The Effects of Nonindependent Rater Sets in Multilevel–Multitrait–Multimethod Models
Research output: Journal contributions › Journal articles › Research › peer-review
Standard
In: Structural Equation Modeling, Vol. 22, No. 3, 03.07.2015, p. 439-448.
Research output: Journal contributions › Journal articles › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - The Effects of Nonindependent Rater Sets in Multilevel–Multitrait–Multimethod Models
AU - Schultze, Martin
AU - Koch, Tobias
AU - Eid, Michael
PY - 2015/7/3
Y1 - 2015/7/3
N2 - Multiple ratings are becoming increasingly popular for the assessment of a wide range of behaviors. Teaching evaluation designs and 360-degree feedback often use multiple raters alongside self-ratings. Eid and colleagues (2008) proposed a multilevel structural equation model for the analysis of data stemming from such designs that assumes that raters stem from independent populations of raters. However, it is quite common for raters to rate multiple targets, thus implying a cross-classified data structure. A simulation study was conducted to assess the effects of this rater nonindependence on parameter and standard error estimates in multilevel structural equation models. Results show parameter estimation biases to be small, whereas standard errors pertaining to the Level 1 covariance matrices as well as the mean structure on Level 2 are underestimated.
AB - Multiple ratings are becoming increasingly popular for the assessment of a wide range of behaviors. Teaching evaluation designs and 360-degree feedback often use multiple raters alongside self-ratings. Eid and colleagues (2008) proposed a multilevel structural equation model for the analysis of data stemming from such designs that assumes that raters stem from independent populations of raters. However, it is quite common for raters to rate multiple targets, thus implying a cross-classified data structure. A simulation study was conducted to assess the effects of this rater nonindependence on parameter and standard error estimates in multilevel structural equation models. Results show parameter estimation biases to be small, whereas standard errors pertaining to the Level 1 covariance matrices as well as the mean structure on Level 2 are underestimated.
KW - cross-classified
KW - MTMM
KW - multilevel
KW - simulation study
KW - structural equation modeling
KW - Economics, empirical/statistics
UR - http://www.scopus.com/inward/record.url?scp=84930572250&partnerID=8YFLogxK
U2 - 10.1080/10705511.2014.937675
DO - 10.1080/10705511.2014.937675
M3 - Journal articles
VL - 22
SP - 439
EP - 448
JO - Structural Equation Modeling
JF - Structural Equation Modeling
SN - 1070-5511
IS - 3
ER -