A generalized least squares regression approach for computing effect sizes in single-case research: Application examples

Maggin, Daniel M.; Swaminathan, Hariharan; Rogers, H. Jane; O’Keeffe, Breda V.; Sugai, George; Horner, Robert H.

doi:10.1016/j.jsp.2011.03.004

Cited by 128 publications

(126 citation statements)

References 38 publications

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“…It was compulsory to include a condition with n = 20 data points for the whole series, given that it is the minimum recommended for the autocorrelation estimation in the GLS (Maggin et al, 2011). In the current study, it was specified as n A = n B = 10, for the baseline and treatment phases respectively.…”

Section: Methods Data Generationmentioning

confidence: 99%

“…'s (1989) proposal there are two regression equations, one for each phase, but here all the predicted treatment data are compared, instead of focusing solely on the last one. Maggin et al (2011) illustrate the procedure applying it to real data sets and a useful complement would be to explore via simulation whether this proposal performs better than the previously developed regression-based procedures. This is the reason for centering the present study mainly on the GLS.…”

Section: Regression-based Proceduresmentioning

confidence: 99%

“…The regression procedure GLS (Maggin et al, 2011) 4. Check whether the residuals from the previous regressions are autocorrelated using the Durbin-Watson test.…”

Section: Generalized Least Squares Regression Analysismentioning

confidence: 99%

“…Maggin et al (2011) state that in the estimation of autocorrelation as performed by the GLS it is desirable that: a) both 13 the serial dependence scheme and the degree of autocorrelation be the same for both phases, and b) the series consist of at least 20 measurements. Apart from this assumption, for testing regression coefficients' statistical significance the residual is supposed to be homoscedastic and normally distributed.…”

Section: Generalized Least Squares Regression Analysismentioning

confidence: 99%

“…For the GLS standardizing is already part of the procedure and for the MPD it is also possible to standardize the mean difference between projected and actual data. This can be done dividing the mean difference by the variability around the estimated trend line in the context of the MPD, which is similar to the solution mentioned in Maggin et al (2011) for the GLS.…”

Section: Mean Phase Differencementioning

confidence: 99%

See 4 more Smart Citations

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: Methods Data Generationmentioning

confidence: 99%

Section: Regression-based Proceduresmentioning

confidence: 99%

“…The regression procedure GLS (Maggin et al, 2011) 4. Check whether the residuals from the previous regressions are autocorrelated using the Durbin-Watson test.…”

Section: Generalized Least Squares Regression Analysismentioning

confidence: 99%

Section: Generalized Least Squares Regression Analysismentioning

confidence: 99%

Section: Mean Phase Differencementioning

confidence: 99%

See 3 more Smart Citations

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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Randomization procedures in single‐case intervention research contexts: (Some of) “the rest of the story”

Levin

Kratochwill

Ferron

2019

J Exper Analysis Behavior

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Following up on articles recently published in this journal, the present contribution tells (some of) "the rest of the story" about the value of randomization in single-case intervention research investigations. Invoking principles of internal, statistical-conclusion, and external validity, we begin by emphasizing the critical distinction between design randomization and analysis randomization, along with the necessary correspondence between the two. Four different types of single-case design-and-analysis randomization are then discussed. The persistent negative influence of serially dependent single-case outcome observations is highlighted, accompanied by examples of inappropriate applications of parametric and nonparametric tests that have appeared in the literature. We conclude by presenting valid applications of single-case randomization procedures in various single-case intervention contexts, with specific reference to a freely available Excel-based software package that can be accessed to incorporate the present randomization schemes into a wide variety of single-case intervention designs and analyses.

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Defining and assessing immediacy in single‐case experimental designs

Manolov

Onghena

2022

J Exper Analysis Behavior

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Immediacy is one of six data aspects (alongside level, trend, variability, overlap, and consistency) that has to be accounted for when visually analyzing single‐case data. Given that it is one of the aspects that has received considerably less attention than other data aspects, the current text offers a review of the proposed conceptual definitions of immediacy (i.e., what it refers to) and also of the suggested operational definitions (i.e., how exactly is it assessed and/or quantified). Provided that a variety of conceptual and operational definitions is identified, we propose following a sensitivity analysis using a randomization test for assessing immediate effects in single‐case experimental designs, by identifying when changes were most clear. In such a sensitivity analysis, the immediate effects are tested for multiple possible intervention points and for different possible operational definitions. Robust immediate effects can be detected if the results for the different operational definitions converge.

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A generalized least squares regression approach for computing effect sizes in single-case research: Application examples

Cited by 128 publications

References 38 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

Randomization procedures in single‐case intervention research contexts: (Some of) “the rest of the story”

Defining and assessing immediacy in single‐case experimental designs

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