Does the choice of a linear trend-assessment technique matter in the context of single-case data?

Manolov, Rumen

doi:10.3758/s13428-022-02013-0

Cited by 4 publications

(5 citation statements)

References 135 publications

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“…Although SCEDs are growing in popularity, single-case data face some challenges such as autocorrelation, trend, small data, and missing data (Manolov, 2023; Peng & Chen, 2021; Shadish & Sullivan, 2011; Smith, 2012; Tanious & Onghena, 2021). Such challenges can make it difficult to analyze SCED data validly and reliably.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Aydin

2023

Behav Modif

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Single-case experimental designs (SCEDs) have grown in popularity in the fields such as education, psychology, medicine, and rehabilitation. Although SCEDs are valid experimental designs for determining evidence-based practices, they encounter some challenges in analyses of data. One of these challenges, missing data, is likely to be occurred frequently in SCEDs research due to repeated measurements over time. Since missing data is a critical factor that can weaken the validity and generalizability of a study, it is important to determine the characteristics of missing data in SCEDs, which are especially conducted with a small number of participants. In this regard, this study aimed to describe missing data features in SCEDs studies in detail. To accomplish this goal, 465 published SCEDs studies within the recent 5 years in six journals were included in the investigation. The overall results showed that the prevalence of missing data among SCEDs articles in at least one phase, as at least one data point, was approximately 30%. In addition, the results indicated that the missing data rates were above 10% within most studies where missing data occurred. Although missing data is so common in SCEDs research, only a handful of studies (5%) have handled missing data; however, their methods are traditional. In analyzing SCEDs data, several methods are proposed considering missing data ratios in the literature. Therefore, missing data rates determined in this study results can shed light on the analyses of SCEDs data with proper methods by improving the validity and generalizability of study results.

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Section: Introductionmentioning

confidence: 99%

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Aydin

2023

Behav Modif

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“…These field results indicate that the PND-LLT could more sensitively predict the intervention effects compared to the PND-STB. In the original studies, the N-of-1 data were conventionally compared between the baseline and intervention phases using not only the PND-STB but also visual inspection, split-middle line, ARIMA, or Tau-U [12][13][14][15]42]. In visual inspection, the trend and level of N-of-1 data are visually compared between phases without mathematical operations [42].…”

Section: Discussionmentioning

confidence: 99%

“…Once the N-of-1 data in the baseline and intervention phases are obtained, they are conventionally compared between the phases using various analysis methods, such as the percentage of non-overlapping data assuming a stable slope (PND-STB), the split-middle line, autoregressive integrated moving average model (ARIMA), and Tau-U [12][13][14][15]. However, interpretation of these conventional analysis methods is limited to over-and under-estimation because these methods assume a linearly stable slope of the data changes.…”

Section: Introductionmentioning

confidence: 99%

Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies

Tanaka

Saito

Cho

et al. 2023

JPM

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The simulation study investigated the relationship between the local linear trend model’s data-comparison accuracy, baseline-data variability, and changes in level and slope after introducing the N-of-1 intervention. Contour maps were constructed, which included baseline-data variability, change in level or slope, and percentage of non-overlapping data between the state and forecast values by the local linear trend model. Simulation results showed that baseline-data variability and changes in level and slope after intervention affect the data-comparison accuracy based on the local linear trend model. The field study investigated the intervention effects for actual field data using the local linear trend model, which confirmed 100% effectiveness of previous N-of-1 studies. These results imply that baseline-data variability affects the data-comparison accuracy using a local linear trend model, which could accurately predict the intervention effects. The local linear trend model may help assess the intervention effects of effective personalized interventions in precision rehabilitation.

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“…However, not only should a parameter be projected, but its first and second derivatives should be especially projected because the latter represent the values of a society [64]. The appropriateness of this technique is explained and acknowledged by Manolov [65], Şen [66], Ahamer [67,68], and Garcia [69]. Overall, the data is taken from the most trusted international sources, such as IEA [70] and UNSTAT [71] Data consists of a country-wise three-decade time series of over a dozen economic sectors.…”

Section: Materials and Methods: An Evaluation Of Long-term Trends And...mentioning

confidence: 99%

Building Blocks for an Energy Transition

Ahamer

2024

J Energ Power Technol

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The present need for an energy transition in the wake of a global climate catastrophe led to the “EU green deal” which requires a transition of the energy system in all countries. This study aimis survey aims to identify strategies within such an energy transition based on global trend analyses and reports on available technologies for implementation. Based on a literature analysis of over 300 pieces (of feasibility studies and technology implementation reports with a focus on Central Europe) and a statistical analysis of the levels of “energy intensity” (E/GDP) covering three decades and all countries worldwide, the diagnosis is made that steady improvements in several dedicated technological focus areas can be made and were made and could still be increased in the future, as required by climate targets. Across several distinct fields of energy economy, the concrete options for such improvements are portrayed: Energy infrastructure, Smart grids, Transmission grid management, Electricity storage, Heat storage, and Industrial waste heat; and linked with the diagnosed long-term trends emerging from the “Global Change Data Base” GCDB, allowing for a sectorial analysis of the so-called energy intensity, which was not yet delivered until now as time series in the literature. The main findings show which economic sectors and clusters of technologies can be most appropriate to achieve climate targets while safeguarding social aspects of sustainability.

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Does the choice of a linear trend-assessment technique matter in the context of single-case data?

Cited by 4 publications

References 135 publications

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies

Building Blocks for an Energy Transition

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