userfriendlyscience (UFS)

Userfriendlyscience contains a number of functions that serve two goals. First, to make R more accessible to people migrating from SPSS by adding a number of functions that behave roughly like their SPSS equivalents (also see http://rosettastats.com). Second, to make a number of slightly more advanced functions more user friendly to relatively novice users. The package also conveniently houses a number of additional functions that are intended to increase the quality of methodology and statistics in psychology, not by offering technical solutions, but by shifting perspectives, for example towards reasoning based on sampling distributions as opposed to on point estimates.

The package imports functions from many other packages, which is in line with its function as a ‘wrapper package’: UFS aims to make many existing functions easier for users coming from SPSS, so sometimes a function is added when it saves the user just some data preparing.

The only publications where the package has been mentioned so far are available at:

• Peters, G.-J. Y. (2017). Diamond Plots: a tutorial to introduce a visualisation tool that facilitates interpretation and comparison of multiple sample estimates while respecting their inaccuracy. PsyArXiv, https://doi.org/10.17605/OSF.IO/9W8YV

• Peters, G.-J. Y. (2014). The alpha and the omega of scale reliability and validity: why and how to abandon Cronbach’s alpha and the route towards more comprehensive assessment of scale quality. The European Health Psychologist, 16, 56–69.

• Crutzen, R. (2014). Time is a jailer : what do alpha and its alternatives tell us about reliability? The European Health Psychologist, 1(2), 70–74.

• Crutzen, R., & Peters, G.-J. Y. (2015). Scale quality: alpha is an inadequate estimate and factor-analytic evidence is needed first of all. Health Psychology Review. http://dx.doi.org/10.1080/17437199.2015.1124240

Four more have been submitted for publication and are currently available as preprints at PsyArXiv:

• Peters, G.-J. Y. & Crutzen, R. (2017). Knowing exactly how effective an intervention, treatment, or manipulation is and ensuring that a study replicates: accuracy in parameter estimation as a partial solution to the replication crisis. http://osf.io/cjsk2/

• Peters, G.-J. Y. & Gruijters, S. (2017). Why your experiments fail: sample sizes required for randomization to generate equivalent groups as a partial solution to the replication crisis. http://osf.io/38vfn/

• Crutzen, R., Peters, G.-J. Y., & Noijen, J. (2017). How to Select Relevant Social-Cognitive Determinants and Use them in the Development of Behaviour Change Interventions? Confidence Interval-Based Estimation of Relevance. http://osf.io/5gnmz/

• Gruijters, S., & Peters, G.-J. Y. (2017). Introducing the Numbers Needed for Change (NNC): A practical measure of effect size for intervention research. http://osf.io/2bau7/

All are (and will be) Open Access. Please cite the manual and/or one of these publications when you use the package.

If you have any questions, you can reach me at gjalt-jorn@userfriendlyscience.com.