The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model

A three-level piecewise growth model (3L-PGM) can be used to break up nonlinear growth into multiple components, providing the opportunity to examine potential sources of variation in individual and contextual growth within different segments of the model. The conventional 3L-PGM assumes that the data are strictly hierarchical in nature, where measurement occasions (level 1) are nested within individuals (level 2) who are members of a single cluster (level 3). However, in longitudinal research, it is sometimes difficult for data structures to remain purely clustered during a study, such as when some students change classrooms or schools over time. One resulting data structure in this situation is known as a multiple membership structure, where some lower-level units are members of more than one higher-level unit. The new multiple membership PGM (MM-PGM) extends the 3L-PGM to handle multiple membership data structures frequently found in the social sciences. This study sought to examine the consequences of ignoring individual mobility across clusters when estimating a 3L-PGM in comparison to estimating a MM-PGM. MM-PGM estimates were less biased (especially in the cluster-level coefficient estimates), although we found substantial bias in cluster-level variance components across some conditions for both models.     

Computer‐based training to teach observers to accurately score problem behavior using fast forwarding at 5x normal speed

The current study evaluated whether a computer‐based training program could improve observers' accuracy in scoring discrete instances of problem behavior at 5x normal speed using a multiple‐baseline design across subjects. During pretraining and posttraining, observers attempted to score multiple examples of problem behavior at 5.0x without feedback. During training, participants scored multiple examples of problem behavior at 5.0x with automated feedback. Researchers measured omission (missing problem behavior) and commission (scoring other behavior as problem behavior) errors and the total duration of scoring time to determine the observers' accuracy and efficiency, respectively. After training, all participants scored instances of problem behavior with less than 11% error using 5.0x. The time required to score the videos across 90‐min observations was reduced by 66%. Results extend previous evaluations of fast forwarding by demonstrating that the training program could be used to teach observers to accurately score problem behavior using a speed faster than 3.5x.     

Comparing computer‐based training and lecture formats to teach visual analysis of baseline‐treatment graphs

Accurate analysis of data is vital to the validation of interventions. As such, there has been a recent increase in studies evaluating visual analysis training procedures. However, past investigations have not evaluated direct and indirect visual analysis training methods with matched instructional content that was systematically designed. Furthermore, training has rarely included assessment of generalization and maintenance of visual analysis skills. The purpose of the current dissertation study was to compare the effectiveness and efficiency of (a) computer‐based training, (b) lecture formats with and (c) without the opportunity to pause, and (d) a no‐training group to teach visual analysis of AB graphs to university students. To make these formats directly comparable, the instructional content was equated by ensuring information and examples were identical across the three training procedures. Eighty‐three students were randomly assigned to one of the four groups. Results showed that all three training formats produced increases in accurate responding compared to the no‐training group. Visual analysis skills generalized to novel graphs and maintained approximately 2 weeks following all trainings. These results suggest that structured approaches that are carefully designed to train visual analysis are effective and lead to gains that generalize and maintain in the absence of training.     

Preference structures and threshold models

Preferences are often represented in terms of a function, in the deterministic case as well as in the probabilistic case. In the present paper we develop a new numerical representation of preference structures for which the strict preference relation (P) is without circuit but not necessarily transitive. Moreover, we investigate the consequences of the representation for the usual preference structures. In particular, we propose new formulations for the numerical representation of the interval order structure.     

A brief review of technology-based antecedent training procedures

This brief review summarizes recent research on technology-based antecedent staff training procedures, highlights key methodological components, and concludes with potential directions for future research.     

Idiographic network analysis of discrete mood states prior to treatment

Idiographic network models based on time‐series data have received recent attention for their ability to model relationships among symptoms and behaviours as they unfold in time within a single individual (cf. Epskamp, Borsboom, & Fried, 2018; Fisher, Medaglia, & Jeronimus, 2018). Rather than examine the correlational relationships between variables in a sample of individuals, an idiographic network examines correlations within a single person, averaged over many time points. Because the approach averages over time, the data must be stationary (i.e. relatively consistent over time). If individuals experience varying states over time—different mixtures of symptoms and behaviours in one moment or another—then averaging over categorically different moments may undermine model accuracy. Fisher and Bosley (2019) address these concerns via the application of Gaussian finite mixture modelling to identify latent classes of time points in intraindividual time‐series data from a sample of adults with major depressive disorder and/or generalised anxiety disorder (n = 45). The present paper outlines an extension of this work, wherein network analysis is used to model within‐class covariation of symptoms. To illustrate this approach, network models were constructed for each intraindividual class identified by Fisher and Bosley (137 networks across the 45 participants, mean classes/person = ~3, range = 2–4 classes/person). We examine the relative consistency in symptom organisation between each individual's multiple mood state networks and assess emergent group‐level patterns. We highlight opportunities for enhanced treatment personalisation and review nomothetic patterns relevant to transdiagnostic conceptualisations of psychopathology. We address opportunities for integrating this approach into clinical practice and outline potential shortcomings.     

Dimensions of fatigue in different working populations

Perceived fatigue related to work has often been measured in one dimension. The main purpose of the present study was to validate a proposed five-factor model of perceived fatigue in a new sample. 597 persons, employed in five occupations with different types of work loads, rated their fatigue after work. The ratings were subjected to analyses of linear structural equation models. The results suggest a slightly revised model for perceived fatigue, still with the five dimensions: Lack of energy, Physical exertion, Physical discomfort, Lack of motivation and Sleepiness. As expected, the rating profiles describing fatigue states differed between the five occupations. On the basis of these results, a revised version of the Swedish Occupational Fatigue Inventory (SOFI) is presented.     

Applications of computer-based instruction: using specialized software to aid letter-name and letter-sound recognition

We evaluated computerized training and testing programs with children who were having difficulties learning prereading skills. The programs were derived from equivalence research and were written in authoring software designed for educators. After learning to match uppercase and lowercase printed letters to the corresponding letter names (Tasks 1 and 2), the children matched the letters to one another (Tasks 4 and 5). Then, after learning to match uppercase letters to sounds (Task 3), they also matched lowercase letters to sounds (Task 6) and matched printed to spoken words (Tasks 7 and 8). The results recommend equivalence-based protocols and user-friendly software in further development of prereading instruction.     

Relations among equivalence, naming, and conflicting baseline control

Three studies were conducted with different groups of 6 students each to explore the effects of training class-inconsistent relations and naming on demonstrations of emergent arbitrary stimulus relations. In all studies, two three-member equivalence classes of Greek symbols (A1B1C1 and A2B2C2) emerged as a result of training in conditional discriminations. Two new symbols were introduced (X and Y), and additional conditional discriminations were trained, whereby X was designated as the positive discriminative stimulus (S+) and Y was designated as the negative discriminative stimulus (S-) for A1 and B2. Conversely, Y was designated as the S+ and X as the S- for B1 and A2. This introduced conflicting sources of control within and between classes. In Study 1, subjects were not provided with names for the stimuli. In Study 2, the experimenter provided common names for the stimuli within each class. In Study 3, the subjects were required to use the common names during conditional discrimination training and test-trial blocks. In all experiments, equivalence responding with respect to the original classes was disrupted for some subjects subsequent to learning the new relations. Furthermore, in Studies 2 and 3, there were frequent examples of noncorrespondence between observed (listener or speaker) naming patterns and derived relations. These results support the view that demonstrations of equivalence are subject to control from a variety of sources rather than being fundamentally dependent on naming.     

验证性因素分析:问卷题数及小样本应用策略

验证性因素分析在心理、教育等研究应用日广。本文引用三个模拟数据研究,提出一些在分析时的应用策略,包括用较多题目及强制同一因子的负荷相等。样本容量一般愈大愈好,但当资源等条件有限制时,上述两个技巧,可协助解决叠代收敛问题,并能更准确估计各重要参数。此外,一些方法如：每因子只得两题目、当样本只得约１００人时每因子用少於四题目、将题目合并以其平均数为指标,均可能导致不收敛、高标准误及偏差估计量等问题,不值得推荐。