Psychology in the Schools, Vol. 00(0), 2017 C  2017 Wiley Periodicals, Inc. View this article online at wileyonlinelibrary.com/journal/pits DOI: 10.1002/pits.22022 EMPIRICAL SYNTHESIS OF THE EFFECT OF STANDARD ERROR OF MEASUREMENT ON DECISIONS MADE WITHIN BRIEF EXPERIMENTAL ANALYSES OF READING FLUENCY MATTHEW K. BURNS, CRYSTAL N. TAYLOR, KRISTY L. WARMBOLD-BRANN, AND JUNE L. PREAST University of Missouri JOHN L. HOSP University of Massachusetts Amherst JEREMY W. FORD Boise State University Intervention researchers often use curriculum-based measurement of reading fluency (CBM-R) with a brief experimental analysis (BEA) to identify an effective intervention for individual students. The current study synthesized data from 22 studies that used CBM-R data within a BEA by computing the standard error of measure (SEM) for the median data point from the baseline and intervention data. The median CBM-R score from the intervention that the authors of each study identified as most effective fell within the SEM (68% confidence interval) of the baseline data approximately 30% of the time, but the ranges for the two author-identified most effective interventions overlapped over 75% of the time. Extended analyses were consistent with the BEA results for approximately three-fourths of the instances after considering the SEM of the baseline and intervention phases. Using matched passages did not improve the overlap of the ranges, but there was less overlap when the study used three data points per condition. Results emphasize the importance of considering SEM of CBM-R data when comparing interventions within a BEA. Further implications for practice and future research are included. C  2017 Wiley Periodicals, Inc. Children that learn to read by third grade are less likely to drop out of school and to be involved with the juvenile justice system (Connor, Alberto, Compton, & O’Connor, 2014), but some students require additional support to master reading (Snow, Burns, & Griffin, 1998). School psychologists are often involved in collecting data to determine how to best intervene with individual and groups of students (Chafouleas, Riley-Tillman, & Eckert, 2003) because interventions are more effective if they are correctly targeted to student needs (Burns, VanDerHeyden, & Zaslofsky, 2014). School personnel should identify student reading needs and adapt instructional variables to improve performance (Daly, Witt, Martens, & Dool, 1997). Brief experimental analysis (BEA) is one approach to identify instructional variables that can be adapted to improve student learning. BEA is the process of delivering multiple interventions over a brief period of time and assessing their effectiveness with brief measures to test hypotheses regarding student deficits (Jones & Wickstrom, 2002). BEAs use logic from single-case design (SCD) to test hypotheses by collecting baseline data and examining immediate changes in scores after implementing an intervention (Riley-Tillman & Burns, 2009). For example, Andersen, Daly, and Young (2013) compared reading fluency after an intervention with multiple instructional components to providing a reward and to a control condition with six students using one instructional session and one reading assessment for each condition. The results indicated that the instructional condition plus the reward led to the highest reading fluency for three of the students, and the reward only led to highest fluency for three other students. Thus, three students likely had a reading deficit and three were demonstrating a motivational issue. The Correspondence to: Matthew Burns, University of Missouri, 109 Hill Hall, Columbia, MO 65211. E-mail: burnsmk@missouri.edu 1