R version of PowerUp! < https://www.causalevaluation.org/>. Includes tools to calculate statistical power, minimum detectable effect size, and minimum required sample size for various multilevel randomized experiments with continuous outcomes.

To install CRAN version

`install.packages("PowerUpR")`

For an introductory tutorial on `PowerUpR`

package click on
vignettes.

`PowerUpR`

is an implementation of *PowerUp!* in R environment (R Core Team, 2016). *PowerUp!* is a statistical power analysis tool to calculate
minimum detectable effect size (MDES) and top level minimum required sample size (MRSS)
for various multilevel randomized experiments and quasi-experiments (Dong & Maynard, 2013).
`PowerUpR`

package solely focuses on multilevel randomized experiments with several additional features.
The package bases its framework on three fundemental concepts in statistical power analysis; power calculation, MDES calculation, and sample size calculation. Congruent with this framework, `PowerUpR`

provides tools to calculate power, MDES, MRSS for any level, and to solve constrained optimal sample allocation (COSA) problems (Hedges & Borenstein, 2014; Raudenbush, 1997; Raudenbush & Liu, 2000).
COSA problems can be solved in the following forms,
(i) under budgetary constraints given marginal costs per unit while maximizing the power,
(ii) under power constraints given marginal costs per unit while minimizing the cost,
(ii) under MDES constraints given marginal costs per unit while minimizing the cost, and
(iv) under sample size constraints for one or more levels along with any of the i ii, or iii options.
Congruent with the three fundemental concepts the package also provides tools for graphing two or three dimensional relationships to investiage relative standing of power, MDES, MRSS or a component of COSA.

A design parameter (one of the power, MDES, MRSS, or COSA) can be requested by using approriate function
given design characteristics. Except for graphing functions, each function begins with an **output** name,
following by a period, and a **design** name. There are four types of output; `mdes`

, `power`

, `mrss`

, and `optimal`

,
and 14 types of design; `ira1r1`

, `bira2r1`

, `bira2f1`

, `bira2c1`

, `cra2r2`

, `bira3r1`

, `bcra3r2`

, `bcra3f2`

, `cra3r3`

,
`bira4r1`

, `bcra4r2`

, `bcra4r3`

, `bcra4f3`

, and `cra4r4`

. The first three letters of the design stands for
the type of assignment, for individual random assignment `ira`

, for blocked individual random assignment `bira`

,
and for cluster random assignment `cra`

, for blocked cluster random assignment `bcra`

.
It is followed by a number indicating number of levels. A single letter followed by a number indicates
whether a block is considered to be `r`

, random; `f`

, fixed; or `c`

, constant and the level at which
random assingment takes place. For example, to find MDES for 3-level blocked (random) cluster randomized design where
random assignment is at level 2, function `mdes.bcra3r2`

is used.

Each function requires slightly different arguments depending on the output it produces and the design. Most of the arguments have default values to provide users a starting point. Default values are

`mdes`

= .25`power`

= .80`alpha`

= .05`two.tail`

=`TRUE`

`P`

= .50`g1`

,`g2`

,`g3`

,`g4`

= 0- any sequence of
`R12`

,`R22`

,`R32`

,`R42`

= 0 - any sequence of
`RT22`

,`RT32`

,`RT42`

= 0

Users should be aware of default values and change them if necessary. Minimum required arguments to successfully run a function are

- any sequence of
`rho2`

,`rho3`

,`rho4`

- any sequence of
`omega2`

,`omega3`

,`omega4`

- any one of, any sequence of, or any combination of
`n`

,`J`

,`K`

,`L`

For definition of above-mentioned parameters see Dong & Maynard (2013) and Hedges & Rhoads (2009),
or help files in *man* folder for individual functions. For reference intraclass correlation (`rho2`

, `rho3`

) values
see Dong, Reinke, Herman, Bradshaw, and Murray (2016), Hedberg and Hedges (2014), Hedges and Hedberg (2007, 2013), Kelcey, and Phelps (2013), Schochet (2008), Spybrook, Westine, and Taylor (2016).
For reference variance (`R12`

, `R22`

, `R32`

) values see Bloom, Richburg-Hayes, and Black (2007),
Deke et al. (2010), Dong et al. (2016), Hedges and Hedberg (2013), Kelcey, and Phelps (2013), Spybrook, Westine,and Taylor (2016), Westine, Spybrook, and Taylor (2013).
Users can also obtain design parameters for various levels using publicly available state or district data.

Please email us any issues or suggestions.

Metin Bulus [email protected]

Nianbo Dong [email protected]

**Suggested citation:**

Bulus, M., & Dong, N. (2016). `PowerUpR`

: Power Analysis Tools for Individual/Cluster Randomized Trials. R package version 0.1.2.

Dong, N., & Maynard, R. A. (2013). PowerUp!: A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes
for Experimental and Quasi-Experimental Design Studies, *Journal of Research on Educational Effectiveness, 6(1)*, 24-6.

Bloom, H. S., Richburg- Hayes, L. & Black, A. R. (2007).
Using Covariates to Improve Precision for Studies that Randomize Schools to Evaluate Educational Interventions.
*Educational Evaluation and Policy Analysis, 29(1)*, 0-59.

Deke, John, Dragoset, Lisa, and Moore, Ravaris (2010). Precision Gains from Publically Available School Proficiency Measures Compared to Study-Collected Test Scores in Education Cluster-Randomized Trials (NCEE 2010-4003). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from https://ies.ed.gov/ncee/pubs/20104003/

Dong, N., & Maynard, R. A. (2013). PowerUp!: A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes
for Experimental and Quasi-Experimental Design Studies, *Journal of Research on Educational Effectiveness, 6(1)*, 24-6.

Dong, N., Reinke, W. M., Herman, K. C., Bradshaw, C. P., & Murray, D. W. (2016). Meaningful effect sizes, intraclass correlations, and proportions of variance explained by covariates for panning two-and three-level cluster randomized trials of social and behavioral outcomes. *Evaluation Review*. doi: 10.1177/0193841X16671283

Hedges, L. V., & Borenstein, M. (2014). Conditional Optimal Design in Three- and Four-Level Experiments.
*Journal of Educational and Behavioral Statistics, 39(4)*, 257-281.

Hedberg, E., & Hedges, L. V.(2014). Reference Values of Within-District Intraclass Correlations of Academic Achivement
by District Characteristics: Results From a Meta-Analysis of District-Specified Values. *Evaluation Review, 38(6)*, 546-582.

Hedges, L. V., & Hedberg, E. (2007). Interclass correlation values for planning group-randomized trials in education.
*Educational Evaluation and Policy Analysis, 29(1)*, 60-87.

Hedges, L. V., & Hedberg, E. (2013). Interclass Correlations and Covariate Outcome Correlations for Planning
Two- and Three-Level Cluster-Randomized Experiments in Education. *Evaluation Review, 37(6)*, 445-489.

Hedges, L. & Rhoads, C.(2009). Statistical Power Analysis in Education Research (NCSER 2010-3006). Washington, DC: National Center for Special Education Researc , Institute of Education Sciences, U.S. Department of Education. Retrieved from https://ies.ed.gov/ncser/.

Kelcey, B., & Phelps, G. (2013). Strategies for improving power in school randomized studies of professional development. *Evaluation Review, 37(6)*, 520-554.

R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.r-project.org/

Raudenbush, S. W. (1997). Statistical analysis and optimal design for cluster randomized trials.
*Psychological Methods, 2*, 173-185.

Raudenbush, S. W., & Liu, X. (2000). Statistical power and optimal design for multisite trials.
*Psychological Methods, 5*, 199-213.

Schochet, P. Z. (2008). Statistical Power for Random Assignment Evaluations of Education Programs.
*Journal of Educational and Behavioral Statistics, 33(1)*, 62-87.

Spybrook, J., Westine, C. D., & Taylor, J. A. (2016). Design Parameters for Impact Research in Science Education:
A Multisite Anlaysis. *AERA Open, 2(1)*, 1-15.

Westine, C. D., Spybrook, J., & Taylor, J. A. (2013). An Empirical Investigation of Variance Design Parameters
for Planning Cluster-Randomized Trials of Science Achievement. *Evaluation Review, 37(6)*, 490-519.

- CITATION info
- Vignettes
- Title change
- Bug fixes for object conversion
- Minor typos in the help files

- Statistical power analysis of main effects with continious outcomes.
- Functions to calculate statistical power, MDES, MRSS, and to solve COSA problems under various constraints.
- Function to visualize type I and type II error rates for didactic purpose.
- Functions to visualize duo and trio relatioship between statistical power, MDES, MRSS, and COSA.