The chain mediating effect of learning goal distress and excessive learning motivation from unknown locus of control on migrant middle school students' learning anxiety
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The prevalence of learning anxiety among Chinese migrant middle school students is a notable concern in academia. There is a wealth of research on the effects of internal and external locus of control on learning anxiety, but there is still a lack of research on unknown locus of control. To grasp the situation of migrant middle school students in terms of learning anxiety, to understand the relationship between migrant middle school students' unknown locus of control and learning anxiety, this study surveyed 351 migrant middle school students, using Mental Health Test, Multidimensional Measure of Children's Perceptions of Control, Middle School Students Learning Motivation Scale to do the questionnaire survey. The data analysis conducted through SPSS software revealed the following findings: (1) There is a positive prediction of learning anxiety from an unknown locus of control (β = 0.139, p < 0.05). (2) Unknown locus of control indirectly influences learning anxiety through both learning goal distress and excessive learning motivation. Learning goal distress and excessive learning motivation partially mediate the relationship between unknown locus of control and learning anxiety. The total magnitude of the indirect effects is 0.15 (p<0.05). The effect is significant, but the size of the effect is small and the issue of generalizability should be considered. The research findings suggest that the unknown locus of control indirectly affects learning anxiety through difficulties with learning goals and excessive learning motivation. It is suggested that increasing opportunities for students to make independent choices and to develop their sense of self-control in daily lessons; guiding students to set appropriate learning goals, avoiding too high or too low, emphasizing refinement of goals and the combination of long-term and short-term goals.Keywords:
Locus of control
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.
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Variance components
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Clinical studies, especially randomized, controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic review to understand the practice of generalizability assessment. We identified 187 relevant articles and systematically organized these studies in a taxonomy with three dimensions: (i) data availability (i.e., before or after trial (a priori vs. a posteriori generalizability)); (ii) result outputs (i.e., score vs. nonscore); and (iii) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but < 30% of studies reported positive generalizability results. As a priori generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, < 40% of the studies in our review assessed a priori generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.
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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.
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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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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.
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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.
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Internal 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.
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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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