| Title: | Single-Case Meta-Analysis |
|---|---|
| Description: | Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. |
| Authors: | Isis Bulte, Tamal Kumar De, Patrick Onghena |
| Maintainer: | Tamal Kumar De <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3.1 |
| Built: | 2024-12-01 08:39:36 UTC |
| Source: | CRAN |
Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method).
| Package: | SCMA |
| Type: | Package |
| Version: | 1.3.1 |
| Date: | 2019-12-20 |
| License: | GPL version 2 or newer |
Isis Bulte, Tamal Kumar De, Patrick Onghena
Maintainer: Tamal Kumar De <[email protected]>
Hypothetical data collected in an AB phase design with 27 measurement times.
data(AB)data(AB)
A data frame with 27 observations.
The first column contains the condition/phase labels ("A" and "B").
The second column contains the obtained scores.
The rows and columns are not labeled.
Bulte, I., & Onghena, P. (2012). When the truth hits you between the eyes: A software tool for the visual analysis of single-case experimental data. Methodology, 8, 104-114.
data(AB)data(AB)
Calculates a general p-value by statistically combining the p-values of a number of independent studies, to determine whether a general significant result is obtained.
combine(method, pvalues = read.table(file.choose(new = FALSE)))combine(method, pvalues = read.table(file.choose(new = FALSE)))
method |
Indicates which combining function should be used: "x" (multiplicative) or "+" (additive) |
pvalues |
File in which the p-values can be found. Default: a window pops up in which the appropriate file can be selected. |
When using the default 'pvalues' argument, a window will pop up to ask in what file the pvalues can be found. This text file containing the pvalues should consist of 1 column with all the obtained pvalues.
Isis Bulte
Bulte, I., & Onghena, P. (2008). An R package for single-case randomization tests. Behavior Research Methods, 40, 467-478.
Bulte, I., & Onghena, P. (2009). Randomization tests for multiple baseline designs: An extension of the SCRT-R package. Behavior Research Methods, 41, 477-485.
http://ppw.kuleuven.be/cmes/SCRT-R.html
data(P) combine(method="+",pvalues=P)data(P) combine(method="+",pvalues=P)
Calculates the specified effect size measure.
ES(design, ES, data = read.table(file.choose(new = FALSE)))ES(design, ES, data = read.table(file.choose(new = FALSE)))
design |
Type of single-case design: "AB", "ABA", "ABAB", "CRD"(completely randomized design), "RBD" (randomized block design), "ATD" (alternating treatments design), "MBD" (multiple-baseline AB design) or "Custom" (user specified design). |
ES |
Type of effect size that has to be calculated: "SMD" (standardized mean difference), "SMDpool" (pooled standardized mean difference), "PND+" / "PND-" (percentage of nonoverlapping data, depending on the expected direction of the treatment effect), "PEM+" / "PEM-" (percentage of data points exceeding the median, depending on the expected direction of the treatment effect), or "NAP+" / "NAP-" (nonoverlap of all pairs, depending on the expected direction of the treatment effect). |
data |
File in which the data can be found. Default: a window pops up in which the appropriate file can be selected. |
When using the default 'data' argument, a window will pop up to ask in what file the data can be found. This text file containing the data should consist of two columns for single-case phase and alternation designs: the first with the condition labels and the second with the obtained scores.
For multiple-baseline designs, it should consist of these two columns for EACH unit. This way, each row represents one measurement occasion. It is important not to label the rows or columns.
Missing data should be indicated as NA. For calculations, missing data are omitted.
Isis Bulte
Bulte, I., & Onghena, P. (2008). An R package for single-case randomization tests. Behavior Research Methods, 40, 467-478.
Bulte, I., & Onghena, P. (2009). Randomization tests for multiple baseline designs: An extension of the SCRT-R package. Behavior Research Methods, 41, 477-485.
http://ppw.kuleuven.be/cmes/SCRT-R.html
data(AB) ES(design = "AB", ES = "SMD", data = AB)data(AB) ES(design = "AB", ES = "SMD", data = AB)
Each p-value (i.e., each row) corresponds to one participant in the study.
data(P)data(P)
A data frame with 7 observations on the following variable.
V1P-value
data(P)data(P)