Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. Diffusion is a special type of communication concerned with the spread of messages that are perceived as new ideal. Communication is a process in which participants create and share information with one another in order to reach a mutual understanding. Diffusion has a special character because of the newness of the idea in the message content. Thus some degree of uncertainty and perceived risk is involved in the diffusion process. An individual can reduce this degree of uncertainty by obtaining information. Information is a difference in matter energy that affects uncertainty in a situation where a choice exists among a set of alternatives.

The Diffusion of Innovations

The impact of nonrheumatic atrial fibrillation, hypertension, coronary heart disease, and cardiac failure on stroke incidence was examined in 5,070 participants in the Framingham Study after 34 years of follow-up. Compared with subjects free of these conditions, the age-adjusted incidence of stroke was more than doubled in the presence of coronary heart disease (p less than 0.001) and more than trebled in the presence of hypertension (p less than 0.001). There was a more than fourfold excess of stroke in subjects with cardiac failure (p less than 0.001) and a near fivefold excess when atrial fibrillation was present (p less than 0.001). In persons with coronary heart disease or cardiac failure, atrial fibrillation doubled the stroke risk in men and trebled the risk in women. With increasing age the effects of hypertension, coronary heart disease, and cardiac failure on the risk of stroke became progressively weaker (p less than 0.05). Advancing age, however, did not reduce the significant impact of atrial...

https://www.ahajournals.org/doi/pdf/10.1161/01.STR.22.8.983

Atrial fibrillation as an independent risk factor for stroke: the Framingham Study.

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.

/pdf/matching-methods-for-causal-inference-a-review-and-a-look-1133erv7vp.pdf

Matching Methods for Causal Inference: A Review and a Look Forward

Previous research has shown that trait concepts and stereotypes become active automatically in the presence of relevant behavior or stereotyped-group features. Through the use of the same priming procedures as in previous impression formation research, Experiment l showed that participants whose concept of rudeness was primed interrupted the experimenter more quickly and frequently than did participants primed with polite-related stimuli. In Experiment 2, participants for whom an elderly stereotype was primed walked more slowly down the hallway when leaving the experiment than did control participants, consistent with the content of that stereotype. In Experiment 3, participants for whom the African American stereotype was primed subliminally reacted with more hostility to a vexatious request of the experimenter. Implications of this automatic behavior priming effect for self-fulfilling prophecies are discussed, as is whether social behavior is necessarily mediated by conscious choice processes.

/pdf/automaticity-of-social-behavior-direct-effects-of-trait-20w2uuep4n.pdf

Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action.

Past behavior guides future responses through 2 processes. Well-practiced behaviors in constant contexts recur because the processing that initiates and controls their performance becomes automatic. Frequency of past behavior then reflects habit strength and has a direct effect on future performance. Alternately, when behaviors are not well learned or when they are performed in unstable or difficult contexts, conscious decision making is likely to be necessary to initiate and carry out the behavior. Under these conditions, past behavior (along with attitudes and subjective norms) may contribute to intentions, and behavior is guided by intentions. These relations between past behavior and future behavior are substantiated in a meta-analytic synthesis of prior research on behavior prediction and in a primary research investigation. In everyday explanations of behavior, habits denote one's customary ways of behaving. Claiming that one performed a behavior because of habit provides an understandable explanation for an act that otherwise might seem irrational or even harmful. Habits also are featured in the popular psychology literature in the form of self-help books designed to identify readers' existing habits, evaluate habits' effectiveness in meeting goals, and establish more desirable habits. Habits are not, however, important constructs in most contemporary social psychological models of human behavior. Early in their careers, most psychology graduate students learn that frequency of past behavior, a standard indicator of habit strength (Triandis, 1977, 1980), is the best predictor of

/pdf/habit-and-intention-in-everyday-life-the-multiple-processes-1xuobe1ggh.pdf

George W. Snedecor

Papers

Statistical methods