A Comparison Between Propensity Score Matching, Weighting, and Stratification in Multiple Treatment Groups: A Simulation Study

The application of propensity score techniques (matching, stratification, and weighting) with multiple treatment levels are similar to those used in binary groups. However, given that the application of propensity scores in multiple treatment groups is new, factors affecting the performance of matching, stratification, and weighting in multiple treatment groups are less explored. Therefore, this study was conducted to determine the performance of different propensity score techniques with multiple treatment groups under various circumstances. Specifically, the study focused on examining how the three propensity score corrective techniques perform in estimating treatment effects under (1) overt and (2) hidden types of selection bias. In this study, the performance of propensity score matching, stratification, and weighting techniques were tested under three different sample sizes and three levels of overt and hidden bias. A Monte Carlo simulation was used to generate data with specific sample sizes and levels of overt and hidden bias. A total of 54 data conditions with 1000 replications for each condition was generated to compute the average treatment effect (ATE). The difference between the pre-specified ATE and estimated ATE was calculated to evaluate the performance of propensity score techniques. Two 3x3x3x2 analyses of variance were conducted to assess the effect of propensity score technique, level of bias, sample size, and type of treatment effect on the amount of bias in estimating the treatment effect under overt and hidden bias conditions. The results provided four key findings of information about the application of propensity score analysis in multiple treatment groups. The first key finding is that the treatment effect estimate will be underestimated after imposing propensity score adjustments. Second, the treatment effect estimates are affected by the level of overt bias. Third, propensity score analysis does not account for hidden bias. The fourth finding is that the propensity score techniques performed differently in a small sample size condition. Overall, these four key findings provide cautionary notes to the users of propensity score analysis in multiple treatment groups. The study is concluded with the limitations of this study and the recommendations for future research. Keywords: Propensity score, multiple treatmen

Detecting Socially Desirable Responses In Personality Inventory

This study attempted to detect the socially desirable responses within differential responses. Besides, this study also examined items and personality dimensions that are vulnerable to social desirability. In the experimental design, a sample of 521 students was tested twice with the International Personality Item Pool (IPIP) under honest and socially desirable instructions. Responses from the first administration were classified as honest group responses and those from the second administration were grouped as socially desirable responses. The mean dispersion and fit analysis were applied in detecting socially desirable responses

A Practical Guide for Using Propensity Score Weighting in R

Propensity score weighting is one of the techniques used in controlling for selection biases in non-experimental studies. Propensity scores can be used as weights to account for selection assignment differences between treatment and comparison groups. One of the advantages of this approach is that all the individuals in the study can be used for the outcomes evaluation. In this paper, we demonstrate how to conduct propensity score weighting using R. The purpose is to provide a step-by-step guide to propensity score weighting implementation for practitioners. In addition to strengths, some limitations of propensity score weighting are discussed. Accessed 24,338 times on https://pareonline.net from June 27, 2015 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Propensity Scores: A Practical Introduction Using R

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

Cracking the code: Investigating the relationship between big five personality traits and stem education

The ultimate objective of this research is to provide light on the complex relationship between Big Five personality traits and jobs in science, technology, engineering, and mathematics (STEM). This study examines the role of personality traits, notably openness, conscientiousness, extraversion, agreeableness, and neuroticism, in growing one's interest and proclivity for STEM careers by examining data from a large sample of studies. The study's findings have far-reaching implications for both individuals and society as a whole. Understanding the influence of personality traits on STEM career pursuits can assist educational institutions, career counsellors, and policymakers in creating environments that encourage and support persons with specific personality profiles to pursue STEM jobs. Individuals can also gain substantial insights into their own predispositions and aptitudes, allowing them to make informed career decisions that are aligned with their own personality traits. By cracking the code and unravelling the deep relationship between Big Five personality traits and STEM career choices, this research hopes to contribute to the greater goal of broadening and strengthening the STEM workforce, ultimately paving the way for a more inclusive and innovative futur

Revolutionizing STEM Education: Unleashing the Potential of STEM Interest Careers in Malaysia

STEM education and STEM oriented occupations have emerged as major drivers of talent pool expansion, economic growth, and technical advancement in Malaysia's dynamic terrain, where innovation and progress reign supreme. Malaysia recognises the critical need to create a solid talent pool capable of navigating this fast evolving terrain as the globe becomes increasingly reliant on science, technology, engineering, and mathematics. STEM education, with its multidisciplinary approach that integrates these important disciplines, has become the cornerstone of Malaysia's educational system, empowering a generation of STEM-literate persons ready to face the future's problems and opportunities. Simultaneously, the appeal of STEM interest employment has grabbed aspirant persons, providing avenues that merge enthusiasm and proficiency, ensuring Malaysia remains at the forefront of global innovation. We delve into the domains of STEM education and STEM oriented careers in Malaysia, finding their transformative impact on nurturing and extending the nation's talent pool, boosting economic competitiveness, and propelling scientific and technological brilliance in this enthralling exploration

Empowering minds: Harnessing the potential of cognitive field independence and dependence in STEM education

The purpose of this article is to discuss the importance of cognitive field independence and field dependency in STEM (Science, Technology, Engineering, and Mathematics) education. It discusses the critical topic of cognitive types and their impact on STEM learning and problemsolving. This study intends to illuminate how educators might harness these cognitive features to empower students in their STEM learning journeys by investigating the characteristics and benefits of field independence and reliance. The study focuses on the success of fieldindependent learners in analytical and abstract thinking, as opposed to field-dependent learners' ability for holistic and context-based comprehension. It also looks into methodologies and pedagogical approaches for accommodating diverse cognitive styles in order to create inclusive and engaging learning environments. Recognizing and cultivating the qualities of field independence and dependency can help educators unlock students' full potential and foster a diverse range of problem-solving skills in the STEM industry. Finally, this paper emphasizes the necessity of recognizing cognitive variety and harnessing it as a significant tool for increasing creativity and achievement in STEM education

Charting the course: STEM interest career survey among secondary school students in Malaysia

STEM (Science, Technology, Engineering, and Mathematics) career interest has expanded in tandem with the rising emphasis on STEM education in schools and universities. Researchers investigated aspects and consequences connected to students' interest in STEM disciplines, which is critical for recognising their potential and inclination towards STEM subjects, particularly in the context of Malaysia's ongoing STEM education implementation. Kier et al. (2013) created the STEM-Career Interest Survey (STEM-CIS) as a theoretical framework for assessing STEM career interest. The STEM-CIS, which is based on the Social Cognitive profession Theory, takes into account factors such as profession choice, self-efficacy, outcome expectations, personal aspirations, and contextual factors. The STEM-CIS theory is made up of four sets of characteristics that describe careers in science, technology, engineering, and mathematics. These dimensions include self-efficacy, personal objectives, outcome expectations, interest in STEM courses, contextual support, and personal input. Understanding STEM career interest and using the STEM-CIS has important consequences for educational institutions, legislators, and career counsellors. This knowledge helps to produce a skilled and diversified workforce, which drives innovation and progress in STEM-related sectors

Exploring the impact of cognitive factors on learning, motivation and career in Malaysia’s STEM education

Advancements in science, technology, engineering, and mathematics (STEM) are critical to the success of modern societies. However, STEM education and careers are often hindered by cognitive factors, such as mindset, motivation, and learning strategies. This paper examines the complex interplay between cognitive factors and STEM education and careers, highlighting the profound influence of these factors on success in these fields. Through a comprehensive review of existing literature and empirical evidence, this paper presents a compelling case for the need to prioritize cognitive development in STEM education and career pathways. We argue that by fostering a growth mindset, cultivating intrinsic motivation, and promoting effective learning strategies, individuals can overcome cognitive barriers and achieve success in STEM education and careers. Ultimately, this paper underscores the critical role of cognitive factors in shaping the future of STEM fields and offers practical recommendations for educators, policymakers, and STEM professionals to support cognitive development and enhance STEM outcomes

Examining Moderator Factors Influencing Students' Interest in STEM Careers: The Role of Demographic, Family, and Gender

This study investigates the characteristics that influence rural high school students' interest in jobs in Science, Technology, Engineering, and Mathematics (STEM). It focuses on the impact of socioeconomic class, family background, and gender on their ideas and desires for STEM jobs. The findings show that socioeconomic factors have a major impact on rural students' interest in STEM subjects. Due to enhanced access to resources, educational opportunities, and exposure to STEM-related activities, students from higher socioeconomic backgrounds in rural areas display greater interest and drive for STEM. Furthermore, parental education and occupation have a significant impact on rural children' perceptions of STEM vocations and self-confidence in these domains. The report also emphasises the impact of gender dynamics, with gender preconceptions and a lack of diverse role models contributing to rural students' underrepresentation of girls and marginalised genders in STEM jobs. It is critical to develop inclusive learning settings, challenge gender prejudices, and offer equal access to STEM education for rural children in order to increase interest and participation in STEM. By addressing these concerns, educators and politicians can encourage rural kids to pursue STEM careers, resulting in a more diversified and skilled STEM workforce and propelling rural areas forward in the face of technological breakthroughs