Search

Single-case designs (SCDs) are used to evaluate the effects of interventions on individual participants. By repeatedly measuring participants under different conditions, SCD studies focus on individual effects rather than on group summaries. The main limitation of SCDs remains its generalisability to wider populations, reducing the relevance of their findings for practice and policy making. With this limitation in mind, methodological developments for synthesising SCD data from different studies that investigate the same research question have intensified in the past decades (e.g. multilevel modelling). However, these techniques are restricted to comparing two interventions at a time and can only incorporate evidence from studies that directly compare the two treatments of interest. These limitations could be addressed by using network meta-analysis that incorporates both direct and indirect evidence to simultaneously compare multiple interventions. Despite its potential, network meta-analytical techniques have yet to be applied to SCD data. Thus, in this paper, we argue that network meta-analysis can be a valuable tool to synthesise SCD data. We demonstrate the use of network meta-analysis in SCD data using a real dataset, and we conclude by reflecting on the challenges that SCD researchers might face when applying network meta-analysis methods to their data.

Children differ in their sensitivity to positive and negative environmental influences, which can be measured with the Highly Sensitive Child (HSC) scale. The present study introduces the HSC-21, an adaptation of the original 12 item scale with new items and factor structure that are meant to be more informative than the original ones. The psychometric properties of the HSC-21 were investigated in 1,088 children across Belgium and the Netherlands, including child and mother reports. Results showed evidence for (a) bifactor model with a general sensitivity factor and two specific factors (i.e., Ease of Excitation-Low Sensory Threshold and Aesthetic Sensitivity); (b) (partial) measurement invariance across gender, developmental stage, country, and informants; (c) moderate child-mother agreement; (d) good reliability; (e) normally distributed item scores; and (f) meaningful associations with personality and temperament across both samples. No evidence was found for HSC-21 as a moderator in the relationship between parenting and problem behaviors. ispartof: Assessment vol:29 issue:4 pages:1-23 ispartof: location:United States status: Published online

This article presents an updated meta-analysis of survey experiments comparing the performance of the item count technique (ICT) and the direct questioning method. After synthesizing 246 effect sizes from 54 studies, we find that the probability that a sensitive item will be selected is .089 higher when using ICT compared to direct questioning. In recognition of the heterogeneity across studies, we seek to explain this variation by means of moderator analyses. We find that the relative effectiveness of ICT is moderated by cultural orientation in the context in which ICT is conducted (collectivism vs. individualism), the valence of topics involved in the applications (socially desirable vs. socially undesirable), and the number of nonkey items. In the Discussion section, we elaborate on the methodological implications of the main findings.

Previous research has introduced several effect size measures (ESMs) to quantify data aspects of single-case experimental designs (SCEDs): level, trend, variability, overlap, and immediacy. In the current article, we extend the existing literature by introducing two methods for quantifying consistency in single-case A-B-A-B phase designs. The first method assesses the consistency of data patterns across phases implementing the same condition, called CONsistency of DAta Patterns (CONDAP). The second measure assesses the consistency of the five other data aspects when changing from baseline to experimental phase, called CONsistency of the EFFects (CONEFF). We illustrate the calculation of both measures for four A-B-A-B phase designs from published literature and demonstrate how CONDAP and CONEFF can supplement visual analysis of SCED data. Finally, we discuss directions for future research. ispartof: Behavior Modification vol:44 issue:4 pages:518-551 ispartof: location:United States status: published

Interpersonal theories of depression postulate that depressed individuals’ experience of social isolation is attributable, in part, to their tendency to behave in ways that elicit rejection from others. Depression contagion has been implicated as a factor that may account for the rejection of depressed individuals. In the current study, we revisit this hypothesis by using a controlled, but realistically motivated, setting: speed dating. Approximately 2 weeks before the speed-dating event, participants’ depression levels were assessed. During the event, participants had 4-min “dates” with opposite-sex partners. After each date, they responded to items that measured their affect and romantic attraction. At the end of the evening, participants indicated which partners they wanted to see again. Our results did not support depression contagion: After 4 min of interaction with partners with high levels of depressive symptoms, participants did not experience increased negative affect; instead, they experienced reduced positive affect, which led to the rejection of these partners.

In special education, multilevel models of single-case research have been used as a method of estimating treatment effects over time and across individuals. Although multilevel models can accurately summarize the effect, it is known that if the model is misspecified, inferences about the effects can be biased. Concern with the potential for model misspecification motivates our method for evaluating multilevel models of single-case data. This method is based on the visual analysis of graphs that have the model-implied individual trajectories superimposed on plots of the raw data. Through the reanalysis of a published study, we show how this visual analysis approach can identify model misspecifications and motivate the consideration of alternative model specifications that lead to better fit.

Uncertainty persists regarding adequate measurement of parenting behavior during early adolescence. The present study aimed to clarify the conceptual structure of parenting by evaluating three different models that include support, psychological control, and various types of behavioral control (i.e., proactive, punitive, and harsh punitive control). Furthermore, we examined measurement invariance of parenting ratings by 1,111 Flemish adolescents from Grade 7 till 9, their mother, and father. Finally, criterion validity of parenting ratings was estimated in relation to adolescent problem behavior. Results supported a five-factor parenting model indicating multiple aspects of behavioral control, with punitive and harsh punitive control as more intrusive forms and proactive control as a more supportive form. Similar constructs were measured for adolescents, mothers, and fathers (i.e., configural and metric invariance), however on a different scale (i.e., scalar noninvariance). Future research and clinical practices should acknowledge these findings in order to fully grasp the parenting process.

The quantitative methods for analyzing single-subject experimental data have expanded during the last decade, including the use of regression models to statistically analyze the data, but still a lot of questions remain. One question is how to specify predictors in a regression model to account for the specifics of the design and estimate the effect size of interest. These quantitative effect sizes are used in retrospective analyses and allow synthesis of single-subject experimental study results which is informative for evidence-based decision making, research and theory building, and policy discussions. We discuss different design matrices that can be used for the most common single-subject experimental designs (SSEDs), namely, the multiple-baseline designs, reversal designs, and alternating treatment designs, and provide empirical illustrations. The purpose of this article is to guide single-subject experimental data analysts interested in analyzing and meta-analyzing SSED data.

Multiple imputation (MI) has become a highly useful technique for handling missing values in many settings. In this article, the authors compare the performance of a MI model based on empirical Bayes techniques to a direct maximum likelihood analysis approach that is known to be robust in the presence of missing observations. Specifically, they focus on handling of missing item scores in multilevel cross-classification item response data structures that may require more complex imputation techniques, and for situations where an imputation model can be more general than the analysis model. Through a simulation study and an empirical example, the authors show that MI is more effective in estimating missing item scores and produces unbiased parameter estimates of explanatory item response theory models formulated as cross-classified mixed models.

In this article, the authors compare the multilevel meta-analysis approach with the more traditional meta-analytical approaches. After a description and comparison of the under-lying models and some of the major techniques, the results of the multilevel approach are compared with those of the traditional approaches, using a simulation study. The results of the simulation study suggest that the maximum likelihood multilevel approach is in general superior to the fixed-effects approaches, unless only a small number of studies is available. For models without moderators, the results of the multilevel approach, however, are not substantially different from the results of the traditional random-effects approaches.