How to assess and take into account trend in single-case experimental design data

One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend, under the premise that there is not a single correct answer to each of these questions. First, there are multiple ways in which even linear trend can be operatively defined. Second, different criteria can be followed when deciding whether the trend observed needs to be taken into account when assessing intervention effectiveness or, in contrast, its magnitude is sufficiently small to be considered inconsequential. If a decision is made to take trend into account, there are different ways in which this can be done. Finally, a challenge arises when there is a trend for some of the comparisons between conditions, but not for others. Several options are reviewed for each of these questions in the context of real data, and tentative recommendations are provided. A new user-friendly website is developed to implement the options for fitting a trend line and a criterion for selecting a specific technique for that purpose. Trend-related and more general data analytical recommendations are provided for applied researchers.