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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.

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.

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.

This article presents a rejoinder to Rogosa's (2004) commentary on the author's (Molenaar, 2004) focus article titled, "A Manifesto on Psychology as Idiographic Science." The expert commentary of Rogosa brings up some central issues that require careful evaluation. The basic message of the author's focus article was straightforward: In general, there exists no relation between the results obtained in analyses of interindividual variation (IEV) and the results of analogous analyses of intraindividual variation (IAV). This far-reaching conclusion follows directly from general mathematical-statistical theorems, the so-called classical ergodic theorems. It concerns the fundamental difference between the phenomenon of IEV (individual differences) and the phenomenon of IAV. The author stresses that this fundamental difference pertains to the kind of variation itself: variation between participants (IEV) on one hand and variation within a participant (IAV) on the other hand. Rogosa (2004) discussed a number of statistical model types such as individual growth-curve models and other so-called individual-unit models. The models referred to have been applied only in analyses of IEV but never in analyses of IAV. However, that is not mandatory; the renewal models mentioned by Rogosa, for instance, have important applications in intraindividual signal analysis of, for example, cardiac interbeat intervals. The important question is whether applications are to IEV or to IAV, knowing that in general the results may differ in arbitrary ways if the empirical processes under scrutiny are nonergodic. In this rejoinder, the author elaborates these qualifications further with respect to the simplest version of the individual growth-curve model referred to by Rogosa. He explains that the individual growth-curve model is an instance of T-technique as defined by Cattell (1963) and hence, pertains only to analysis of IEV.

Results obtained with interindividual techniques in a representative sample of a population are not necessarily generalizable to the individual members of this population. In this article the specific condition is presented that must be satisfied to generalize from the interindividual level to the intraindividual level. A way to investigate whether this condition is satisfied is by means of multivariate time series analysis. More generally, time series analysis can be used to investigate psychological processes situated within individuals. In this article we consider a well established class of multivariate stationary time series models that may be used to study the intraindividual covariance structure. We demonstrate the application of some of these models with an empirical example consisting of state measurements of behavior associated with the Five Factor Model of Personality. We illustrate how one can investigate whether individuals are similar with respect to their intraindividual structure of variation, and whether this structure is similar to the structure of interindividual variation.

In this article we show the one-factor model can be rewritten as a quasi-simplex model. Using this result along with addition theorems from time series analysis, we describe a common general model, the nonstationary autoregressive moving average (NARMA) model, that includes as a special case, any latent variable model with continuous indicators and continuous latent variables. As an example, we show the NARMA representations of the linear growth curve model and the growth curve model with estimated basis vector coefficients. In certain instances rewriting competing models may help the investigator to compare different models. Here we compare the "hybrid" behavior genetics model of Eaves and Hewitt to the quasi-simplex model of Boomsma and Molenaar and show that both have equivalent NARMA representations which differ only in order.

The esthetics of anterior lithium disilicate restorations can be enhanced if the buccal aspect is layered with a feldspathic ceramic. However, whether fractures and chipping of this layer are a prevalent complication is unclear. The purpose of this retrospective study was to evaluate the incidence of incisal fracture of a specially designed lithium disilicate reinforcement of the incisal edge for indirect anterior bilayered restorations on both teeth and implants. A total of 924 anterior bilayered pressed lithium disilicate restorations in 324 patients and made in one dental laboratory were delivered by 4 restorative dentists. The restorations had the palatal side of the incisal edge in monolithic lithium disilicate and the facial side in feldspathic porcelain. The restorations were evaluated for survival and the occurrence of fracture or chipping. Survival analyses were performed by using the Kaplan-Meier and log rank (Mantel-Cox) tests (α=.05). Of the 924 restorations, 798 (236 complete crowns, 562 partial restorations) were placed on teeth and 126 on implants. The mean observation time was 38 months (3 to 72 months). The survival rate was 96.5%, with 14 failures occurring. The failures were fracture after dental trauma (n=5), ceramic fracture (n=1), debonding (n=6), poor shade match (n=1), and tooth loss (n=2). Restorations in patients with parafunctional habits and endodontically treated teeth showed a significant decrease in survival rate (P =.018). No significant differences were found between the survival of restorations on teeth and implants and between complete crowns and partial restorations (P =.021). No chipping was observed on any restorations in the study. Modified anterior bilayered ceramic restorations showed good survival rates, and no chipping was observed up to 6 years of follow-up. Parafunctional habits and endodontic treatment had a negative effect on the survival rate of restorations. The support of tooth or implant and the restoration type had no effect on the survival. [ABSTRACT FROM AUTHOR]

Neural networks are applied to a theoretical subject in developmental psychology: modeling developmental transitions. Two issues that are involved will be discussed: discontinuities and acquiring qualitatively new knowledge. We will argue that by the appearance of a bifurcation, a neural network can show discontinuities and may acquire qualitatively new knowledge. First, it is shown that biological principles of neurite outgrowth result in self-organization in a neural network, which is strongly dependent on a bifurcation in the activity dynamics. Second, the effect of a bifurcation due to morphological change is investigated in an Adaptive Resonance Theory (ART) network. Exact ART networks with quantitative differences in network structure at the category level show qualitatively different dynamical regimes, which are separated by bifurcations. These qualitative differences in dynamics affect the cognitive function of Exact ART: Representations of learned categories are local or distributed.

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators always results in an increase in the coefficient of determination and so in the power. We note that the coefficient of determination plays a similar role in determining the error variance of predicted factor scores.

Psychology is focused on variation between cases (interindividual variation). Results thus obtained are considered to be generalizable to the understanding and explanation of variation within single cases (intraindividual variation). It is indicated, however, that the direct consequences of the classical ergodic theorems for psychology and psychometrics invalidate this conjectured generalizability: only under very strict conditions--which are hardly obtained in real psychological processes--can a generalization be made from a structure of interindividual variation to the analogous structure of intraindividual variation. Illustrations of the lack of this generalizability are given in the contexts of psychometrics, developmental psychology, and personality theory. (Contains 3 footnotes.)

Studied the relationship between event-related brain activity, age, and intelligence using a visual oddball task presented to girls at 9, 10, and 11 years of age. Findings for 26 girls suggest a qualitative shift in the relation between event-related brain activity and intelligence between 9 and 10 years of age. (SLD)

Demonstrated, through simulation, that stationary autoregressive moving average (ARMA) models may be fitted readily when T>N, using normal theory raw maximum likelihood structural equation modeling. Also provides some illustrations based on real data. (SLD)

Developed a method to determine the required number and position of sensors for human brain electromagnetic source analysis. Studied the method through a simulation study and an empirical study on visual evoked potentials in one adult male. Results indicate the method is fast and reliable and improves source precision. (SLD)

Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those obtained by stochastic model preliminary estimation and are not true maximum likelihood estimates. (SLD)

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average. (Author/SLD)

Presents a method for estimating the random coefficients model using covariance structure modeling and allowing one to estimate both fixed and random effects. The method is applied to real and simulated data, including marriage data from J. Belsky and M. Rovine (1990). (SLD)

Presents a LISREL model for the estimation of the repeated measures analysis of variance (ANOVA) with a patterned covariance matrix. The model is demonstrated for a 5 x 2 (Time x Group) ANOVA in which the data are assumed to be serially correlated. Similarities with the Statistical Analysis System PROC MIXED model are discussed. (SLD)

Pseudo-Maximum Likelihood (p-ML) and Asymptotically Distribution Free (ADF) estimation methods for estimating dynamic factor model parameters within a covariance structure framework were compared through a Monte Carlo simulation. Both methods appear to give consistent model parameter estimates, but only ADF gives standard errors and chi-square statistics that appear consistent. (SLD)

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using autoand cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. [ABSTRACT FROM AUTHOR]

Studied the conditions under which the quasi-Markov simplex model fits a linear growth curve covariance structure and determined when the model is rejected. Presents a quasi-Markov simplex model with structured means and gives an example. (SLD)

The dynamic factor model proposed by P. C. Molenaar (1985) is exhibited, and a dynamic nonstationary factor model (DNFM) is constructed with latent factor series that have time-varying mean functions. The use of a DNFM is illustrated using data from a television viewing habits study. (SLD)