Causality in a Social World

Moderation, Mediation and Spill-over

Guanglei Hong

• Publisher: Wiley
• Year: 2015
• Print ISBN: 9781118332566
• eBook ISBN: 9781119030607
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data.

The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory.

Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.

Part I
OVERVIEW

1
Introduction

According to an ancient Chinese fable, a farmer who was eager to help his crops grow went into his field and pulled each seedling upward. After exhausting himself with the work, he announced to his family that they were going to have a good harvest, only to find the next morning that the plants had wilted and died. Readers with minimal agricultural knowledge may immediately point out the following: the farmer’s intervention theory was based on a correct observation that crops that grow taller tend to produce more yield. Yet his hypothesis reflects a false understanding of the cause and the effect—that seedlings pulled to be taller would yield as much as seedlings thriving on their own.

In their classic Design and Analysis of Experiments, Hinkelmann and Kempthorne (1994; updated version of Kempthorne, 1952) discussed two types of science: descriptive science and the development of theory. These two types of science are interrelated in the following sense: observations of an event and other related events, often selected and classified for description by scientists, naturally lead to one or more explanations that we call “theoretical hypotheses,” which are then screened and falsified by means of further observations, experimentation, and analyses (Popper, 1963). The experiment of pulling seedlings to be taller was costly, but did serve the purpose of advancing this farmer’s knowledge of “what does not work.” To develop a successful intervention, in this case, would require a series of empirical tests of explicit theories identifying potential contributors to crop growth. This iterative process gradually deepens our knowledge of the relationships between supposed causes and effects—that is, causality—and may eventually increase the success of agricultural, medical, and social interventions.

1.1 Concepts of moderation, mediation, and spill-over

Although the story of the ancient farmer is fictitious, numerous examples can be found in the real world in which well-intended interventions fail to produce the intended benefits or, in many cases, even lead to unintended consequences. “Interventions” and “treatments,” used interchangeably in this book, broadly refer to actions taken by agents or circumstances experienced by an individual or groups of individuals. Interventions are regularly seen in education, physical and mental health, social services, business, politics, and law enforcement. In an education intervention, for example, teachers are typically the agents who deliver a treatment to students, while the impact of the treatment on student outcomes is of ultimate causal interest. Some educational practices such as “teaching to the test” have been criticized to be nearly as counterproductive as the attempt of helping seedlings grow by pulling them upward. “Interventions” and “treatments” under consideration do not exclude undesired experiences such as exposure to poverty, abuse, crime, or bereavement. A treatment, planned or unplanned, becomes a focus of research if there are theoretical reasons to anticipate its impact, positive or negative, on the well-being of individuals who are embedded in social settings including families, classrooms, schools, neighborhoods, and workplaces.

In social science research in general and in policy and program evaluations in particular, questions concerning whether an intervention works and, if so, which version of the intervention works, for whom, under what conditions, and why are key to the advancement of scientific and practical knowledge. Although most empirical investigations in the social sciences concentrate on the average effect of a treatment for a specific population as opposed to the absence of such a treatment (i.e., the control condition), in-depth theoretical reasoning with regard to how the causal effect is generated, substantiated by compelling empirical evidence, is crucial for advancing scientific understanding.

First, when there are multiple versions or different dosages of the treatment or when there are multiple versions of the control condition, a binary divide between “the treatment” and “the control” may not be as informative as fine-grained comparisons across, for example, “treatment version A...

Table of contents

• Cover
• Title page
• Table of Contents
• Preface
• Part I: OVERVIEW
• Part II: MODERATION
• Part III: MEDIATION
• Part IV: SPILL-OVER
• Index
• End User License Agreement