vertical_jump_data: Vertical jump data
In mladenjovanovic/bmbstats: Bootstrap Magnitude-Based Statistics
Description
Simple RCT data using Change in vertical jump height as a target effect. Consists of
two groups: Treatment and Control with N=15 in each. Training intervention
involves plyometric training, with vertical jump height measured Pre-test
and Post-test. Additional covariate involve Squat 1RM
Usage
1
Format
A data frame with 30 rows and 7 variables:
- Athlete
String. Athlete name
- Squat 1RM
Relative back squat 1RM
- Group
Treatment group. Two levels: Treatment and Control
- Pre-test
Vertical jump height pre-treatment
- Post-test
Vertical jump height post-treatment
- Change
Difference between Post-test and Pre-test
- Magnitude
Factor with three levels: Lower, Equivalent, and Higher.
This represent magnitude of change using SESOI of +/- 2.5cm
mladenjovanovic/bmbstats documentation built on Aug. 5, 2020, 4:20 p.m.
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Description
Simple RCT data using Change in vertical jump height as a target effect. Consists of
two groups: Treatment and Control with N=15 in each. Training intervention
involves plyometric training, with vertical jump height measured Pre-test
and Post-test. Additional covariate involve Squat 1RM
Usage
1 |
Format
A data frame with 30 rows and 7 variables:
- Athlete
String. Athlete name
- Squat 1RM
Relative back squat 1RM
- Group
Treatment group. Two levels: Treatment and Control
- Pre-test
Vertical jump height pre-treatment
- Post-test
Vertical jump height post-treatment
- Change
Difference between
Post-testandPre-test- Magnitude
Factor with three levels: Lower, Equivalent, and Higher. This represent magnitude of change using SESOI of +/- 2.5cm
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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