An Application of the Stressor-Vulnerability Model of Drinking in College Student Drinkers
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Abstract:
The stressor vulnerability model provides theoretical support for conditions under which drinking to cope (DTC) is likely to occur (i.e., decreased adaptive coping, increased positive alcohol expectancies). However, research has only tested this model in a college sample reporting trauma. Generalizability to a non-trauma-specific sample college drinkers would support applications of the model and inferences about coping-related drinking across a broader group of young adults.Keywords:
Stressor
Affect
Binge drinking
Moderated mediation
Mediator
Cognitive appraisal
Abstract Parenting was observed in videotaped interactions in 30 families referred for child conduct problems. Generalizability coefficients and the impact of varying numbers of raters were estimated. Two measurement designs were compared: All raters observed all families ("crossed" design) and a different rater observed each family ("nested" design). The crossed design provided higher generalizability coefficients than a nested design, implying inflated generalizability estimates if a crossed estimation model is used for a nested data collection. Three and four raters were needed to obtain generalizability coefficients in the .70–.80 range for monitoring and discipline, respectively. One rater was sufficient for a corresponding estimate for positive involvement and for an estimate in .80–.90 range for problem-solving. Estimates for skill encouragement were non-acceptable.
Inter-Rater Reliability
Research Design
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Variance components
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Generalizability theory consists of a conceptual framework and a methodology that enable an investigator to disentangle multiple sources of error in a measurement procedure. The roots of generalizability theory can be found in classical test theory and analysis of variance (ANOVA), but generalizability theory is not simply the conjunction of classical theory and ANOVA. In particular, the conceptual framework in generalizability theory is unique. This framework and the procedures of generalizability theory are introduced and illustrated in this instructional module using a hypothetical scenario involving writing proficiency.
Classical test theory
Test theory
Variance components
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Abstract Falsificationist and confirmationist approaches provide two well-established ways of evaluating generalizability. Yarkoni rejects both and invents a third approach we call neo-operationalism . His proposal cannot work for the hypothetical concepts psychologists use, because the universe of operationalizations is impossible to define, and hypothetical concepts cannot be reduced to their operationalizations. We conclude that he is wrong in his generalizability-crisis diagnosis.
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The main purpose of the current research was to explore the effects of work stress on employees' mental health, especially the mediation role of emotional exhaustion and the moderation role of cognitive reappraisal. A total of 446 fulltime employees completed the survey. The results of SPSS Process indicated that (a) emotional exhaustion mediated the negative effect of both challenge and hindrance stressors on mental health; (b) cognitive appraisal negatively moderated the indirect effect of challenge stressors on mental health through emotional exhaustion, that is, with higher level of cognitive reappraisal this indirect effect was lower.
Moderated mediation
Mental model
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Abstract Generalizability theory is a conceptual and statistical framework for the analysis and construction of measurement instruments. Among the most important concepts of generalizability discussed in the first section of this entry are the universe of admissible observations, universe and observed scores, random and fixed facets, crossed and nested designs, variance components, generalizability studies, decision studies, and generalizability coefficients. A discussion of the results of a generalizability study and a decision study of a crossed one‐facet random effects design and of a two‐facet crossed one‐facet random effects design is presented in the two following sections. A number of other designs that illustrate the versatility of generalizability theory are presented in the final section.
Variance components
Facet (psychology)
Section (typography)
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Falsificationist and confirmationist approaches provide two well-established ways of evaluating generalizability. Yarkoni rejects both and invents a third approach we call neo-operationalism. His proposal cannot work for the hypothetical concepts psychologists use, because the universe of operationalizations is impossible to define, and hypothetical concepts cannot be reduced to their operationalizations. We conclude that he is wrong in his generalizability-crisis diagnosis.
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Purpose: To examine the concepts of external validity and generalizability, and explore strategies to strengthen generalizability of research findings, because of increasing demands for knowledge utilization in an evidence‐based practice environment. Framework: The concepts of external validity and generalizability are examined, considering theoretical aspects of external validity and conflicting demands for internal validity in research designs. Methodological approaches for controlling threats to external validity and strategies to enhance external validity and generalizability of findings are discussed. Conclusions: Generalizability of findings is not assured even if internal validity of a research study is addressed effectively through design. Strict controls to ensure internal validity can compromise generalizability. Researchers can and should use a variety of strategies to address issues of external validity and enhance generalizability of findings. Enhanced external validity and assessment of generalizability of findings can facilitate more appropriate use of research findings.
External validity
Internal validity
Compromise
Incremental validity
Validity
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Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework affirms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research.
Relevance
Empirical Research
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Introduction. 1. What is Generalizability Theory? 2. Generalizability Theory: Concepts and Principles. 3. Using EduG: The Generalizability Theory Software. 4. Applications to the Behavioral and Social Sciences. 5. Practice Exercises. 6. Current Developments and Future Possibilities. Appendixes.
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