scholarly journals A comprehensive method of single-case data analysis: Interrupted Time-Series Simulation (ITSSIM).

2018 ◽  
Vol 33 (4) ◽  
pp. 590-603 ◽  
Author(s):  
Kevin R. Tarlow ◽  
Daniel F. Brossart
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Single Case ◽  
Interrupted Time Series ◽  
Comprehensive Method ◽  
Time Series Simulation ◽  
Case Data

scholarly journals A Comprehensive Method of Single-Case Data Analysis: Interrupted Time-Series Simulation (ITSSIM)

2018 ◽  
Author(s):  
Kevin Tarlow ◽  
Daniel Brossart
Keyword(s):  
Time Series ◽  
Effect Size ◽  
Single Case ◽  
Meta Analysis ◽  
Interrupted Time Series ◽  
Simulation Method ◽  
Psychological Research ◽  
Multilevel Methods ◽  
Time Series Simulation ◽  
Case Data

Single-case experimental methods are used across a range of educational and psychological research. Single-case data are analyzed with a variety of methods, but no statistic has demonstrated clear superiority over other methods. The time-series nature of single-case designs requires special consideration for baseline trend and autocorrelation when estimating intervention effect size. However, standard correction methods are limited because they assume precise statistical estimation of trend and autocorrelation. Unlike standard correction methods, Monte Carlo simulation methods can address the poor precision of single-case effect size indices. This paper presents the rationale for a new simulation method, Interrupted Time-Series Simulation (ITSSIM). A small field test was also conducted, and ITSSIM performed similarly to sophisticated multilevel methods for single-case research. ITSSIM is accessible as a free software application that requires no prior knowledge of statistical computing or syntax. ITSSIM may be used to estimate the effect size of a single interrupted time-series (AB design), and multiple ITSSIM effect size estimates may be combined via meta-analysis.

Fitting Bayesian Models for Single-Case Experimental Designs

Methodology ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 147-156 ◽  
Author(s):  
Prathiba Natesan
Keyword(s):  
Time Series ◽  
Single Case ◽  
Interrupted Time Series ◽  
Experimental Designs ◽  
Short Time Series ◽  
Strong Basis ◽  
Steep Learning Curve ◽  
Methodological Rigor ◽  
Parametric Analyses ◽  
Short Time

Abstract. Single-case experimental designs (SCEDs) are interrupted time-series designs that have recently gained recognition as being able to provide a strong basis for establishing intervention effect. Typically, SCED data are short time series and autocorrelated, which renders maximum likelihood and parametric analyses inadequate for data analysis, respectively. Although Bayesian methods overcome these challenges, most practitioners do not use Bayesian estimation because of: (a) its steep learning curve, (b) lack of Bayesian training, and (c) lack of knowledge of Bayesian software solutions. This study demonstrates two Bayesian interrupted time-series models using freeware programs R and JAGS. Practitioners could modify these codes and run them for their own data by changing the values in the codes where indicated. Providing practitioners with such tools to facilitate their analysis is one way to improve methodological rigor in applied research.

scholarly journals Robust regression for single-case data analysis: How can it help?

2011 ◽  
Vol 43 (3) ◽  
pp. 710-719 ◽  
Author(s):  
Daniel F. Brossart ◽  
Richard I. Parker ◽  
Linda G. Castillo
Keyword(s):  
Data Analysis ◽  
Robust Regression ◽  
Single Case ◽  
Case Data

Time-series statistical analysis of single-case data.

2013 ◽  
pp. 251-266 ◽  
Author(s):  
Jeffrey J. Borckardt ◽  
Michael R. Nash ◽  
Wendy Balliet ◽  
Sarah Galloway ◽  
Alok Madan
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Single Case ◽  
Case Data

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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