \code{mirt} package (Chalmers, 2012).
y=SAS3f[,1]
FP=as.matrix(SAS3f[,2:4])
fg=SAS3f[,5]
PHI=cbind(c(1,0.408,0.504),c(0.408,1,0.436),c(0.504,0.436,1))
unival(y = y, FP = FP, fg = fg, PHI = PHI)
|
Unival: Assessing essential unidimensionality using external validity information
Differential validity assessment:
0.5719 (0.4428 - 0.7014)
0.2101 (0.0412 - 0.3894)
0.3339 (0.2293 - 0.4587)
Maximum difference
0.2380 (0.0989 - 0.3574) *
Incremental validity assessment:
0.2963 (0.2258 - 0.3640)
0.4005 (0.3332 - 0.4596)
Incremental value estimate
0.1042 (0.0396 - 0.1565) **
* Some factors are more strongly or weakly related to the criterion that can be predicted from their relations to the general factor
** There is a significant increase in accuracy between the prediction based on the primary factor score estimates and that based on the general factor score estimates.
Warning message:
In unival(y = y, FP = FP, fg = fg, PHI = PHI) :
The type of factor scores was not provided, ML scores were assumed.
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