The use of nonparametric effect sizes in single study musculoskeletal physiotherapy research: A practical primer
There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often resear...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pautz, Nikolas [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: A sum-bracket theorem for simple Lie algebras - Dona, Daniele ELSEVIER, 2023, [S. l.] |
|---|---|
| Übergeordnetes Werk: |
volume:33 ; year:2018 ; pages:117-124 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ptsp.2018.07.009 |
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ELV043868975 |
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| 520 | |a There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often researchers use parametric statistics where nonparametric measures would be more appropriate. This holds true for nonparametric measures of effect size, where even when researchers use nonparametric statistics, some use parametric effect size measures to interpret the result. This paper attempts to provide a practical overview and illustration of the correct usage and interpretation of effect size measures for nonparametric statistics for single study designs using real-world physiotherapy data in the worked examples. This primer covers a range of different formulae based on categorical measures of effect size, as well as between- and within-group designs using ranked data. While this primer does use examples focusing on physiotherapy research, the applications of the information can be used in any field of research. | ||
| 520 | |a There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often researchers use parametric statistics where nonparametric measures would be more appropriate. This holds true for nonparametric measures of effect size, where even when researchers use nonparametric statistics, some use parametric effect size measures to interpret the result. This paper attempts to provide a practical overview and illustration of the correct usage and interpretation of effect size measures for nonparametric statistics for single study designs using real-world physiotherapy data in the worked examples. This primer covers a range of different formulae based on categorical measures of effect size, as well as between- and within-group designs using ranked data. While this primer does use examples focusing on physiotherapy research, the applications of the information can be used in any field of research. | ||
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10.1016/j.ptsp.2018.07.009 doi GBV00000000000341.pica (DE-627)ELV043868975 (ELSEVIER)S1466-853X(17)30486-8 DE-627 ger DE-627 rakwb eng 510 VZ 31.20 bkl Pautz, Nikolas verfasserin aut The use of nonparametric effect sizes in single study musculoskeletal physiotherapy research: A practical primer 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often researchers use parametric statistics where nonparametric measures would be more appropriate. This holds true for nonparametric measures of effect size, where even when researchers use nonparametric statistics, some use parametric effect size measures to interpret the result. This paper attempts to provide a practical overview and illustration of the correct usage and interpretation of effect size measures for nonparametric statistics for single study designs using real-world physiotherapy data in the worked examples. This primer covers a range of different formulae based on categorical measures of effect size, as well as between- and within-group designs using ranked data. While this primer does use examples focusing on physiotherapy research, the applications of the information can be used in any field of research. There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often researchers use parametric statistics where nonparametric measures would be more appropriate. This holds true for nonparametric measures of effect size, where even when researchers use nonparametric statistics, some use parametric effect size measures to interpret the result. This paper attempts to provide a practical overview and illustration of the correct usage and interpretation of effect size measures for nonparametric statistics for single study designs using real-world physiotherapy data in the worked examples. This primer covers a range of different formulae based on categorical measures of effect size, as well as between- and within-group designs using ranked data. While this primer does use examples focusing on physiotherapy research, the applications of the information can be used in any field of research. Within-subject Elsevier Nonparametric Elsevier Physiotherapy Elsevier Between-subject Elsevier Differences Elsevier Effect sizes Elsevier Olivier, Benita oth Steyn, Faans oth Enthalten in Churchill Livingstone Dona, Daniele ELSEVIER A sum-bracket theorem for simple Lie algebras 2023 [S. l.] (DE-627)ELV010318380 volume:33 year:2018 pages:117-124 extent:8 https://doi.org/10.1016/j.ptsp.2018.07.009 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.20 Algebra: Allgemeines VZ AR 33 2018 117-124 8 |
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A sum-bracket theorem for simple Lie algebras |
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The use of nonparametric effect sizes in single study musculoskeletal physiotherapy research: A practical primer |
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The use of nonparametric effect sizes in single study musculoskeletal physiotherapy research: A practical primer |
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Pautz, Nikolas |
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A sum-bracket theorem for simple Lie algebras |
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A sum-bracket theorem for simple Lie algebras |
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Pautz, Nikolas |
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Pautz, Nikolas |
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10.1016/j.ptsp.2018.07.009 |
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use of nonparametric effect sizes in single study musculoskeletal physiotherapy research: a practical primer |
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The use of nonparametric effect sizes in single study musculoskeletal physiotherapy research: A practical primer |
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There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often researchers use parametric statistics where nonparametric measures would be more appropriate. This holds true for nonparametric measures of effect size, where even when researchers use nonparametric statistics, some use parametric effect size measures to interpret the result. This paper attempts to provide a practical overview and illustration of the correct usage and interpretation of effect size measures for nonparametric statistics for single study designs using real-world physiotherapy data in the worked examples. This primer covers a range of different formulae based on categorical measures of effect size, as well as between- and within-group designs using ranked data. While this primer does use examples focusing on physiotherapy research, the applications of the information can be used in any field of research. |
| abstractGer |
There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often researchers use parametric statistics where nonparametric measures would be more appropriate. This holds true for nonparametric measures of effect size, where even when researchers use nonparametric statistics, some use parametric effect size measures to interpret the result. This paper attempts to provide a practical overview and illustration of the correct usage and interpretation of effect size measures for nonparametric statistics for single study designs using real-world physiotherapy data in the worked examples. This primer covers a range of different formulae based on categorical measures of effect size, as well as between- and within-group designs using ranked data. While this primer does use examples focusing on physiotherapy research, the applications of the information can be used in any field of research. |
| abstract_unstemmed |
There is a strong push for the inclusion of effect size indexes alongside the reporting of statistical analysis in academic journals. Nonparametric methods of analysis have generally been developed less than their parametric counterparts have, and are also generally less well known. Too often researchers use parametric statistics where nonparametric measures would be more appropriate. This holds true for nonparametric measures of effect size, where even when researchers use nonparametric statistics, some use parametric effect size measures to interpret the result. This paper attempts to provide a practical overview and illustration of the correct usage and interpretation of effect size measures for nonparametric statistics for single study designs using real-world physiotherapy data in the worked examples. This primer covers a range of different formulae based on categorical measures of effect size, as well as between- and within-group designs using ranked data. While this primer does use examples focusing on physiotherapy research, the applications of the information can be used in any field of research. |
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The use of nonparametric effect sizes in single study musculoskeletal physiotherapy research: A practical primer |
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https://doi.org/10.1016/j.ptsp.2018.07.009 |
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Olivier, Benita Steyn, Faans |
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