Assessing Intervention Effects in the Presence of Missing Scores
Due to repeated observations of an outcome behavior in <i>N</i>-of-1 or single-case design (SCD) intervention studies, the occurrence of missing scores is inevitable in such studies. Approximately 21% of SCD articles published in five reputable journals between 2015 and 2019 exhibited ev...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
|
| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/11/2/76 |
| _version_ | 1797396626173591552 |
|---|---|
| author | Chao-Ying Joanne Peng Li-Ting Chen |
| author_facet | Chao-Ying Joanne Peng Li-Ting Chen |
| author_sort | Chao-Ying Joanne Peng |
| collection | DOAJ |
| description | Due to repeated observations of an outcome behavior in <i>N</i>-of-1 or single-case design (SCD) intervention studies, the occurrence of missing scores is inevitable in such studies. Approximately 21% of SCD articles published in five reputable journals between 2015 and 2019 exhibited evidence of missing scores. Missing rates varied by designs, with the highest rate (24%) found in multiple baseline/probe designs. Missing scores cause difficulties in data analysis. And inappropriate treatments of missing scores lead to consequences that threaten internal validity and weaken generalizability of intervention effects reported in SCD research. In this paper, we comprehensively review nine methods for treating missing SCD data: the available data method, six single imputations, and two model-based methods. The strengths, weaknesses, assumptions, and examples of these methods are summarized. The available data method and three single imputation methods are further demonstrated in assessing an intervention effect at the class and students’ levels. Assessment results are interpreted in terms of effect sizes, statistical significances, and visual analysis of data. Differences in results among the four methods are noted and discussed. The extensive review of problems caused by missing scores and possible treatments should empower researchers and practitioners to account for missing scores effectively and to support evidence-based interventions vigorously. The paper concludes with a discussion of contingencies for implementing the nine methods and practical strategies for managing missing scores in single-case intervention studies. |
| first_indexed | 2024-03-09T00:53:08Z |
| format | Article |
| id | doaj.art-fb33a21e09a74cdba38859cfbc5b5254 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-09T00:53:08Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-fb33a21e09a74cdba38859cfbc5b52542023-12-11T17:04:40ZengMDPI AGEducation Sciences2227-71022021-02-011127610.3390/educsci11020076Assessing Intervention Effects in the Presence of Missing ScoresChao-Ying Joanne Peng0Li-Ting Chen1Department of Psychology, National Taiwan University, Taipei 10617, TaiwanDepartment of Educational Studies, University of Nevada, Reno, NV 89557, USADue to repeated observations of an outcome behavior in <i>N</i>-of-1 or single-case design (SCD) intervention studies, the occurrence of missing scores is inevitable in such studies. Approximately 21% of SCD articles published in five reputable journals between 2015 and 2019 exhibited evidence of missing scores. Missing rates varied by designs, with the highest rate (24%) found in multiple baseline/probe designs. Missing scores cause difficulties in data analysis. And inappropriate treatments of missing scores lead to consequences that threaten internal validity and weaken generalizability of intervention effects reported in SCD research. In this paper, we comprehensively review nine methods for treating missing SCD data: the available data method, six single imputations, and two model-based methods. The strengths, weaknesses, assumptions, and examples of these methods are summarized. The available data method and three single imputation methods are further demonstrated in assessing an intervention effect at the class and students’ levels. Assessment results are interpreted in terms of effect sizes, statistical significances, and visual analysis of data. Differences in results among the four methods are noted and discussed. The extensive review of problems caused by missing scores and possible treatments should empower researchers and practitioners to account for missing scores effectively and to support evidence-based interventions vigorously. The paper concludes with a discussion of contingencies for implementing the nine methods and practical strategies for managing missing scores in single-case intervention studies.https://www.mdpi.com/2227-7102/11/2/76missingattritionSCD<i>N</i>-of-1interventionevidence-based |
| spellingShingle | Chao-Ying Joanne Peng Li-Ting Chen Assessing Intervention Effects in the Presence of Missing Scores Education Sciences missing attrition SCD <i>N</i>-of-1 intervention evidence-based |
| title | Assessing Intervention Effects in the Presence of Missing Scores |
| title_full | Assessing Intervention Effects in the Presence of Missing Scores |
| title_fullStr | Assessing Intervention Effects in the Presence of Missing Scores |
| title_full_unstemmed | Assessing Intervention Effects in the Presence of Missing Scores |
| title_short | Assessing Intervention Effects in the Presence of Missing Scores |
| title_sort | assessing intervention effects in the presence of missing scores |
| topic | missing attrition SCD <i>N</i>-of-1 intervention evidence-based |
| url | https://www.mdpi.com/2227-7102/11/2/76 |
| work_keys_str_mv | AT chaoyingjoannepeng assessinginterventioneffectsinthepresenceofmissingscores AT litingchen assessinginterventioneffectsinthepresenceofmissingscores |