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題名 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作者 黃任清
Huang, Jen-Ching貢獻者 游琇婷
Yu, Hsiu-Ting
黃任清
Huang, Jen-Ching關鍵詞 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日期 2022 上傳時間 2-Sep-2022 15:04:09 (UTC+8) 摘要 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.參考文獻 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/10.1080/23311908.2018.1518687Bulté, 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/1383280020Chen, 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/10.1177/0145445515603706Declercq, 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/s13428-019-01216-2Drager, 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/10.1177/0145445519860219Harrington, 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/10.1080/00273171.2014.973989Huitema, B. E., & McKean, J. W. (2016). Design specification issues in time-series interventionmodels. Educational and Psychological Measurement, 60(1), 38-58. https://doi.org/10.1177/00131640021970358Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137-152. https://doi.org/10.1037/a0028086Lee, 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-16-0197Ma, 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/10.1177/0145445504272974Manolov, 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.2016.04.008Manolov, R., & Onghena, P. (2018). Analyzing data from single-case alternating treatments designs. Psychological Methods, 23(3), 480-504. https://doi.org/10.1037/met0000133Manolov, R., & Solanas, A. (2008). Comparing N = 1 effect size indices in presence of autocorrelation. Behavior Modification, 32(6), 860-875. https://doi.org/10.1177/0145445508318866Manolov, R., & Solanas, A. (2009). Percentage of nonoverlapping corrected data. Behavior Research Methods, 41(4), 1262-1271. https://doi.org/10.3758/BRM.41.4.1262Manolov, 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.2016.00032Manolov, 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/a000006Manolov, 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.2010.12.003Parker, R. I. (2006). Increased reliability for single-case research results: is the bootstrap theanswer? Behavior Therapy, 37(4), 326-338. https://doi.org/10.1016/j.beth.2006.01.007Parker, 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.2007.07.004Parker, 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.2008.10.006Parker, 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/s10864-012-9153-1Parker, 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/10.1177/00224669070400040101Parker, 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/10.1177/001440290907500201Parker, 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/10.1177/0145445511399147Parker, 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/10.1177/0022466912456430Parker, 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.2010.08.006Peng, 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/1430454060Pustejovsky, 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.suppPustejovsky, J. E. (2019). Procedural sensitivities of effect sizes for single-case designs withdirectly observed behavioral outcome measures. Psychological Methods, 24(2), 217-235. https://doi.org/10.1037/met0000179Scruggs, 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/10.1177/074193258700800206Shadish, W. R. (2014). Statistical analyses of single-case designs. Current Directions in Psychological Science, 23(2), 139-146. https://doi.org/10.1177/0963721414524773Solanas, A., Manolov, R., & Onghena, P. (2010). Estimating slope and level change in N = 1 designs. Behavior Modification, 34(3), 195-218. https://doi.org/10.1177/0145445510363306Tarlow, 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/10.1177/0145445516676750Tarlow, 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/spq0000273Vannest, 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.12038White, 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 描述 碩士
國立政治大學
心理學系
106752003資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106752003 資料類型 thesis dc.contributor.advisor 游琇婷 zh_TW dc.contributor.advisor Yu, Hsiu-Ting en_US dc.contributor.author (Authors) 黃任清 zh_TW dc.contributor.author (Authors) Huang, Jen-Ching en_US dc.creator (作者) 黃任清 zh_TW dc.creator (作者) Huang, Jen-Ching en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-Sep-2022 15:04:09 (UTC+8) - dc.date.available 2-Sep-2022 15:04:09 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2022 15:04:09 (UTC+8) - dc.identifier (Other Identifiers) G0106752003 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141635 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 心理學系 zh_TW dc.description (描述) 106752003 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 1第一節 單一受試者實驗設計 1第二章 單一受試者實驗設計之資料分析 3第一節 SCED的資料特性 3第二節 分析SCED資料的六個面向 4第三節 SCED資料之分析目的 10第三章 文獻回顧 12第一節 非重疊型指標 12第二節 研究回顧總整 27第四章 研究問題與設計 34第一節 研究問題整理 34第二節 研究設計 35第五章 研究結果 43第一節 序列相依性 43第二節 序列長度 52第三節 趨勢 57第四節 變異程度 68第五節 效果即刻性 72第六章 討論 76第一節 研究問題之結論 76第二節 選用指標之建議 83第三節 本研究貢獻 87第四節 本研究限制與未來研究方向 88參考文獻 90 zh_TW dc.format.extent 2661269 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106752003 en_US dc.subject (關鍵詞) 單一個案研究 zh_TW dc.subject (關鍵詞) 非重疊型指標 zh_TW dc.subject (關鍵詞) 效果量指標 zh_TW dc.subject (關鍵詞) 時間序列資料 zh_TW dc.subject (關鍵詞) Single-case research en_US dc.subject (關鍵詞) Nonoverlap indices en_US dc.subject (關鍵詞) Effect size en_US dc.subject (關鍵詞) Time series data en_US dc.title (題名) 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 zh_TW dc.title (題名) The Robustness of Nonoverlap Indices as Effect Size Measures in Single Case Experimental Design: A Simulation Study en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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/10.1080/23311908.2018.1518687Bulté, 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/1383280020Chen, 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/10.1177/0145445515603706Declercq, 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/s13428-019-01216-2Drager, 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/10.1177/0145445519860219Harrington, 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/10.1080/00273171.2014.973989Huitema, B. E., & McKean, J. W. (2016). Design specification issues in time-series interventionmodels. Educational and Psychological Measurement, 60(1), 38-58. https://doi.org/10.1177/00131640021970358Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137-152. https://doi.org/10.1037/a0028086Lee, 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-16-0197Ma, 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/10.1177/0145445504272974Manolov, 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.2016.04.008Manolov, R., & Onghena, P. (2018). Analyzing data from single-case alternating treatments designs. Psychological Methods, 23(3), 480-504. https://doi.org/10.1037/met0000133Manolov, R., & Solanas, A. (2008). Comparing N = 1 effect size indices in presence of autocorrelation. Behavior Modification, 32(6), 860-875. https://doi.org/10.1177/0145445508318866Manolov, R., & Solanas, A. (2009). Percentage of nonoverlapping corrected data. Behavior Research Methods, 41(4), 1262-1271. https://doi.org/10.3758/BRM.41.4.1262Manolov, 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.2016.00032Manolov, 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/a000006Manolov, 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.2010.12.003Parker, R. I. (2006). Increased reliability for single-case research results: is the bootstrap theanswer? Behavior Therapy, 37(4), 326-338. https://doi.org/10.1016/j.beth.2006.01.007Parker, 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.2007.07.004Parker, 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.2008.10.006Parker, 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/s10864-012-9153-1Parker, 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/10.1177/00224669070400040101Parker, R. I., Vannest, K. J., & Brown, L. (2009). 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