Public schools with impressive records of serving lower-performing students are often overlooked because their average test scores, even when students are growing quickly, are lower than scores in schools that serve higher-performing students. Schools may appear to be doing poorly either because baseline achievement is not easily accounted for or because changing demographic trends result in successive cohorts of students that are not comparable. These situations are common and problematic for practitioners, policymakers, and researchers who are increasingly tasked with identifying, replicating and communicating effective educational practice. The purpose of this study is to (1) explore alternatives to value-added models that are simple to calculate and easy to interpret; and (2) describe the conditions that must be met for such alternatives to provide results that are similar to those found with value-added methods. The authors focus on three alternative methods that have been the subject of prior reports (see Glazerman & Potamites 2011; Castellano & Ho 2013): average gain scores, calculated as the difference between a school's average score in a given grade in one year and that school's average score in the previous grade the previous year; average cohort differences, calculated as the difference between a school's average score in a given grade in one year and that school's average score in the "same" grade the previous year; and residual gain scores, calculated as the difference between the observed and expected average score in a given grade in one year given the previous year's average score. The study asks the following questions: (1) How similar are results obtained from average gain, cohort difference, and residual gain methods to results obtained using value-added methods? and (2) Under what conditions can results from average gain, cohort difference, or residual gain scores provide an acceptable substitute for value-added results? The authors focus on the relationship between student background characteristics and growth and student attrition (or student composition changes) across years. The first question is addressed through the use of empirical school district data and the second through simulation studies. The goal of this analysis is to provide practical evidence for districts and states that can inform analyses of achievement growth trends. Two tables and a figure are appended. [SREE documents are structured abstracts of SREE conference symposium, panel, and paper or poster submissions.]