Background: This paper provides an introduction to propensity scores for evaluation practitioners. Purpose: The purpose of this paper is to provide the reader with a conceptual and practical introduction to propensity scores, matching using propensity scores, and its implementation using statistical R program/software. Setting: Not applicable Intervention: Not applicable Research Design: Not applicable Data Collection and Analysis: Not applicable Findings: In this demonstration paper, we describe the context in which propensity scores are used, including the conditions under which the use of propensity scores is recommended, as well as the basic assumptions needed for a correct implementation of the technique. Next, we describe some of the more common techniques used to conduct propensity score matching. We conclude with a description of the recommended steps associated with the implementation of propensity score matching using several packages developed in R, including syntax and brief interpretations of the output associated with every step. Keywords: propensity score analysis; propensity score matching; R.
Purpose The Brief Symptom Inventory-18 (BSI-18) is a tool used to measure clinically relevant psychological symptoms to support clinical decision-making at intake and during the course of treatment in various settings. The BSI-18 has frequently been evaluated for construct validity via analysis of its structure. However, these studies showed mixed results of the factor solutions and no consensus on the dimensionality. Therefore, the purpose of this paper is to synthesize the empirical findings about the factor structure to reach an overall conclusion about the factor structure of the BSI-18. Design/methodology/approach A meta-analysis of factor analysis results using an aggregated co-occurrence matrix approach was conducted to synthesize the factor structure. The item factor loading information from seven published studies is gathered, combined and summarized to conclude the factor structure of the instrument. Multidimensional scaling (MDS) was used to quantify the similarity between the underlying factor structures of BSI-18 from different empirical articles. Findings The perceptual map from MDS-found items was clustered into three distinctive factors matching the original intent. The findings highlight the consistency of the BSI-18’s factor structure. However, the findings should be used with caution owing to the small sample size and conclusions made from visual representation. Originality/value This original study contributes to research in the provision of empirically tested measures that take a focus on factor analysis and the use of meta-analysis technique to account for an understanding of the factor structure.
This manuscript reports results of an empirical assessment of a newly developed measure designed to assess apprentice teaching proficiency. In this study, Many Facets Rasch model software was used to evaluate the psychometric quality of the Framework for Equitable and Effective Teaching (FEET), a rater-mediated assessment. The analysis focused on examining variability in (1) supervisor severity in ratings, (2) level of item difficulty, (3) time of assessment, and (4) teacher apprentice proficiency. Added validity evidence showed moderate correlation with self-reports of apprentice teaching. The findings showed support for the FEET as yielding reliable ratings with a need for added rater training.
In Education sectors many researchers have focused student's life style and effects on mental health. Currently there are many factors which are contributing to develop depressive symptoms among University Students. Study was conduct to investigate the relationship between skipping breakfast and lack of physical exercise are contributing risk factor of depression among University students. To conduct this survey research, sample (N=350) students participated from different faculties, of Sultan Idris Education University (UPSI), Malaysia. Students (178% male, 172% female), age (19-27 years) were selected by using convenience sampling. This survey was conduct by administering the structured questionnaire of Breakfast information and Habitual physical exercise questionnaire, level of depression was measured by Beck Depression Inventory (Beck, et al., 1996). Results revealed that there was strongly negative correlation between the scores of having breakfast, Habitual physical activity and depression. Multiple regression shows that skipping breakfast and lack of physical activities were significant, F(2,347=87.99, p<.000), R 2 33%, which revealed skipping breakfast and physical activity are significant at (p<.000). Result indicate that skipping breakfast and lack of physical activity can predict depression. Student's life style behavior of skipping meal and lack of physical are significant for mental and emotional health. Present research predict that skipping breakfast, lack of physical exercise are risk factors toward depression among students. This study is important to learn about the contributing factors of mental health, we can encourage students to take healthy and timely meal and participate regularly in exercise to increase mental health.
Many facet Rasch measurement (MFRM) is a type of measurement application that aims to perform analysis of multiple variables that potentially influence results of a test or outcome measure. A facet is a component with a systematic contribution to the variability of the measurement error. Linacre (1989) created the technique as an extension of the Rasch model to model the consistency of judges/raters in rating performances. The purpose is to provide a step-by step guide for practitioners on how to conduct an MFRM analysis in FACETS software using real data.
Teaching and facilitation implementation methods among lecturers and their influence on students' interests in learning geography.
The increasing prevalence of exploratory factor analysis (EFA) applications in scholarly literature reflects its popularity and the convenience of computer‐assisted analysis. With advancements in computer hardware and software, the complexity and variations of EFA analysis have also grown. Despite the availability of sophisticated computer programming, the appropriate utilization of EFA necessitates users to make informed judgments. Additionally, users are responsible for searching and identifying suitable statistical software to accommodate their data and analysis requirements. This review aims to enhance understanding of the EFA technique and summarize the analysis options available for EFA in R packages. A total of 50 packages were examined in this study. Specifically, the review focuses on (1) diagnostic functions, (2) factor extraction, (3) factor retention, (4) factor rotation, and (5) complex data and technique features provided by these packages. The review summarizes the available function options in R packages by outlining these five crucial steps in conducting an EFA analysis. This synthesis offers an overview of the similarities and distinctive features of each package, serving as a valuable resource for users in selecting a suitable EFA technique. It is important to note that there is no definitive approach to conducting an exploratory factor analysis. Users need to deliberately select and combine appropriate techniques to achieve optimal results.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Dimension Reduction Software for Computational Statistics > Software/Statistical Software
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