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Relative to the randomized controlled trial (RCT), the basic regression discontinuity (RD) design suffers from lower statistical power and lesser ability to generalize causal estimates away from the treatment eligibility cutoff. This chapter seeks to mitigate these limitations by adding an untreated outcome comparison function that is measured along all or most of the assignment variable. When added to the usual treated and untreated outcomes observed in the basic RD, a comparative RD (CRD) design results. One version of CRD adds a pretest measure of the study outcome (CRD-Pre); another adds posttest outcomes from a nonequivalent comparison group (CRD-CG). We describe how these designs can be used to identify unbiased causal effects away from the cutoff under the assumption that a common, stable functional form describes how untreated outcomes vary with the assignment variable, both in the basic RD and in the added outcomes data (pretests or a comparison group’s posttest). We then create the two CRD designs using data from the National Head Start Impact Study, a large-scale RCT. For both designs, we find that all untreated outcome functions are parallel, which lends support to CRD’s identifying assumptions. Our results also indicate that CRD-Pre and CRD-CG both yield impact estimates at the cutoff that have a similarly small bias as, but are more precise than, the basic RD’s impact estimates. In addition, both CRD designs produce estimates of impacts away from the cutoff that have relatively little bias compared to estimates of the same parameter from the RCT design. This common finding appears to be driven by two different mechanisms. In this instance of CRD-CG, potential untreated outcomes were likely independent of the assignment variable from the start. This was not the case with CRD-Pre. However, fitting a model using the observed pretests and untreated posttests to account for the initial dependence generated an accurate prediction of the missing counterfactual. The result was an unbiased causal estimate away from the cutoff, conditional on this successful prediction of the untreated outcomes of the treated.

A sample of elementary schools and a sample of high schools were employed in a replication of a study conducted on corporations to determine the influence exerted by principals on the instructional performance of schools. An analysis of the components of variation was conducted to estimate principals' effects while controlling for environmental and organizational factors. It was found that principals exert a small but important influence on school performance, as measured by standardized achievement tests.

The study here helps to fill the gap between the current practices of management performance audits for firms and government agencies. The study advances recent theories of program evaluation and marketing management auditing. While the application in this chapter refers to government agencies managing destination marketing programs (tourism agencies), the algorithmic model construction is applicable for all management audits. The study applies the perspectives from two streams of theory to describe five relevant activities for managing destination marketing programs: scanning, planning, implementation, assessing, and administering. The analysis proposes impact assessments to improve management performances of DMOs via checklists for assessing the quality of information in tourism-management performance audits. Checklists can serve as a management tool by management performance auditors and by DMO executives to enhance the quality in executing destination marketing programs. A meta-evaluation of 10 tourism management audit reports identifies good and bad practices. The findings indicate that substantial improvements are possible in the practice of DMO’s management performance auditing, and the proposed checklist may ensure both high quality performance audit reports and improved performances in DMO practices.

Conventional tests of the regression discontinuity design’s identifying restrictions can perform poorly when the running variable is discrete. This paper proposes a test for manipulation of the running variable that is consistent when the running variable is discrete. The test exploits the fact that if the discrete running variable’s probability mass function satisfies a certain smoothness condition, then the observed frequency at the threshold has a known conditional distribution. The proposed test is applied to vote tally distributions in union representation elections and reveals evidence of manipulation in close elections that is in favor of employers when Republicans control the NLRB and in favor of unions otherwise.

ONLY as events recede can we view them in proper perspective. It is then that we discover how often initial judgments were wrong, our fears con‐founded or our hopes dispelled. Treaties to end wars, pacts of eternal friendship and alliance are the debris which litter our uneasy world.

This study aims to provide a response to the commentary by Yuan on the paper “Marketing or Methodology” in this issue of EJM.

Conceptual argument and statistical discussion.

The authors find that some of Yuan’s arguments are incorrect, or unclear. Further, rather than contradicting the authors’ conclusions, the material provided by Yuan in his commentary actually provides additional reasons to avoid partial least squares (PLS) in marketing research. As such, Yuan’s commentary is best understood as additional evidence speaking against the use of PLS in real-world research.

This rejoinder, coupled with Yuan’s comment, continues to support the strong implication that researchers should avoid using PLS in marketing and related research.

Marketing researchers should avoid using PLS in their work.

This rejoinder supports the earlier conclusions of “Marketing or Methodology,” with additional argumentation and evidence.

Achievement goal theory traces people's behaviors, thoughts, and emotions in achievement situations to the broad goals they pursue in that activity, whether in education, sports, work, or other achievement domains (Dweck, 1986; Maehr & Midgley, 1991; Nicholls, 1984). Two goals have featured prominently: mastery goals (also sometimes called learning goals) and performance goals (also called ego goals or ability validation goals). Both goals concern the pursuit of competence and the assessment of one's own skill level, yet they do so in distinct ways. People pursuing a mastery goal strive to develop their skill or expertise, while those pursuing a performance goal instead strive to demonstrate and validate their existing skill, typically by outperforming peers. As such, those pursuing mastery goals typically use self-referential standards to define success versus failure, while those pursuing performance goals instead use normative standards to define success versus failure.

Regression discontinuity (RD) models are commonly used to nonparametrically identify and estimate a local average treatment effect. Dong and Lewbel (2015) show how a derivative of this effect, called treatment effect derivative (TED) can be estimated. We argue here that TED should be employed in most RD applications, as a way to assess the stability and hence external validity of RD estimates. Closely related to TED, we define the complier probability derivative (CPD). Just as TED measures stability of the treatment effect, the CPD measures stability of the complier population in fuzzy designs. TED and CPD are numerically trivial to estimate. We provide relevant Stata code, and apply it to some real datasets.