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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15402326 |
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| _version_ | 1866901605209604096 |
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| author | Mohammad Ahsan Khodami |
| author_facet | Mohammad Ahsan Khodami |
| contents | <p>The <strong>SCIA (Single-Case</strong> <strong>I</strong>nterpretation & <strong>A</strong>nalysis) package is a Python-based toolkit designed for the statistical analysis of single-case experimental designs (SCEDs). It offers a suite of methods tailored to evaluate intervention effects at the individual level, making it particularly useful in fields such as psychology, medicine, and social sciences. The package includes functionalities for handling missing data, computing descriptive statistics, and applying various effect size metrics like the Reliable Change Index (RCI), Improvement Rate Difference (IRD), Nonoverlap of All Pairs (NAP), and Percentage of All Non-Overlapping Data (PAND). Additionally, it provides tools for autocorrelation analysis, facilitating a comprehensive examination of time-series data within single-case studies.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15402326 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SCIA Single-Case Interpretation & Analysis Package in Python Mohammad Ahsan Khodami Single-Case Studies as Topic Statistical analysis Psychology Medicine <p>The <strong>SCIA (Single-Case</strong> <strong>I</strong>nterpretation & <strong>A</strong>nalysis) package is a Python-based toolkit designed for the statistical analysis of single-case experimental designs (SCEDs). It offers a suite of methods tailored to evaluate intervention effects at the individual level, making it particularly useful in fields such as psychology, medicine, and social sciences. The package includes functionalities for handling missing data, computing descriptive statistics, and applying various effect size metrics like the Reliable Change Index (RCI), Improvement Rate Difference (IRD), Nonoverlap of All Pairs (NAP), and Percentage of All Non-Overlapping Data (PAND). Additionally, it provides tools for autocorrelation analysis, facilitating a comprehensive examination of time-series data within single-case studies.</p> |
| title | SCIA Single-Case Interpretation & Analysis Package in Python |
| topic | Single-Case Studies as Topic Statistical analysis Psychology Medicine |
| url | https://doi.org/10.5281/zenodo.15402326 |