Standard Error Estimates Research Articles

Factors affecting investor demand in a health emergency

This paper investigates the influence of COVID-19 exposure and China exposure disclosed by companies in their annual reports on firm value. Research data were obtained from annual reports. The study used observations of 430 companies listed on the Indonesia Stock Exchange during 2019 and analyzed using ordinary least squares (OLS) with fixed industry effects and standard error estimation with Stata 17.0 software. The study also used the Heckman two-stage as a robustness test. The results show a positive and statistically significant relationship between COVID-19 and firm value. The same result is also shown in the relationship between China exposure and firm value. Our research provides evidence that exposure to the COVID-19 situation resulted in an increase in firm value during the period. Additionally, these results were found to be robust by the Heckman two-stage test. With these research findings, companies can identify factors that can influence an increase in firm value, including providing clear disclosure on company conditions such as COVID-19 and China exposure. Companies that comprehensively disclose their information during uncertain environmental situations will provide opportunities for them to generate a more positive performance, as stakeholders provided funding facilities for companies to pursue growth during the COVID-19 period. Therefore, the COVID-19 factor as a form of exposure for companies is expected to encourage companies to continue to adapt and innovate.

Heteroscedasticity of unknown form: a comparison of five heteroscedasticity-consistent covariance matrix (hccm) estimators

Regression model applications frequently involve violations of the homoscedasticity assumption and the presence of high leverage points (HLPs). The Heteroscedasticity-Consistent Covariance Matrix (HCCM) estimator's impact in the presence of heteroscedasticity of an unknown form was investigated in this study. The effectiveness of five variations of HCCM namely White’s estimator (HC0), White-Hinkley (HC1), Mackinnon White (HC2), Davison –Mackinnon (HC3), and Cribari-Neto (HC4) were accessed to identify the optimal Heteroscedasticity-Consistent Covariance Matrix (HCCM) estimator. In the study a simulated dataset was analysed using the Econometric View Software Version 12. The Breush-Pagan Godfery’s test for heteroscedasticity was applied and p-value of 0.0123 was obtained indicating presence of heteroscedasticity in the model. Applying the HCCM estimators and comparing the Heteroskedasticity-consistent standard errors estimates showed that HCO had 124.104, HC1 had 1189.222, HC2 had 1175.282, HC3 had 1106.94 and HC4 had 1140.707. These results reveal that HC3 and HC4 produced smaller errors compared to HC0, HC1 and HC2. The study hence comes to the conclusion that when doing inferential tests using OLS regression, the use of HCSE estimator increases the researcher's confidence in the accuracy and potency of those tests. This study therefore suggests that to ensure that findings are not affected by heteroscedasticity; researchers should use HCCM estimator but precisely HC3 and HC4, as the presented better results in comparison to others.

Inference on subgroups identified based on a heterogeneous treatment effect in a post hoc analysis of a clinical trial.

Due to the many benefits of understanding treatment effect heterogeneity in a clinical trial, an exploratory post hoc subgroup analysis is often performed to find subpopulations of patients with conditional average treatment effect that suggests better treatment efficacy than in the overall population. A naive re-substitution approach uses all available data to identify a subgroup and then proceeds with estimation and inference using the same data set. This approach generally leads to an overly optimistic estimate of conditional average treatment effect. In this article, in a post hoc analysis, we estimate the target optimal subgroup through maximizing a utility function, from candidates systematically identified with a penalized regression. We then compare two resampling-based bias-correction methods, cross-validation and debiasing bootstrap, for obtaining approximately unbiased estimates and valid inference of conditional average treatment effect in the identified subgroup, with either an empirical or an augmented estimator. Our results show that both the cross-validation and the debiasing bootstrap methods reduce the re-substitution bias effectively. The cross-validation method appears to have less biased point estimates, smaller standard error estimates, but poorer coverages than the debiasing bootstrap method when using the empirical estimator and the sample size is moderate. Using the augmented estimator in the debiasing bootstrap method leads to less biased point estimates but poorer coverages. We conclude that bias correction should be a part of every exploratory post hoc subgroup analysis to eliminate re-substitution bias and to obtain a proper confidence interval for the estimated conditional average treatment effect in the selected subgroup.

Estimating the VaR-induced Euler allocation rule

AbstractThe prominence of the Euler allocation rule (EAR) is rooted in the fact that it is the only return on risk-adjusted capital (RORAC) compatible capital allocation rule. When the total regulatory capital is set using the value-at-risk (VaR), the EAR becomes – using a statistical term – the quantile-regression (QR) function. Although the cumulative QR function (i.e., an integral of the QR function) has received considerable attention in the literature, a fully developed statistical inference theory for the QR function itself has been elusive. In the present paper, we develop such a theory based on an empirical QR estimator, for which we establish consistency, asymptotic normality, and standard error estimation. This makes the herein developed results readily applicable in practice, thus facilitating decision making within the RORAC paradigm, conditional mean risk sharing, and current regulatory frameworks.

Correlates of Parental Consent to Human Papillomavirus Vaccine Uptake by Their Adolescent Daughters in ZAMBIA: Application of the Health Belief Model.

Parental consent for adolescent human papillomavirus (HPV) vaccine uptake is important; however, refusal is prevalent. Therefore, this study aimed to understand factors associated with parental consent for their adolescent daughter's HPV vaccination. A cross-sectional study was conducted in Lusaka, Zambia, between September and October 2021. We recruited parents from different social settings. The means and standard deviations or median and interquartile ranges were used as appropriate to summarise continuous variables. Simple and multiple logistic regression models were fitted with robust estimation of standard errors. The odds ratios are presented with 95% CI. Mediation analysis was conducted using a generalised structural equation model. The study enrolled 400 parents, mean age 45.7 years [95% CI, 44.3-47.1]. Two hundred and fifteen (53.8%) parents reported consenting to their daughters' HPV vaccination, and their daughters received it. None of the health belief model (HBM) construct scores showed an independent association with parental consent. Higher, compared to lower wealth index (AOR; 2.32, 95% CI: 1.29-4.16), knowing someone with genital warts (AOR = 2.23, 95 CI: 1.04-4.76), cervical cancer screening uptake (AOR = 1.93, 95% CI: 1.03-3.62) were associated with increased odds of parental consent. This study highlights factors influencing parental consent for their daughters' HPV vaccination. Ongoing sensitisation programs are important to improve their decision-making.

Pathologist pupil dilation reflects experience level and difficulty in diagnosing medical images.

Purpose: Digital whole slide imaging allows pathologists to view slides on a computer screen instead of under a microscope. Digital viewing allows for real-time monitoring of pathologists' search behavior and neurophysiological responses during the diagnostic process. One particular neurophysiological measure, pupil diameter, could provide a basis for evaluating clinical competence during training or developing tools that support the diagnostic process. Prior research shows that pupil diameter is sensitive to cognitive load and arousal, and it switches between exploration and exploitation of a visual image. Different categories of lesions in pathology pose different levels of challenge, as indicated by diagnostic disagreement among pathologists. If pupil diameter is sensitive to the perceived difficulty in diagnosing biopsies, eye-tracking could potentially be used to identify biopsies that may benefit from a second opinion. Approach: We measured case onset baseline-corrected (phasic) and uncorrected (tonic) pupil diameter in 90 pathologists who each viewed and diagnosed 14 digital breast biopsy cases that cover the diagnostic spectrum from benign to invasive breast cancer. Pupil data were extracted from the beginning of viewing and interpreting of each individual case. After removing 122 trials ( ) with poor eye-tracking quality, 1138 trials remained. We used multiple linear regression with robust standard error estimates to account for dependent observations within pathologists. Results: We found a positive association between the magnitude of phasic dilation and subject-centered difficulty ratings and between the magnitude of tonic dilation and untransformed difficulty ratings. When controlling for case diagnostic category, only the tonic-difficulty relationship persisted. Conclusions: Results suggest that tonic pupil dilation may indicate overall arousal differences between pathologists as they interpret biopsy cases and could signal a need for additional training, experience, or automated decision aids. Phasic dilation is sensitive to characteristics of biopsies that tend to elicit higher difficulty ratings and could indicate a need for a second opinion.

Environmental implication of international migration on high- and middle-income countries: A comparative analysis

In this paper, we researched the implication of international migration and pollution on environmental development of the selected countries which we classified as middle- and upper-income countries. Maintaining good environmental performance while keeping pollution at minimum level amidst international migration has being the global focus towards climate goal. On this premises, this study examines the relationship between international migration and carbon dioxide emission (CO2). Scientific approaches (random-effects model and Driscoll-Kraay standard error estimator) with panel data for the period from 2010 to 2019 are adopted for indepth analysis of the selected countries. The findings according to random-effects model/Driscoll-Kraay standard error estimator suggest the incresae of CO2 due to increase in international migration for the case of middle-income countries. Also, a decrease in CO2 due to increase in international migration is found for high-income countries. Following our main findings, as a function of CO2 emission, we recommend to take identity of people who migrate into consideration for both economics and environment policy. On the other hand, policies should be based on mitigating the negative impact of international migration and economic growth for the middle-income countries. Moreover, environmentally friendly growth technologies are suggested.

PALEO‐PGEM‐Series: A spatial time series of the global climate over the last 5 million years (Plio‐Pleistocene)

AbstractMotivationClimate change plays an important role in the generation and maintenance of biodiversity by driving processes such as diversification and range shifts. As a result, biodiversity patterns are often found to carry the imprints of palaeoclimatic changes. However, we still know little about the spatial and temporal variation in climate over the scale of millennia affecting eco‐evolutionary dynamics, mostly because of the scarcity of user‐friendly and freely available spatio‐temporal palaeoclimate series at such temporal scales. Here, we address this gap by presenting PALEO‐PGEM‐Series, a global spatio‐temporal dataset of the last 5 Myr, with 1 kyr resolution, spatially downscaled from emulations performed with the intermediate‐complexity atmosphere–ocean general circulation model PALEO‐PGEM. PALEO‐PGEM‐Series holds the potential to advance our understanding of the mechanisms behind the strong relationship between biodiversity and climate, a pressing need given projected biodiversity responses to anthropogenic climatic change.Main Types of Variables ContainedSpatio‐temporal series of monthly temperature and precipitation and 17 derived bioclimatic variables over the Pliocene–Pleistocene, along with standard error estimates from multiple runs of the emulator.Spatial Location and GrainGlobal landmasses, at 1° × 1°.Time Period and GrainLast 5 Myr at 1000 year resolution.Major Taxa and Level of MeasurementNot applicable.Software FormatTab‐delimited text files and accompanying R code to derive bioclimatic variables.

Risk perceptions of driving under the influence of cannabis: Comparing medical and non-medical cannabis users

BackgroundReduced perception of potential harm resulting from cannabis use is associated with greater risk of driving under the influence of cannabis (DUIC). Understanding risk perceptions of DUIC among subgroups of users will positively inform targeting and content of anti-DUIC driver education and public awareness campaigns. The current study compared DUIC-related risk perceptions among individuals who use cannabis exclusively for non-medical purposes versus those who use cannabis for exclusively medical or for both medical and non-medical purposes (i.e., dual-purpose users). MethodData were drawn from a regionally stratified cross-sectional general population telephone survey of adults (18 + years) in Ontario, Canada conducted in 2017. Respondents who reported past-year use of cannabis and answered questions regarding DUIC risk perceptions were selected (weighted N = 343). Modified Poisson regression models with robust standard error estimation were conducted for three attitudes: disagree that DUIC increases motor vehicle collision risk, agree that DUIC is safer than driving under the influence of alcohol (DUIA), agree that chances of being caught by police for DUIA higher than DUIC. In addition to type of cannabis user (non-medical only versus medical or dual-purpose), covariates included demographic characteristics and past-year alcohol and cannabis use frequency. ResultsAdjusting for other covariates, regression analyses indicated that medical or dual-purpose cannabis users were 45 % more likely than non-medical users to agree that DUIC is safer than DUIA (Adjusted Prevalence Ratio = 1.45, 95 %CI = 1.10, 1.91, p = 0.007). No other differences in risk perception between medical or dual-purpose and non-medical cannabis users were found. DiscussionAlthough no difference between medical or dual-purpose and non-medical cannabis users was found in perceptions of collision risk associated with DUIC or risk of police enforcement, the perception that DUIC is safer than DUIA was stronger among medical or dual-purpose than non-medical users. Recognizing that the shift in public perception of DUIA as a high-risk behaviour contributed to reductions in incidence of DUIA, results of the current study support education and awareness campaigns that target medical or dual-purpose users and emphasize that, like DUIA, DUIC is also a high-risk behaviour.

Information and communication technology development and health gap nexus in Africa.

Development of information and communication technology has been identified as a tool for fast and effective information gathering and dissemination, and as a means through which almost every social and economic sector (including the health sector) could achieve economic, operational, and service delivery efficiencies that can enable the realization of targeted outcomes. ICT can serve as a tool for achieving international agreements (including the Alma Ata Declaration of 1978), thereby accelerating the achievement of various global development targets. Consequently, based on a sample of 38 countries from 2000 to 2018, this study investigates the effect of ICT development on the health gap, and whether the effect varies by gender and sub-region in Africa. The dependent variable (health gap) was measured as the difference between the achieved life expectancy at birth of 60 years and the Alma Ata Declaration of 1978 targeted life expectancy at birth of 60 years. The main independent variables are ICT indicators (ICT index, mobile cellular subscriptions, and internet access), while the gross domestic product (GDP), which is the measure of economic growth, healthcare expenditure, urbanization, and labor market outcome, is employed as control variables. The effect was examined using Driscoll-Kraay standard errors, feasible generalized least squares (FGLS), and panel-corrected standard error (PCSE). The findings of the Driscoll-Kraay standard errors estimation technique supported by those of FGLS and PCSE suggest that ICT does act as an indispensable stimulator for Africa to significantly exceed the international health target of life expectancy at birth of 60 years. It can be concluded that African leaders need to take advantage and maximize the health-enhancing potential of the internet component of ICT through relevant policies that would improve internet coverage, connectivity, and access for individuals and health institutions.

Age determination on panoramic radiographs using the Kvaal method with the aid of artificial intelligence.

This study aimed to assess and compare age estimation on panoramic radiography using the Kvaal method and machine learning (ML). 554 panoramic radiographs were selected from a Brazilian practice. To estimate age using the Kvaal method, the following measurements were performed on the upper left central incisors and canines: tooth, pulp and root length; root and pulp width at three different levels: at the enamel-cementum junction (ECJ); midpoint between the enamel-cementum junction and; at the mid root level. For ML age estimation, radiomic, semantic and the radiomic-semantic attribute extractions were assessed. Nineteen semantic and 14 radiomic attributes and a single set of 33 semantic-radiomic attributes were extracted. Logistic Regression, Linear Regression, KNN, SVR, Decision Tree Reg, Random Forest Reg, Gradient Boost Reg e XG Boosting Reg were used for ML classification. For the Kvaal method, Mann-Whitney test, Spearman correlation coefficient, Student's t-test and linear regression with its respective coefficient of determination were used to estimate age and to assess data variability. Mean absolute error (MAE) and standard error estimate (SEE) were assessed. For the Kvaal method, upper incisors presented higher precision than canines (R²: 0.335, SSE: 7.108). Males presented better MAE and SEE values (5.29,6.96) than females (5.69,7.37). The radiomic-semantic attributes presented superior precision (MAE: 4.77) than the radiomic and semantic (MAE: 5.23) attributes. The XG Boosting Reg classifier performed better than the other six assessed classifiers (MAE: 4.65). ML (MAE: 4.77 presented higher age estimation precision than the Kvaal method (MAE: 5.68). The use of ML on panoramic radiographs can improve age estimation.

The correlates of regional cooperation: A regional dyadic approach

AbstractUnder what conditions do regional countries engage in cooperation? Regional cooperation arguably matters as an essential factor for the peace and development of nations, yet few quantitative studies have observed only countries. Cooperation nevertheless occurs between countries. This study presents the first global analysis of the correlates of regional cooperation through an analyses of dyads—where cooperation occurs. The independence concerns of interstate dyads are addressed using dyadic clustering robust standard error estimation. The analyses offered here, of memberships in 76 regional organizations from 1945 to 2012, yield several factors as significant forces of regional cooperation. In order of importance, these are: joint democracy, joint language, equal material capability, and trade interdependence. I also explore if weaker countries are more hesitant to cooperate with stronger ones in the regions studied. This is an interesting contribution at the theoretical level, as it seems to privilege a liberal explanation of regional organizations (trade, democracy) over a hegemonic realist explanation (power asymmetry).Related ArticlesBishwakarma, Jham Kumar, and Zongshan Hu. 2021. “Problems and Prospects for the South Asian Association for Regional Cooperation (SAARC).”Politics & Policy50(1): 154–79.https://doi.org/10.1111/polp.12443.Genna, Gaspare M., and David A. Mayer‐Foulkes. 2011. “Beyond Borders: Migration, Security, and Cooperation in North America.”Politics & Policy39(1): 5–9.https://doi.org/10.1111/j.1747‐1346.2010.00280.x.Levine, Steven. 2007. “Asian Values and the Asia Pacific Community: Shared Interests and Common Concerns.”Politics & Policy35(1): 102–35.https://doi.org/10.1111/j.1747‐1346.2007.00050.x.

The impact of geopolitical risk on cash holdings policy: evidence from an emerging market

PurposeThis study aims to examine whether geopolitical risk (GPR) impacts the cash holdings behavior of 210 Turkish firms between 2005 and 2019. The authors choose Turkey as a country of interest because Turkey has an important place in terms of geographical location and serves as a bridge between Europe and Asia. Considering the prominent role that can play in decision-making processes, the authors thought that analyzing the impact of GPR on the cash holdings determinants of Turkish firms would be important and interesting. A widely accepted view is that GPRs play an important role in the economic decisions of emerging countries, such as Turkey.Design/methodology/approachThe authors examine models with fixed effects (FE), random effects (RE) and pooled ordinary least squares (POLS), respectively. First, the authors analyzed whether POLS, FE or RE would be the most appropriate model. According to the F-test and the Breusch–Pagan LM test, the FE and the RE models are more suitable than POLS. Then, according to the Hausman test results, the authors found that FE is this study's most appropriate model. After determining the validity of FE, the diagnostics tests of heteroscedasticity, autocorrelation and serial correlation tests are examined. Due to the presence of these problems, Driscoll and Kraay's (1998) test, which is the robust standard error estimator, is used.Findings The authors find a positive relationship between GPR and cash holdings after controlling firm-level control variables. Firms faced with uncertainty prefer to hoard cash as a precautionary measure. In keeping with real options theory, firms postpone the investments of firms under uncertain conditions. The use of alternative measurements for GPR and cash holdings ensures the validity of our results. The authors' research reveals that investors and politicians should pay more attention to the influence of GPR on the determinants of the cash holdings of firms.Research limitations/implicationsThere are limitations for this study, but this study may provide opportunities for further studies. First, this study has only data from Turkey. This situation mitigates cross-country effects. In future studies, the number of firms, countries of focus and time span can be expanded. Second, this study does not consider the period of coronavirus disease 2019 (COVID-19) that increased risk and uncertainty worldwide. Further studies may consider the impact of COVID-19 and geographical risks relating to cash holdings. Third, the authors try to choose more relied independent and control variables.Practical implicationsThe authors' results provide some insights that are relevant to practitioners and policymakers. Managers need to consider GPR in managers' financial decisions based on managers' firm-specific characteristics. Turkish policymakers should target improving policies to alleviate the negative effects of GPRs. Regulators should postulate more encouraging policies to firms in an environment of GPR. Regulators can give firms more time to understand and analyze the GPRs and the impacts of GPRs to adjust regulators' day-to-day activities.Originality/valueThere are fewer studies in the literature that analyzed the relationship between GPR and cash holdings. This study aims to full this gap in the literature.

Effects of outliers on remote sensing‐assisted forest biomass estimation: A case study from the United States national forest inventory

Abstract Large‐scale ecological sampling networks, such as national forest inventories (NFIs), collect in situ data to support biodiversity monitoring, forest management and planning, and greenhouse gas reporting. Data harmonization aims to link auxiliary remotely sensed data to field‐collected data to expand beyond field sampling plots, but outliers that arise in data harmonization—questionable observations because their values differ substantially from the rest—are rarely addressed. In this paper, we review the sources of commonly occurring outliers, including random chance (statistical outliers), definitions and protocols set by sampling networks, and temporal and spatial mismatch between field‐collected and remotely sensed data. We illustrate different types of outliers and the effects they have on estimates of above‐ground biomass population parameters using a case study of 292 NFI plots paired with airborne laser scanning (ALS) and Sentinel‐2 data from Sawyer County, Wisconsin, United States. Depending on the criteria used to identify outliers (sampling year, plot location error, nonresponse, presence of zeros and model residuals), as many as 53 of the 292 Forest Inventory and Analysis plot observations (18%) were identified as potential outliers using a single criterion and 111 plot observations (38%) if all criteria were used. Inclusion or removal of potential outliers led to substantial differences in estimates of mean and standard error of the estimate of biomass per unit area. The simple expansion estimator, which does not rely on ALS or other auxiliary data, was more sensitive to outliers than model‐assisted approaches that incorporated ALS and Sentinel‐2 data. Including Sentinel‐2 predictors showed minimal increases to the precision of our estimates relative to models with ALS predictors alone. Outliers arise from many causes and can be pervasive in data harmonization workflows. Our review and case study serve as a note of caution to researchers and practitioners that the inclusion or removal of potential outliers can have unintended consequences on population parameter estimates. When used to inform large‐scale biomass mapping, carbon markets, greenhouse gas reporting and environmental policy, it is necessary to ensure the proper use of NFI and remotely sensed data in geospatial data harmonization.

Toward efforts to lessen income inequality in Asia: Exploring synergies between tourism and governance quality

While several studies have investigated the influence of tourism development on income inequality, the moderation of governance quality on the tourism-income inequality relationship remains scarce. Therefore, this study tries to fill the gap in the literature by examining the moderating effect of governance quality on the impact of tourism on income inequality in 30 Asian countries over the period 1996–2020. This study uses the dynamic ordinary least square (DOLS) and Panel corrected standard errors (PCSE) estimation techniques. The study unveils that the impact of tourism on income inequality is positive and statistically significant. However, the results show that the impact of governance quality on income inequality is negative and statistically significant. Importantly, the results show that the interaction term of tourism and governance quality has a negative and statistically significant effect on income inequality. Policy implications are discussed.

A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients.

Principal component regression (PCR) is a popular technique in data analysis and machine learning. However, the technique has two limitations. First, the principal components (PCs) with the largest variances may not be relevant to the outcome variables. Second, the lack of standard error estimates for the unstandardized regression coefficients makes it hard to interpret the results. To address these two limitations, we propose a model-based approach that includes two mean and covariance structure models defined for multivariate PCR. By estimating the defined models, we can obtain inferential information that will allow us to test the explanatory power of individual PCs and compute the standard error estimates for the unstandardized regression coefficients. A real example is used to illustrate our approach, and simulation studies under normality and nonnormality conditions are presented to validate the standard error estimates for the unstandardized regression coefficients. Finally, future research topics are discussed.

Risk of Pseudophakic Cystoid Macular Edema in Fellow-Eye Cataract Surgeries: A Multicenter Database Study

To quantify the risk of pseudophakic cystoid macular edema (pCME) in fellow-eye cataract surgery and to determine risk factors, including prior first-eye pCME. Retrospective, clinical database study. Patients undergoing bilateral nonsimultaneous cataract surgeries in 8 UK National Health Service clinical centers between July 2003 and March2015. We excluded patients with a history of diabetic macular edema (DME) or CME and perioperative topical nonsteroidal anti-inflammatory drug use in either eye. We calculated the overall risk of pCME and used Poisson model with robust estimation of standard error to identify potential risk factors for pCME in the fellow eye. The risk of postoperative clinical pCME in the fellow eye. A total of 54 209 patients were included. The mean age was 74.6 ± 10.4 years, and 38.8% were male. The fellow eye developed pCME in 544 patients (1%). The risk of fellow-eye pCME among patients without first-eye pCME was 0.9%. However, the risk of fellow-eye pCME among those with first-eye pCME was 10.7%. In the fully adjusted model, we found that the risk factors for the development of fellow-eye pCME were first-eye pCME (RR, 8.55, 95% confidence interval [CI], 6.19-11.8), epiretinal membrane (ERM) (RR, 4.1, CI, 2.63-6.19), history of retinal vein occlusion (RR, 2.94, CI, 1.75-4.93), diabetes without history of DME (RR, 2.08, CI, 1.73-2.5), advanced cataract (RR, 1.75, CI, 1.16-2.65), prostaglandin analogue use preoperatively (RR, 1.49, CI, 1.13-1.97), and male sex (RR, 1.19, CI, 1.0-1.41). History of pCME in the first-operated eye is the strongest independent risk factor for the development of pCME in the fellow eye. Our findings may guide clinicians in counseling patients on the risk of pCME before performing cataract surgery in the fellow eye and help in identifying high-risk patients who may benefit from prophylactic therapy. Proprietary or commercial disclosure may be found after the references.

Zero Hunger by 2030 – Are we on track? Climate variability and change impacts on food security in Africa

According to the World Food Programme (WFP), the projected increase in the human population stands at 2 billion people by 2050. At the same time, world food production has been witnessing a declining trend over recent years, and 690 million (8.9%) of the world’s population are already in severe starvation. Climate variability and climate change impacts on food security are very eminent today. Against this backdrop, this study explored the real effects of climate variability and change on food security in Africa by applying the system Generalized Method of Moments (GMM) and the Panel Corrected Standard Errors (PCSEs) estimation techniques on data from 2001 to 2018 for 38 selected African countries. The findings reveal that higher amounts of precipitation positively influence food security along two dimensions: food availability and utilization. Hotter temperatures negatively impact food availability and utilization. However, it aids food accessibility in Africa. Similarly, carbon dioxide emissions improve food availability and are harmful to food accessibility and utilization in Africa. Consequently, the effects of climate variability and change on food security in Africa are undesirable, thereby putting the continent at risk of food insecurity over the long run. These findings provide practical implications for policy change to address the disastrous effects of climate variability and change on food security in Africa.

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

On the Common but Problematic Specification of Conflated Random Slopes in Multilevel Models

For multilevel models (MLMs) with fixed slopes, it has been widely recognized that a level-1 variable can have distinct between-cluster and within-cluster fixed effects, and that failing to disaggregate these effects yields a conflated, uninterpretable fixed effect. For MLMs with random slopes, however, we clarify that two different types of slope conflation can occur: that of the fixed component (termed fixed conflation) and that of the random component (termed random conflation). The latter is rarely recognized and not well understood. Here we explain that a model commonly used to disaggregate the fixed component—the contextual effect model with random slopes—troublingly still yields a conflated random component. Negative consequences of such random conflation have not been demonstrated. Here we show that they include erroneous interpretation and inferences about the substantively important extent of between-cluster differences in slopes, including either underestimating or overestimating such slope heterogeneity. Furthermore, we show that this random conflation can yield inappropriate standard errors for fixed effects. To aid researchers in practice, we delineate which types of random slope specifications yield an unconflated random component. We demonstrate the advantages of these unconflated models in terms of estimating and testing random slope variance (i.e., improved power, Type I error, and bias) and in terms of standard error estimation for fixed effects (i.e., more accurate standard errors), and make recommendations for which specifications to use for particular research purposes.