These hypothetical data are displayed in Table 1. If a relation is found, then these data would lend some credence to the hypothesis that the student's out-of-seat behavior is attention maintained. Using the generalized matching equation, she plans to determine whether or not there is indeed a relation between levels of behavior and contiguous (i.e., occurring together in time) attention from individuals in the classroom (cf. In addition, she records the number of vocal statements directed to the student by all classroom staff and peers (including praise and reprimand statements) while the student is either in seat or out of seat. In particular, as part of her descriptive assessment, she records the duration of in-seat and out-of-seat behavior across five 1-hr observations.
Thus, she hopes to confirm these findings without having the student participate in another full functional analysis. The results of the previous functional analysis suggested that her student's out-of-seat behavior was maintained by social approval and disapproval (i.e., attention).
As she reviews the student's case history, she notices that a functional analysis was conducted by the student's previous consultant less than 1 month ago. In one classroom in which the analyst consults, she is asked to conduct a functional assessment of a recently admitted student's out-of-seat behavior. Although the focus of this tutorial is on Excel 2007, any noteworthy differences in the task analysis for previous versions of Excel are detailed.Īs an example, imagine that an applied behavior analyst recently took a position in a private school setting serving children with developmental disabilities. The purpose of this technical report is to guide the reader through the use of Excel in the calculation and plot generation of two alternative generalized matching analyses, using hypothetical data from an example case study. Fortunately, Microsoft Office Excel 2007, a widely available and commonly used spreadsheet program, can quickly compute all of the regression calculations necessary for matching analyses using the generalized matching equation, as well as making matching plots. Although the calculations involved in regression and matching analyses are surprisingly easy with regards to the difficulty of the mathematical operations involved, they are also relatively time consuming and may be tedious to compute by hand or calculator. Ultimately, such translation provides practitioners with the analytical tools used by basic and translational researchers, which achieves two goals: (a) Practitioners will better understand principles of learning theory and how basic research analyzes such data, and (b) practitioners will have an additional analytical tool that they can translate to their clinical work to better understand behavior–consequence interactions. Through this process, applied behavior analysts offer generalizability to experimental procedures and ultimately arrive at interventions that are conceptually consistent with basic operant principles. As discussed by Lerman (2003), the successful translation of basic findings to applied settings represents our field's primary goal of understanding the basic processes that underlie behavior in an effort to effectively and efficiently target behaviors for change. These discussions highlight an increasing interest in bridge research, which seeks to translate the findings from basic laboratory studies to social concerns (see Fisher & Mazur, 1997). However, it can be inferred that all behavior-change procedures rely on some deviation from the matching law given the presence of concurrent schedules of reinforcement or differential consequences that underlie any reinforcement- or punishment-based procedure (see McDowell, 1988). In recent years, behavior analysts have applied the generalized matching equation (see Baum, 1974, 1979 Davison & McCarthy, 1988) to topics outside the laboratory, such as problem behavior ( Borrero & Vollmer, 2002), sports ( Reed, Critchfield, & Martens, 2006 Romanowich, Bourret, & Vollmer, 2007 Vollmer & Bourret, 2000), academics ( Mace, Neef, Shade, & Mauro, 1994 Reed & Martens, 2008), and social dynamics ( Borrero et al., 2007).