This study investigates the measurement invariance of the TMMS-18, the adjusted version of the Trait Meta-Mood Scale 24 (TMMS-24) by Górriz et al. (2021), among Spanish adolescents. The TMMS-18,...Show moreThis study investigates the measurement invariance of the TMMS-18, the adjusted version of the Trait Meta-Mood Scale 24 (TMMS-24) by Górriz et al. (2021), among Spanish adolescents. The TMMS-18, which assesses three core dimensions of emotional intelligence—attention to feelings, clarity of feelings, and mood repair—is widely used for its reliable representation of emotional awareness and regulation. Utilizing a publicly available dataset from the Open Science Framework repository, originally compiled by Tejada-Gallardo et al. (2022), involving Spanish adolescents, we conducted a multi-group confirmatory factor analysis (MGCFA) to explore whether the TMMS-24 demonstrates measurement invariance across sexes. The analysis followed a hierarchical testing procedure, assessing configural, metric, scalar, and residual invariance. Our findings suggest full invariance across male and female groups, indicating that the TMMS-24 measures emotional intelligence consistently across sexes without bias. These results support the scale’s applicability in educational and psychological settings, providing a reliable tool for assessing emotional intelligence in diverse adolescent populations. The study contributes to the existing literature by confirming the TMMS 24's utility and robustness, reinforcing its role in developmental and educational research.Show less
In recent years the use of network analysis has seen an increase across multiple fields. Using features derived from network analysis in a random forest model has already shown promising results in...Show moreIn recent years the use of network analysis has seen an increase across multiple fields. Using features derived from network analysis in a random forest model has already shown promising results in an experiment of the Netherlands Labour Inspection to predict risk scores of violating Dutch labour laws for Dutch companies. In order to see if this is the best way for the NLA to use network analysis this method has been compared with using node classification, a deep learning method that uses sampling and aggregation to label nodes of a network based on its neighbours and the network structure. In this study node classification has shown to perform better at predicting risk scores for Dutch companies than a random forest model with network features does. A simulation study was done on the node classification method to test its robustness and has shown that it is important that there are enough labels in the training set in order for the method to perform well and that the quality of these labels influences the extent to which the model overfits. If the data scientist of the NLA choose to use node classification in future projects it is important that they make an ethical selection of the variables to use for prediction and that they ensure that there are enough labels in the dataset for the node classification model to perform well.Show less
The ability to correctly classify an individual’s clinical status could help pave the way for early treatment programs for disorders and illnesses of mental- and physical nature alike. Neuroimaging...Show moreThe ability to correctly classify an individual’s clinical status could help pave the way for early treatment programs for disorders and illnesses of mental- and physical nature alike. Neuroimaging data could serve as a basis in reaching this goal of accurate classification. The use of said data, however, does come with challenges. A prominent one of which is the fact that neuroimaging data is highly dimensional, meaning that the amount of features largely exceeds the number of subjects within the data set. Furthermore, research has indicated that the use of heterogeneous, but complimentary, data derived from multiple modalities can be an asset to a model used in a classification setting (Zhang et al., 2010; Schouten et al., 2016). The challenge arises in how to combine the different modalities within a single model. A solution could be the use of machine learning algorithms which search for patterns in the data to draw conclusions. Current literature is, however, lacking meaningful comparisons between different machine learning techniques. Within this project, three different algorithms (support vector machines, Gaussian process classification and multiple kernel learning) have been selected to get insight into (1) whether or not machine learning is able to cope with challenges in the use of neuroimaging data, (2) the difference in performance between these methods and (3) whether or not the use of multiple modalities leads to better results in classification. To this end, 16 alcohol-dependent respondents have been selected along with 32 age-matched healthy controls and have been subjected to both MRI and PET. Models have been trained on data from both separate modalities and on data combining the two modalities. The performances of the models have been assessed by leave-one-subject-out cross-validation and expressed in balanced accuracy and area under the curve. Results indicate that the chosen methods are effective in overcoming challenges arising in the use of neuroimaging data as a means of classification. High balanced accuracies have been found ranging from 76.56% (GPC using PET data) to 100% (GPC using MRI data). Different situations are cause for different solutions and the right choice of algorithm seems to be dependent on, for instance, the fact if either unimodal- or multimodal data is used. Also, settings/optimization of parameters within the model can make a large impact on accuracy. It is therefore advised that researchers try different algorithms and settings before selecting a technique. Different options need to be weighed in order to receive the best possible outcome.Show less
An often-encountered challenge in neuroscience is the reliable prediction of a subject’s disease status based on brain data to, for example, disentangle Alzheimer’s disease (AD) patients from...Show moreAn often-encountered challenge in neuroscience is the reliable prediction of a subject’s disease status based on brain data to, for example, disentangle Alzheimer’s disease (AD) patients from healthy control (HC) subjects. In this regard, functional Magnetic Resonance Imaging (fMRI) has been applied successfully to study the functional and structural differences between the brains of AD patients and HC’s. However, when using fMRI data for AD classification, the complex multivariate nature of these data faces researchers with two main statistical challenges that need to be appropriately addressed to improve the AD classification performance: high dimensionality (i.e., the data containing many variables/voxels) and multimodality (i.e., the data consisting of different brain modalities, like structural and functional brain information). In this study, both statistical challenges are tackled. To address high dimensionality, dimension reduction is proposed. Regarding the multimodality challenge, a ‘concatenated strategy’, which consists of a simultaneous dimension reduction of the information present in all the modalities involved, is compared to a ‘separate strategy’, which reduces the data for each modality separately. Combined with these strategies, three common feature extraction methods are compared in terms of classification performance: (1) Principal Component Analysis (PCA), (2) Partial Least Squares Regression (PLS-R) and (3) Generalized Regularized Canonical Correlation Analysis (RGCCA). Testing these methods and strategies on multimodal data consisting of three different neuroimaging properties that are related to AD, it is found that the best classification accuracies are obtained with PLS-R (compared to PCA and CCA) and the concatenated strategy (compared to the separate strategy). PLS-R combined with the concatenated strategy, however, is outperformed by a whole-brain analysis applied to all data modalities simultaneously and performs at the same level as a whole-brain analysis applied to one of the brain modalities used (i.e., the structural one).Show less
