Early Childhood Teachers’ Competence to Evaluate Children’s Mathematical Skills

This study examined German early childhood teachers’ absolute and relative judgment accuracy with respect to the mathematical skills of the children under their supervision. The two types of judgment accuracy are crucial prerequisites for pacing activities in early childhood education and offering differentiated activities adapted to children’s individual skill levels. Data from 39 early childhood teachers and 268 children were analyzed using multilevel modeling. Teachers rated children’s skills on a structured observation instrument (“Kinder Diagnose Tool”, KiDiT). Children were assessed on their mathematical skills with a standardized test (“Mathematische Basiskompetenzen im Kindesalter”, MBK-0). On average, 65% of the variation in teachers’ judgments on the KiDiT could be explained by children’s MBK-0 scores which suggests that teachers are – on average – well able to rank children within their groups. Teachers were also able to judge the mathematical level of children’s skills as assessed by the MBK-0. Neither teachers’ mathematical content knowledge (MCK), nor their mathematics pedagogical content knowledge (MPCK) or their general pedagogical knowledge (GPK) moderated the relationship between teachers’ judgments and children’s test scores or the relationship between the level of the judgments and the level of test scores. Conclusions for future research and practice are drawn

Within- and Between-Persons Effects of Self-Esteem and Affective State as Antecedents and Consequences of Dysfunctional Behaviors in the Everyday Lives of Patients With Borderline Personality Disorder

Dysfunctional behaviors are conceptualized as maladaptive affective coping attempts in borderline personality disorder (BPD). The recent benefits-and-barriers model extended the affective function assumption by adding self-esteem as a barrier to engaging in dysfunctional behaviors. Patients with BPD (N = 119) carried e-diaries to report their current selfesteem, emotional valence, tense arousal, and whether they engaged in dysfunctional behaviors 12 times a day for 4 days. Dynamic structural equation modeling revealed that on the within-person level, high momentary negative affect predicted dysfunctional behaviors, and on the between-person level, low trait self-esteem predicted dysfunctional behaviors. We also found an association between engaging in dysfunctional behaviors and momentary self-esteem and trait levels of valence and tense arousal. Moreover, our results indicate a deterioration of, rather than relief from, negative affective state after dysfunctional behaviors. These findings highlight the importance of emotion-regulation skills and reestablishing a positive self-view as important treatment targets to reduce dysfunctional behaviors in BPD

Discontinuous Change Models

This repository contains the R-syntax for the contribution "Modeling Discontinuous Change with Hierarchical Linear Models"

A probabilistic measurement model for the assessment of intraindividual variability

In der vorliegenden Arbeit wird ein probabilistisches Item-Response-Modell zur Erfassung von intraindividueller Variabilität in multivariaten, diskreten Zeitreihen generiert. Manifester Indikator der Variabilität ist die mittlere absolute Differenz auf den Items (MASD). Potentielles Anwendungsgebiet ist die Skalierung von intraindividueller Variabilität im Rahmen von Ambulatory Assessments. Im Theorieteil werden einige Theorien zur intraindividuellen Variabilität dargelegt und der Modellierungshintergrund von Rasch zur Erzeugung von probabilistischen Testmodellen und die Maximum-Entropie-Methode zur Modellgenerierung werden gegenübergestellt. Es wird gezeigt, wie gängige Testmodelle, wie das dichotome Rasch-Modell, das Partial-Credit-Modell und das bedingte Rasch-Modell aus der Anwendung der Methode resultieren. Im Teil zur Modellentwicklung wird die Maximum-Entropie-Methode angewendet, um ein neues IRT-Modell zur Erfassung intraindividueller Variabilität auf Basis sukzessiver, absoluter Differenzen in Zeitreihen zu generieren. Es resultiert ein Modell, dass einen Markov-Prozess erster Ordnung abbildet, wobei die manifeste Variabilität durch einen personenbezogenen, latenten Parameter bestimmt wird. Die Eigenschaften des Modells werden untersucht, indem die vorhergesagten Wahrscheinlichkeiten, die Item-Response-Kurven und die Likelihood-Funktion dargestellt werden. Anhand der Likelihood-Funktion zeigt sich, dass das Modell suffiziente Statistiken zur Schätzung der Parameter besitzt. Um die Bewertung der Modellpassung auf der Basis von standardisierten Residuen zu ermöglichen, werden die Erwartungswerte und die Varianz der manifesten Variable unter dem Modell hergeleitet. Die Eigenschaften der latenten Trait-Skala des Modells werden anhand der Bildung der Logits der Kategorien-Wahrscheinlichkeiten untersucht. Es zeigt sich, dass das Modell auf einer Differenzen-Skala misst. Der Zusammenhang zwischen der manifesten Variable und der Trait-Skala sowie der empirische Bias und die empirische Varianz der MCMC-Parameterschätzer werden simulativ untersucht. Es zeigt sich, dass die latente Trait-Skala in einem monotonen Verhältnis zu der Variabilität in den manifesten Zeitreihen steht. In nicht-extremen Regionen der Trait-Skala ist der Bias relativ gering und die Varianz der Schätzer steigt in Extrembereichen der Trait-Skala. Die Ergebnisse der simulativen Untersuchung des Bias und der Varianz sind mit gängigen Ergebnissen zu Rasch-Modellen kompatibel. Auf Basis der Ergebnisse der Modellentwicklung wird geschlossen, dass es sich bei dem generierten Modell um ein Rasch-Modell handelt und die Maximum-Entropie-Methode verwendet werden kann, um neue, probabilistische, psychometrische Modelle zu generieren. Zur Bewertung der Reliabilität wird die Andrich-Reliabilität vorgeschlagen. Im Anwendungteil der Arbeit wird das Modell auf Daten angewendet, die im Rahmen eines Ambulatory Assessments zur Affektregulation von Crayen et al. (in Druck) mit einer Kurzform des Mehrdimensionalen Befindlichkeitsfragebogens (Steyer et al., 1997) angefallen sind. Es wird überprüft, ob das Modell zur Bewertung intraindividueller Variabilität auf reale Daten angewendet werden kann. Anhand der standardisierten Residuen des Modells zeig sich, dass das Modell relativ gut passt. Die Andrich-Reliabilität für alle drei Skalen ist relativ hoch, was darauf hindeutet, dass die intraindividuelle Variabilität auf den drei Skalen des MDBF reliabel erfasst wird. Die individuellen Variabilitäts-Parameter der drei Skalen sind hoch miteinander korrelieren, ein Ergebnis, dass mit demjenigen von Eid und Diener (1999) zur Affekt-Variabilität kompatibel ist. Die Korrelationen der Variabilitäts-Parameter mit der manifesten Variabilität ist hoch, was darauf hindeutet, dass die Parameter die manifeste Variabilität in den Zeitreihen abbilden. Die Korrelation der Variabilitäts-Skalen und der manifesten Indikatoren der Variabilität mit drei Skalen des NEO-FFI (Neurotizismus, Extraversion und Gewissenhaftigkeit) zeigte jedoch keine deutlichen, signifikanten Zusammenhänge, was darauf hindeutet, dass die Variabilität der Skalen des MDBF in der Stichprobe etwas spezifisch anderes erfasst als die drei Skalen des NEO-FFI.The present thesis focuses on the development of an item response model for the assessment of intraindividual variability in multivariate, discrete time series based on the mean absolute successive difference of the time-series. A potential application of the model is the scaling of intraindividual variability in ambulatory assessments. In the theoretical part some current theories with regard to intraindividual variability are presented and the IRT- modeling framework by Rasch and the maximum entropy method for model construction are discussed. It is shown that well known IRT-models, such as the dichotomous Rasch model, the partial credit model and the conditional Rasch model can be obtained by applying the maximum entropy modeling framework. Then the maximum entropy modeling framework is applied to the problem of generating a new psychometric model for the assessment of intraindividual variability based on absolute successive differences in multivariate, discrete time series. The resulting model describes a first order markov process that is governed by a latent trait parameter. The properties of the resulting model are described by examining the predicted category response probabilities, the item characteristic curves and the likelihood function. An detailed examination of the likelihood function reveals that the model features sufficient statistics for parameter estimation. To allow for the assessment of model fit by standardized residuals, the expectations and the variance of the expected responses under the model are derived. To clarify the measurement structure of the model, the logits of adjacent response categories are examined. It is found that the model captures intraindividual variability on a difference scale. The relationship between the latent trait scale and the manifest variability of the responses are examined by simulation. The simulation showed a monotonous relationship between the variability of the manifest variables and the latent trait scale. A simulative examination of the MCMC-estimators' empirical bias and variance indicated that bias is relatively small in non extreme regions of the trait scale. The variance of the estimators increases in extreme regions of the trait scale. These results are in accordance with known results for Rasch models. It is concluded that the generated model is a Rasch model and that the maximum entropy framework can be used to generate new probabilistic psychometric models. It is proposed to assess measurement reliability by Andrich's method and to examine model fit based on standardized residuals. The model's practical applicability is examined based on a longitudinal ambulatory assessment data set for the study of affect regulation by Crayen et al. (in press). The generated model is applied to a short form of the multidimensional mood questionnaire (MDBF) (Steyer et al., 1997) with the aim to check if the model could be used to measure mood variability in ambulatory assessment studies. The model shows overall good fit based on standardized residuals and good reliability of measurement based on Andrich's reliability coefficient. It is concluded that the model is capable of reliably differentiating individuals with regard to intraindividual variability on a latent scale. The three sub- scales of the MDBF were highly intercorrelated. To otain first hints with regard to validity, the variability parameters were correlated with three scales of the NEO-FFI (neuroticism, extraversion and conscientiousness). The model's latent variability parameters were highly intercorrelated with the manifest variability in the time series. However, there were no pronounced correlations between the variability scales and the three avaliable scales of the NEO-FFI. The same counts for the manifest variability in the time series

Development of the Aesthetic Emotions Scale (AESTHEMOS)

Despite a long-standing scientific interest in aesthetic emotions, we still are lacking an assessment tool to capture the broad range of emotions involved in aesthetic experiences across different domains. Elicitors of aesthetic emotions are not limited to the arts in the strict sense but extend to design, built environments, and nature. This project is devoted to the development of a questionnaire that is applicable across many of these domains: the Aesthetic Emotions Scale (AESTHEMOS). Based on theoretical accounts of aesthetic emotions and an extensive review of extant measures of aesthetic emotions within specific domains like music, literature, film, painting, advertisement, design, and architecture, we arrived at an item set to study aesthetic emotions. In a field study, we tested 75 items and selected items to be included in the first version of our new assessment tool. It contains 21 subscales with two items each, designed to assess the emotional signature of an aesthetic experience in a highly differentiated manner. These encompass prototypical aesthetic emotions (e.g., the feeling of beauty, being moved, fascination, and awe), epistemic emotions (e.g., interest and insight), and emotions indicative of amusement (humor and joy). In addition, the AESTHEMOS subscales capture both activating (energy and vitality) and calming (relaxation) effects of aesthetic experiences and negative emotions that may contribute to aesthetic displeasure (e.g., the feeling of ugliness, boredom, and confusion)

Conceptualizing and Measuring Aesthetic Emotions

An aesthetic experience is not merely a cognitive process but also involves feelings. Therefore, the empirical study of this type of experience requires conceptualization and measurement of aesthetic emotions. The aim of this project is to increase our understanding of the nature of aesthetic emotions and to develop assessment tools that allow for their encompassing yet parsimonious measurement. Thus far, we have conducted two projects to accomplish this. The study "Conceptual Space of Aesthetic Emotions" employed the pile-sort methodolgy to map the conceptual domain of aesthetic emotions. The ongoing project "Development of the Aesthetic Emotions Scale (AESTHEMOS)" is devoted to creating a new assessment tool for aesthetic emotions that is applicable across different aesthetic domains

Questionnaire Examples of AESTHEMOS

Presents examples of the AESTHEMOS questionnaire in German and Englis

Path Models of Vocal Emotion Communication

We propose to use a comprehensive path model of vocal emotion communication, encompassing encoding, transmission, and decoding processes, to empirically model data sets on emotion expression and recognition. The utility of the approach is demonstrated for two data sets from two different cultures and languages, based on corpora of vocal emotion enactment by professional actors and emotion inference by naive listeners. Lens model equations, hierarchical regression, and multivariate path analysis are used to compare the relative contributions of objectively measured acoustic cues in the enacted expressions and subjective voice cues as perceived by listeners to the variance in emotion inference from vocal expressions for four emotion families (fear, anger, happiness, and sadness). While the results confirm the central role of arousal in vocal emotion communication, the utility of applying an extended path modeling framework is demonstrated by the identification of unique combinations of distal cues and proximal percepts carrying information about specific emotion families, independent of arousal. The statistical models generated show that more sophisticated acoustic parameters need to be developed to explain the distal underpinnings of subjective voice quality percepts that account for much of the variance in emotion inference, in particular voice instability and roughness. The general approach advocated here, as well as the specific results, open up new research strategies for work in psychology (specifically emotion and social perception research) and engineering and computer science (specifically research and development in the domain of affective computing, particularly on automatic emotion detection and synthetic emotion expression in avatars)