Normality assumption in latent interaction models.

Latent moderated structural equation (LMS) is one of the most common techniques for estimating interaction effects involving latent variables (i.e., XWITH command in Mplus). However, empirical applications of LMS often overlook that this estimation technique assumes normally distributed variables and that violations of this assumption may lead to seriously biased parameter estimates. Against this backdrop, we study the robustness of LMS to different shapes and sources of nonnormality and examine whether various statistical tests can help researchers detect such distributional misspecifications. In four simulations, we show that LMS can be severely biased when the latent predictors or the structural disturbances are nonnormal. On the contrary, LMS is unaffected by nonnormality originating from measurement errors. As a result, testing for the multivariate normality of observed indicators of the latent predictors can lead to erroneous conclusions, flagging distributional misspecifications in perfectly unbiased LMS results and failing to reject seriously biased results. To solve this issue, we introduce a novel Hausman-type specification test to assess the distributional assumptions of LMS and demonstrate its performance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023).

In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that "[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR]." In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.

Sustainable antibullying program implementation: School profiles and predictors.

We examined the sustainability of the KiVa antibullying program in Finland from its nationwide roll-out in 2009 to 2016. Using latent class analyses, we identified four different patterns of implementation. The persistent schools (43%) maintained a high likelihood of participation throughout the study period. The awakened (14%) had a decreasing trend during the first years, but then increased the likelihood of program participation. The tail-offs (20%) decreased in the likelihood of participating after the third year, and the drop-offs (23%) already after the first year. The findings suggest that many schools need support during the initial years to launch and maintain the implementation of evidence-based programs; yet a large proportion of schools manage to sustain the program implementation for several years. The logistic regression analyses showed that large schools persisted more likely than small schools. Lower initial level of victimization was also related to the sustainability of the program. Finally, persistent program participation was predicted by several school-level actions during the initial years of implementing the program. These results imply that the sustainability of evidence-based programs could be enhanced by supporting and guiding schools when setting up the program during the initial implementation.

A cautionary note on the finite sample behavior of maximal reliability.

Several calls have been made for replacing coefficient α with more contemporary model-based reliability coefficients in psychological research. Under the assumption of unidimensional measurement scales and independent measurement errors, two leading alternatives are composite reliability and maximal reliability. Of these two, the maximal reliability statistic, or equivalently Hancock's H, has received a significant amount of attention in recent years. The difference between composite reliability and maximal reliability is that the former is a reliability index for a scale mean (or unweighted sum), whereas the latter estimates the reliability of a scale score where indicators are weighted differently based on their estimated reliabilities. The formula for the maximal reliability weights has been derived using population quantities; however, their finite-sample behavior has not been extensively examined. Particularly, there are two types of bias when the maximal reliability statistic is calculated from sample data: (a) the sample maximal reliability estimator is a positively biased estimator of population maximal reliability, and (b) the true reliability of composites formed with maximal reliability weights calculated from sample data is on average less than the population reliability. Both effects are more pronounced in small-sample scenarios (e.g., <100). We also demonstrate that the composite reliability estimator for equally weighted composite exhibits substantially less bias, which makes it a more appropriate choice for the small-sample case. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Being bullied by same- versus other-sex peers: does it matter for adolescent victims?

The negative consequences of peer victimization on psychosocial adjustment are well documented. The consequences, however, may depend on who the bullies are. In this study, we examined the consequences of same- versus other-sex victimization. The sample consisted of 4,941 Finnish adolescents (ages 14-15; 47.7% boys). We used structural equation modeling to examine both concurrent and longitudinal associations of same- and other-sex victimization with depression, negative perception of peers, and social self-esteem. Both same- and other-sex victimization were related to psychosocial adjustment. Concurrently, the victimization experiences with same-sex peers in particular were associated with generalized cognitions about peers, whereas being bullied by other-sex peers was related to adolescents' social self-esteem more strongly than victimization by same-sex peers. The longitudinal associations, in turn, showed that only being bullied by boys had carry-over effects on girls' adjustment. Other-sex victimization can have serious consequences especially on girls' psychosocial adjustment.