Improved Data Collection Methods Research Articles

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.

PREDICTIVE MODELING FOR DISEASE OUTBREAKS: A REVIEW OF DATA SOURCES AND ACCURACY

This review explores the dynamic field of predictive modelling for disease outbreaks, focusing on the data sources, modelling techniques, accuracy, challenges, and future directions integral to its advancement. It underscores the significance of diverse data sources, including epidemiological, environmental, social media, and mobility data. It also discusses various modelling approaches, from statistical models to advanced machine learning algorithms and network analysis. The review highlights the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and the ethical use of personal data. It outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies such as AI and IoT to enhance predictive capabilities. This comprehensive overview emphasizes the importance of predictive modelling in informing public health decisions and the continuous need for innovation and collaboration to address the complex dynamics of disease outbreaks.
 Keywords: Predictive Modeling, Disease Outbreaks, Data Sources, Machine Learning, Public Health, Emerging Technologies.

IoT-enabled Smart Weather Stations: Innovations, Challenges, and Future Directions

The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

A health system's journey to preventing workplace violence: Use of multidisciplinary teamwork to unify efforts across a health system.

Workplace violence (WPV) is known to threaten the safety of patients and staff. In 2018, a wellness survey showed many employees had not received training on WPV prevention and felt unprepared to manage aggression. The health network's leaders knew they needed to take action. From various multidisciplinary committees, the leaders were able to create a comprehensive WPV prevention program. Some of the highlights of this program include forming a centralized security department, codes of conduct, and crisis response process, adopting tools to predict violence, and providing a range of education. Data from WPV events showed the health network had a statistically significant reduction in WPV events from 2020 to 2021. However, WPV events increased in 2022. This increase in 2022 mirrors national trends in WPV. There are a number of factors that may have impacted this increase. Regardless, the leaders at the health network are dedicated to continuously improving the WPV prevention program. Some of the ongoing projects include improving data collection methods and building a long-term notification for highly violent individuals. This WPV prevention program relies on the commitment of its multidisciplinary team members and focuses on taking care of patients while also prioritizing the wellness of the staff.

Introducing the Nitrogen Footprint in SIMAP: A Review of Improvements in Nitrogen Footprint Methodology for Institutions

Nitrogen (N) footprint assessments have served as tools for sustainability, research, and education at institutions of higher learning by connecting institutional activities and resource consumption with environmental impacts through the lens of the N cycle. The Nitrogen Footprint Tool (NFT) Network consists of colleges, universities, and research institutions that have committed to assessing and reporting their own N footprints while contributing novel research and feedback that improves footprinting tools and methods. Institutions in the network since its inception in 2014 initially calculated their N footprints using the Excel-based NFT. In 2017, institutions began using the web-based Sustainability Indicator Management & Analysis Platform (SIMAP), which integrates the institution's N footprint with a carbon footprint. In this article, the N footprint results generated by the NFT are compared to those generated by SIMAP for six of the seven institutions that made up the first cohort of the NFT Network. A comparison, of how the changes in methodology between platforms and the changes that occurred at the institution level affected the results, and yield insights into the numerous factors that influence N footprint assessments. The accuracy of the results of an N footprint using the Excel-NFT have been improved by SIMAP. Key factors include updates to emissions factors used in SIMAP, reductions in emissions due to institution sustainability efforts, and improved data collection methods by participating institutions. The NFT Network recommends using SIMAP for institutional N footprint data tracking, goal setting, and reporting. Several best practice recommendations for using the tool for N tracking are proposed.

Strategic insights in kabaddi: Applying game theory and mathematical modeling for enhanced performance

This paper explores the application of game theory and mathematical modeling in Kabaddi, a dynamic contact sport requiring strategic decision-making. By analyzing the interactions between raiders and defenders through game-theoretic models, insights are gained into optimal strategies for both offense and defense. The study discusses key concepts such as mixed-strategy Nash equilibria and Bayesian games, highlighting their relevance in Kabaddi strategy. Challenges, including the assumption of rationality, real-time decision complexities, and data quality issues, are addressed. Future directions include refining models to better reflect real-time play, improving data collection methods, and integrating theoretical insights into practical coaching. The application of these mathematical approaches promises to enhance strategic planning and performance in Kabaddi.

Evaluation of blood product transfusion therapies in acute injury care in low- and middle-income countries: a systematic review

BackgroundWorldwide, injuries account for approximately five million mortalities annually, with 90% occurring in low- and middle-income countries (LMICs). Although guidelines characterizing data for blood product transfusion in injury resuscitation have been established for high-income countries (HICs), no such information on use of blood products in LMICs exists. This systematic review evaluated the available literature on the use and associated outcomes of blood product transfusion therapies in LMICs for acute care of patients with injuries. MethodsA systematic search of PubMed, EMBASE, Global Health, CINAHL and Cochrane databases through November 2018 was performed by a health sciences medical librarian. Prospective and cross-sectional reports of injured patients from LMICs involving data on blood product transfusion therapies were included. Two reviewers identified eligible records (κ=0.92); quality was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. Report elements, patient characteristics, injury information, blood transfusion therapies provided and mortality outcomes were extracted and analyzed. ResultsOf 3411 records, 150 full-text reports were reviewed and 17 met inclusion criteria. Identified reports came from the World Health Organization regions of Africa, the Eastern Mediterranean, and South-East Asia. A total of 6535 patients were studied, with the majority from exclusively inpatient hospital settings (52.9%). Data on transfusion therapies demonstrated that packed red blood cells were given to 27.0% of patients, fresh frozen plasma to 13.8%, and unspecified product types to 50.1%. Among patients with blunt and penetrating injuries, 5.8% and 15.7% were treated with blood product transfusions, respectively. Four reports provided data on comparative mortality outcomes, of which two found higher mortality in blood transfusion-treated patients than in untreated patients at 17.4% and 30.4%. The overall quality of evidence was either low (52.9%) or very low (41.2%), with one report of moderate quality by GRADE criteria. ConclusionThere is a paucity of high-quality data to inform appropriate use of blood transfusion therapies in LMIC injury care. Studies were geographically limited and did not include sufficient data on types of therapies and specific injury patterns treated. Future research in more diverse LMIC settings with improved data collection methods is needed to inform injury care globally.

Occupational health and safety incidents at a university school of nursing: A retrospective, descriptive appraisal

Nursing is recognised internationally as a high-risk occupation for graduates and students. Developing an understanding of incident data may facilitate prevention of future adverse events. A descriptive, retrospective review of occupational health and safety incidents reported by personnel at an Australian university school of nursing was conducted, spanning a period of eight-and-a-half years. Classification of incident reports was determined using the existing electronic system with manual categorisation of data not suited to electronic analysis, such as narrative descriptions. In total, 142 incidents were reported, of which 135 were included in this study. Students reported 98 incidents on placement and 10 on campus. Frequently reported student incidents involved sharps, fainting, manual handling activities and workplace violence. Fifteen reports were hazard notifications. Reviewing data gathered over a prolonged duration provided useful patterns of information. Gaps in data and reporting system constraints were identified. Dual reporting systems may be barriers to providing an all-inclusive overview. Improving data collection methods could include recording demographic information such as year level, drop-down menus for commonly reported incidents to facilitate recording, and consideration of how best to capture events that involve multiple personnel.

Epidemiology of basal and cutaneous squamous cell carcinoma in the U.K. 2013-15: a cohort study.

SummaryBackgroundBasal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), together known as keratinocyte cancers (KCs), are the commonest cancer in white ethnic populations. Recent improvements to registry data collection in England has allowed more accurate analysis of the epidemiology of BCC and cSCC and for the first time we are able to provide an accurate (representative) tumour burden for KC in the U.K.ObjectivesTo estimate the incidence of BCC and cSCC in the U.K.MethodsA cohort of patients with KCs between 2013 and 2015 were identified using linkage to diagnostic codes derived from pathology reports collected into the national cancer registry. Data from England's cancer registry were combined with data from Scotland, Northern Ireland and Wales. European age‐standardized incidence rates (EASRs) of the first BCC and cSCC per patient per annum (PPPA) were calculated.ResultsIn the U.K, the EASR of the first BCC and cSCC PPPA in 2013–15 were 285 and 77 per 100 000 person years, respectively (211 120 KCs total in 2015). The mean annual percentage increase was 5% between 2013 and 2015 for both BCC and cSCC. By counting the first KC PPPA, we include an additional 51% KCs compared with the previous reporting technique which counts only the first BCC and cSCC in a patient's lifetime, yet it represents a probable underestimation of 5–11% of the true tumour count.ConclusionsBased on an improved methodology, a more representative incidence of KC is presented, which is essential to healthcare planning and will lead to improved understanding of the epidemiology of KC. What's already known about this topic? Keratinocyte cancers (KCs) are the most common cancers affecting white ethnic populations.The incidence of basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) is increasing worldwide including the U.K., most commonly in elderly male Caucasian patients.These cancers are traditionally substantially underreported and frequently excluded from national cancer statistics. What does this study add? Using improved data collection methods in England and validated tumour‐reporting techniques, we report the most accurate BCC and cSCC incidence data for the U.K. ever published.Identifying the first BCC and cSCC per patient per annum, the incidence of BCC and cSCC in the U.K. (excluding Wales) was 285 and 77 per 100 000 person years, respectively, between 2013 and 2015, with more than 210 000 KCs in the U.K. in 2015.

Desarrollando métodos comparativos realistas críticos para investigar las religiones, los sistemas de creencias y la educación

Education is increasingly implicated in religious and other ideological issues, but contributions from comparative education have been surprisingly rare. From the definition ‘belief-systems’, this paper reviews relevant comparative methodologies, and proposes that a ‘critical realist comparative’ approach can reduce methodological weaknesses. It concludes that ontology and epistemology can apply current perspectives on ‘fake-news’ and ‘fact checking’. The UNESCO ‘disarming history’ framework can encourage non-confrontational educational discussions and the participatory creation of curricula and teaching materials. Appropriate research frameworks entail locating studies in terms of people, places, and/or systems, and the methodologies for researching powerful people and organisations can improve data collection methods. Crowdsourcing research and online research, including big data, increase the scope of these methods. Diverse participation improves research, and the paper provides ideas for involving young people.

Design Methodology of Microservices to Support Predictive Analytics for IoT Applications.

In the era of digital transformation, the Internet of Things (IoT) is emerging with improved data collection methods, advanced data processing mechanisms, enhanced analytic techniques, and modern service platforms. However, one of the major challenges is to provide an integrated design that can provide analytic capability for heterogeneous types of data and support the IoT applications with modular and robust services in an environment where the requirements keep changing. An enhanced analytic functionality not only provides insights from IoT data, but also fosters productivity of processes. Developing an efficient and easily maintainable IoT analytic system is a challenging endeavor due to many reasons such as heterogeneous data sources, growing data volumes, and monolithic service development approaches. In this view, the article proposes a design methodology that presents analytic capabilities embedded in modular microservices to realize efficient and scalable services in order to support adaptive IoT applications. Algorithms for analytic procedures are developed to underpin the model. We implement the Web Objects to virtualize IoT resources. The semantic data modeling is used to promote interoperability across the heterogeneous systems. We demonstrate the use case scenario and validate the proposed design with a prototype implementation.

Fool’s gold? A critical assessment of sources of data on heritage crime

PurposeThe purpose of this paper is to explore critically various sources of data available on heritage crime, and consider how these may be utilised and improved.Design/methodology/approachThis study is primarily a scoping review of the current heritage crime data climate, embedding examples from a range of existing and potential information sources. It highlights opportunities to improve data resources.FindingsA lack of consistency in reporting and recording practices means there is little meaningful evidence about heritage crime trends and patterns. This needs to change in order to develop and evaluate appropriate strategies to reduce the problem of heritage crime nationally and internationally.Research limitations/implicationsIt is hoped that urging improvement of data resources in the heritage crime sector will inspire a greater number of researchers to analyse and address key problems within heritage crime.Practical implicationsThis paper encourages the development of new and improved data collection methods to foster effective assessment of existing heritage crime reduction schemes and better support victims of heritage crime.Social implicationsIncreasing availability and accessibility of high-quality data on heritage crime would allow for developing better protections and resource allocation for vulnerable heritage.Originality/valueThis paper has drawn together, for the first time, evidence of the existing state of affairs of data availability within heritage crime. It is a position paper which encourages the development of improved recording and reporting practices both formally and informally across heritage and criminal justice sectors in order to support further research and understanding of the heritage crime problem.

EEG data collection in children with ASD: The role of state in data quality and spectral power

BackgroundElectroencephalography can elucidate neurobiological mechanisms underlying heterogeneity in ASD. Studying the full range of children with ASD introduces methodological challenges stemming from participants’ difficulties tolerating the data collection process, leading to diminished EEG data retention and increased variability in participant ‘state’ during the recording. Quantifying state will improve data collection methods and aide in interpreting results. ObjectivesObservationally quantify participant state during the EEG recording; examine its relationship to child characteristics, data retention and spectral power. MethodsParticipants included 5–11 year-old children with ASD (N = 39) and age-matched TD children (N = 16). Participants were acclimated to the EEG environment using behavioral strategies. EEG was recorded while participants watched a video of bubbles. Participant ‘state’ was rated using a Likert scale (Perceived State Rating: PSR). ResultsParticipants with ASD had more elevated PSR than TD participants. Less EEG data were retained in participants with higher PSR scores, but this was not related to age or IQ. TD participants had higher alpha power compared with the ASD group. Within the ASD group, participants with high PSR had decreased frontal alpha power. ConclusionsGiven supportive strategies, EEG data was collected from children with ASD across cognitive levels. Participant state influenced both EEG data retention and alpha spectral power. Alpha suppression is linked to attention and vigilance, suggesting that these participants were less ‘at rest’. This highlights the importance of considering state when conducting EEG studies with challenging participants, both to increase data retention rates and to quantify the influence of state on EEG variables.

African swine fever in wild boar.

The European Commission requested EFSA to compare the reliability of wild boar density estimates across the EU and to provide guidance to improve data collection methods. Currently, the only EU‐wide available data are hunting data. Their collection methods should be harmonised to be comparable and to improve predictive models for wild boar density. These models could be validated by more precise density data, collected at local level e.g. by camera trapping. Based on practical and theoretical considerations, it is currently not possible to establish wild boar density thresholds that do not allow sustaining African swine fever (ASF). There are many drivers determining if ASF can be sustained or not, including heterogeneous population structures and human‐mediated spread and there are still unknowns on the importance of different transmission modes in the epidemiology. Based on extensive literature reviews and observations from affected Member States, the efficacy of different wild boar population reduction and separation methods is evaluated. Different wild boar management strategies at different stages of the epidemic are suggested. Preventive measures to reduce and stabilise wild boar density, before ASF introduction, will be beneficial both in reducing the probability of exposure of the population to ASF and the efforts needed for potential emergency actions (i.e. less carcass removal) if an ASF incursion were to occur. Passive surveillance is the most effective and efficient method of surveillance for early detection of ASF in free areas. Following focal ASF introduction, the wild boar populations should be kept undisturbed for a short period (e.g. hunting ban on all species, leave crops unharvested to provide food and shelter within the affected area) and drastic reduction of the wild boar population may be performed only ahead of the ASF advance front, in the free populations. Following the decline in the epidemic, as demonstrated through passive surveillance, active population management should be reconsidered.

Opioid Discussion in the Twittersphere

ABSTRACTBackground: The rise in opioid use and overdose has increased the importance of improving data collection methods for the purpose of targeting resources to high-need populations and responding rapidly to emerging trends. Objective: To determine whether Twitter data could be used to identify geographic differences in opioid-related discussion and whether opioid topics were significantly correlated with opioid overdose death rate. Methods: We filtered approximately 10 billion tweets for keywords related to opioids between July 2009 and October 2015. The content of the messages was summarized into 50 topics generated using Latent Dirchlet Allocation, a machine learning analytic tool. The correlation between topic distribution and census region, census division, and opioid overdose death rate were quantified. Results: We evaluated a tweet cohort of 84,023 tweets from 72,211 unique users across the US. Unique opioid-related topics were significantly correlated with different Census Bureau divisions and with opioid overdose death rates at the state and county level. Drug-related crime, language of use, and online drug purchasing emerged as themes in various Census Bureau divisions. Drug-related crime, opioid-related news, and pop culture themes were significantly correlated with county-level opioid overdose death rates, and online drug purchasing was significantly correlated with state-level opioid overdoses. Conclusions: Regional differences in opioid-related topics reflect geographic variation in the content of Twitter discussion about opioids. Analysis of Twitter data also produced topics significantly correlated with opioid overdose death rates. Ongoing analysis of Twitter data could provide a means of identifying emerging trends related to opioids.

The epidemiology and hotspots of road traffic injuries in Moshi, Tanzania: An observational study

Road traffic injuries (RTIs) continue to increase with the proliferation of motor vehicles, especially in low-income countries where safe road infrastructure is lacking. Knowing where and why RTIs occur would allow for increased safety and prevention planning. In this study, police records of 300 motor vehicle collisions which occurred between February 2013 and January 2014 in Moshi, Tanzania were reviewed. Analysis of variables including victim age, gender, type of collision, conditions, and use of safety equipment were analyzed. Geographic information system (GIS) analysis was performed to identify areas with the most collisions. Most injuries occurred at four intersections on two main corridor. Car crashes represented 48% of reports while motorcycle collisions were 35% of reports. Victims were predominantly male. The majority (64%) of RTI victims in cars used seatbelts while only 43% of motorcyclists wore helmets; none of those who used the helmet or seatbelt suffered a grievous injury. These data demonstrate that RTIs in Moshi occur in predictable high traffic locations. RTIs injure victims of all backgrounds and safety equipment is not universally utilized. More investment is needed in improved data collection methods, and a greater emphasis on intersection safety is needed to reduce these preventable injuries.

The joint effect of investment motives and institutional context on Russian international acquisitions

PurposeThe purpose of this paper is to advance knowledge about factors that influence the location of Russian foreign direct investments. In particular, it focusses on the role of institutional distance (represented by corruption perception distance, political distance and cultural distance) as a moderator of the relationships between traditional investment motives and the number of M&A deals made by Russian companies in a specific country.Design/methodology/approachThe analysis is conducted on panel data of Russian cross-border M&As launched in 46 countries during the period 2007-2013. The final data set includes 322 observations. Due to the nature of dependent variable and the results of pre-tests, negative binomial regression is used in the main analysis.FindingsThe key finding of the study reveals the importance of institutional distance, in particular, the moderating effect of different dimensions of institutional distance on the relationships between internationalization motives and the number of Russian M&As. Corruption, political and cultural differences show different effects in terms of both direction and strength, but all three were found to be significant.Research limitations/implicationsThe major concern stems from the type of secondary data used in the paper. This indicates the necessity to improve data collection methods which could allow for better transparency of Russian foreign investments, would facilitate more sophisticated research and probably more accurate business forecasts.Originality/valueBy conducting a systematic examination of Russian cross-border M&As the authors contribute to the literature on emerging markets firms by addressing the important yet under-researched domain of Russian foreign direct investments. Building on the macroeconomic and institutional logic proposed in this study, future research on Russian cross-border activities could add to the understanding by providing more generalizable and critical evidence. The study provides a point of departure from prior studies on Russian outward FDI which the authors hope to inspire future research to further analyze the drivers of Russian M&As and foreign investments in general.

Monitoring the inputs required to extend and sustain hygiene promotion: findings from the GLAAS 2013/2014 survey.

There are significant gaps in information about the inputs required to effectively extend and sustain hygiene promotion activities to improve people's health outcomes through water, sanitation and hygiene (WASH) interventions. We sought to analyse current country and global trends in the use of key inputs required for effective and sustainable implementation of hygiene promotion to help guide hygiene promotion policy and decision-making after 2015. Data collected in response to the GLAAS 2013/2014 survey from 93 countries of 94 were included, and responses were analysed for 12 questions assessing the inputs and enabling environment for hygiene promotion under four thematic areas. Data were included and analysed from 20 External Support Agencies (ESA) of 23 collected through self-administered surveys. Firstly, the data showed a large variation in the way in which hygiene promotion is defined and what constitutes key activities in this area. Secondly, challenges to implement hygiene promotion are considerable: include poor implementation of policies and plans, weak coordination mechanisms, human resource limitations and a lack of available hygiene promotion budget data. Despite the proven benefits of hand washing with soap, a critical hygiene-related factor in minimising infection, GLAAS 2013/2014 survey data showed that hygiene promotion remains a neglected component of WASH. Additional research to identify the context-specific strategies and inputs required to enhance the effectiveness of hygiene promotion at scale are needed. Improved data collection methods are also necessary to advance the availability and reliability of hygiene-specific information.

Getting something out of nothing: Analyzing patterns of null responses to improve data collection methods in sub-Saharan Africa

Getting something out of nothing: Analyzing patterns of null responses to improve data collection methods in sub-Saharan Africa

Getting something out of nothing: Analyzing patterns of null responses to improve data collection methods in sub-Saharan Africa

Careful development and adaptation of assessments are imperative for cultural psychological research. However, despite the best efforts, the use of assessments in new contexts can reveal atypical and/or unexpected patterns of performance. We found this to be the case in the testing of assessments to be used for a larger investigation of Specific Reading Disabilities in Zambia. In a sample of 207 children (100 female) from grades 2 to 7, we illustrated that assessment characteristics (i.e., stimulus type, answer choice, and response type) differentially impact patterns of responsiveness. The number of missing values was the highest for assessments that (1) used written stimuli, (2) had an open-ended answer choice, and (3) required an action response. Age and socio-economic status explained some of the variance in responsiveness in selected, but not all assessments. Consideration of the impact of stimulus and response types when adapting assessments cross-linguistically and cross-culturally is essential.