Assigning and combining probabilities in single-case studies.

Manolov, Rumen; Solanas, Antonio

doi:10.1037/a0029248

Cited by 9 publications

(9 citation statements)

References 104 publications

(181 reference statements)

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“…Among the possibilities for integrating data, there have already been studies using simple (Schlosser, Lee, & Wendt, 2008) or weighted averages of the primary indicators (Schneider, Goldstein, & Parker, 2008). A proposal for weighting has also been made for primary indicators for which the standard error is unknown (Manolov & Solanas, 2012). However, before carrying out meta-analysis it is important to select the primary indicator for summarizing the results, a choice which has found so far no consensus.…”

Section: Quantitative Analysismentioning

confidence: 99%

“…For obtaining a single indicator for the whole design we used here the simple average of the two-phase comparisons, although weighting is also possible (Manolov & Solanas, 2012). It seems appropriate to give the same sign to the differences in the desired direction (i.e., an increase after introducing the intervention, a decrease after 19 withdrawing it, and another increase with the re-introduction).…”

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confidence: 99%

“…Currently there is a proposal for making such kind of information available after estimating the relevant sampling distribution (Manolov & Solanas, 2012), but more discussion on this issue is necessary.…”

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A comparison of mean phase difference and generalized least squares for analyzing single-case data

Manolov

Solanas

2013

Journal of School Psychology

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The present study focuses on single-case data analysis and specifically on two procedures for quantifying differences between baseline and treatment measurementsThe first technique tested is based on generalized least squares regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns (i.e., independent measurements, different serial dependence underlying processes, constant or phase-specific autocorrelation and data variability, different types of trend, and slope and level change). The results suggest that the two techniques perform adequately for a wide range of conditions and researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.

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Section: Quantitative Analysismentioning

confidence: 99%

Section: Insert Figure 2 About Herementioning

confidence: 99%

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A comparison of mean phase difference and generalized least squares for analyzing single-case data

Manolov

Solanas

2013

Journal of School Psychology

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“…First, it might not be necessary to derive the sampling distribution of an effect-size index analytically (e.g., Hedges et al, 2012) or via simulation (e.g., Manolov & Solanas, 2012) in order to be able to obtain its variance and then use it as a weighting factor. Regarding the variance of standardized mean difference measures such as Δ, it has been claimed that the presence of serial dependence in the data makes the sampling distribution unknown and thus the formulae for the variances might not be correct (Beretvas and Chung, 2008b), which is the one of the reasons for the current developments in the field by Hedges and colleagues (2012).…”

Section: Results and Implicationsmentioning

confidence: 99%

Weighting strategies in the meta-analysis of single-case studies

2014

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Establishing the evidence base of interventions taking place in areas such as psychology and special education is one of the research aims of single-case designs in conjunction with the aim of improving the well-being of participants in the studies. The scientific criteria for solid evidence focus on the internal and external validity of the studies, and for both types of validity, replicating studies and integrating the results of these replications (i.e., meta-analyzing) is crucial. In the present study we deal with one of the aspects of meta-analysis, namely the weighting strategy used when computing an average effect size across studies. Several weighting strategies suggested for single-case designs are discussed and compared in the context of both simulated and real-life data. The results indicated that there are no major differences between the strategies and, thus we consider that it is important to choose weights with a sound statistical and methodological basis, while scientific parsimony is another relevant criterion. More empirical research and conceptual discussion are warranted regarding the optimal weighting strategy in single-case designs, alongside investigation of the optimal effect size measure in these types of design.Key words: single-case designs; meta-analysis; weight; effect size 3 The evidence-based movement has now been salient for several years in a variety of disciplines including psychology (APA Presidential Task Force on Evidence-Based Practice, 2006), medicine (Sackett, Rosenberg, Gray, Hayness, & Richardson, 1996), and special education . In this context, single-case designs ( Sidman, 1960) and, apart from using random assignment of conditions to measurement times, it is also favored by replication of the behavioral change contiguous with the change in conditions (Kratochwill et al., 2013;Wolery, 2013). On the other hand, replication is also related to generalization (Sidman, 1960), which benefits from research synthesis and metaanalysis. In that sense, the evidence-based movement has also paid attention to the metaanalytical integration of replications or studies on the same topic (Beretvas & Chung, 2008b;Jenson, Clark, Kircher, & Kristjansson, 2007). The quantitative integration is deemed especially useful when moderator variables are included in the meta-analyses (Burns, 2012;Wolery, 2013).Finally, it has been stressed that meta-analysis and the assessment of internal and external 1 We chose the term single-case designs (SCD) in order to be consistent with the labeling used in the articles recently published in this journal by, for instance, Baek and Ferron (2013) ). The present study represents an effort to discuss and obtain evidence regarding the metaanalysis of single-case studies; its focus is on weighting strategies rather than on the effect size measures that summarize the results. In that sense, it should be stressed that we do not advocate here for or against specific procedures for SCD data analysis. We consider that, while the debate on the optimal analytical...

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“…The first alternative tested here is called the Maximal reference approach (MRA) and it deals with assigning probabilities according to the likelihood of the results in case there was not intervention effect (Manolov & Solanas, 2012). MRA was proposed for (a) assessing the magnitude of effect according to whether the difference in conditions is likely to take place only by chance and (b) making the results expressed in different metrics (e.g., overlap, R-squared) comparable, for instance, in order to make possible integrating quantitatively such results.…”

Section: A Probability-based Approachmentioning

confidence: 99%

Probability and Visual Aids for Assessing Intervention Effectiveness in Single-Case Designs

et al. 2015

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Single-case data analysis still relies heavily on visual inspection, and, at the same time, it is not clear to what extent the results of different quantitative procedures converge in identifying an intervention effect and its magnitude when applied to the same data; this is the type of evidence provided here for two procedures. One of the procedures, included due to the importance of providing objective criteria to visual analysts, is a visual aid fitting and projecting split-middle trend while taking into account data variability. The other procedure converts several different metrics into probabilities making their results comparable. In the present study, we expore to what extend these two procedures coincide in the magnitude of intervention effect taking place in a set of studies stemming from a recent meta-analysis. The procedures concur to a greater extent with the values of the indices computed and with each other and, to a lesser extent, with our own visual analysis. For distinguishing smaller from larger effects, the probability-based approach seems somewhat better suited. Moreover, the results of the field test suggest that the latter is a reasonably good mechanism for translating different metrics into similar labels. User friendly R code is provided for promoting the use of the visual aid, together with a quantification based on nonoverlap and the label provided by the probability approach.

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Assigning and combining probabilities in single-case studies.

Abstract: There is currently a considerable diversity of quantitative measures available for summarizing the results in single-case studies. Given that the interpretation of some of them is difficult due to the lack of established benchmarks, the current paper proposes

Cited by 9 publications

References 104 publications

A comparison of mean phase difference and generalized least squares for analyzing single-case data

A comparison of mean phase difference and generalized least squares for analyzing single-case data

Weighting strategies in the meta-analysis of single-case studies

Probability and Visual Aids for Assessing Intervention Effectiveness in Single-Case Designs

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