Appendix C — Default settings

Some of the default settings of scan can be changed with the options() argument. Table C.1 shows a complete list of options and their default values.

# get the current value of an option
getOption("scan.print.rows")

[1] 15

# set option to a different value
options(scan.print.rows = 5, scan.print.scdf.name = FALSE)
print(exampleAB)

#A single-case data frame with three cases

 values mt phase ｜ values mt phase ｜ values mt phase ｜
     54  1     A ｜     41  1     A ｜     55  1     A ｜
     53  2     A ｜     59  2     A ｜     58  2     A ｜
     56  3     A ｜     56  3     A ｜     53  3     A ｜
     58  4     A ｜     51  4     A ｜     50  4     A ｜
     52  5     A ｜     52  5     A ｜     52  5     A ｜
# ... up to 15 more rows

options(scan.print.rows = 15, scan.print.scdf.name = TRUE)
print(exampleAB)

#A single-case data frame with three cases

 Johanna: values mt phase ｜ Karolina: values mt phase ｜ Anja: values mt phase
              54  1     A ｜               41  1     A ｜           55  1     A
              53  2     A ｜               59  2     A ｜           58  2     A
              56  3     A ｜               56  3     A ｜           53  3     A
              58  4     A ｜               51  4     A ｜           50  4     A
              52  5     A ｜               52  5     A ｜           52  5     A
              61  6     B ｜               57  6     B ｜           55  6     B
              62  7     B ｜               56  7     B ｜           68  7     B
              71  8     B ｜               67  8     B ｜           68  8     B
              66  9     B ｜               75  9     B ｜           81  9     B
              64 10     B ｜               66 10     B ｜           67 10     B
              78 11     B ｜               69 11     B ｜           78 11     B
              70 12     B ｜               68 12     B ｜           73 12     B
              74 13     B ｜               73 13     B ｜           72 13     B
              82 14     B ｜               77 14     B ｜           78 14     B
              77 15     B ｜               79 15     B ｜           81 15     B
 ｜
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# ... up to five more rows

Table C.1: Scan Options

Option	Default	What it does ...
scan.print.cases	"fit"	Max number of cases printed for scdf objects
scan.print.rows	15	Max number of rows printed for scdf objects
scan.print.cols	"all"	Max number of columns printed for scdf objects
scan.print.digits	2	Max number of digits printed for scdf objects
scan.print.long	FALSE	If TRUE, prints scdf objects in long format
scan.print.scdf.name	TRUE	If TRUE, prints case names of scdf
scan.deprecated.warning	FALSE	When TRUE returns information on deprecated functions
scan.export.kable	list(digits = 2, linesep = "", booktab = TRUE)	List with default arguments for the kable argument of the export function
scan.export.kable_styling	list(bootstrap_options = c("bordered", "condensed"), full_width = FALSE, position = "left", latex_options = "hold_position", htmltable_class = "lightable-classic")	List with default arguments for the kable_styling argument of the export function
scan.export.engine	"gt"	NA
scan.export.footnote.collapse	"; "	NA
scan.plot.style	"grid"	NA
scan.print.bar	"｜" \|	A \|

D Example datasets

Table D.1: Example scdfs

Name	Info	Author
Beretvas2008		Beretvas, S., & Chung, H. (2008). An evaluation of modified R2-change effect size indices for single-subject experimental designs. Evidence-Based Communication Assessment and Intervention, 2, 120-128.
Borckardt2014		Borckardt, J. J., & Nash, M. R. (2014). Simulation modelling analysis for small sets of single-subject data collected over time. Neuropsychological Rehabilitation, 24(3-4), 492-506.
Grosche2011	Direct instruction intervention on reading accuracy.	Grosche, M. (2011). Effekte einer direkt-instruktiven Förderung der Lesegenauigkeit. Empirische Sonderpädagogik, 3(2), 147-161.
Grosche2014	Data from a multiple material multi person intervention study on reading.	Michael Grosche, Timo Lueke, and Juergen Wilbert
GruenkeWilbert2014	Data from an intervention study on text comprehension.	Gruenke, M., Wilbert, J., & Stegemann-Calder, K. (2013). Analyzing the effects of story mapping on the reading comprehension of children with low intellectual abilities. Learning Disabilities: A Contemporary Journal, 11(2), 51-64.
Huber2014	Behavioral data (compliance in percent).	Christian Huber
Huitema2000	Example from Huitema, B. E., & Mckean, J. W. (2000). Design specification issues in time-series intervention models. Educational and Psychological Measurement, 60(1), 38-58.
Leidig2018		Leidig, T., Casale, G., Wilbert, J., Hennemann, T., Volpe, R. J., Briesch, A., & Grosche, M. (2022). Individual, generalized, and moderated effects of the good behavior game on at-risk primary school students: A multilevel multiple baseline study using behavioral progress monitoring. Frontiers in Education, 7. https://www.frontiersin.org/articles/10.3389/feduc.2022.917138
Leidig2018_l2
Lenz2013		Lenz, A. S. (2013). Calculating Effect Size in Single-Case Research: A Comparison of Nonoverlap Methods. Measurement and Evaluation in Counseling and Development, 46(1), 64-73.
Parker2007		Parker, R. I., Hagan-Burke, S., & Vannest, K. (2007). Percentage of All Non-Overlapping Data (PAND) An Alternative to PND. The Journal of Special Education, 40(4), 194-204.
Parker2009		Parker, R. I., Vannest, K. J., & Brown, L. (2009). The improvement rate difference for single-case research. Exceptional Children, 75(2), 135-150.
Parker2009b		Parker, R. I., & Vannest, K. (2009). An improved effect size for single-case research: Nonoverlap of all pairs. Behavior Therapy, 40(4), 357-367.
Parker2011		Parker, R. I., Vannest, K. J., Davis, J. L., & Sauber, S. B. (2011). Combining Nonoverlap and Trend for Single-Case Research: Tau-U. Behavior Therapy, 42(2), 284-299.
Parker2011b		Parker, R. I., Vannest, K. J., & Davis, J. L. (2011). Effect Size in Single-Case Research: A Review of Nine Nonoverlap Techniques. Behavior Modification, 35(4), 303-322. https://doi.org/10.1177/0145445511399147
SSDforR2017	Example from the SSDforR package.	Charles Auerbach, PhD & Wendy Zeitlin, PhD; Yeshiva University, Wurzweiler school of social work.
Tarlow2017		Tarlow, K. R. (2017). An Improved Rank Correlation Effect Size Statistic for Single-Case Designs: Baseline Corrected Tau. Behavior Modification, 41(4), 427-467. https://doi.org/10.1177/0145445516676750
Waddell2011	Example from Waddell, D. E., Nassar, S. L., & Gustafson, S. A. (2011). Single-Case Design in Psychophysiological Research: Part II: Statistical Analytic Approaches. Journal of Neurotherapy, 15(2), 160 - 169.
byHeart2011	Data from university students learning vocabulary by heart and checking their progress with 20 flashcards each session.	Juergen Wilbert, 2011
exampleA1B1A2B2
exampleA1B1A2B2_zvt
exampleAB	Randomly created data with normal distributed dependent variable.
exampleABAB	Randomly created data with uniform distribution.
exampleABC
exampleABC_150	Random data-set for testing out hplm. Level and slope effects vary.
exampleABC_outlier	Random data-set based on exampleABC but with outliers.
exampleAB_50
exampleAB_50.l2
exampleAB_add	Random data-set for testing out plm with additional variables.
exampleAB_decreasing	Random data-set from a poisson distribution. Level effect is negative.
exampleAB_mpd	A multiple phase design study.	Juergen Wilbert
exampleAB_score	Random data-set for binomial data.
exampleAB_simple	A simple multiple baseline AB Design.	Juergen Wilbert
example_A24	Number of injuries on a German autobahn before and after implementation of a speedlimit (130km/h).	Ministerium fuer Infrastruktur und Landesplanung. Land Brandenburg.