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First-order autoregressive models are popular to assess the temporal dynamics of a univariate process. Researchers often extend these models to include time-varying covariates, such as contextual factors, to investigate how they moderate processes' dynamics. We demonstrate that doing so has implications for how well one can estimate the autoregressive and covariate effects, as serial dependence in the variables can imply predictor collinearity. This is a noteworthy contribution, since in current practice serial dependence in a time-varying covariate is rarely considered important. We first recapitulate the role of predictor collinearity for estimation precision in an ordinary least squares context, by discussing how it affects estimator variances, covariances and correlations. We then derive a general formula detailing how predictor collinearity in first-order autoregressive models is impacted by serial dependence in the covariate. We provide a simulation study to illustrate the implications of the formula for different types of covariates. The simulation results highlight when the collinearity issue becomes severe enough to hamper interpretation of the effects. We also show that the effect estimates can be biased in small samples (i.e., 50 time points). Implications for study design, the use of time as a predictor, and related model variants are discussed. [ABSTRACT FROM AUTHOR]

Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications. [ABSTRACT FROM AUTHOR]

Students with dyslexia face particular challenges in higher education. Individuals with disabilities have a right to accommodations to guarantee equal opportunities and participation in education. The present study addresses (1) the frequency of functioning and participation problems of students with dyslexia in higher education, (2) the teaching and evaluation methods during which the functioning and participation problems emerge, and (3) the effectiveness of accommodations perceived by students with dyslexia. Results showed that students with dyslexia primarily have problems with reading comprehension, reading speed, spelling and text writing. Furthermore, most students with dyslexia experience more problems during classical teaching (e.g. lecture) or classical evaluation methods (e.g. written exam with open questions) than during dynamic or activating teaching methods (e.g. excursion or internship) or alternative evaluation methods (e.g. peer evaluation). Finally, perception of the effectiveness of accommodations depends strongly on the individual functioning and participation problems students with dyslexia experienced. [ABSTRACT FROM AUTHOR]

The communicative behavior of young children with significant cognitive and motor developmental delays is generally considered to be limited, idiosyncratic and non-intentional. At present, changes between and within children over time regarding their communicative behavior are hard to detect. This article describes an exploratory observational study that draws on data from the first data point of 38 children who are participating in a longitudinal project on the developmental trajectories of children with significant cognitive and motor developmental delays. The aims of this study were to (a) describe the participants' communicative behavior in detail with communication-related variables that reflect differences across individuals, (b) create summarizing variables and (c) explore whether subgroups of children can be detected. A self-developed coding scheme and descriptive statistics combined with correlational analyses were used, followed by a principal component analysis and visual inspection of the outcome of this analysis. The within-group differences related to communicative behavior was characterized using 16 variables. Based on these variables, three overarching components were formulated: communication proficiency, Expressions of Discomfort and Rejection and Differentiation According to Focus. All participating children were found to be unique in terms of their component scores and the relationship among their component scores. [ABSTRACT FROM AUTHOR]

The current study explored dynamics of secure state attachment expectations in everyday life in middle childhood, specifically state attachment carry-over and reactivity to experiences of caregiver support in the context of stress. In two independent samples (one community sample, N = 123; one adoption sample, N = 69), children (8–12 years) daily reported on their state attachment for respectively 14 and 7 consecutive days. Additionally, they reported daily on their experiences of distress and subsequent experiences of caregiver support. Results in both samples indicated that secure state attachment on a day-to-day basis is characterized by a significant positive carry-over effect, suggesting that state attachment fluctuations are (partially) self-predictive. In Study 1, experiencing no support following distress significantly related to intraindividual decreases in secure state attachment; in Study 2, experiencing effective support during distress related to intra-individual increases in secure state attachment. Taken together, the current studies provide novel and important insights into how state attachment temporally evolves on a day-to-day basis in middle childhood. [ABSTRACT FROM AUTHOR]

In many psychological research domains stimulus-response profiles are explained by conjecturing a sequential process in which some variables mediate between stimuli and responses. Charting sequential processes is often a complex task because (1) many possible mediating variables may exist, and (2) interindividual differences may occur in the relationship between these mediating variables and the response. Recently, Ceulemans and Van Mechelen ("Psychometrika" 73(1):107-124, 2008) addressed these challenges by developing the CLASSI model. A major drawback of CLASSI is that it requires information about the same set of stimuli for all participants (i.e., crossed data), whereas recently a number of data gathering techniques have been proposed in which the set of stimuli differs across participants, yielding nested data. Therefore we present the CLASSI-N model, which extends the CLASSI model to nested data. A simulated annealing algorithm is proposed. The results of a simulation study are discussed as well as an application to data concerning depression.

The popularity of today's blended courses in higher education is driven by the assumption that students are provided with a rich toolset that supports them in their learning process. However, little is known on how students actually use these tools and how this affects their performance for the course. The current study investigates how students use the entire toolset at their disposal, whether tool-use patterns can be found and if these patterns affect performance for the course. Logging students (n = 156) actions throughout the content management system and registering students' use of the face-to-face support in an undergraduate course, the study reveals large student differences and an underuse for some tool-types. Further to this, K-means cluster analysis reveals three distinct tool-use patterns or user profiles: the no-users, the intensive users and the incoherent users. These patterns are characterized by different tool-choices and even different use intensity among students. Evidence is retrieved that these tool-use differences are problematic since multivariate analysis of variance reveals significant performance effects. Hence, these results imply that not all students seem to profit from the learning affordances that are provided. Similar as evidence in controlled settings, the results suggest that learner control in using tools cannot be taken for granted. Consequently, this study legitimates more research into the influencing (student and context) variables that can explain these differences. (Contains 6 tables and 4 figures.)

Often problems result in the collection of coupled data, which consist of different N-way N-mode data blocks that have one or more modes in common. To reveal the structure underlying such data, an integrated modeling strategy, with a single set of parameters for the common mode(s), that is estimated based on the information in all data blocks, may be most appropriate. Such a strategy implies a global model, consisting of different N-way N-mode submodels, and a global loss function that is a (weighted) sum of the partial loss functions associated with the different submodels. In this paper, such a global model for an integrated analysis of a three-way three-mode binary data array and a two-way two-mode binary data matrix that have one mode in common is presented. A simulated annealing algorithm to estimate the model parameters is described and evaluated in a simulation study. An application of the model to real psychological data is discussed.

In psychological research, one often aims at explaining individual differences in S-R profiles, that is, individual differences in the responses (R) with which people react to specific stimuli (S). To this end, researchers often postulate an underlying sequential process, which boils down to the specification of a set of mediating variables (M) and the processes that link these mediating variables to the stimuli and responses under study. Obviously, a crucial task is to chart how the individual differences in the S-R profiles are caused by individual differences in the S-M link and/or by individual differences in the M-R link. In this paper we propose a new model, called CLASSI, which was explicitly designed for this task. In particular, the key principle of CLASSI consists of reducing the S, M, and R nodes of a sequential process to a few mutually exclusive types and inducing an S-M and an M-R person typology from the data, with the S-M person types being characterized in terms of "if" S type "then" M type rules and the M-R person types in terms of "if" M type "then" R type rules. As such, the S-M and M-R person types and their associated if-then rules represent the important individual differences in the S-M and M-R links of the sequential process under study. An algorithm to fit the CLASSI model is described and evaluated in a simulation study. An application of CLASSI to data from the behavioral domain of anger and sadness is discussed. Finally, we relate CLASSI to other methods and discuss possible extensions.

Hierarchical classes (HICLAS) models constitute a distinct family of structural models for N-way N-mode data. All members of the family include N simultaneous and linked classifications of the elements of the N modes implied by the data; those classifications are organized in terms of hierarchical, if-then-type relations. Moreover, the models are accompanied by comprehensive, insightful graphical representations. Up to now, the hierarchical classes family has been limited to dichotomous or dichotomized data. In the present paper we propose a novel extension of the family to two-way two-mode rating data (HICLAS-R). The HICLAS-R model preserves the representation of simultaneous and linked classifications as well as of generalized if-then-type relations, and keeps being accompanied by a comprehensive graphical representation. It is shown to bear interesting relationships with classical real-valued two-way component analysis and with methods of optimal scaling.

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite well in recovering the underlying truth but frequently end in a local minimum. In this paper we evaluate whether or not this local minimum problem can be mitigated by means of two common strategies for avoiding local minima in combinatorial data analysis: simulated annealing (SA) and use of a multistart procedure. In particular, we propose a generic SA algorithm for hierarchical classes analysis and three different types of random starts. The effectiveness of the SA algorithm and the random starts is evaluated by reanalyzing data sets of previous simulation studies. The reported results support the use of the proposed SA algorithm in combination with a random multistart procedure, regardless of the properties of the data set under study.

Several hierarchical classes models can be considered for the modeling of three-way three-mode binary data, including the INDCLAS model (Leenen, Van Mechelen, De Boeck, and Rosenberg, 1999), the Tucker3-HICLAS model (Ceulemans,VanMechelen, and Leenen, 2003), the Tucker2-HICLAS model (Ceulemans and Van Mechelen, 2004), and the Tucker1-HICLAS model that is introduced in this paper. Two questions then may be raised: (1) how are these models interrelated, and (2) given a specific data set, which of these models should be selected, and in which rank? In the present paper, we deal with these questions by (1) showing that the distinct hierarchical classes models for three-way three-mode binary data can be organized into a partially ordered hierarchy, and (2) by presenting model selection strategies based on extensions of the well-known scree test and on the Akaike information criterion. The latter strategies are evaluated by means of an extensive simulation study and are illustrated with an application to interpersonal emotion data. Finally, the presented hierarchy and model selection strategies are related to corresponding work by Kiers (1991) for principal component models for three-way three-mode real-valued data.

Recent studies showed that attachment security can change within persons, suggesting that there might be an interplay between a rather stable (trait) and rather variable (state) part of attachment. The study's first aim was to investigate whether attachment priming could influence the level of state attachment. The second aim was to explore possible moderators explaining individual differences in the relation between state attachment responses to attachment versus neutral primes. This can shed light on individual differences in attachment prime-induced state attachment. We conducted a within-subjects priming study in which children (9–13 years) were allocated to three priming procedures (neutral, secure, avoidant). Results showed an increase in secure state attachment after secure attachment priming. Individual differences in the strength of this effect were moderated by trait attachment and state attachment volatility. First findings cautiously suggest that state attachment security can change in response to environmental cues under certain circumstances. [ABSTRACT FROM AUTHOR]

This paper presents a new hierarchical classes model, called Tucker2-HICLAS, for binary three-way three-mode data. As any three-way hierarchical classes model, the Tucker2-HICLAS model includes a representation of the association relation among the three modes and a hierarchical classification of the elements of each mode. A distinctive feature of the Tucker2-HICLAS model, being closely related to the Tucker3-HICLAS model (Ceulemans, Van Mechelen & Leenen, 2003), is that one of the three modes is minimally reduced and, hence, that the differences among the association patterns of the elements of this mode are maximally retained in the model. Moreover, as compared to Tucker3-HICLAS, Tucker2-HICLAS implies three rather than four different types of parameters and as such is simpler to interpret. Two types of Tucker2-HICLAS models are distinguished: a disjunctive and a conjunctive type. An algorithm for fitting the Tucker2-HICLAS model is described and evaluated in a simulation study. The model is illustrated with longitudinal data on interpersonal emotions.