Path Analysis With Mixed-Scale Variables: Categorical ML, Least Squares, and Bayesian Estimations.

A healthy lifestyle is fundamental for the prevention and treatment of cardiovascular disease and other noncommunicable diseases (NCDs). Investment in primary prevention, including modification of health risk behaviors, could result in a 4-fold improvement in health outcomes compared with secondary prevention based on pharmacological treatment. The American Heart Association (AHA) emphasized the importance of lifestyle in its 2020 goals for cardiovascular health promotion and disease reduction. In addition to defining “cardiovascular health” based on criteria for blood pressure and biochemical markers (lipids and glycemia), the AHA Strategic Planning Committee further identified lifestyle characteristics of central importance: nutrition, physical activity, smoking, and maintenance of a healthy body weight.1 The World Health Organization estimated that ≈80% of NCDs could be prevented if 4 key lifestyle practices were followed: a healthy diet, being physically active, avoidance of tobacco, and alcohol intake in moderation.2 To support healthy lifestyle initiatives, major changes are necessary at the societal level to improve population health. Numerous strategies might help to create a culture that promotes and facilitates healthy behaviors, including creating laws and regulations, mounting large-scale public awareness and education campaigns, implementing local community programs, and providing individual counseling.3 Physicians are uniquely positioned to encourage individuals to adopt healthy lifestyle behaviors: Approximately 80% of Americans visit their primary care physician at least once a year. Physicians directly communicate with their patients during clinical encounters across numerous settings, and research indicates that patients highly value recommendations provided by their physicians.4,5 However, data further indicate that lifestyle counseling does not routinely occur in physicians’ offices, thereby representing a lost opportunity. Physicians report that they perform lifestyle counseling during ≈34% of clinic visits.4 Patients, in turn, report an even lower frequency of physician lifestyle counseling. For example, obese patients reported receiving physical activity and …

The importance of providing structural validity evidence for test score(s) derived from psychometric test instruments is highlighted by several institutions; for example, the American Psychological Association (2014) demands that evidence for the validity of an instruments' internal structure and its underlying measurement model must be provided before it is applied in psychological assessment. The knowledge about the latent structure of data obtained with tests addressing the major question "What is/are the construct[s] being measured" by psychological tests under investigation (Ziegler, 2014 (Ziegler, , 2020)) . The study of structural validity is typically addressed with factor analyses when the test scores reflect continuous latent traits. As most submissions to Psychological Test Adaptation and Development (PTAD) deal with the adaptation and further development of existing measures, authors typically test a measurement model that is based on theoretical considerations and prior findings on original versions (or adaptations) of the test under investigation. Our literature review of PTAD's publications showed that more than 90% of the articles contain at least one confirmatory factor analysis (CFA). As editor and reviewers of PTAD, we appreciate that authors are rigorous in providing evidence on the structural validity of their tests' data. However, since PTAD's inception in 2019, we experience that one comment is frequently communicated to authors during the review process, namely, the request to adjust the analytic approach in CFA from maximum likelihood (ML) estimation toward using the mean-and variance-adjusted weighted least squares (WLSMV; Muthén et al., 1997) estimator to account for the ordinal nature of the data that psychological instruments typically generate on the item level. In this editorial, we discuss the rationale behind choosing the WLSMV estimator when analyzing test adaptations and developments that are based on ordinal categorical data and concisely illustrate the problems associated with using the ML estimator (potentially in combination with robust tests of model fit) for such data.

Etude des deux formes de communication utilisee par les humains : le langage et la communication non verbale ; cette derniere forme etant utilisee par les primates pour communiquer. Commentaires, reponse de l'auteur.

The majority of respondents who took part in a survey were of the opinion that there should be a greater focus placed on behavioural and social sciences within the curriculum of medical schools. This is done to ensure that graduates of medical schools will be able to practise medicine in a manner that is both safe and effective. Despite the fact that behavioural and social sciences make significant contributions to the effectiveness of health care delivery, traditional medical school curricula have not traditionally placed a significant amount of focus on the study of these subjects. This article's objective is to provide the reader with a more in-depth comprehension of the value of social and behavioural sciences in medical education as well as the breadth of their application in a variety of different settings. Additionally, it discusses the areas of social and behavioural sciences that are significant to medicine, as well as the efficacy of incorporating them into the curricula of medical schools in order to educate and train future medical professionals to practise medicine in a manner that is fully informed. Place of Study: Foundation University Islamabad Study Duration: February 2022 to July 2022 Study Design: Empirical research Conclusion: This study examines the importance of teaching future doctors about medicine's social and behavioural aspects. It gives medical school educators the latest information on how to best teach medical students to succeed in the medical industry. Medical educators, administrators, policymakers, and other stakeholders must work together to integrate social and behavioural sciences into medical curricula. Keywords: Medical curriculum's courses, the social and behavioral sciences, and the foundations of medical education.

Hume observed that our minds are mirrors to one another: they reflect one another's passions, sentiments, and opinions.' This "sympathy," or "propensity we have to sympathize with others, to ... receive by communication [the] inclinations and sentiments [of others], however different from, or even contrary to, our own," he held to be the chief source of moral distinctions.2 Hume presented an account of how this mirroring of minds works. After a brief presentation of the account, I will show how it needs to be updated and corrected in the light of recent empirical research. Then I will give some reasons to think that the mirroring of minds is more pervasive than even Hume had thought: that mirroring is an essential part of the way in which we think about other minds. Finally, I will make some remarks about the relevance of mirroring to ethics.

Social Science and Its Frontiers Myron P. Gutmann (bio) Mark Solovey,Social Science for What? Battles over Public Funding for the “Other Sciences” at the National Science Foundation. Cambridge, Massachusetts: MIT Press, 2020. X+ 398pp. Figures, notes, index. $50.00. Americans often date the emergence of a strong commitment to government support of science to the launch of the Soviet Union’s Sputnik 1 satellite in October 1957. That event certainly spurred policy decisions that increased federal investments in education and science, and thus is an appropriate starting point for the popular narrative about science. At the same time, policy developments of the Sputnik era built on earlier events, widely recognized by historians of science. That perspective starts the story with the presentation in July 1946 of Vannever Bush’s report, Science, The Endless Frontier, to President Truman, advocating for a large, organized federal investment in scientific research, based on the role of science and technology in the Second World War. Early efforts to enact legislation based on the Bush report failed (Truman vetoed the first bill that passed because it lacked presidential control over the appointment of the Foundation’s leadership), but in 1950 Truman signed the National Science Foundation Act, establishing an enduring basis for publicly—especially federally—funded scientific research in the United States. The debates about the creation of the National Science Foundation pitted progressives against conservatives and advocates of public and congressional control of science against advocates of exclusive control by scientists.1 One of the topics of debate—although hardly the loudest—was whether the social sciences would be included in the Foundation’s charge.2 Vannever Bush was opposed to their inclusion, sometimes arguing that they should be supported by a separate organization; on the other side, Democratic West Virginia Senator Harley M. Kilgore, a leading sponsor of a more progressive approach, supported their inclusion in the Foundation’s mission. In the end, the compromise legislation that Truman signed in 1950 did not include support for the social sciences, but at the same time did not prohibit such support. The Foundation did not totally exclude the social sciences for long; it hired sociologist Harry Alpert in 1953, and in 1954 introduced a first, extremely modest, program to support the linkage between the social and natural sciences. [End Page 396] The first Social Sciences Division was not established until 1960 (in an era in which the Foundation was divided into four scientific divisions reflecting major disciplinary categories). Later, when the Foundation was reorganized into seven directorates (three of them disciplinary, one for education, and three for administrative activities) in 1975, the Divisions of Social Sciences and Behavioral and Neural Sciences were part of an expanded Directorate for Biological, Behavioral and Social Sciences (p. 179). Only in 1991–92 did the Foundation establish a separate Directorate for the Social, Behavioral and Economic (SBE) Sciences, an organizational status that still exists today. The road from the origin of the Foundation to the creation of the SBE Directorate was not linear, with ups and downs in support for the social and behavioral sciences mostly reflecting political and institutional challenges. This history spanning the period from the first discussions of the National Science Foundation through the end of the 1980s (with an added discussion of recent events and recommendations for the future) is the topic of Mark Solovey’s Social Science for What? Battles over Public Funding for the “Other Sciences” at the National Science Foundation. In this book he builds on his earlier book, Shaky Foundations: The Politics-Patronage-Social Science Nexus in Cold War America (2013), on extensive archival research, and on interviews with surviving participants. Social Science for What? is an impressive accomplishment, capturing the connections between partisan politics, scientific inquiry, tensions among scientific disciplines, and the institutional development of the Foundation. It is instructive for all readers, including for me, who served for four years (2009–13) as one of the Foundation’s Assistant Directors and head of the Directorate for Social Behavioral and Economic Sciences (SBE). Social Science for What? articulates consistent themes that define social science at NSF, along with a lively narrative arc. To define that arc, Solovey divides the main...

In the social and behavioral sciences, observed variables of mixed scale types (i.e., both continuous and categorical observed variables) have long been included in structural equation models. However, little is known about the impact of mixed continuous and categorical observed variables on the performance of existing estimation methods. This study compares two popular estimation methods with robust corrections, robust maximum likelihood (MLR) and diagonally weighted least squares (DWLS), when mixed continuous and categorical observed data are analyzed, evaluating the behavior of DWLS and MLR estimates in both measurement and full structural equation models. Monte Carlo simulation was carried out to examine the performance of DWLS and MLR in estimating model parameters, standard errors, and chi-square statistics. Two population models, a correlated three-factor measurement model and a five-factor structural equation model, were tested in combination with 36 other experimental conditions characterized by the number of observed variables' categories (2, 3, 4, 5, 6, and 7), categorical observed distribution shape (symmetry and slight asymmetry), and sample size (200, 500, and 1000). Data generation and analysis were performed with Mplus 8. Results reveal that (1) DWLS yields more accurate factor loading estimates for categorical observed variables than MLR, whereas DWLS and MLR produce comparable factor loading estimates for continuous observed variables; (2) inter-factor correlations and structural paths are estimated equally well by DWLS and MLR in nearly all conditions; (3) robust standard errors of parameter estimates obtained by MLR are slightly more accurate than those produced by DWLS in almost every condition, but the superiority of MLR over DWLS is not clearly evident once a medium or large sample is used (i.e., n = 500 or 1000); and (4) DWLS is systematically superior to MLR in controlling Type I error rates, but this superiority is attenuated with increasing sample size. The article concludes with a general discussion of the findings and some recommendations for practice and future research.

Proposed Revisions to the Common Rule for the Protection of Human Subjects in the Behavioral and Social Sciences examines how to update human subjects protections regulations so that they effectively respond to current research contexts and methods. With a specific focus on social and behavioral sciences, this consensus report aims to address the dramatic alterations in the research landscapes that institutional review boards (IRBs) have come to inhabit during the past 40 years. The report aims to balance respect for the individual persons whose consent to participate makes research possible and respect for the social benefits that productive research communities make possible.The ethics of human subjects research has captured scientific and regulatory attention for half a century. To keep abreast of the universe of changes that factor into the ethical conduct of research today, the Department of Health and Human Services published an Advance Notice of Proposed Rulemaking (ANPRM) in July 2011. Recognizing that widespread technological and societal transformations have occurred in the contexts for and conduct of human research since the passage of the National Research Act of 1974, the ANPRM revisits the regulations mandated by the Act in a correspondingly comprehensive manner. Its proposals aim to modernize the Common Rule and to improve the efficiency of the work conducted under its auspices. Proposed Revisions to the Common Rule for the Protection of Human Subjects in the Behavioral and Social Sciences identifies issues raised in the ANPRM that are critical and feasible for the federal government to address for the protection of participants and for the advancement of the social and behavioral sciences. For each identified issue, this report provides guidance for IRBs on techniques to address it, with specific examples and best practice models to illustrate how the techniques would be applied to different behavioral and social sciences research procedures.

to general feelings such as liking/disliking of mathematics, nor is it meant to exclude perceptions of the difficulty, usefulness, and appropriateness of mathematics as a school subject. There are several ways affective variables are related to mathematics learning. It is likely that a student who feels very positive about mathematics will achieve at a higher level than a student who has a negative attitude toward mathematics. It is also likely that a high achiever will enjoy mathematics more than a student who

BackgroundCost-effectiveness analyses in patients with migraine require estimates of patients’ utility values and how these relate to monthly migraine days (MMDs). This analysis examined four different modelling approaches to assess utility values as a function of MMDs.MethodsDisease-specific patient-reported outcomes from three erenumab clinical studies (two in episodic migraine [NCT02456740 and NCT02483585] and one in chronic migraine [NCT02066415]) were mapped to the 5-dimension EuroQol questionnaire (EQ-5D) as a function of the Migraine-Specific Quality of Life Questionnaire (MSQ) and the Headache Impact Test (HIT-6™) using published algorithms. The mapped utility values were used to estimate generic, preference-based utility values suitable for use in economic models. Four models were assessed to explain utility values as a function of MMDs: a linear mixed effects model with restricted maximum likelihood (REML), a fractional response model with logit link, a fractional response model with probit link and a beta regression model.ResultsAll models tested showed very similar fittings. Root mean squared errors were similar in the four models assessed (0.115, 0.114, 0.114 and 0.114, for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model respectively), when mapped from MSQ. Mean absolute errors for the four models tested were also similar when mapped from MSQ (0.085, 0.086, 0.085 and 0.085) and HIT-6 and (0.087, 0.088, 0.088 and 0.089) for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model, respectively.ConclusionsThis analysis describes the assessment of longitudinal approaches in modelling utility values and the four models proposed fitted the observed data well. Mapped utility values for patients treated with erenumab were generally higher than those for individuals treated with placebo with equivalent number of MMDs. Linking patient utility values to MMDs allows utility estimates for different levels of MMD to be predicted, for use in economic evaluations of preventive therapies.Trial registrationClinicalTrials.gov numbers of the trials used in this study: STRIVE, NCT02456740 (registered May 14, 2015), ARISE, NCT02483585 (registered June 12, 2015) and NCT02066415 (registered Feb 17, 2014).

Communication Climate as Predictor of Perceived Corporate Governance and Organizational Success

Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness. The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families. The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services. Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.

Objectives Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness. Methods The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families. Results The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services. Conclusions Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.KeywordsArtificial intelligenceBehavioral and social sciencesMachine learningFamiliesChildren

Accurate estimates of the intraclass correlation coefficient (ICC) are important for calculating appropriate sample sizes for cluster-randomized trials. The ICC and hence the sample size may be reduced through adjustment for baseline covariates. A method exists for calculating adjusted ICCs for binary outcomes based on the logit link, used to calculate odds ratios. Recent interest in presenting relative risks rather than odds ratios indicates that a method based on the log link is needed. To determine and evaluate a method for calculating adjusted ICCs based on the log link, and to provide and compare unadjusted and adjusted ICCs from a cluster-randomized trial in primary care based on the logit and log link. Two methods are proposed for calculating adjusted ICCs for the log link based on a first-order Taylor series expansion and properties of the lognormal distribution. The methods are evaluated by simulation. Unadjusted and adjusted ICCs are calculated for binary outcomes from the Point of Care Testing (PoCT) Trial using the logit and log link. The methods for calculating adjusted ICCs for the log link produced similar results unless the between cluster variance was large. Unadjusted ICCs for the PoCT Trial ranged from 0.001 to 0.048. The impact of adjustment on the ICC varied between outcomes and link functions, ranging from a 59% reduction to an 89% increase. The true ICC was unknown for the simulation study. Adjustment was made for age and gender only for the PoCT Trial. The method for calculating adjusted ICCs for binary outcomes depends on the link function. For the log link, the method based on the lognormal distribution is recommended. This method will be useful for cluster-randomized trials where the relative risk, rather than the odds ratio, is the effect measure of interest.

The Institute of Medicine (IOM) report on social and behavioral sciences (SBS) indicated that 50% of morbidity and mortality in the United States is associated with SBS factors, which the report also found were inadequately taught in medical school. A multischool collaborative explored whether the Association of American Medical Colleges Graduation Questionnaire (GQ) could be used to study changes in the six SBS domains identified in the IOM report. A content analysis conducted with the GQ identified 30 SBS variables, which were narrowed to 24 using a modified Delphi approach. Summary data were pooled from nine medical schools for 2006 and 2007, representing 1,126 students. Data were generated on students' perceptions of curricular experiences, attitudes related to SBS curricula, and confidence with relevant clinical knowledge and skills. The authors determined the sample sizes required for various effect sizes to assess the utility of the GQ. The 24 variables were classified into five of six IOM domains representing a total of nine analytic categories with cumulative scale means ranging from 60.8 to 93.4. Taking into account the correlations among measures over time, and assuming a two-sided test, 80% power, alpha at .05, and standard deviation of 4.1, the authors found that 34 medical schools would be required for inclusion to attain an estimated effect size of 0.50 (50%). With a sample size of nine schools, the ability to detect changes would require a very high effect size of 107%. Detecting SBS changes associated with curricular innovations would require a large collaborative of medical schools. Using a national measure (the GQ) to assess curricular innovations in most areas of SBS is possible if enough medical schools were involved in such an effort.