EFFECTS OF SERIAL DEPENDENCY ON THE AGREEMENT BETWEEN VISUAL AND STATISTICAL INFERENCE<sup>1</sup>

Jones, Richard R.; Weinrott, Mark R.; Vaught, Russell S.

doi:10.1901/jaba.1978.11-277

Cited by 167 publications

(166 citation statements)

References 10 publications

Supporting

Mentioning

154

Contrasting

Unclassified

Order By: Relevance

“…Jones et al (1978) conduded that visual analysis was adversely affected by increased serial dependence. However, their analysis of autocorrelation was incorrect (Huitema, 1986a).…”

mentioning

confidence: 99%

Visual Analysis of Single‐case Time Series: Effects of Variability, Serial Dependence, and Magnitude of Intervention Effects

Matyas

Greenwood

1990

J of App Behav Analysis

193

184

View full text Add to dashboard Cite

Visual analysis is the dominant method of analysis for single-case time series. The literature assumes that visual analysts will be conservative judges. We show that previous research into visual analysis has not adequately examined false alarm and miss rates or the effect of serial dependence. In order to measure false alarm and miss rates while varying serial dependence, amount of random variability, and effect size, 37 students undertaking a postgraduate course in single-case design and analysis were required to assess the presence of an intervention effect in each of 27 AB charts constructed using a first-order autoregressive model. Three levels of effect size and three levels of variability, representative of values found in published charts, were combined with autocorrelation coefficients of 0, 0.3 and 0.6 in a factorial design. False alarm rates were surprisingly high (16% to 84%). Positive autocorrelation and increased random variation both significantly increased the false alarm rates and interacted in a nonlinear fashion. Miss rates were relatively low (0% to 22%) and were not significantly affected by the design parameters. Thus, visual analysts were not conservative, and serial dependence did influence judgment.

show abstract

“…Jones et al (1978) conduded that visual analysis was adversely affected by increased serial dependence. However, their analysis of autocorrelation was incorrect (Huitema, 1986a).…”

mentioning

confidence: 99%

Visual Analysis of Single‐case Time Series: Effects of Variability, Serial Dependence, and Magnitude of Intervention Effects

Matyas

Greenwood

1990

J of App Behav Analysis

193

184

View full text Add to dashboard Cite

show abstract

“…Since the recognition that serial dependency (Jones et al, 1978) and other individual subject data characteristics (e.g., DeProspero, & Cohen, 1979) …”

Section: Discussionmentioning

confidence: 99%

“…Unlike other decision aids, such as visual analysis or analysis of variance, ITSA accommodates serial dependency, a common property of single organism behavioral scores. Serial dependency violates assumptions underlying traditional statistical models, such as the analysis of variance (Glass et al, 1975;Gottman & Glass, 1978), 543 1980,133,[543][544][545][546][547][548][549][550][551][552][553][554][555][556][557][558][559] NUMBER 4 (WINTER 1980) and appears to hinder the use of visual analysis as well (Jones, Weinrott, & Vaught, 1978).…”

Section: Q: What Is Interrupted Time-series Analysis?mentioning

confidence: 99%

“…That is, far too many interventions are found to be statistically significant when no real effect exists. Because visual analyses also (and appropriately) take account of the apparent variability in the data to estimate the strength of an experimental effect, they too are biased in the presence of autocorrelation (Jones et al, 1978 A: A major goal of ITSA is to model the structure of the stochastic components of the timeseries observations. Once a model is fitted to the stochastic component of the data, the systematic part of the error can be subtracted from each observation.…”

Section: Q: What Is Interrupted Time-series Analysis?mentioning

confidence: 99%

See 1 more Smart Citation

Interrupted Time‐series Analysis and Its Application to Behavioral Data

Hartmann

Gottman

Jones

et al. 1980

J of App Behav Analysis

Self Cite

140

View full text Add to dashboard Cite

This paper uses a question-and-answer format to present the technical aspects of interrupted time-series analysis (ITSA). The reader should understand that he or she will not be able to perform an ITSA as a result of reading our paper. It is not intended as a cookbook for performing an ITSA. It is instead a description of the novel aspects of ITSA written in reasonably ordinary language. We have written the material that follows in a questionand-answer format. This format allows the reader the options of obtaining an answer to a specific question by turning to the relevant portion of the paper or obtaining a general understanding of ITSA and related issues by reading the paper straight through.

show abstract

“…All of the available statistical techniques for singlesubject research designs are somewhat influenced by autocorrelation (even visual analysis is affected by autocorrelation [28][29]). Autocorrelation is a problem because it violates the assumption of independence that most methods require.…”

Section: Use Of Statistics In Analysis Of Single-case Researchmentioning

confidence: 99%

Small N designs for rehabilitation research

Barnett¹,

Heinemann²,

Libin³

et al. 2012

JRRD

View full text Add to dashboard Cite

Abstract-Rehabilitation research presents unique and challenging problems to investigators during both the design and analysis periods. Statistical issues regarding sample size requirements for an adequately powered study may be in direct conflict with realistic recruitment and subject retention goals. Issues of underpowered studies, sample size requirements, and recruitment goals plague rehabilitation research. Randomized clinical trials (RCTs) are typically narrow in scope and thus lack generalizability to everyday, yet specific, clinical problems; they are also costly and time-consuming and require large numbers of participants for randomization to have optimal, desired effects. Further, the RCT design may not be applicable to assistive technologies and environmental modifications-vital components of disability and rehabilitation research-nor is it appropriate in situations in which theoretical models of change are lacking or premature. Single-case designs are better suited for studies in which understanding and changing patient behavior and functional status are primary goals and the targeted sample sizes are less than 30 and frequently less than 10. Theoretical, methodological, and clinical reasons for using experimental and quasiexperimental single-case designs are presented. Recommendations for designing and conducting single-case studies that contribute to the evidence base are also discussed.

show abstract

EFFECTS OF SERIAL DEPENDENCY ON THE AGREEMENT BETWEEN VISUAL AND STATISTICAL INFERENCE¹

Cited by 167 publications

References 10 publications

Visual Analysis of Single‐case Time Series: Effects of Variability, Serial Dependence, and Magnitude of Intervention Effects

Visual Analysis of Single‐case Time Series: Effects of Variability, Serial Dependence, and Magnitude of Intervention Effects

Interrupted Time‐series Analysis and Its Application to Behavioral Data

Small N designs for rehabilitation research

Contact Info

Product

Resources

About

EFFECTS OF SERIAL DEPENDENCY ON THE AGREEMENT BETWEEN VISUAL AND STATISTICAL INFERENCE1

Cited by 167 publications

References 10 publications

Visual Analysis of Single‐case Time Series: Effects of Variability, Serial Dependence, and Magnitude of Intervention Effects

Visual Analysis of Single‐case Time Series: Effects of Variability, Serial Dependence, and Magnitude of Intervention Effects

Interrupted Time‐series Analysis and Its Application to Behavioral Data

Small N designs for rehabilitation research

Contact Info

Product

Resources

About

EFFECTS OF SERIAL DEPENDENCY ON THE AGREEMENT BETWEEN VISUAL AND STATISTICAL INFERENCE¹