An Examination of Parametric and Nonparametric Dimensionality Assessment Methods with Exploratory and Confirmatory Mode


  •  Hakan Kogar    

Abstract

The aim of the present research study was to compare the findings from the nonparametric MSA, DIMTEST and DETECT and the parametric dimensionality determining methods in various simulation conditions by utilizing exploratory and confirmatory methods. For this purpose, various simulation conditions were established based on number of dimensions, number of items, item discrimination levels, sample size and correlation between dimensions values. The performance of dimensionality determining methods based on MSA and factor analysis are similar, yet MSA is more effective in determining the number of dimensions. However, the method of DETECT has displayed a more powerful performance when compared with the other dimensionality methods. Particularly the confirmatory DETECT method could reveal the true dimensionality in conditions of both low discrimination and high discrimination methods. On the other hand, the exploratory DETECT method was affected by discrimination and, thus, could perform well only with high-discrimination items. In conditions where the exploratory dimensionality reduction methods are used to determine the number of dimensions, it is beneficial to confirm this structure by using confirmatory dimensionality reduction methods. For this purpose, using confirmatory DETECT is particularly recommended.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1927-5250
  • Issn(Onlne): 1927-5269
  • Started: 2012
  • Frequency: bimonthly

Journal Metrics

Google-based Impact Factor (2017): 2.87

h-index (February 2018): 13

i10-index (February 2018): 29

h5-index (February 2018): 11

h5-median (February 2018): 20

Learn more

Contact