Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/141635
|
| Title: | 非重疊型指標作為量化單一受試者實驗設計介入效果之強韌性研究
The Robustness of Nonoverlap Indices as Effect Size Measures in Single Case Experimental Design: A Simulation Study |
| Authors: | 黃任清
Huang, Jen-Ching |
| Contributors: | 游琇婷
Yu, Hsiu-Ting
黃任清
Huang, Jen-Ching |
| Keywords: | 單一個案研究
非重疊型指標
效果量指標
時間序列資料
Single-case research
Nonoverlap indices
Effect size
Time series data |
| Date: | 2022 |
| Issue Date: | 2022-09-02 15:04:09 (UTC+8) |
| Abstract: | 在臨床心理學和特殊教育領域常使用單一受試者實驗設計進行研究。此研究法比較個 案的目標行為於介入前後的差異以評估介入方案的效果。單一受試者實驗設計可用目視分 析評量介入方案是否有效，但需要客觀的量化分析指標以進行跨受試者的比較。量化分析 方法中的非重疊型效果量指標計算基線期與介入期間資料的不重疊比例，可作為介入效果 的效果量指標。非重疊型指標計算簡單、解釋直觀且對依變項分配並無假設，然而卻可能 受到時間序列資料等資料特徵的影響而產生偏誤。可能影響指標的資料特徵包含序列相依 性、序列長度、趨勢、資料變異程度以及效果即刻性。本研究以模擬研究操弄資料特徵之 型態與強度，比較十種非重疊型指標（ECL、PND、PAND、 PEM、PNCD、IRD、 NAP、Tau、Tau-U 以及基線期校正 Tau）估計介入效果時受資料特徵影響的程度及強韌 性。研究發現NAP、Tau、和 Tau-U 能避免受到序列相依性的影響；NAP 和 Tau 對序列長 度有最好的強韌性；PNCD 和基線期校正 Tau 於資料中有直線趨勢時有最好的強韌性，而 Tau-U則 可應用於各類趨勢型態；PEM 可以避免變異程度的影響；PND 對效果未即刻顯 現時有最好的強韌性。本研究建議應依照資料特性選擇適切的非重疊型指標以估計單一受 試者實驗設計的介入效果。
Single-case experimental design (SCED) commonly applies to clinical psychology and special education research for examining the treatment effect of an intervention. The typical approach is comparing the changes in target behavior of participant before and after an intervention. While visual analysis can be easily used to assess intervention effect, quantitative methods are required for cross-subject comparisons. The nonoverlap effect size measures are one of the SCED objective indices which evaluate the percentage of nonoverlapping data between baseline and intervention phases. Nonoverlap indices are easy to calculate and intuitive to interpret, and they can be applied without making assumptions about the distributions of dependent variables. However, data features of repeated measurements may impact the accuracy of nonoverlap indices in quantifying the treatment effect. Common data features in SCED are serial dependence, series length, trend, variability, and immediacy of effect. A series of simulation studies were conducted to assess the performance of nonoverlap indices for data sets with difference data features. Performance of ten nonoverlap indices (ECL, PND, PAND, PEM, PNCD, IRD, NAP, Tau, Tau-U and baseline corrected Tau) were compared and evaluated the robustness of these indices. Research results suggested NAP, Tau and Tau-U perform well under data sets with serial dependence; The two indices, NAP and Tau, were robust under difference series lengths; PNCD and baseline corrected Tau are suitable for data with linear trend; Tau-U can be applied to data with various trend types; PEM is the least among the compared indices affected by the data variability; and PND is the most robust effect size measure when the effect is not exhibited immediately after the intervention. We suggest SCED researchers to select appropriate nonoverlap indices on the bases of data features to quantify the intervention effect of their study accurately. |
| Reference: | 李怡嫻、張正芬（2019）。正向行為支持計畫對國中特教班自閉症學生行為問題處理成效之個案研究。中華民國特殊教育學會年刊，108 年度，87-105。
紐文英、吳裕益（2015）。單一個案研究法：研究設計與後設分析。心理出版社。
Brossart, D. F., Laird, V. C., Armstrong, T. W., & Walla, P. (2018). Interpreting Kendall’s Tau and Tau-U for single-case experimental designs. Cogent Psychology, 5(1), 1-26. https://doi.org/10.1080/23311908.2018.1518687
Bulté, I., & Onghena, P. (2013). The single-case data analysis package: Analysing single-case experiments with R software. Journal of Modern Applied Statistical Methods, 12(2), 450-478. https://doi.org/10.22237/jmasm/1383280020
Chen, L. T., Peng, C. Y., & Chen, M. E. (2015). Computing tools for implementing standards for single-case designs. Behavior Modification, 39(6), 835-869. https://doi.org/10.1177/0145445515603706
Declercq, L., Cools, W., Beretvas, S. N., Moeyaert, M., Ferron, J. M., & Van den Noortgate, W. (2020). MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling. Behavior Research Methods, 52(1), 177-192. https://doi.org/10.3758/s13428-019-01216-2
Drager, K. D. R., Postal, V. J., Carrolus, L., Castellano, M., Gagliano, C., & Glynn, J. (2006). The effect of aided language modeling on symbol comprehension and production in 2 preschoolers with Autism. American Journal of Speech-Language Pathology, 15(2), 112-125. https://doi.org/10.1044/1058-0360(2006/012)
Giannakakos, A. R., & Lanovaz, M. J. (2019). Using AB designs with nonoverlap effect size measures to support clinical decision-making: A Monte Carlo Validation. Behavior Modification. Advance online publication. 1–16.https://doi.org/10.1177/0145445519860219
Harrington, M., & Velicer, W. F. (2015). Comparing visual and statistical analysis in single-case studies using published studies. Multivariate Behavioral Research, 50(2), 162-183. https://doi.org/10.1080/00273171.2014.973989
Huitema, B. E., & McKean, J. W. (2016). Design specification issues in time-series intervention
models. Educational and Psychological Measurement, 60(1), 38-58. https://doi.org/10.1177/00131640021970358
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137-152. https://doi.org/10.1037/a0028086
Lee, J. B., & Cherney, L. R. (2018). Tau-U: A quantitative approach for analysis of single-case experimental data in aphasia. American Journal of Speech-Language Pathology, 27(1S), 495–503. https://doi.org/10.1044/2017_AJSLP-16-0197
Ma, H. H. (2006). An alternative method for quantitative synthesis of single-subject researches: percentage of data points exceeding the median. Behavior Modification, 30(5), 598-617. https://doi.org/10.1177/0145445504272974
Manolov, R., & Moeyaert, M. (2017). Recommendations for choosing single-case data analytical techniques. Behavior Therapy, 48(1), 97-114. https://doi.org/10.1016/j.beth.2016.04.008
Manolov, R., & Onghena, P. (2018). Analyzing data from single-case alternating treatments designs. Psychological Methods, 23(3), 480-504. https://doi.org/10.1037/met0000133
Manolov, R., & Solanas, A. (2008). Comparing N = 1 effect size indices in presence of autocorrelation. Behavior Modification, 32(6), 860-875. https://doi.org/10.1177/0145445508318866
Manolov, R., & Solanas, A. (2009). Percentage of nonoverlapping corrected data. Behavior Research Methods, 41(4), 1262-1271. https://doi.org/10.3758/BRM.41.4.1262
Manolov, R., Losada, J. L., Chacon-Moscoso, S., & Sanduvete-Chaves, S. (2016). Analyzing two-phase single-case data with non-overlap and mean difference indices: Illustration, software tools, and alternatives. Front Psychology, 7, 32-47. https://doi.org/10.3389/fpsyg.2016.00032
Manolov, R., Solanas, A., & Leiva, D. (2010). Comparing “visual” effect size indices for single-case designs. Methodology, 6(2), 49-58. https://doi.org/10.1027/1614-2241/a000006
Manolov, R., Solanas, A., Sierra, V., & Evans, J. J. (2011). Choosing among techniques for quantifying single-case intervention effectiveness. Behavior Therapy, 42(3), 533-545. https://doi.org/10.1016/j.beth.2010.12.003
Parker, R. I. (2006). Increased reliability for single-case research results: is the bootstrap the
answer? Behavior Therapy, 37(4), 326-338. https://doi.org/10.1016/j.beth.2006.01.007
Parker, R. I., & Hagan-Burke, S. (2007). Single case research results as clinical outcomes. Journal of School Psychology, 45(6), 637-653. https://doi.org/10.1016/j.jsp.2007.07.004
Parker, R. I., & Vannest, K. (2009). An improved effect sizefor single case research: Non-overlap of all pairs (NAP). Behavior Therapy, 40(4), 357-367.https://doi.org/10.1016/j.beth.2008.10.006
Parker, R. I., & Vannest, K. J. (2012). Bottom-up analysis of single-case research designs. Journal of Behavioral Education, 21(3), 254-265. https://doi.org/10.1007/s10864-012-9153-1
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. https://doi.org/10.1177/00224669070400040101
Parker, R. I., Vannest, K. J., & Brown, L. (2009). The improvement rate difference for single-case research. Exceptional Children, 75(2), 135-150. https://doi.org/10.1177/001440290907500201
Parker, R. I., Vannest, K. J., & Davis, J. L. (2011a). Effect size in single-case research : A review of nine nonoverlap techniques. Behavior Modification, 35(4), 303-322. https://doi.org/10.1177/0145445511399147
Parker, R. I., Vannest, K. J., & Davis, J. L. (2012). A simple method to control positive baseline trend within data nonoverlap. The Journal of Special Education, 48(2), 79-91. https://doi.org/10.1177/0022466912456430
Parker, R. I., Vannest, K. J., Davis, J. L., & Sauber, S. B. (2011b). Combining nonoverlap and trend for single-case research : Tau-U. Behavior Therapy, 42(2), 284-299. https://doi.org/10.1016/j.beth.2010.08.006
Peng, C.-Y. J., & Chen, L.-T. (2015). Algorithms for assessing intervention effects in single-case studies. Journal of Modern Applied Statistical Methods, 14(1), 276-307. https://doi.org/10.22237/jmasm/1430454060
Pustejovsky, J. E. (2015). Measurement-comparable effect sizes for single-case studies of free-operant behavior. Psychological Methods, 20(3), 342-359. https://doi.org/10.1037/met0000019.supp
Pustejovsky, J. E. (2019). Procedural sensitivities of effect sizes for single-case designs with
directly observed behavioral outcome measures. Psychological Methods, 24(2), 217-235. https://doi.org/10.1037/met0000179
Scruggs, T. E., Mastropieri, M. A., & Casto, G. (1987). The quantitative synthesis of single subject research : Methodology and validation. Remedial and Special Education, 8(2), 24-33. https://doi.org/10.1177/074193258700800206
Shadish, W. R. (2014). Statistical analyses of single-case designs. Current Directions in Psychological Science, 23(2), 139-146. https://doi.org/10.1177/0963721414524773
Solanas, A., Manolov, R., & Onghena, P. (2010). Estimating slope and level change in N = 1 designs. Behavior Modification, 34(3), 195-218. https://doi.org/10.1177/0145445510363306
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
Tarlow, K. R., & Brossart, D. F. (2018). A comprehensive method of single-case data analysis: Interrupted time-series simulation (ITSSIM). School Psychology Quarterly, 33(4), 590-603. https://doi.org/10.1037/spq0000273
Vannest, K. J., & Ninci, J. (2015). Evaluating intervention effects in single-case research designs. Journal of Counseling & Development, 93(4), 403-411. https://doi.org/10.1002/jcad.12038
White, O. R., & Haring, N. G. (1980). Exceptional teaching: A multimedia training package
(2nd ed). Merrill.
Wolery, M., Busick, M., Reichow, B., & Barton, E. E. (2008). Comparison of overlap methods for quantitatively synthesizing single-subject data. The Journal of Special Education, 44(1), 18-28. https://doi.org/10.1177/0022466908328009 |
| Description: | 碩士
國立政治大學
心理學系
106752003 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106752003 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201209 |
| Appears in Collections: | [心理學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 200301.pdf | | 2598Kb | Adobe PDF2 | 102 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
