Stratified Sampling Strategy Research Articles

Teachers’ Classroom Management Styles and Practices: A Case Study of Government Girls’ Secondary Schools of Peshawar, Khyber Pakhtunkhwa

Classroom management is one of the primary areas of concern expressed by teachers, wherein teachers’ personal belief influences their choice of classroom management approaches. Using a quantitative research design, the study used survey questionnaire to gather data from teachers focusing upon exploring relationships between teachers' classroom management styles and practices in government girls’ secondary schools of Peshawar. The study was conducted with the following objectives to explore classroom management styles and practices of female secondary school teachers, investigate how teachers' attitudes and personal experiences influence their classroom management styles and practices. The doctoral research from which this paper is drawn focused upon secondary school teachers and students in grades 9th and 10th. Using stratified sampling strategy, the data were gathered from 105 teachers from fifteen (15) girls' secondary schools in Peshawar. The study revealed that the classroom management styles and practices of the majority of the participating teachers aligned with the modern instructional approach. Nevertheless, the teachers' scores on the inventory indicated a greater implementation of student-centered classroom management styles and practices compared to teacher-centered practices. However, in an environment that prioritizes student-centered learning, learners should autonomously make decisions to become self-regulated learners. The cause of this phenomenon may be attributed to the overcrowded classrooms, with 77% of the participants reporting having more than 30 students in their classrooms. Educators in the modern day need to be extra careful to meet the demands of students all over the world in the twenty-first century. It is the responsibility of educators to prepare students for real-world challenges and inspire them to pursue further practical education. The study recommended that Teachers may undergo training to develop self-assurance and the capacity to maintain discipline, organize workshops in KP to build curriculum based on classroom management approaches, focusing on both theoretical and practical aspects, as per the respondents' comments.

Testing Phylogenetic Placement Accuracy of DNA Barcode Sequences on a Fish Backbone Tree: Implications of Backbone Tree Completeness and Species Representation.

Advancements in DNA sequencing technology have facilitated the generation of a vast number of DNA sequences, posing opportunities and challenges for constructing large phylogenetic trees. DNA barcode sequences, particularly COI, represent extensive orthologous sequences suitable for phylogenetic analysis. Phylogenetic placement analysis offers a promising method to integrate COI data into tree-building efforts, yet the impacts of backbone tree completeness and species composition remain under-explored. Using a dataset comprising 27 genes and 4520 species of bony fishes, we assessed the accuracy of phylogenetic inference by "placing" COI sequences onto backbone trees. The backbone tree completeness was varied by subsampling 20%, 40%, 60%, 80%, and 99% of the total species separately, followed by placement of those missing species based on their COI sequences using software packages EPA-ng and APPLES. We also compared the effects of biased, random, and stratified sampling strategies; the latter ensured the representation of all major lineages (Family) of bony fish. Our findings indicate that the placement accuracy is consistently high across all levels of backbone tree completeness, where 70%-78% missing species are correctly placed (by EPA-ng) in the same locations as the reference tree derived from the complete data. High completeness produces slightly high placement accuracy, although in many cases the differences are nonsignificant. For example, at the 99% completeness level with stratified sampling, EPA-ng placed 78% missing species correctly, and when only considering placement with high confidence (LWR > 0.9), the percentage is 87%. Additionally, stratified sampling outperforms random sampling in most cases, and biased sampling has the worst performance. The likelihood-based EPA-ng consistently provide higher accurate placements than the distance-based APPLES. In conclusion, COI-based placement analysis represents a potential route of using the available vast barcoding data for building large phylogenetic trees.

Difference-Cum-Exponential-type estimators for estimation of finite population mean in survey sampling.

Extensive research work has been done for the estimation of population mean using bivariate auxiliary information based on conventional measures. Conventional measures of the auxiliary variables provide suspicious results in the presence of outliers/extreme values. However, non-conventional measures of the auxiliary variables include quartile deviation, mid-range, inter-quartile range, quartile average, tri-mean, Hodge-Lehmann estimator etc. give efficient results in case of extreme values. Unfortunately, non-conventional measures are not used by survey practitioners to enhance the estimation of unknown population parameters using bivariate auxiliary information. In this article, difference-cum-exponential-type estimators for population mean utilizing bivariate auxiliary information based on non-conventional measures under simple and stratified random sampling schemes have been suggested. Mathematical properties such as bias and mean squared error are derived. To support theoretical findings, various real-life applications are used to confirm the superiority of the suggested estimators as compared to the competing estimators under study.

Project Manager's Soft Skills and Project Performance: A Case of Transforming Eastern Province Through Adaptation Project in Bugesera District, Rwanda

The purpose of this study was to investigate the effect of project manager’s soft skills on performance of Transforming Eastern Province Through Adaptation (TREPA) Project in Bugesera District, in Rwanda. The specific objectives of this study was to assess the effect of project manager’s conflict resolution skills on performance of TREPA Project in Bugesera District; To find out the effect of project manager’s motivation skills on performance of TREPA Project in Bugesera District; To determine the effect of project manager’s leadership skills on performance of TREPA Project in Bugesera District; and to examine the effect of project manager’s communication skills on performance of TREPA Project in Bugesera. The theories that guided this study were Theory of goal setting, Theory of communication, Theory of agency and Theory of decision. The study employed a descriptive and correlational research designs. Target population was 1753 participants including project managers, project team members and beneficiaries. The sample size was 326 respondents selected by using formula proposed by Yamane. Researchers used a random sample sampling to select participants from the entire population, followed by a stratified sampling strategy to divide them into manageable subsets. When collecting data, the researcher used questionnaire, documentary analysis, interview schedule and observation guide. The collected data was analyzed using mixed methods, descriptive statistics, correlational analysis and multiple linear regression analysis with the help of Statistical MS Excel. The data presented, analyzed and interpreted in line with research objectives. The predictors (conflict resolution skills, leadership skills, communication skills and motivation skills) and the performance of TREPA Project in Bugesera District are well correlated (R value of 0.819). There is a 0.341coefficient for conflict resolution skills (t=6.205, p=0.000), a 0.143 coefficient for leadership skills (t=2.641, p=0.009), a 0.182 coefficient for communication skills (t=3.420, p=0.001), and a 0.276 coefficient for motivation skills (t=5.316, p=0.000). This means that Transforming Eastern Province Through Adaptation Project iproves in direct correlation with the level of each of these factors. These coefficients have meaningful statistical significance and major roles in contributing to the performance of TREPA, since their p-values are considerably below the normal significance thresholds of 0.05. Based on these findings, it is recommended that Bugesera District invest in comprehensive training programs focused on developing project managers' conflict resolution and leadership skills. Furthermore, the implementation of regular communication workshops and motivational strategies would be highly beneficial for the TREPA Project.

Valid overall odds ratio estimators using different stratified sampling schemes

ABSTRACT The study presents valid estimators for determining the overall odds ratio between two independent groups within stratified populations, utilizing both simple stratified sampling (SSRS) and stratified ranked set sampling (SRSS) methodologies. Through analytical derivations, we establish the expected values and variances for these estimators. Two distinct types of estimators namely, the naive weighted and the Cochran Mantel–Haenszel–Haenszel approaches are thoroughly examined. Our investigation encompasses an in-depth analysis of the expectation and variance of these estimators, shedding light on their performance characteristics. Through intensive simulation experiments, we discern that estimators based on SRSS exhibit notable advantages over their SSRS counterparts. To validate the efficacy of our proposed estimators, we conduct an empirical assessment utilizing data from the (2009–2010) National Health and Nutrition Examination Survey (NHANES). Through this analysis, we glean insights into the performance of the estimators in a real-world context. In summary, our study contributes valuable insights into the estimation of the overall odds ratio within stratified populations. By comparing SSRS and SRSS methodologies and evaluating different estimation approaches, we provide researchers with robust tools for analyzing odds ratios in diverse settings. Moreover, our empirical validation using NHANES data underscores the practical utility of the proposed estimators in real-world applications.

Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers

PM2.5 air pollution is a critical global health issue. This paper introduces an innovative framework to explore the multi-scale relationship between urban morphology and PM2.5 concentrations. An enhanced Land Use Regression (LUR) model integrates geographic, architectural, and visual factors, enabling analysis from neighborhood to regional scales. A stratified sampling strategy, combined with standardized mobile monitoring and fixed-site data, establishes a robust and verifiable data collection methodology. Cross-validation (CV R2 > 0.70) further confirms the model’s reliability and robustness. The nested buffer analysis reveals scale-dependent effects of urban morphology on PM2.5 concentrations, providing quantitative evidence for planning interventions. Quantitative analysis shows land use (β = 0.42, p < 0.01), visual factors (β = 0.38, p < 0.01), and building density (β = 0.35, p < 0.01) in descending order of influence. Geographic factors are significant at the regional scale (2000–3000 m) while architectural parameters dominate at the neighborhood scale (50–500 m), informing both macro-scale spatial optimization and micro-scale design. This framework, through standardized parameters and reproducible procedures, supports cross-regional and cross-scale air quality assessments, providing quantitative metrics for urban planning, neighborhood optimization, and public space design.

The Breeding Bird Survey of the United Kingdom

ABSTRACTMotivationInformation on species' population trends is essential to assess species' conservation status, make informed environmental decisions and ultimately reduce biodiversity loss. Robust population trends require a long‐term monitoring programme, often using citizen scientists, that ideally generates representative and unbiased data from the study area. Here we present the dataset of the Breeding Bird Survey, the main scheme for monitoring the population changes of common and widespread breeding birds in the United Kingdom, which achieves this through a randomised sampling scheme and defined field methodology. We also describe the modelling approach used to calculate the population trends, which are the main output of the survey.Main Types of Variable ContainedThe main published dataset contains 7,070,577 records detailing counts of 217 bird species in 7010 grid cells over 30 years. Data for 78 species that are currently regarded as too sensitive to be released at fine resolution are omitted. As an illustration of the main use of this dataset, we provide population change estimates for 119 bird species.Spatial Location and GrainGrid squares (1 × 1 km) are randomly selected using a stratified sampling scheme throughout the United Kingdom, Isle of Man and Channel Islands. Data in a grid square are collected along two 1‐km‐long transects which are subdivided into 200‐m‐long sections.Time Period and GrainData have been collected every year since 1994, with two major disruptions in 2001 and 2020, when people's movements were nationally restricted. Grid squares are surveyed twice a year during the main breeding season (April to June). Here we present the data collected from 1994 to 2023.Major Taxa Studied and Level of MeasurementBird species.Software FormatData are supplied as comma‐separated text files.

Adverse Drug Events in Ambulatory Care: A Cross-Sectional Study.

Adverse drug events (ADEs) are understudied in the ambulatory care setting. We aim to estimate the prevalence and characteristics of ADEs in outpatient care using electronic health records (EHRs). This cross-sectional study included EHR data for patients who had an outpatient encounter at an academic medical center from 1 October 2018 through 31 December 2019. We developed a stratified sampling strategy based on a comprehensive set of 994 ADE-related International Classification of Disease (ICD-10) codes to identify clinical encounters and notes likely to contain ADEs. Within each ICD-10 likelihood group, clinical notes were randomly sampled and annotated for present or possible ADE-drug relationships and severity. The overall estimated population prevalence of ADEs presenting in the outpatient setting was calculated. The generalizability of the findings was assessed by comparing ICD-10 code frequencies against a large commercial database. The study included 3126 notes (unique patient encounters) from 2882 unique patients. Of these, 1383 patient encounters (44.2%) had a present or possible ADE documented (6308 mentions). Of the 6038 ADEs mentioned, 14.1% were hypersensitivity reactions, 1.1% were life-threatening, 22.4% were serious, and 60.4% were significant. Main causal agents included anti-infectives (19.3%), central nervous system agents (12.8%), and cardiovascular agents (11.5%). The overall prevalence of present ADEs mentioned in the clinical notes was estimated to be 1.97 per 100 patient encounters (or 2.52 per 100 patient encounters when possible ADEs are included). This study identified the overall population prevalence per encounter of ADEs in the outpatient population by leveraging ICD-10 codes and investigating ADEs documented in clinical notes. Understanding the ADE characteristics in a large corpus of outpatient documentation advances pharmacovigilance knowledge, enhancing the detection, monitoring, and prevention of ADEs in ambulatory care.

The Human Energy Crisis: Employee’s Performance and Need for Resilience

Purpose: This study aims to investigate the impact of the human energy crisis on employee productivity, particularly within the IT sector in India. The study also provide recommendations to overcome this crisis and enhancing productivity of employees. Methodology: Employing a positivist philosophy and deductive approach, quantitative methods were utilized via a cross-sectional survey with a stratified sampling strategy. The sample comprised 65 employees, ensuring reliability and validity through established scales and rigorous statistical analysis. Findings: The outcomes highlight that human energy crisis significantly impacts employee’s performance. Employees in IT face adjusting to a changing workplace culture, impacting their mental health. This human energy crisis includes factors like stress, workload, burnout, and lack of work-life balance. To avoid burnout and improve workplace, organisations should implement stress reduction techniques, encourage active work, social support, involve staff in decision making, and implement excellence performance management. These policies aim to advance worker autonomy, skills development, and job equity. Practical Implications: This research contributes to academic literature and provides insights into the human energy crisis, a common issue causing stress, exhaustion, and workload. It suggests that employees prioritise theory well-being over work, and suggests strategies for HR, managers, and administrators to improve employee performance. The data can also be used by the government, as employees work in both public and private sectors. Originality: The research fills a gap in literature concerning the mediating factors of the human energy crisis and contributes to both academic understanding and practical application in public and private sector workplaces.

Peace and Security Dynamics as A Result of Conflict Intervention by United Nations Stabilization Mission in the Democratic Republic of Congo

The study examines chronology and nature of external interventions, including peacekeeping missions, diplomatic initiatives, and economic engagement. By assessing both positive and negative consequences, the study explored how the conflict intervention has shaped the political landscape, security dynamics, and socio-economic conditions in the Democratic Republic of Congo. Studies on or and around United Nations Stabilization Mission in the Democratic Republic of Congo with respect to conflict, peace and security, portray shortcomings bordering on ineffectiveness. This presents a serious paradox and a question on conflict intervention by MONUSCO’s peace and security processes. The study objective was to interrogate peace and security dynamics in the DRC as a result of the conflict intervention by MONUSCO. The study explored Liberal Theory, Power Theory and Theory of Functionalism from which a conceptual model was drawn. The study was conducted in Eastern DRC. A descriptive survey research design was adopted. The target population was 49,871 with a unit of analysis of 21,105 civilians comprising 5052 refugees, 4053 internally displaced persons and 12,000 local traders, 16,161 MONUSCO staff, 7,895 from major none governmental organizations (NGOs), and 4,710 from the political class, the rebel groups, and immigration. The study adopted simple random stratified sampling, snowballing and purposive sampling strategies. The sample size was 384. The primary and secondary data were collected using questionnaires, focus group discussions, observation guides, and key informant interviews. Content analysis was done for all qualitative data; statistical data was analyzed using descriptive statistics. The findings of the study indicate that, MONUSCO is occupational and not an interventional force and that the government forces could handle the conflict in the DRC without MONUSCO. MONUSCO is ineffective due to the long chain of command within the UN security system and that the International community wants MONUSCO to exit DRC. The study concludes that MONUSCO should exit the DRC in order to achieve peace in the Democratic Republic of Congo (DRC). That MONUSCO’S presence in the DRC is both occupational and inconsequential. Therefore, MONUSCO has failed to intervene effectively in DRC and restore peace and security to a large extent. The study recommends that MONUSCO's Operational Capacity be boosted to enhance its mandate to include more robust peace enforcement measures, including disarmament, demobilization, and reintegration (DDR) programs for ex-combatants.

Racial Discrimination and Mental Health: Analyzing the Impact of Discrimination, Stress, Depressive Symptoms and Self-Esteem on Anxiety Experiences

ABSTRACT Background Existing research demonstrates that racial inequity influences mental health outcomes. However, few studies simultaneously analyze the multiple factors influencing racial disparities regarding these outcomes. Our study addresses this gap by analyzing the impact of discrimination, stress, self-esteem, racial identity, depressive symptoms, and demographic factors on anxiety symptoms. Methods Participants were adults recruited from a primary care clinic in the Southeastern region of the United States. We utilized a stratified random sampling strategy to select 383 participants. Participants completed a survey designed to assess levels of anxiety, depression, perceived stress, self-esteem, racial identity, experiences with discrimination, and demographic information. A hierarchical regression analyzed which factors most impact symptoms of anxiety. Results Of the 363 participants in our study, the majority were female (71.3%), 57% were White identifying, 43% were Black identifying, and the average age was 54.4 (SD = 12.3). Model 4 accounted for 58% of the variance of anxiety symptoms and included a combination of depressive symptoms, perceived stress, self-esteem, experiences of everyday discrimination, and interaction terms. Within this model, perceived stress (t = 3.81, p < .01), experiences of everyday discrimination, (t = 2.33, p < .01), and income (t = -1.96, p < .01) were significantly associated with increased anxiety. Conclusions and implications Multiple contextual factors influence the relationship between race and anxiety. In particular, anxiety increases with lower rates of self-esteem and income. Conversely, anxiety increases with higher rates of stress, discrimination, and depressive symptoms. Social workers should consider each of these factors when treating patients presenting with symptoms of anxiety.

Hygienic Production Practices and Microbiological Quality of Sheep Milk in Ararso District of Jarar Zone, Somali Regional State, Ethiopia

This study was conducted to evaluate the hygienic production practices and microbiological quality of sheep milk in Ararso district of Jarar Zone, Somali Regional State, Ethiopia. A stratified sampling strategy was used to choose 180 households specifically for this investigation. A total of 60 pooled raw sheep milk samples (each containing 200 mL) were collected from the udders and milk handling equipment of selected milk producers in the research area. The samples were examined to determine microbiological quality. The data were gathered via a questionnaire, field observation, key informant interviews, and focus group discussions. The vast majority of respondents in pastoral (86.7%) and agro-pastoral (64.4%) production systems were illiterate. The majority of respondents (70.6%) in the research area cleaned the sheep home (kraal) once every two days. The majority (98.3%) of respondents in the research area milk their sheep in open kraals with no roof or walls. As a consequence, 36.7% of pastoral and agro-pastoral respondents (81.1%) reported cleaning milk vessels on a regular basis. Almost all of the responders in the research area used plastic equipment for milking and handling. The majority of responders in pastoral and agro-pastoralist categories placed barn hygiene first, with indexes of 0.24 and 0.23, respectively. The average total bacterial count (TBC), coiform count (CC) and yeast and mould count (YMC) of raw sheep milk samples were 5.13±0.21, 2.89±0.27, and 0.77±0.21 log10 cfu/mL, respectively. Raw sheep milk samples taken from producers’ milk handling equipment had a substantially higher mean total bacterial count (5.78±0.32 log10 cfu/mL) compared to samples collected from the udder (4.48±0.23 log10 cfu/mL). Milk samples from pastoral and agro-pastoral households had an average coliform count of 3.18 and 2.60 log10 cfu/mL, respectively. The average YMC in milk samples from pastoralists and agro-pastoralists were 0.74 and 0.80 log10 cfu/mL, respectively. In general, pastoralists and agro-pastoralists in the district do not practice hygienic milking and handling. Consequently, the implementation of hygienic milk production and handling practices is essential to enhance the safety and quality of sheep milk in the study area. Additionally, further research should be undertaken to investigate the microbiological aspects and assess the safety and quality parameters of sheep milk in greater detail.

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Abstract. Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery (Planet Team, 2017). Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World (Brown et al., 2022), based on Sentinel-2, and the NASA IMPACT model (Paul and Ganju, 2021), based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29 (Mukherjee et al., 2024).

The Effect of Mindfulness-Based SEL Programs on Reducing Stress and Improving Mental Health in Adolescents in Jordan

This study employed quantitative methodologies to investigate the impact of a 12-week mindfulness-based Social and Emotional Learning (SEL) program on lowering stress and improving mental well-being in a sample of 300 adolescents from Jordan. The study employed a stratified random sampling strategy to choose participants from diverse urban and rural populations. Data was collected utilizing the Perceived Stress Scale (PSS) and the Strengths and Difficulties Questionnaire (SDQ). The data evaluation involved the use of descriptive statistics, paired-sample t-tests, regression analysis, and an Analysis of Covariance (ANCOVA). The results indicate a significant decrease in stress levels and positive changes in mental health outcomes following the intervention. The regression analysis revealed that age was a likely predictor of changes in stress levels, indicating that older persons saw more significant declines. The analysis of covariance (ANCOVA) indicated significant gender-specific discrepancies in the impacts of the intervention. Independent samples t-tests revealed statistically significant variations in the average rankings across specific age groups and school environments. Proposed measures include implementing mindfulness-based social and emotional learning (SEL) programs in school systems, tailoring treatments to suit different demographic groups, examining gender-specific factors, and advocating for policy integration. Performing a longitudinal study is typically advised to assess the long-term sustainability of intervention results.

Toward inclusive and equitable LGBTIQ+ measurement: Assessing gender and sexual orientation measures and scale validity in national surveys across 21 countries

AbstractDespite growing global interest in lesbian, gay, bisexual, trans, intersex, and queer/questioning (LGBTIQ+) research, variations in measurement practices across countries have remained underexplored. In this work, we focused on two fundamental aspects of measurement vital to understanding the experiences of LGBTIQ+ people. Specifically, we documented current measures of sex, gender, and sexual orientation used in national mental well‐being‐related surveys and reviewed whether the mental well‐being scales in those surveys have been validated for LGBTIQ+ people. We employed a stratified sampling strategy and evaluated national surveys from a list of randomly selected countries representing 10% of global nations (N = 21). Fewer than half of the countries measured sexual orientation and fewer than one‐third measured gender beyond the binary in their national surveys. Among the countries that measured gender or sexual orientation, the response options and question phrasing were often not inclusive. In addition, most of the mental well‐being scales lacked validity evidence for LGBTIQ+ populations. Finally, we outline recommendations for the future of reimagining LGBTIQ+ research in terms of measurement, highlighting the importance of research engagement with the global LGBTIQ+ community.

Defying the flat-Earth myth: Soil organic carbon monitoring in topographically complex temperate pasture landscapes

Soil sampling design to capture the spatiotemporal variability of soil organic carbon (SOC) stocks in complex landscapes is challenged by the influence of topographical features on primary production, animal behaviour, and carbon and nutrient return to grasslands. Here we explore a range of approaches for streamlining the sampling process using measures of SOC stock data (2003, 2020, 2021) from a long-term hill-country fertilizer experiment. The stepwise procedure included a) evaluation of sampling efficiency between stratified and random soil sampling designs to capture spatial variability of SOC stocks, b) semi-parametric regression analysis to evaluate the representativeness of certain strata (e.g. medium slopes), c) exploring the appropriateness of using shallow SOC stock estimates to represent the full sampling depth, and d) the use of a process-based model to determine expected SOC stock changes and temporal variability to estimate the number of representative soil samples required to achieve a significant minimum change. Our analyses suggest that a) the stratified sampling design was more efficient reflected in the number of samples required to identify 5 % deviation in SOC stocks, b) estimates obtained on the medium slope were a reasonable approximation of the SOC stocks and its spatial variation across the farmlets, c) shallow (0–75 mm) SOC stock estimates from an adjusted regression model offered an accurate inference of its vertical distribution (0–300 mm), and d) the Grass-NEXT model provided insights on where more intensive sampling is required to detect a significant minimum change in SOC stocks over time. Tailored, stratified soil sampling and context-specific strategies, such as using representative strata and shallow sampling as effective steps for estimating SOC stocks and addressing data gaps, are crucial for simplifying monitoring and minimizing the cost of on-farm soil sampling designs in topographically complex landscapes covered by long-term pastures.

Efficient population mean estimation via stratified sampling with dual auxiliary information: A real estate perspective

Auxiliary information is an essential component in the field of survey sampling since it enables precise estimation of population parameters like mean, variance, distribution function, and so on, which in turn guarantees the best possible outcomes. In order to estimate the population mean of a study variable, this study makes use of auxiliary information in a two-fold approach. Through a stratified random sampling scheme, we introduce a novel class of estimators that utilize auxiliary information and their corresponding ranks. By conducting a thorough evaluation based on metrics such as mean square error and percentage relative efficiency, these proposed estimators have been shown to be effective in the estimation process. Empirical validation is conducted using a real dataset sourced from the domain of real estate. Exploring the relationship between Assessed Value (X) and Sale Amount (Y) during a five-year period extending from 2017 to 2021 is the primary emphasis of the empirical validation process, which is carried out with the assistance of a real dataset of real estate data. Furthermore, in order to demonstrate that our suggested estimator is superior to conventional unbiased estimators, as well as traditional regression estimators and other estimators that have been considered in the literature, a full simulation analysis is carried out. Our proposed estimator appears to be the most effective choice after being subjected to a comparison study against a variety of preexisting approaches. The findings of this study not only make a significant contribution to the development of the methodology of survey sampling but also offer vital insights for predictive modeling within the real estate sector.

Scalar-on-function regression: Estimation and inference under complex survey designs.

Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of this sort requires novel methodologic work at the intersection of survey statistics and functional data analysis. Here, we address this crucial gap in the literature by proposing an estimation and inferential framework for generalizable scalar-on-function regression models for data collected under a complex survey design. We propose to: (1) estimate functional regression coefficients using weighted score equations; and (2) perform inference using novel functional balanced repeated replication and survey-weighted bootstrap for multistage survey designs. This is the first frequentist study to discuss the estimation of scalar-on-function regression models in the context of complex survey studies and to assess the validity of various inferential techniques based on re-sampling methods via a comprehensive simulation study. We implement our methods to predict mortality using diurnal activity profiles measured via wearable accelerometers using the National Health and Nutrition Examination Survey 2003-2006 data. The proposed computationally efficient methods are implemented in R software package surveySoFR.

Does distribution and type of aid affect internal migration following a cyclone? Evidence from Bangladesh

PurposeThe purpose of this study is to explore the impacts of aid in the form of food, cash, and agricultural inputs on internal migration following a series of cyclones in the southern coastal areas of Bangladesh. The impacts of sources of aid, such as institutional or social network sources, were also analysed. With the increasing frequency of extreme climatic events, it becomes crucial to understand the impacts of different post-cyclonic aid on human mobility.Design/methodology/approachA total of 472 households across 16 blocks (moujas) in the Patharghata sub-district of Barguna district were selected for participation using a stratified sampling strategy. Data were captured via a survey which included individual and household-level demographics, migration and aid-receipt following cyclones. Data were analysed using a mixture of descriptive and inferential statistical methods.FindingsThe analysis revealed that migration was significantly higher among households which did not receive institutional food aid. More specifically, non-receipt of food aid and cash aid after a cyclone, different sources of income, non-availability of alternative sources of income, lack of land ownership and severity of cyclones up to a certain level increased migration among the households where a minority of household members migrated. In contrast, the absence of food aid and the severity of cyclones were found to be significant factors in increasing migration among the households where a majority of household members migrated. The authors argue that the decision to migrate from the households where most members migrated increased with the rise in cyclone severity.Originality/valueThe contribution of this research in the field of aid and migration is unique. To the best of the authors’ knowledge, no research was conducted before on the impacts of distribution and type of aid on internal migration following a cyclone.

Korean Early Childhood Education and Care Panel Study: Data Utilization Strategies for Policy and Practice

The Korean Early Childhood Education and Care Panel Study (K-ECEC panel study), commencing in 2021, aimed to collect data related to early life growth and development. In particular, based on Bronfenbrenner’s ecological model, the study aims to collect intrinsic and extrinsic parental factors, educational and caregiving experiences in institutions, and environmental factors from the prenatal stage through infancy, toddlerhood, and early childhood. This is important for understanding child development with varied dimensional aspects. The K-ECEC panel study uses a stratified sampling strategy in order to recruit pregnant women based on regions and numbers of deliveries to individual hospitals. This resulted in the participation of 3,372 women, 95 of whom were from low-income families and 72 from multicultural families. The K-ECEC panel study is based on a household survey collecting information related to pregnancy, birth, and childrearing. The survey included an annual follow-up for individual households of newborns from 2022, and the second round of the survey was conducted in 2023. A total of 2,775 children participated in the first round of the survey, 1,423 of whom were boys. As there are few birth cohort studies considering policy-oriented aspects in Korea, the K-ECEC panel study is expected to provide data to understand childhood trajectories for growth and development. Furthermore, in terms of childcare and education, it is expected to be utilized to evaluate the policy effects of early life and parenting support.