Why is it that childlessness rates are increasing in societies where childbearing has been, and is currently, promoted by a multitude of factors? This is the case in Poland, where the childlessness...Show moreWhy is it that childlessness rates are increasing in societies where childbearing has been, and is currently, promoted by a multitude of factors? This is the case in Poland, where the childlessness rate is amongst the highest in Europe. Factors like single status and advanced education are associated with childlessness in women, but these don’t fully explain the childlessness phenomena. Exploring motivations towards childbearing may reveal a clearer picture. This can be done with the Childbearing Questionnaire (CBQ), which divides childbearing motivations into two positive and negative scales and can aid in family planning, necessitating comparable scores for men and women. Although the CBQ has been psychometrically validated, no previous testing has been conducted into the gender-based measurement invariance of the CBQ, limiting result comparability. Therefore, this study explores the extent of gender-based MI of the Polish CBQ. Data was collected from an open-source site and 939 childless participants of childbearing age completed the Polish CBQ. Multigroup confirmatory analysis established configural non-invariance after specification search and model modification, suggesting that the CBQ does not capture childbearing motivations in the same way for men and women. Separate exploratory model analyses on both groups revealed that the theorized two-factor model is a better fit for men than it is for women. A possible explanation of women facing more ambivalence towards childbearing than men is given, but the sparse research of men’s childbearing motivations makes this explanation theoretically unfounded.Show less
Nomophobia is the fear of being without one’s phone and is an increasing phobia in today’s digital society. This study examines the measurement invariance of a modified Chinese Nomophobia scale ...Show moreNomophobia is the fear of being without one’s phone and is an increasing phobia in today’s digital society. This study examines the measurement invariance of a modified Chinese Nomophobia scale (NMP-C) across gender groups using multigroup confirmatory factor analysis. Gender differences in nomophobia vary significantly based on cultural context. Generally, females have been found to exhibit higher levels of nomophobia. However, understanding how gender influences the assessment of nomophobia is crucial for developing effective interventions to address its negative consequences, such as depressive states or sleep problems. The NMP-C scale was translated to Chinese in 2020, yet the measurement invariance across genders was not assessed. Ensuring measurement invariance is important to confirm that the scale measures the construct equivalently across different groups. The publicly available dataset used in this study was collected from 673 college students in China using a 16-item Nomophobia scale for Chinese. The results of the analysis revealed partial scalar measurement invariance. These findings indicate that while the nomophobia scale captures the same underlying construct for both males and females, intercepts needed to be adjusted for certain items and residual variances differed across groups. In addition, the results suggest that NMP-C should be adjusted for the Chinese population. Further implication shows a repeating pattern of gender differences in nomophobia scoring. Hence, this study highlights the importance of measurement invariance across gender testing in future nomophobia scale validations.Show less
This study tested the measurement invariance (MI) across gender for the Dutch version of the Awareness of Narrative Identity Questionnaire (ANIQ-NL). ANIQ was developed to measure the awareness and...Show moreThis study tested the measurement invariance (MI) across gender for the Dutch version of the Awareness of Narrative Identity Questionnaire (ANIQ-NL). ANIQ was developed to measure the awareness and coherence of narrative identity. During validating ANIQ into multiple languages, studies have also compared the mean scores for male and female respondents. However, these studies so far have not tested for MI across gender. Without first establishing MI, meaningful and unbiased comparisons between groups cannot be made. To address this gap in the literature, this study tested whether the ANIQ-NL is measurement invariant across gender. The MI was tested using multi-group CFA through fitting a series of nested models with increasing constraints. The data used in this study is publicly available and it can be accessed via the Open Science Framework. Firstly, the results indicate support for the four-factor structure of ANIQ-NL, namely: Awareness, Temporal Coherence, Causal Coherence, Thematic Coherence. Secondly, the results indicate that ANIQ-NL achieved MI on the scalar level. Thus, the latent mean scores between men and women can be meaningfully interpreted.Show less
Brain activity in fMRI studies is represented by voxels; units of graphic information defining a small location in the brain. In a typical case, the brain is visualized using somewhere around 200...Show moreBrain activity in fMRI studies is represented by voxels; units of graphic information defining a small location in the brain. In a typical case, the brain is visualized using somewhere around 200.000 voxels. To measure activity every location or voxel is tested individually, with every voxel using a separate hypothesis test; this leads to a massive multiple testing problem. One way this problem is solved is by Bonferroni-like corrections on single voxels, however Bonferroni is notorious for it’s conservativeness (Samuel-Cahn, 1996). Instead of correcting for every test at the voxel level, one can also test groups (called clusters) of voxels. Hypothesis-testing on clusters reduces the multiple testing problem by accept- or rejecting entire clusters, but leads to a new problem known as the ‘spatial specificity paradox’: inference on the voxel level accurately locates activation at the cost of having low power for each test, whereas inference on the cluster level has more power but cannot localize activation any more accurate than ”there is at least one voxel active in this cluster”. Recently a solution called All-Resolutions Inference (ARI) was developed based on closed-testing to tackle this problem (Rosenblatt, Finos, Weeda, Solari, & Goeman, 2018). This method offers one way to quantify activation within clusters, without losing too much power. This project aims to assess and compare the quality of these new methods using simulation studies and real data applications.Show less
Psychological research has mostly been focused on finding an explanation for behavior, rather than on finding a model that accurately predicts behavior. This approach often results in the use of...Show morePsychological research has mostly been focused on finding an explanation for behavior, rather than on finding a model that accurately predicts behavior. This approach often results in the use of models that fit very well to the sample used for testing but are difficult to generalize to new samples. Similarly, models are often too complex and take into consideration too many variables. Classically, replications would be performed to account for these issues. This is however an expensive, time-consuming, and laborious process, leading to the perpetuation of nonreplicated studies in psychology. This thesis presents a possible solution stemming from the field of machine learning, namely using cross-validation. With cross-validation, the predictive performance of a model can be assessed using only one dataset. This thesis examined the use of cross-validation by applying it over an existing dataset and comparing its output to the output of conventional null-hypothesis testing. The results show that using cross-validation reduces the likelihood of making overly optimistic claims, by reducing the chances of using excessively complex models unable to generalize to new samples. Furthermore, cross-validation gives the opportunity to examine the predictability of models while preserving the explanatory power. It therefore proves to be a useful tool in the field of psychological research.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Association of neurological and psychological conditions with changes in coactivation patterns of brain regions in ’resting state’ is of recent interest in neuroscience. To uncover such latent...Show moreAssociation of neurological and psychological conditions with changes in coactivation patterns of brain regions in ’resting state’ is of recent interest in neuroscience. To uncover such latent functional connectivity, series of functional Magnetic Resonance Imaging (fMRI) scans are typically reduced by averaging activations in brain atlas regions. The averaged activations are further reduced to pairwise correlation in sliding fixed width time windows. Unfortunately such reduction in dimensions also reduces the scan resolution and complicates interpretation. Changing to a text mining perspective, this thesis interprets the high dimensional scans as documents with categorical words drawn from a study bag. Consecutive scans measure the activation in V discrete voxels of brain volumes. Activation series in each voxel are segmented into stationary subsequences. Similar correlated segments within voxels and from distinct voxels are then bagged as words. The words capture correlated activation both within- and between-voxels. Instead of being predefined in an atlas, regions emerge as neighbourhoods of voxels drawing the same word at the original scan resolution. The word counts that document voxels draw from the bag of categorical words defines the document state. Document state transition probabilities measure the dynamics in coactivated brain locations at the original fMRI resolution, as a possible marker for a neurological condition. This alternative fMRI activation reduction method avoids a-priori selection of regions, tuning of fixed time window widths, and selection of the number of principal components of the contrasted existing method; the alternative method allows a more direct interpretation of activations. However, the direct state switching interpretation of scan document voxels drawing categorical word counts, does not sufficiently separate subject groups for reliable classification of neurological conditions.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Functional connectivity (FC) is an important metric to characterize brain mechanisms. Assessment of resting-state FC is a popular tool for studying brain disease mechanisms. Correlations between...Show moreFunctional connectivity (FC) is an important metric to characterize brain mechanisms. Assessment of resting-state FC is a popular tool for studying brain disease mechanisms. Correlations between functional magnetic resonance imaging (fMRI) blood-oxygenation-level-dependent (BOLD) time courses in different brain regions can measure FC which has revealed a meaningful organization of spontaneous fluctuations in the brain during rest. Therefore, in most studies, the presence of temporal and spatial dynamics of FC are usually measured by the correlation coefficients between the fMRI signals of several brain regions. However, recent research has shown that FC is not stationarity. That is, FC dynamically changes over time reflecting additional and rich information about brain organization. In 2013, Leonardi et al. proposed a new approach which was based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. This thesis evaluates this new approach in a simulation study. Moreover, also a framework to test the new approach is proposed. The simulation study showed advantages and disadvantages of the new approach. The results of the simulation study showed that the new approach can extract the most important dynamic connectivity features underlying fMRI data. It can retrieve timevarying connectivity between dynamic brain regions during rest effectively. The new approach identified connections with similar fluctuations, and gave an efficient linear representation, but only sensitive to linear relations between connectivity pairs, and it yielded robust results in restricted conditions. Finally, some recommendations for researchers using this method to study dynamic brain functional brain connectivity at rest are provided.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Aerosols are tiny particles of various kinds and compositions suspended in the atmosphere, some of which have a critical, adverse impact on public health. Hence, modelling the prevalence and...Show moreAerosols are tiny particles of various kinds and compositions suspended in the atmosphere, some of which have a critical, adverse impact on public health. Hence, modelling the prevalence and distribution of these separate types is vital for giving shape to informed policy on air quality. In this work, methods are described to identify clusters of similar aerosol type mixtures in the Earth’s atmosphere on a global scale, on the basis of microphysical data from the space-borne remote sensing instrument POLDER-3. We report an unsupervised learning approach using the Self-Organizing Map (SOM) and k-means clustering, which allows for clustering without a priori assumptions on existing aerosol types, nature or prevalence. Two methods are introduced to stabilize these clustering algorithms over multiple equal runs to manage their local optima convergence property: the k-means nstart option is extended to the SOM and a set-up is given for a new method, Expectation-Maximization-centered Mahalanobis clustering (EMcMc). A (repeated) v-fold cross-validation framework is presented to find the optimal number of clusters k in the data by means of cluster validation measures, currently including Prediction Strength and validated variants of the Silhouette Width. Using a separate test set, the method can be used to optimize a generic k, countering overfitting. A novel validation index is developed which extends the Silhouette Width to data sets with many observations (large N): the Gridded Silhouette Width. All described methods are implemented in the statistical software package R and shown to work for simulated examples, originating from scaled Gaussian distributions with varying degrees of overlap. Analysis of the POLDER-3 data indicated that using only four variables, 8 clusters can be found in a stable and reproducable fashion. The Silhouette indices did not appear to perform well for data so widely dispersed as here. The found clusters were characterized based on their variable distributions and geographical occurence, which proved to be feasible and meaningful for real-life interpretations. The proposed aerosol types were dust, marine, urban-industrial, smoke and mixtures thereof. Keywords: aerosol typing; unsupervised learning; self-organizing map; k-means clustering; cluster validation measures; cross-validation; gridded silhouette.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Synchronous neuronal responses across subjects is also known as neural reliability. The level of neural reliability evoked by natural stimuli is shown to be a predictor to larger audience...Show moreSynchronous neuronal responses across subjects is also known as neural reliability. The level of neural reliability evoked by natural stimuli is shown to be a predictor to larger audience preferences (Dmochowski et al., 2014). The same authors also proposed the state-of-the-art method for calculating neural reliability in an EEG setting (Dmochowski et al., 2014). However, the method is indirect and rather ad hoc, therefore, some existing alternative methods are proposed as well as an own proposed algorithm of calculating neural reliability. All the different methods are compared by means of a simulation study. Here, the performance is tested in their ability to recover the actual neural reliability in the data, but also their performance in predicting a population measure. Furthermore, wavelet transform as a denoising step in the setting of EEG data is investigated. The results of the simulation study show that Dmochowski and colleagues’ (2014) is performing well on undenoised data and when the relationship between the “true” ISC and buying behaviour is strong. However, the adapted neural reliability method of Hasson and colleagues’ (2004) and originally intended for fMRI studies stands out not only in terms of performance, but also in consistency of performance under different data characteristics, like the strength of the ISC, the signal to noise ratio and the strength of the relation between true ISC and buying behavior. Moreover, this method is also more direct and easier to calculate. The proposed way of denoising by wavelet transform only hurts the performance of the proposed neural reliability methods. It can be concluded that the adapted method of Hasson and colleagues’ (2004) can be recommended both for determining the ISC as the relation between ISC and a population measure.Show less
