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The intelligent tutoring system of structure strategy (ITSS) is a web-based digital tutoring system proven to be effective in helping students recognize and use text structures to comprehend and recall texts. However, little is known about the dynamic learning processes within the ITSS. This study aims to investigate the effects of feedback dosage on lesson mastery throughout the progression of ITSS lessons. We applied a confirmatory factor analysis and extended vector autoregressive model to assess the dynamic relationships among three tasks embedded within the ITSS and found: (1) significant cross-regression effects among the three reading tasks; (2) distinct effects of feedback dosage on the specific reading task; and (3) different effect sizes of feedback across lessons. Results provide helpful insights on ways to design better modules in further development of the ITSS.

Introduction: Anti-GABABR encephalitis is a rare disease reported to be often associated with tumors. The current study aims to summarize the clinical characteristics, imaging features, treatments, outcomes and explore the potential prognosis risk factors of patients with anti-GABABR encephalitis. Methods: Patients tested positive for anti-GABABR were retrospective studied from a single medical center in China over a period of 3 years. They were followed up for a maximum period of 18 months. Clinical data were summarized and prognostic factors including demographic characteristics, laboratory tests, and neurological functions were compared between survived and deceased patients at 18 months follow-up. Results: Twenty-six patients, 10 females (38.5%) and 16 males (61.5%), diagnosed with anti-GABABR encephalitis were studied. The median age was 58 years. Of the 23 cases with complete clinical data, their main manifestations were epileptic seizures (65%), mental and behavioral abnormalities (52%), and cognitive impairment (48%). 7 (30.4%) cases had tumors: 5 small cell lung cancer (SCLC), 1 rectum adenocarcinoma (moderately differentiated) and 1 esophageal squamous cell carcinoma. MRI showed 5 (22%) cases had T2 FLAIR increased signals in cortex but with different regions affected. One of the two patients scanned for PET-CT showed hypermetabolism in the left temporal lobe region. The disease course ranged from 5 days to 3 years. 2 patients (one had esophageal carcinoma) without immunotherapy and 3 patients (one had SCLC) that did not response to immunotherapy died soon after diagnosis. 18 patients improved after immunotherapy while 3 (all had SCLC) died after relapses. The prevalence of epileptic seizures and malignancies was significantly lower in the survival group than in the deceased group at 18-months follow-up, the same as the admission mRs score. Serum fibrinogen, cerebrospinal fluid immunoglobulin G quotient, and 24-hour intrathecal synthesis rate were significantly lower in the survival groups as well. Conclusions: Cortex T2 FLAIR abnormalities were only observed in a small proportion of anti-GABABR encephalitis patients with heterogeneous MRI phenotypes. High mRS score at admission, epileptic seizures and the presence of a tumor indicated a poor prognosis, while the underlying mechanism of the later two factors should be investigated further. [ABSTRACT FROM AUTHOR]

An individual's emotions system can be conceived of as a synchronized, coordinated, and/or emergent combination of physiology, experience, and behavioral components. Together, the interplay among these components produce emotional experiences through coordinated excitatory positive feedback (i.e., the mutual amplification of emotion concordance) and/or inhibitory negative feedback (i.e., the damping of emotion regulation) processes. Different system configurations produce differential psychophysiological reactivity profiles, and by implication, differential moment-to-moment emotional experience and long-term development. Applying dynamic systems models to second-by-second psychophysiological and experience time-series data collected from 130 adolescents (age 12.0 to 16.7 years) completing a social stress-inducing speech task, we describe the configuration of adolescents' emotion systems, and examine how differences in the dynamic outputs of those systems (psychophysiological reactivity profile) are related to individual differences in trait anxiety. We found substantial heterogeneity in the coordination patterns of these adolescents. Some individuals' emotion systems were characterized by negative feedback loops (emotion regulation processes), many by unidirectionally connected or independent components, and a few by positive feedback loops (emotion concordance). The reactivity dynamics of respiratory sinus arrhythmia were related to adolescents' level of trait anxiety. Results highlight how dynamic systems models may contribute to our understanding of interindividual and developmental differences.

Objective: This study examined the role of dispositional mindfulness in impacting the relational benefits of yoga in novice practitioners in an undergraduate academic yoga course. Participants: 21 college students enrolled in a yoga course throughout a 15-week academic semester (January-April 2016). Methods: Participants attended lab assessments at the beginning and end of the semester and were asked to complete eight consecutive Internet-based daily surveys across six separate bursts, yielding 48 data points for each participant. Results: Multivariate time-series analyses revealed within-person subgroup differences such that in general, those in the high trait mindfulness group gleaned benefits from yoga practice with respect to relational outcomes. Conclusions: This study demonstrated trait mindfulness impacts relational outcomes in novice yoga practitioners, suggesting those low in trait mindfulness may not garner immediate relational benefits from yoga and could benefit from additional strategies to bolster against any negative influences of initiating yoga practice.

Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A novel feature of P-technique data is that observations are usually sequentially dependent in some way. The current study evaluates 10 criteria for determining the number of factors for data simulated to resemble intensive repeated-measures data for which P-technique is usually applied. The number of factors, loading size, observed-to-latent ratio, serial-dependency, and sample size were simulation design factors with conditions simulated to resemble P-technique data. The methods are compiled from various statistical techniques and have been demonstrated in previous R-technique simulations. Finally, a real data example demonstrates applying the acceleration factor method to several time series data sets.

Transitions requiring the creation of new social networks may be challenging for individuals vulnerable to social anxiety, which may hinder successful adjustment. Using person-specific methodology, this study examined social anxiety in vulnerable university freshman away from home during their first semester of college to understand how day-to-day processes of social anxiety influenced future social anxiety and social withdrawal. Participants completed daily measures of four components of social anxiety which were examined as a process during a single day, and as a process affecting social anxiety and social withdrawal the next day. For most individuals, same-day fear of negative evaluation was associated with maladaptive cognitive processes (anticipatory processing and post-event rumination) and influenced social withdrawal behaviors. Across time relations were less robust and varied between participants suggesting the importance of situational factors and individual differences. These findings may have implications for the effectiveness of various intervention strategies.

In this article, Peter Molenaar responds to three commentaries (this issue) on his article, "An Interpretation of Part of Gilbert Gottlieb's Legacy: Developmental Systems Theory Contra Developmental Behavior Genetics." He addresses aspects of relational developmental systems (RDS) mentioned and questions raised in each of the commentaries. [This commentary relates to Molenaar's "An Interpretation of Part of Gilbert Gottlieb's Legacy: Developmental Systems Theory Contra Developmental Behavior Genetics" (EJ1144157) and the following commentaries: "Developmental Systems Theory and the Person-Oriented Approach" (EJ1144417), "When the Stars Align: On the Contributions of Gilbert Gottlieb and Peter C. M. Molenaar to Developmental Science Theory and Method" (EJ1144159), and "Who Are We Talking About? A Discussion of Peter Molenaar's Interpretation of Gottlieb's Legacy" (EJ1144424).]

The main theme of this paper concerns the persistent critique of Gilbert Gottlieb on developmental behavior genetics and my reactions to this critique, the latter changing from rejection to complete acceptation. Concise characterizations of developmental behavior genetics, developmental systems theory (to which Gottlieb made essential contributions), ergodic theory (the implications of which underlie my change of opinion), as well as their relevant theoretical relations are given.

The fields of psychology and human development are experiencing a resurgence of scientific inquiries about phenomena that unfold at the level of the individual. This article addresses the issues of analyzing intraindividual psychological/developmental phenomena using standard analytical techniques for interindividual variation. When phenomena are characterized by intraindividual variation, analyses must be done at the level of the individual. Research on these processes generally uses methods analyzing interindividual variation, rather than intraindividual, however, a noteworthy fundamental difference exists between the two. The current article provides theoretical and empirical illustrations of the fundamental contrast between variation within subjects and variation between subjects. First, we explain this distinction through the classical ergodic theorems. Next, we discuss the utility of analysis of intraindividual variation for developmental processes. We then provide an empirical example, using recently-developed methods for analyzing person-specific developmental processes. This article presents for the first time a methodological approach in which first the factor model is determined by exploratory and confirmatory P-technique, and then in a separate second phase the dynamic model for the latent factor series is determined.

Background: Anti-GAD65 autoantibodies (GAD65-Abs) may occur in patients with epilepsy and other neurological disorders, but the clinical significance is not clear-cut. Whereas high levels of GAD65-Abs are considered pathogenic in neuropsychiatric disorders, low or moderate levels are only considered as mere bystanders in, e.g., diabetes mellitus type 1 (DM1). The value of cell-based assays (CBA) and immunohistochemistry (IHC) for GAD65-Abs detection has not been clearly evaluated in this context. Objective: To re-evaluate the assumption that high levels of GAD65-Abs are related to neuropsychiatric disorders and lower levels only to DM1 and to compare ELISA results with CBA and IHC to determine the additional value of these tests. Methods: 111 sera previously assessed for GAD65-Abs by ELISA in routine clinical practice were studied. Clinical indications for testing were, e.g., suspected autoimmune encephalitis or epilepsy (neuropsychiatric cohort; n = 71, 7 cases were initially tested positive for GAD65-Abs by ELISA), and DM1 or latent autoimmune diabetes in adults (DM1/LADA cohort (n = 40, all were initially tested positive)). Sera were re-tested for GAD65-Abs by ELISA, CBA, and IHC. Also, we examined the possible presence of GAD67-Abs by CBA and of other neuronal autoantibodies by IHC. Samples that showed IHC patterns different from GAD65 were further tested by selected CBAs. Results: ELISA retested GAD65-Abs level in patients with neuropsychiatric diseases was higher than in patients with DM1/LADA (only retested positive samples were compared; 6 vs. 38; median 47,092 U/mL vs. 581 U/mL; p = 0.02). GAD-Abs showed positive both by CBA and IHC only if antibody levels were above 10,000 U/mL, without a difference in prevalence between the studied cohorts. We found other neuronal antibodies in one patient with epilepsy (mGluR1-Abs, GAD-Abs negative), and in a patient with encephalitis, and two patients with LADA. Conclusion: GAD65-Abs levels are significantly higher in patients with neuropsychiatric disease than in patients with DM1/LADA, however, positivity in CBA and IHC only correlates with high levels of GAD65-Abs, and not with the underlying diseases. [ABSTRACT FROM AUTHOR]

In this short contribution I introduce myself to the readership of the "International Journal of Developmental Science" by describing some high points in my scientific career, leading up to a major change in my perspective on the proper way to conduct empirical research in developmental science.

This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis Index (TLI) to reject misspecified models with varying degrees of misspecification. With a sample size of 20, RMSEA, CFI, and TLI are high in both Type I and Type II error rates, whereas LRT has a high Type II error rate. With a sample size of 100, these indexes generally have satisfactory performance, but CFI and TLI are affected by a confounding effect of their baseline model. Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) have high success rates in identifying the true model when sample size is 100. A comparison with the mixed model approach indicates that separately modeling the means and covariance structures in structural equation modeling dramatically improves the success rate of AIC and BIC. (Contains 4 tables and 5 figures.)

Molenaar (2003, 2011) showed that a common factor model could be transformed into an equivalent model without factors, involving only observed variables and residual errors. He called this invertible transformation the Houdini transformation. His derivation involved concepts from time series and state space theory. This article verifies the Houdini transformation on a general latent variable model using algebraic methods. The results show that the Houdini transformation is illusory in the sense that the Houdini-transformed model remains a latent variable model. Contrary to common knowledge, a model that is a path model with only observed variables and residual errors may, in fact, be a latent variable model.

Objective: The present article aimed to demonstrate that the establishment of dynamic patterns during the course of psychotherapy can create attractor states for continued adaptive change following the conclusion of treatment. Method: This study is a secondary analysis of T. D. Borkovec and E. Costello (1993). Of the 55 participants in the original study, 33 were retained for the present analysis due to the homogeneity of psychotherapy outcome among these participants. Of these 33, the majority were White (88%) and female (70%), and the average age was 35.44 years (SD = 14.46). Participants participated in 12 weeks of either cognitive behavioral therapy or applied relaxation. Daily diary entries from the treatment period were subjected to time series analyses in order to determine the degree of order versus disorder present within individual dynamic systems. These idiographic data were then aggregated for nomothetic analysis of treatment outcome via linear mixed effect models. Results: Spectral power due to daily to intradaily oscillations in thrice-daily diary data significantly moderated reliable change over posttreatment follow-up such that lesser power predicted increases in reliable change over the 1-year follow-up period. Additionally, residual variance for dynamic factor models significantly moderated the slope for change over the follow-up period, such that lesser variance--and thus greater order in dynamic systems--predicted increases in reliable change. Conclusions: The degree of order in dynamic systems established during therapy acted as an adaptive attractor state, promoting continued positive gains 1 year after the conclusion of therapy. The present study represents an important innovation in the study of dynamic systems in psychotherapy. (Contains 4 tables, 6 footnotes and 5 figures.)

Definitions of measurement equivalence in terms of invariance can be specified at a very general formal level. Presently, however, only the operationalization of measurement equivalence in terms of factor models is at stake. In this article, the author discusses the proposed alternative definition in terms of idiographic filters in the context of P-technique, the state space model, and the general dynamic factor model.

It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even though it does not explicitly model lagged information, P-technique's ability to recover the parameters of underlying dynamic processes involving lagged relations among the manifest variables is apparently robust and accurate. An empirical example is presented using 103 days of affective mood self-ratings from a young pregnant woman. Implications of the simulation and empirical findings are briefly discussed. (Contains 6 tables and 1 footnote.)

Individuals change over time, often in complex ways. Generally, studies of change over time have combined individuals into groups for analysis, which is inappropriate in most, if not all, studies of development. The authors explain how to identify appropriate levels of analysis (individual vs. group) and demonstrate how to estimate changes in developmental processes over time using a multivariate nonstationary time series model. They apply this model to describe the changing relationships between a biological son and father and a stepson and stepfather at the individual level. The authors also explain how to use an extended Kalman filter with iteration and smoothing estimator to capture how dynamics change over time. Finally, they suggest further applications of the multivariate nonstationary time series model and detail the next steps in the development of statistical models used to analyze individual-level data. (Contains 1 table and 3 figures.)

In his introduction to this special issue, Roger Millsap has given an excellent summary of its contents. This eases our task considerably. In what follows, we first present some general observations about growth curve modeling of longitudinal data. Among other things, we will address the criticism that the popular latent growth curve model may yield spurious (linear, quadratic, etc.) components. Then we will proceed with a discussion of each of the 5 contributing papers. In closing, we will draw attention to new methodological developments that are of interest to analyses of developmental processes in general.

In this contribution it is shown that Gilbert Gottlieb's theoretical contributions to developmental science, in particular his focus on individual development and his discussion of the limitations of developmental behavior genetics in this respect, are vindicated by recent theoretical developments in mathematical biology and psychometrics.