Choosing among techniques for quantifying single-case intervention effectiveness

Behavior Therapy

Volumn 42, Issue 3, 2011, Pages 533-545

  Manolov, Rumen ^a   Solanas, Antonio ^a   Sierra, Vicenta ^b   Evans, Jonathan J ^c  

^a UNIVERSITY OF BARCELONA   (Spain)

^b ESADE BUSINESS SCHOOL   (Spain)

^c UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLINICAL DECISION MAKING; CONFOUNDING VARIABLE; CORRELATION ANALYSIS; DATA ANALYSIS; EFFECT SIZE; EVIDENCE BASED PRACTICE; EXPERIMENTAL DESIGN; IMPROVEMENT RATE DIFFERENCE; INTERMETHOD COMPARISON; INTERVENTION STUDY; MEDICAL PSYCHOLOGY; MONTE CARLO METHOD; NONOVERLAP OF ALL PAIRS; PERCENTAGE OF NONOVERLAPPING CORRECTED DATA; PRACTICE GUIDELINE; SLOPE AND LEVEL CHANGE; STATISTICAL ANALYSIS;

COMPARATIVE EFFECTIVENESS RESEARCH; COMPUTER SIMULATION; MODELS, STATISTICAL; RESEARCH DESIGN;

EID: 79958103427     PISSN: 00057894     EISSN: 18781888     Source Type: Journal    
DOI: 10.1016/j.beth.2010.12.003     Document Type: Article

References (41)

1. Allison D.B., Gorman B.S. Calculating effect sizes for meta-analysis: The case of the single case. Behaviour Research and Therapy 1993, 31:621-631.
2. Blampied N.M. Single-case research designs: A neglected alternative. American Psychologist 2000, 55:960.
3. Borckardt J.J., Nash M.R., Murphy M.D., Moore M., Shaw D., O'Neil P. Clinical practice as natural laboratory for psychotherapy research: A guide to case-based time-series analysis. American Psychologist 2008, 63:77-95.
4. Bradley J.V. A common situation conducive to bizarre distribution shapes. American Statistician 1977, 31:147-150.
5. Brossart D.F., Parker R.I., Olson E.A., Mahadevan L. The relationship between visual analysis and five statistical analyses in a simple AB single-case research design. Behavior Modification 2006, 30:531-563.
6. Busse R.T., Kratochwill T.R., Elliott S.N. Meta-analysis for single-case consultation outcomes: Applications to research and practice. Journal of School Psychology 1995, 33:269-285.
7. Callahan C.D., Barisa M.T. Statistical process control and rehabilitation outcome: The single-subject design reconsidered. Rehabilitation Psychology 2005, 50:24-33.
8. Center B.A., Skiba R.J., Casey A. A methodology for the quantitative synthesis of intra-subject design research. Journal of Special Education 1985/1986, 19:387-400.
9. Chambless D.L., Ollendick T.H. Empirically supported psychological interventions: Controversies and evidence. Annual Review of Psychology 2001, 52:685-716.
10. Fahmie T.A., Hanley G.P. Progressing toward data intimacy: A review of within-session data analysis. Journal of Applied Behavior Analysis 2008, 41:319-331.
11. Fisch G.S. Evaluating data from behavioral analysis: Visual inspection or statistical models?. Behavioural Processes 2001, 54:137-154.
12. Greenwood K.M., Matyas T.A. Problems with application of interrupted time series analysis for brief single-subject data. Behavioral Assessment 1990, 12:355-370.
13. Harrop J.W., Velicer W.F. A comparison of alternative approaches to the analysis of interrupted time-series. Multivariate Behavioral Research 1985, 20:27-44.
14. Horner R.H., Carr E.G., Halle J., McGee G., Odom S., Wolery M. The use of single-subject research to identify evidence-based practice in special education. Exceptional Children 2005, 71:165-179.
15. Huitema B.E., McKean J.W. Autocorrelation estimation and inference with small samples. Psychological Bulletin 1991, 110:291-304.
16. Huitema B.E., McKean J.W. Design specification issues in time-series intervention models. Educational and Psychological Measurement 2000, 60:38-58.
17. Huitema B.E., McKean J.W., McKnight S. Autocorrelation effects on least-squares intervention analysis of short time series. Educational and Psychological Measurement 1999, 59:767-786.
18. Kazdin A.E. The meanings and measurements of clinical significance. Journal of Consulting and Clinical Psychology 1999, 67:332-339.
19. Kratochwill T.R. Preparing psychologists for evidence-based school practice: Lessons learned and challenges ahead. American Psychologist 2007, 62:829-843.
20. Kratochwill T.R., Levin J.R. Enhancing the scientific credibility of single-case intervention research: Randomization to the rescue. Psychological Methods 2010, 15:124-144.
21. Lieberman R.G., Yoder P.J., Reichow B., Wolery M. Visual analysis of multiple baseline across participants graphs when change is delayed. School Psychology Quarterly 2010, 25:28-44.
22. Ma H.H. An alternative method for quantitative synthesis of single-subject research: Percentage of data points exceeding the median. Behavior Modification 2006, 30:598-617.
23. Manolov R., Solanas A. Percentage of nonoverlapping corrected data. Behavior Research Methods 2009, 41:1262-1271.
24. McCleary R., Hay R.A. Applied time series analysis for the social sciences 1980, Beverly Hills, CA: Sage.
25. Micceri T. The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin 1989, 105:156-166.
26. Parker R.I., Brossart D.F. Evaluating single-case research data: A comparison of seven statistical methods. Behavior Therapy 2003, 34:189-211.
27. Parker R.I., Brossart D.F. Phase contrasts for multiphase single case intervention designs. School Psychology Quarterly 2006, 21:46-61.
28. Parker R.I., Cryer J., Byrns G. Controlling baseline trend in single-case research. School Psychology Quarterly 2006, 21:418-443.
29. Parker R.I., Hagan-Burke S. Median-based overlap analysis for single case data: A second study. Behavior Modification 2007, 31:919-936.
30. Parker R.I., Hagan-Burke S., Vannest K.J. Percentage of all non-overlapping data: An alternative to PND. Journal of Special Education 2007, 40:194-204.
31. Parker R.I., Vannest K.J. An improved effect size for single-case research: Nonoverlap of all pairs. Behavior Therapy 2009, 40:357-367.
32. Parker R.I., Vannest K.J., Brown L. The improvement rate difference for single-case research. Exceptional Children 2009, 75:135-150.
33. Perdices M., Tate R.L. Single-subject designs as a tool for evidence-based clinical practice: Are they unrecognised and undervalued?. Neuropsychological Rehabilitation 2010, 19:904-927.
34. Reichardt C.S. The principle of parallelism in the design of studies to estimate treatment effects. Psychological Methods 2006, 11:1-18.
35. Robey R.R., Schultz M.C., Crawford A.B., Sinner C.A. Single-subject clinical outcome research: Designs, data, effect sizes, and analysis. Aphasiology 1999, 13:445-473.
36. Schneider N., Goldstein H., Parker R.I. Social skills interventions for children with autism: A meta-analytic application of percentage of all non-overlapping data (PAND). Evidence-Based Communication Assessment and Intervention 2008, 2:152-162.
37. Scruggs T.E., Mastropieri M.A., Casto G. The quantitative synthesis of single-subject research: Methodology and validation. Remedial and Special Education 1987, 8:24-33.
38. Shadish W.R., Rindskopf D.M., Hedges L.V. The state of the science in the meta-analysis of single-case experimental designs. Evidence-Based Communication Assessment and Intervention 2008, 2:188-196.
39. Solanas A., Manolov R., Onghena P. Estimating slope and level change in N=1 designs. Behavior Modification 2010, 34:195-218.
40. White D.M., Rusch F.R., Kazdin A.E., Hartmann D.P. Applications of meta-analysis in individual subject research. Behavioral Assessment 1989, 11:281-296.
41. Wolery M., Busick M., Reichow B., Barton E.E. Comparison of overlap methods for quantitatively synthesizing single-subject data. Journal of Special Education 2010, 44:18-29.