Behavior Modification最新文献

There has been growing interest in using statistical methods to analyze data and estimate effect size indices from studies that use single-case designs (SCDs), as a complement to traditional visual inspection methods. The validity of a statistical method rests on whether its assumptions are plausible representations of the process by which the data were collected, yet there is evidence that some assumptions-particularly regarding normality of error distributions-may be inappropriate for single-case data. To develop more appropriate modeling assumptions and statistical methods, researchers must attend to the features of real SCD data. In this study, we examine several features of SCDs with behavioral outcome measures in order to inform development of statistical methods. Drawing on a corpus of over 300 studies, including approximately 1,800 cases, from seven systematic reviews that cover a range of interventions and outcome constructs, we report the distribution of study designs, distribution of outcome measurement procedures, and features of baseline outcome data distributions for the most common types of measurements used in single-case research. We discuss implications for the development of more realistic assumptions regarding outcome distributions in SCD studies, as well as the design of Monte Carlo simulation studies evaluating the performance of statistical analysis techniques for SCD data.

The continuation of a baseline pattern of responding into a treatment phase, sometimes referred to as a "transition state," can obscure interpretation of data depicted in single-case experimental designs (SCEDs). For example, when using visual analysis, transition states may lead to the conclusion that the treatment is ineffective. Likewise, the inclusion of overlapping data points in some statistical analyses may lead to conclusions that the treatment had a small effect size and give rise to publication bias. This study reviewed 20 volumes in a journal that publishes primarily SCEDs studies. We defined a transition state as a situation wherein at least the first three consecutive data points of a treatment phase or condition are within the range of the baseline phase or condition. Results indicate that transitions states (a) were present for 7.4% of graphs that met inclusion criteria and (b) occurred for a mean of 4.9 data points before leading to behavior change. We discuss some implications and directions for future research on transition states.

Multilevel modeling (MLM) is an approach for meta-analyzing single-case experimental designs (SCED). In this paper, we provide a step-by-step guideline for using the MLM to meta-analyze SCED time-series data. The MLM approach is first presented using a basic three-level model, then gradually extended to represent more realistic situations of SCED data, such as modeling a time variable, moderators representing different design types and multiple outcomes, and heterogeneous within-case variance. The presented approach is then illustrated using real SCED data. Practical recommendations using the MLM approach are also provided for applied researchers based on the current methodological literature. Available free and commercial software programs to meta-analyze SCED data are also introduced, along with several hands-on software codes for applied researchers to implement their own studies. Potential advantages and limitations of using the MLM approach to meta-analyzing SCED are discussed.

Single-case experimental designs often require extended baselines or the withdrawal of treatment, which may not be feasible or ethical in some practical settings. The quasi-experimental AB design is a potential alternative, but more research is needed on its validity. The purpose of our study was to examine the validity of using nonoverlap measures of effect size to detect changes in AB designs using simulated data. In our analyses, we determined thresholds for three effect size measures beyond which the type I error rate would remain below 0.05 and then examined whether using these thresholds would provide sufficient power. Overall, our analyses show that some effect size measures may provide adequate control over type I error rate and sufficient power when analyzing data from AB designs. In sum, our results suggest that practitioners may use quasi-experimental AB designs in combination with effect size to rigorously assess progress in practice.

Practitioners frequently use single-case data for decision-making related to behavioral programming and progress monitoring. Visual analysis is an important and primary tool for reporting results of graphed single-case data because it provides immediate, contextualized information. Criticisms exist concerning the objectivity and reliability of the visual analysis process. When practitioners are equipped with knowledge about single-case designs, including threats and safeguards to internal validity, they can make technically accurate conclusions and reliable data-based decisions with relative ease. This paper summarizes single-case experimental design and considerations for professionals to improve the accuracy and reliability of judgments made from single-case data. This paper can also help practitioners to appropriately incorporate single-case research design applications in their practice.

The purpose of this manuscript is to provide an overview of, and rationale for, the increasing adoption of a wide range of cutting-edge technological methods in assessment and intervention which are relevant for treatment. First, we review traditional approaches to measuring and monitoring affect, behavior, and cognition in behavior and cognitive-behavioral therapy. Second, we describe evolving active and passive technology-enabled approaches to behavior assessment including emerging applications of digital phenotyping facilitated through fitness trackers, smartwatches, and social media. Third, we describe ways that these emerging technologies may be used for intervention, focusing on novel applications for the use of technology in intervention efforts. Importantly, though some of the methods and approaches we describe here warrant future testing, many aspects of technology can already be easily incorporated within an established treatment framework.

Although efficacious treatments exist for anxiety disorders, issues remain regarding how best to conceptualize and measure purported change processes in clinical research. In the current study, we examined the relationship between treatment-specific (exposure therapy, attention bias modification [ABM]) as well as more general change processes with symptoms within a transdiagnostic sample using mixed models. Results indicated that slope of self-efficacy across treatment and between-session habituation across identical exposures was associated with slope of symptom change. Although slope of anxiety ratings within session was not associated with slope of symptom change, it did interact with other candidate exposure processes to predict symptoms. Purported ABM change processes were not associated with outcome. Our use of mixed models exemplifies an emerging trend in this research aimed at minimizing loss of data through aggregation, and our results highlight the utility of integrating treatment-specific as well as more general change processes in mechanistic research.

Hierarchical linear modeling (HLM) has been recommended as a meta-analytic technique for the quantitative synthesis of single-case experimental design (SCED) studies. The HLM approach is flexible and can model a variety of different SCED data complexities, such as intervention heterogeneity. A major advantage of using HLM is that participant and-or study characteristics can be incorporated in the model in an attempt to explain intervention heterogeneity. The inclusion of moderators in the context of meta-analysis of SCED studies did not yet receive attention and is in need of methodological research. Prior to extending methodological work validating the hierarchical linear model including moderators at the different levels, an overview of characteristics of moderators typically encountered in the field is needed. This will inform design conditions to be embedded in future methodological studies and ensure that these conditions are realistic and representative for the field of SCED meta-analyses. This study presents the results of systematic review of SCED meta-analyses, with the particular focus on moderator characteristic. The initial search yielded a total of 910 articles and book chapters. After excluding duplicate studies and non peer-reviewed studies, 658 unique peer-reviewed studies were maintained and screened by two independent researchers. Sixty articles met the inclusion criteria and were eligible for data retrieval. The results of the analysis of moderator characteristics retrieved from these 60 meta-analyses are presented. The first part of the results section contains an overview of moderator characteristics per moderator level (within-participant level, participant level, and study level), including the types of moderators, the ratio of the number of moderators relative to the number of units at that level, the measurement scale, and the degree of missing data. The second part of the results section focuses on the metric used to quantify moderator effectiveness and the analysis approach. Based on the results of the systematic review, recommendations are given for conditions to be included in future methodological work.

To strengthen the scientific credibility arguments for single-case intervention studies, randomization design-and-analysis methods have been developed for the multiple-baseline, ABAB, and alternating treatment designs, including options for preplanned designs, wherein the series and phase lengths are established prior to gathering data, as well as options for response-guided designs, wherein ongoing visual analyses guide decisions about when to intervene. Our purpose here is to develop randomization methods for another class of single-case design, the changing criterion design. We first illustrate randomization design-and-analysis methods for preplanned changing criterion designs and then develop and illustrate methods for response-guided changing criterion designs. We discuss the limitations associated with the randomization methods and the validity of the corresponding intervention-effect inferences.

Disruptive behaviors such as elopement, calling-out, and aggression are often a major barrier to instruction in preschool classrooms. One widely used class-wide behavior management system is Class-Wide Function-Related Intervention Teams (CW-FIT). To date, we could only locate two studies on CW-FIT used in preschool settings which found a therapeutic change in on-task behavior as well rates of teacher praise, teacher reprimands, student socials skills, and student problem behaviors. The current study used a withdrawal design to also evaluate the effectiveness of Tier 1 CW-FIT on on-task student behavior and teacher praise and reprimand behavior in a preschool setting during both large and small group activities. Results suggested that the implementation of the Tier 1 components of CW-FIT increased on-task group behavior in both settings. Results for rates of teacher's praise and reprimand statements were variable.