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In de adolescentie, de leeftijdsperiode tussen de kinderjaren en de volwassenheid, nemen psychosociale problemen vaak toe. Dat kunnen emotionele problemen zijn zoals somberheid, maar ook gedragsproblemen of sociale problemen met leeftijdgenoten. Emotionele problemen zoals somberheid, zich terugtrekken of piekeren komen tijdens de adolescentie vaker bij meisjes voor dan bij jongens. Wat is de Strengths and Difficulties Questionnaire (SDQ)? De Strengths and Difficulties Questionnaire (SDQ) is een korte vragenlijst waarmee psychosociale problemen bij jeugdigen gemeten kunnen worden. De SDQ kan gebruikt worden door de Jeugdgezondheidszorg (JGZ) voor jeugdigen in de leeftijd van 3-17 jaar. De SDQ kent verschillende versies, namelijk een voor leerkrachten, een voor ouders en een voor de jeugdige zelf. Deze verschillende versies van de SDQ zijn te vinden op de website www.sdqinfo.com

In modern omics research, it is more rule than exception that multiple data sets are collected in a study pertaining to the same biological organism. In such cases, it is worthwhile to analyze all data tables simultaneously to arrive at global information of the biological system. This is the area of data fusion or multi-set analysis, which is a lively research topic in chemometrics, bioinformatics, and biostatistics. Most methods of analyzing such complex data focus on group means, treatment effects, or time courses. There is also information present in the covariances among variables within a group, because this relates directly to individual differences, heterogeneity of responses, and changes of regulation in the biological system. We present a framework for analyzing covariance matrices and a new method that fits nicely in this framework. This new method is based on combining covariance prototypes using simultaneous components and is, therefore, coined Covariances Simultaneous Component Analysis (COVSCA). We present the framework and our new method in mathematical terms, thereby explaining the (dis)similarities of the methods. Systems biology models based on differential equations illustrate the type of variation generated in real-life biological systems and how this type of variation can be modeled within the framework and with COVSCA. The method is subsequently applied to two real-life data sets from human and plant metabolomics studies showing biologically meaningful results

Exploratory factor analysis (EFA) can be used to determine the dimensionality of a set of items. When data come from clustered subjects, such as pupils within schools or children within families, the hierarchical structure of the data should be taken into account. Standard multilevel EFA is only suited for the analysis of continuous data. However, with the robust weighted least squares estimation procedures that are implemented in the computer program Mplus, it has become possible to easily conduct EFA of multilevel discrete data. In the present paper, we show how multilevel EFA can be used to determine the dimensionality in discrete two-level data. Measurement invariance across clusters implies equal dimensionality across levels. We describe two procedures, one with and one without measurement invariance restrictions across clusters. Data from educational research serve as an illustrative example.

This article provides a review of contemporary instruments for the developmental assessment of children aged 0–4 years and their applicability for children with special needs. The issues involved in the developmental assessment of children with special needs are discussed, and, on the basis of these issues, various instruments are then evaluated. Method: A literature search was carried out for articles about or using standardized developmental assessment instruments for children aged 0–4 years. Results: Eighteen instruments were found, of which 2 were nonverbal and 2 were designed for motor-impaired children. The instruments varied in terms of their suitability for children with special needs. Conclusion: The range of instruments is limited, especially for children younger than 2 years of age. Instruments for children with motor or hearing/language impairments are available, but their psychometric properties need to be researched and improved. For children with a visual impairment, no appropriate instrument is currently available.

Objective: To establish the long-term effectiveness of 3 treatments for DSM-IV panic disorder with or without agoraphobia: cognitive-behavioral therapy (CBT), pharmacotherapy using a selective serotonin reuptake inhibitor (SSRI), or the combination of both (CBT + SSRI). As a secondary objective, the relationship between treatment outcome and 7 predictor variables was investigated. Method: Patients were enrolled between April 2001 and September 2003 and were randomly assigned to treatment. Academic and nonacademic clinical sites participated. Each treatment modality lasted 1 year. Pharmacotherapists were free to choose between 5 SSRIs currently marketed in The Netherlands. Outcome was assessed after 9 months of treatment (posttest 1), after discontinuation of treatment (posttest 2), and 6 and 12 months after treatment discontinuation (follow-up 1 and follow-up 2). Results: In the sample (N = 150), 48% did not suffer from agoraphobia or suffered from only mild agoraphobia, while 52% suffered from moderate or severe agoraphobia. Patients in each treatment group improved significantly from pretest to posttest 1 on the primary outcome measures of level of anxiety (P < .001), degree of coping (P < .001), and remitter status (P < .001), as well as on the secondary outcome measures of depressive symptomatology (P < .001), and from pretest to posttest 2 for health-related quality of life (P < .001). Gains were preserved from posttest 2 throughout the follow-up period. Some superiority of CBT + SSRI and SSRI as compared with CBT was observed at posttest 1. However, at both follow-ups, differences between treatment modalities proved nonsignificant. Client satisfaction appeared to be high at treatment endpoint, while patients receiving CBT + SSRI appeared slightly (P < .05) more satisfied than those receiving CBT only. Conclusions: No fall-off in gains was observed for either treatment modality after treatment discontinuation. SSRIs were associated with adverse events. Gains produced by CBT were slower to emerge than those produced by CBT + SSRI and SSRI, but CBT ended sooner.

Applied Multiway Data Analysis, P.M. Kroonenberg

Recovery in 60 patients with a closed-head injury (CHI) in the first year posttrauma was assessed repeatedly with a series of attention tests. A matched group of healthy subjects was tested at the same intervals to allow us to control for practice effects. The results of a multilevel analysis for longitudinal data show retest effects in all but one of the tests. Patients performed more poorly on all tests, but their results on each test appeared to show recovery over time. The indicator of recovery was an improvement in test performance that was greater than the retest effect shown by the controls. On most tests, the performance of the more severely injured patients was initially worse, but showed more recovery over time. Test results differed with respect to changes over time, sensitivity to severity of injury, and subject specific characteristics Like age and vocational level. Recovery rate was not related to age or vocational status. Despite their recovery, the patient group was still impaired 1 year posttrauma on all tests sensitive to mental slowness. Outcome after 1 year, scored on a modified Glasgow Outcome Scale, was predicted to a small extent by PTA duration and initial performance on the RT-Distraction task. Return to work 2 to 5 years posttrauma was predicted by initial performance and improvement over time on the Stroop Color Word Test.

Large-scale data sets with a large number of variables become increasingly available in behavioral research. Encompassing a wide range of measurements and indicators, they provide behavioral scientists with unprecedented opportunities to synthesize different pieces of information so that novel - and sometimes subtle – subgroups (also called clusters) of populations can be identified. The successful detection of clusters is of great practical significance for a wide range of social and behavioral research topics. For example, in treating depressed patients, the first step in generating personalized recommendations is to accurately link the patients to the many subtypes of depression. In the organization context, it is highly problematic to assume that all leaders should follow the same developmental paths; in fact, tailoring training programs to the unique strengths of different leadership subgroups (e.g., the down-to-earth leaders and the excessively charismatic leaders) is always more effective than general developmental programs. When trying to understand the cognitive process underlying one’s voting behavior, once again, a one-size-fits-all approach likely produces erroneous descriptions. The broad social context as well as the surrounding environment in which a person grows up likely yields clusters of voters; only those belonging to the same cluster share a similar decision-making process for voting. To provide behavioral researchers with the best tool for accurately recovering the clusters hidden in large, complex data sets, this dissertation developed new statistical models and computational tools and implemented these novel approaches in publicly accessible software. Generally speaking, the novel methods developed here advance previous approaches by addressing the following three major challenges. First, as noise is ubiquitous in psychological measures, a considerable number of variables collected may be completely irrelevant to the hidden clusters. These irrelevant variables have to be completely and automatically filtered out during data analysis. Second, when integrating variables from diverse data sources (for example questionnaires and genetic information, GPS coordinates, social media footprints, etc.), it is desirable to capture both the unique characteristics pertaining to each data source and the shared or connected characteristics across the many data sources. Third, when translating data analytics results into substantive conclusions so as to inform critical decisions (e.g., medical decisions, personnel selection, etc.), effective and accurate communication is vital yet not necessarily easy to achieve. The two most prominent difficulties are communicating the confidence and (un)certainty in the clusters recovered and visualizing the results through very accessible graphs. With a variety of computer-simulated data and empirical behavioral data covering topics in clinical, social, personality, and organizational psychology, we were able to conclude that the various methods developed in the dissertation are more versatile, effective, and accurate in identifying subtle clusters in complex data sets, provide rich and unique insights in interpreting these clusters, and, thanks to the development of many software, can be readily accessed without many technical barriers. These methods are therefore useful for behavioral researchers to navigate in an increasingly digitized world and to recognize structures from massive information.

The technological developments of the last decades, e.g., the introduction of the smartphone, have created opportunities to efficiently collect data of many individuals over an extensive period of time. While these technologies allow for intensive longitudinal measurements, they also come with new challenges: data sets collected using these technologies could become extremely large, and in many applications the data collection is never truly `finished'. As a result, the data keep streaming in and analyzing data streams using the standard computation of well-known models becomes inefficient as the computation has to be repeated each time a new data point enters to remain up to date. In this thesis, methods to analyze data streams are developed. The introduction of these methods allows researchers to broaden the scope of their research, by using data streams. In this dissertation, I introduce a commonly used approach to deal with data streams: online learning, a method to update the result of an analysis while the data are entering, without revisiting the previous data points. This approach can deal with data streams, because 1) it is no longer required to store all data in memory; and 2) because the approach updates the results it can easily remain up to date when new data enter. A large range of statistical models can be estimated using this approach, e.g., a linear regression and correlations. However, when observations are clustered, for instance because the same individuals are repeatedly observed, rewriting the estimation procedure to be feasible in a data stream becomes more difficult. This data structure with clustered observations causes dependencies between data points belonging to the same person. Ignoring these dependencies violates an important statistical assumption of independent observations. When a researcher does not deal with the dependencies between the data points, the results are likely to be biased (i.e., inaccurate). There are models which take the dependencies between the observations into account, called multilevel models. These multilevel models, however, are computationally complex to estimate in a data stream, because the estimation procedure requires multiple passes over the data set. These multiple passes over the data set in combination with the continuous stream of new data points make it difficult to estimate the multilevel model in data streams. In this thesis, I developed an online-learning algorithm that updates the multilevel model, while new data enter and without passing over all the data repeatedly. This algorithm, called SEMA (acronym for Streaming Expectation Maximization Approximation), updates the results of the multilevel model with each new data point, without storing the data points. Using this algorithm, researchers can analyze data streams efficiently, while keeping the structure of the data stream into account.