inst/shiny-examples/gem-scd/markdown/using_gem_scd_single.md

In SingleCaseES: A Calculator for Single-Case Effect Sizes

Single-entry Calculator

The single-entry calculator will estimate the gradual effects model using data from a single series. It also provides a graphical representation of the data and the fitted model. By default, the single-entry calculator comes populated with data from Thorne and Kamps (2008) for participant one. Click "Estimate Model" to view example output.

To use the single-entry calculator with your own data, you must first enter information in the following three fields:

• Treatment Assignment: A series of treatment assignment indicators, with 0 corresponding to occasions where the treatment WAS NOT applied and 1 corresponding to occasions where the treatment WAS applied. Treatment assignment indicators can be seperated by commas, semicolons, tabs, or spaces.

• Outcome: A series of outcomes of the same length as the treatment assignment indicators. If you intend to use the quasi-binomial or binomial variance function, and you have data in a percentage format, you should check the box that transforms the data to proportions.

• m: The number of treatment sessions for which you would like to estimate the treatment effect. This should usually correspond to a length of time present within the data. For example, if you had an ABAB design where the length of the two B phases was both 5 measurement occasions, it would not be good practice to estimate the model with m = 10, because that is much longer than any phase in the observed data. If you intend to apply the model across several cases, pick a common value for m that is not longer than most of the treatment phases found in the set of cases to analyzed.

After providing this information, you must then specify a variance function and a link function in the "Modeling" panel.

• quasi-binomial/binomial: These variance functions are appropriate when outcome data are proportions or percentages. The quas-binomial variance function is a less restrictive assumption than the binomial variance function and we would generally encourage use of the quasi-binomial over the binomial. The typical link function for these two variance functions is the logit link.

• quasi-Poisson/Poisson: These variance functions are appropriate when the outcome data are counts or rates (such as behaviors per minute). Similar to the quasi-binomial, the quasi-Poisson variance function is a less restrictive assumption than the Poisson variance function and we would generally encourage the use of the quasi-Poisson over the Poisson. The typical link function for these two variance functions is the log link.

• gaussian: This variance function is typically appropriate for data that are assumed to be normally distributed. This is equivalent to the assumption made about the error structure in OLS regression. The typical link function for this variance function is the identity link.

Once you have selected the variance function and link function, click "Estimate Model" to fit the model and examine the results.

SingleCaseES documentation built on Aug. 12, 2023, 5:13 p.m.