The mediational role of motivation in the model of motor development in childhood: A longitudinal study

The aim of this study was twofold: first, to examine the stability of the hypothesized conceptual model of motor development (without and with including various types of motivation) when children are followed up one-year later, and second to examine longitudinally whether changes in one model variable predict changes in other variables, according to the hypothesized pathways in the model. A sample of 361 Spanish students (50.7% girls, 8–11 years old) voluntarily participated in this study. In relation to the first aim, structural equation modeling revealed the expected positive relationship between the model variables in both measurement times. That is: actual motor competence (MC) predicted physical activity (PA) (p < .001), perceived MC mediated the relationship between actual MC and PA (p < .001), and autonomous motivation mediated the relationship between perceived MC and PA (p < .05). Moreover, the comparison of the invariance analysis showed non-practical differences between the unconstrained model and the constrained model, supporting the stability of the model over time. In relation to the second aim, the hypothesized model in Time 2 controlling for Time 1 values showed that changes in children’s actual MC positively predicted changes in their perceived MC (p < .001), which in turn, predicted changes in their autonomous motivation (p < .001), and PA (p < .001) at Time 2. Based on these findings Physical Education teachers are recommended to foster children’s actual and perceived MC as well as their autonomous motivation over time in order to promote PA strategies for lifelong health.     

No relevant spectator impact on home advantage in male and female professional volleyball – A longitudinal multilevel logistic model analysis over 25 years

Home advantage (HA) regularly occurs in volleyball (Pollard et al., 2017: men: 56.62%, women: 55.26%). Research to date has investigated primarily small samples of mostly female matches and not looked into the potential impact of spectators on HA. This archival analysis uses multilevel modelling to examine HA in professional German volleyball (men & women) over 25 seasons in all regular and play-off matches (N = 6,833). We analyze how spectators drive HA and whether this projects to the COVID-19 season 2020/21.When intercepts varied between teams (2-level model, ICC = 27%), the winning probability increased when playing at home (men: 57.01%, OR_men = 2.39, d = 0.48; women: 55.39%, OR_women = 2.19, d = 0.43), while controlling for team strength, interaction with gender, and travelling distance. More spectators had a negligible effect on the men’s and women’s chances (|d| < 0.07). Similar trends were observed for the probability of winning sets. Contrary to other team sports (e.g., soccer), there is no HA-development over the last decades.     

Temperament and behaviour problems in children: A multilevel analysis of cross-cultural differences

Early temperament attributes have been linked to emerging behaviour problems and significant long-term consequences; however, these relations are rarely examined cross-culturally. The present study addresses this gap, employing multilevel modelling to explain within- and between-culture variances with respect to temperament predicting a spectrum of behaviour problems across 14 nations from the Joint Effort Toddler Temperament Consortium (JETTC). A total of 865 children between 17 and 40 months, with approximately equal age distribution across this developmental period and about equivalent representation of genders, were recruited from 14 nations. Greater negative emotionality was associated with more internalizing problems, whereas higher surgency and effortful control predicted fewer internalizing difficulties. Controlling for age and gender, temperament explained significant within- and between-culture variances in internalizing and externalizing problems (at the broad-band and fine-grained levels), as well as sleep problems. For internalizing difficulties, temperament accounted for more between-culture variance. In contrast, for externalizing difficulties, temperament accounted more for how individuals within the same culture differed from their same-culture counterparts. The within-culture findings suggest universal patterns of temperament-problem relations, informing cultural adaptation of interventions; between-culture findings enhance understanding of the implications of the cultural niche for normative behaviour and adjustment.     

One-year prediction of cognitive decline following cognitive-stimulation from real-world data

Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.     

Multilevel SEM with random slopes in discrete data using the pairwise maximum likelihood

Pairwise maximum likelihood (PML) estimation is a promising method for multilevel models with discrete responses. Multilevel models take into account that units within a cluster tend to be more alike than units from different clusters. The pairwise likelihood is then obtained as the product of bivariate likelihoods for all within-cluster pairs of units and items. In this study, we investigate the PML estimation method with computationally intensive multilevel random intercept and random slope structural equation models (SEM) in discrete data. In pursuing this, we first reconsidered the general ‘wide format’ (WF) approach for SEM models and then extend the WF approach with random slopes. In a small simulation study we the determine accuracy and efficiency of the PML estimation method by varying the sample size (250, 500, 1000, 2000), response scales (two-point, four-point), and data-generating model (mediation model with three random slopes, factor model with one and two random slopes). Overall, results show that the PML estimation method is capable of estimating computationally intensive random intercept and random slopes multilevel models in the SEM framework with discrete data and many (six or more) latent variables with satisfactory accuracy and efficiency. However, the condition with 250 clusters combined with a two-point response scale shows more bias.     

Mixture-modelling-based Bayesian MH-RM algorithm for the multidimensional 4PLM

Several recent works have tackled the estimation issue for the unidimensional four-parameter logistic model (4PLM). Despite these efforts, the issue remains a challenge for the multidimensional 4PLM (M4PLM). Fu et al. (2021) proposed a Gibbs sampler for the M4PLM, but it is time-consuming. In this paper, a mixture-modelling-based Bayesian MH-RM (MM-MH-RM) algorithm is proposed for the M4PLM to obtain the maximum a posteriori (MAP) estimates. In a comparison of the MM-MH-RM algorithm to the original MH-RM algorithm, two simulation studies and an empirical example demonstrated that the MM-MH-RM algorithm possessed the benefits of the mixture-modelling approach and could produce more robust estimates with guaranteed convergence rates and fast computation. The MATLAB codes for the MM-MH-RM algorithm are available in the online appendix.     

An adaptive approach for modelling multiattribute choice

An adaptive approach for modelling individual-level choice among multiattribute alternatives using the binary logit model is presented. The algorithm involves the collection of paired comparison data. In an effort to maximize the amount of information obtainable from each response, it is based on the experimental design criterion of D-optimality. A simulation study indicates that the proposed algorithm outperforms other sequential selection approaches in terms of estimation accuracy and predictive efficiency under certain circumstances. The results appear to encourage the use of such an adaptive algorithm for individual-level modelling in light of the potential reduction in data requirements without significant loss in predictive accuracy.     

Bayesian factor analysis for multilevel binary observations

Multilevel covariance structure models have become increasingly popular in the psychometric literature in the past few years to account for population heterogeneity and complex study designs. We develop practical simulation based procedures for Bayesian inference of multilevel binary factor analysis models. We illustrate how Markov Chain Monte Carlo procedures such as Gibbs sampling and Metropolis-Hastings methods can be used to perform Bayesian inference, model checking and model comparison without the need for multidimensional numerical integration. We illustrate the proposed estimation methods using three simulation studies and an application involving student's achievement results in different areas of mathematics. The authors thank Ian Westbury, University of Illinois at Urbana Champaign for kindly providing the SIMS data for the application.     

Opium of the people? National identification predicts well-being over time

Social group membership and its social-relational corollaries, for example, social contact, trust, and support, are prophylactic for health. Research has tended to focus on how direct social interactions between members of small-scale groups (i.e., a local sports team or community group) are conducive to positive health outcomes. The current study provides evidence from a longitudinal cross-cultural sample (N = 6,748; 18 countries/societies) that the prophylactic effect of group membership is not isolated to small-scale groups, and that members of groups do not have to directly interact, or in fact know of each other to benefit from membership. Our longitudinal analyses suggest that national identification (strength of association with the country/society of which one is a citizen) predicts lower anxiety and improved health; national identification was in fact almost as positively predictive of health status as anxiety was negatively predictive. The findings indicate that identification with large-scale groups, like small-scale groups, is palliative, and are discussed in terms of globalization and banal nationalism.     

The use of mixed modeling to evaluate the impact of treatment integrity on learning

Treatment integrity, or the degree to which an intervention is implemented as intended, is a critical feature of skill acquisition tasks. Single‐case design consistently demonstrates that low treatment integrity slows or inhibits learning, but the relative impact of different types of instructional errors, or the presence of multiple errors, is less clear. The present study utilized a multilevel modeling approach to evaluate the impact of type of error (omission versus omission and commission) and error component (reinforcer delivery versus feedback) on learning. Findings revealed that learning outcomes worsened based on the type of error and as the complexity of errors increased; more specifically, participants performed better when only a single type of error occurred and when only a single error component was manipulated. Additionally, individual characteristics contributed to learning outcomes, highlighting the use of multilevel modeling as a helpful tool to supplement single‐case design. The differential impact of integrity errors on learning may be due to timing of errors (i.e., commission errors more likely to occur early in learning) or how errors affect reinforcement schedule versus discriminative control.