2017
DOI: 10.1007/s40806-017-0125-5
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A Call for, and Beginner’s Guide to, Measurement Invariance Testing in Evolutionary Psychology

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Cited by 59 publications
(38 citation statements)
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“…The results indicate the equivalence of the factorial composition between the groups (Configural Model) and the intercept of the items among the participants of the different groups (Scalar Model). In both cases, the difference in the CFI between the configural and scalar models is less than 0.01 (Wang, Chen, Dai, & Richardson, 2018). The next step was to obtain more information regarding the characteristics of the items through the PCM (Partial Credit Model, Rash Analysis).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The results indicate the equivalence of the factorial composition between the groups (Configural Model) and the intercept of the items among the participants of the different groups (Scalar Model). In both cases, the difference in the CFI between the configural and scalar models is less than 0.01 (Wang, Chen, Dai, & Richardson, 2018). The next step was to obtain more information regarding the characteristics of the items through the PCM (Partial Credit Model, Rash Analysis).…”
Section: Resultsmentioning
confidence: 99%
“…Another important aspect regarding the internal structure of the SSI2-Del-Prette concerns the invariance of the assessed parameters (configural and scalar model) between the different groups, according to the sex and age of the participants, with results that guarantee the equivalence of the factorial structure and of the intercept values of the items for the participants of the different groups. According to Wang et al (2018), the parameter invariance indicates that the observable variables of an instrument are related to latent variables in an equivalent manner between groups. In this sense, such evidence should be considered as a requirement for future comparisons between these groups based on the scores obtained with the instrument, ensuring that possible differences between the groups would be linked to real differences in the constructs and not to measurement errors associated with the instrument.…”
Section: Discussionmentioning
confidence: 99%
“…Although widely neglected in sex differences research, it is important to assess MI for men and women prior to examination of sex differences (Wang, Chen, Dai, & Richardson, 2018). Drawing on original unmodified models (see Table 3), we established strong MI across sexes in Pakistan, Iran, and Turkey (see Table 4).…”
Section: Resultsmentioning
confidence: 99%
“…Pertinent to the current study, inadequate correspondence between theory and measurement models (McGrath, 2005) as well as lack of measurement invariance (Dong & Dumas, 2020;Stevanovich et al, 2017;Chen, Lau, Richardson, & Dai, 2020) were identified as pressing problems. Evolutionary social science is no outlier when it comes to problems such as these and measurement invariance testing, in particular, is relatively rare in this literature (Wang, Chen, Dai, & Richardson, 2018). Lack of attention to the validity of measures in evolutionary social science is important not only because measurement is the foundation of science, but also because the field often raises controversy and seems to receive a proportional degree of scrutiny (e.g., see Von Hippel, Buss, & Richardson, 2020).…”
Section: Discussionmentioning
confidence: 99%