TY - JOUR
T1 - Evidence of Validity Does not Rule out Systematic Bias
T2 - A Commentary on Nomological Noise and Cross-Cultural Invariance
AU - Fischer, Ronald
AU - Karl, Johannes Alfons
AU - Fontaine, Johnny R. J.
AU - Poortinga, Ype H.
PY - 2023/8
Y1 - 2023/8
N2 - We comment on the argument by Wetzel, Brunkert, Kruse and Inglehart (2021) that theoretically expected associations in nomological networks should take priority over invariance tests in cross-national research. We agree that narrow application of individual tools, such as multi-group confirmatory factor analysis with data that violates the assumptions of these techniques, can be misleading. However, findings that fit expectations of nomological networks may not be free of bias. We present supporting evidence of systematic bias affecting nomological network relationships from a) previous research on intelligence and response styles, b) two simulation studies, and c) data on the choice index from the World Value Survey (WVS). Our main point is that nomological network analysis by itself is insufficient to rule out systematic bias in data. We point out how a thoughtful exploration of sources of biases in cross-national data can contribute to stronger theory development.
AB - We comment on the argument by Wetzel, Brunkert, Kruse and Inglehart (2021) that theoretically expected associations in nomological networks should take priority over invariance tests in cross-national research. We agree that narrow application of individual tools, such as multi-group confirmatory factor analysis with data that violates the assumptions of these techniques, can be misleading. However, findings that fit expectations of nomological networks may not be free of bias. We present supporting evidence of systematic bias affecting nomological network relationships from a) previous research on intelligence and response styles, b) two simulation studies, and c) data on the choice index from the World Value Survey (WVS). Our main point is that nomological network analysis by itself is insufficient to rule out systematic bias in data. We point out how a thoughtful exploration of sources of biases in cross-national data can contribute to stronger theory development.
KW - Invariance
KW - Choice index
KW - Cross-cultural differences
KW - Multilevel models
KW - Nomological networks
KW - Simulation
KW - Systematic bias
KW - Values
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=wosstart_imp_pure20230417&SrcAuth=WosAPI&KeyUT=WOS:000779312600001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1177/00491241221091756
DO - 10.1177/00491241221091756
M3 - Article
SN - 0049-1241
VL - 52
SP - 1420
EP - 1437
JO - Sociological Methods & Research
JF - Sociological Methods & Research
IS - 3
ER -