Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada;
Agriculture and Agri-Food Canada | Agriculture et Agroalimentaire Canada;
Publisher: Open Data Canada
En 2021, l'équipe d'observation de la Terre de la Direction générale des sciences et de la technologie (DGST) d'Agriculture et Agroalimentaire Canada (AAC) a répété le processus visant à produire des cartes numériques de l'inventaire annuel des cultures à l'aide d'images satellitaires pour l'ensemble du Canada, afin de soutenir la réalisation d'un inventaire national des cultures. Une méthodologie par arbre de décision a été utilisée à l'aide d'images satellitaires optiques (Landsat-8, Sentinel-2) et radar (MCR) avec une résolution spatiale finale de 30 m. En même temps que les acquisitions par satellite, des données de réalité de terrain ont été fournies par des sociétés d'assurance-récolte provinciales (Saskatchewan, Manitoba et Québec); tandis que des observations ponctuelles provenaient du Ministère d'Environnement, Eau et Changement climatique de l'Île-du-Prince-Édouard; Ministère de l'Agriculture, de l'Alimentation et des Affaires rurales de l'Ontario; Université de Guelph - campus de Ridgetown; Ministère de l'Agriculture de Colombie-Britannique; l'acquisition de données a aussi été supportée par les centres régionaux de recherches et développement d'AAC à Saint-Jean de Terre-Neuve, Charlottetown, Kentville, Fredericton, Guelph et Summerland. Compte tenu des restrictions de voyage liées à la pandémie et le feu de forêt, un échantillonnage complet n'a pu avoir lieu en C-B. La classe 'agriculture' (120) est donc présente dans la province aux endroits exempts de données terrain. In 2021, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2), and radar (RCM) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph - Ridgetown campus; British Columbia Ministry of Agriculture; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Charlottetown, Kentville, Fredericton, Guelph and Summerland. Due to COVID-19 travel restrictions and forest fires, complete sampling coverages in BC was not possible, as a result the general agriculture class (120) is found in this province in areas where there was no ground data collected.
Research data . Audiovisual . 2022 . Embargo End Date: 15 Aug 2022
The rapid spread of COVID-19 around the globe has increased the need to adopt autonomous social robots within our healthcare systems. In particular, socially assistive robots can help to improve the day-to-day functioning of our healthcare facilities including long-term care, while keeping residents and staff safe by performing repetitive tasks such as health screening. In this paper, we present the first human-robot interaction study with an autonomous multi-task socially assistive robot used for non-contact screening in long-term care homes. The robot monitors temperature, checks for face masks, and asks screening questions to minimize human-to-human contact. We investigated staff perceptions of 7 attributes: screening experience without and with the robot, efficiency, cognitive attitude, freeing up staff, safety, affective attitude, and intent to use the robot. Furthermore, we investigated the influence of demographics on these attributes. Study results show that, overall, staff rated these attributes high for the screening robot, with a statistically significant increase in cognitive attitude and safety after interacting with the robot. Differences between gender and occupation were also determined. Our study highlights the potential application of an autonomous screening robot for long-term care homes.
Social robots have been introduced in different fields such as retail, health care and education. Primary education in the Netherlands (and elsewhere) recently faced new challenges because of the COVID-19 pandemic, lockdowns and quarantines including students falling behind and teachers burdened with high workloads. Together with two Dutch municipalities and nine primary schools we are exploring the long-term use of social robots to study how social robots might support teachers in primary education, with a focus on mathematics education. This paper presents an explorative study to define requirements for a social robot math tutor. Multiple focus groups were held with the two main stakeholders, namely teachers and students. During the focus groups the aim was 1) to understand the current situation of mathematics education in the upper primary school level, 2) to identify the problems that teachers and students encounter in mathematics education, and 3) to identify opportunities for deploying a social robot math tutor in primary education from the perspective of both the teachers and students. The results inform the development of social robots and opportunities for pedagogical methods used in math teaching, child-robot interaction and potential support for teachers in the classroom.
Regular exercise provides many mental and physical health benefits. However, when exercises are done incorrectly, it can lead to injuries. Because the COVID-19 pandemic made it challenging to exercise in communal spaces, the growth of virtual fitness programs was accelerated, putting people at risk of sustaining exercise-related injuries as they received little to no feedback on their exercising techniques. Co-located robots could be one potential enhancement to virtual training programs as they can cause higher learning gains, more compliance, and more enjoyment than non-co-located robots. In this study, we compare the effects of a physically present robot by having a person exercise either with a robot (robot condition) or a video of a robot displayed on a tablet (tablet condition). Participants (N=25) had an exercise system in their homes for two weeks. Participants who exercised with the co-located robot made fewer mistakes than those who exercised with the video-displayed robot. Furthermore, participants in the robot condition reported a higher fitness increase and more motivation to exercise than participants in the tablet condition.
In maart 2020 moesten gerechtsgebouwen ruim twee maanden sluiten vanwege het coronavirus. Zaken werden schriftelijk afgedaan en zittingen voor urgente zaken vonden telefonisch of online plaats. Het project ‘De impact van de coronacrisis op de rechtspraak en de positie van kwetsbare rechtszoekenden’ onderzocht de gevolgen hiervan voor de rechtspraak en kwetsbare rechtzoekenden in het strafrecht, civiele jeugdrecht en vreemdelingenrecht. Data werden verzameld door middel van interviews met rechters, advocaten en andere professionals, enquêtes onder rechtzoekenden en een grondrechtenanalyse. Het onderzoek werd uitgevoerd door onderzoekers van de rechtenfaculteiten van de universiteiten van Leiden, Utrecht en Nijmegen, met een subsidie van ZonMW. Looptijd: september 2020 – maart 2022. Deze niet-openbare data collection bevat de dataset van het onderzoek. De data van de survey onder gedetineerden worden voor hergebruik beschikbaar gemaakt via het DANS archiveringssysteem.
Publisher: ICPSR - Interuniversity Consortium for Political and Social Research
Cross-Section Weights: For the Kenya National Bureau of Statistics (KNBS) and Random Digit Dialing (RDD) samples, to make the sample nationally representative of the current population of households with mobile phone access, the research team created weights in two steps. Step 1: Constructed raw weights combining the two national samples: The population consisted of (I) households that existed in 2015/16, and did not change phone numbers, (II) households that existed in 2015/16, but changed phone number, (III) households that did not exist in 2015/16. Abstracting from differential attrition, the weights from the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot made the KIHBS sample representative of type (I) households. RDD households were asked whether they existed in 2015/16, when they had acquired their phone number, and where they lived in 2015/16, allowing them to be classified into type (I), (II), and (III) households and assigned to KIHBS strata. The weights of each RDD household were adjusted to be inversely proportional to the number of mobile phone numbers used by the household, and scaled relative to the average number of mobile phone numbers used in the KIHBS within each stratum. RDD therefore gave a representative sample of type (II) and (III) households. The research team then combined RDD and KIHBS type (I) households by ex-post adding RDD households into the 2015/16 sampling frame and adjusting weights accordingly. Last, the research team combined the representative samples of type (I), type (II), and type (III), using the share of each type within each stratum from RDD (inversely weighted by number of mobile phone numbers). Variable: WEIGHT_RAW Step 2: Scaled the weights to population proportions in each county and urban/rural stratum: The research team used post stratification to adjust for differential attrition and response rates across counties and rural/urban strata. They scaled the raw weights from step 1 to reflect the population size in each county and rural/urban stratum as recorded in the 2019 Kenya Population and Housing Census conducted by the KNBS (2019 Kenya Population and Housing Census, Volume II: Distribution of Population by Administrative Units, December 2019, Kenya National Bureau of Statistics, https://www.knbs.or.ke/?wpdmpro=2019-kenya-population-and-housing-census-volume-ii-distribution-of-population-by-administrative-units). Variable: WEIGHT Panel Weights: To construct panel weights, the research team followed the approach outlined in Himelein (2014): "Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking". One target respondent was followed in each household. Wherever households were split, only the current household of the target respondent was interviewed. The weights for the wave 1 and 2 balanced panel were constructed by applying the following steps to the full sample of Kenyan nationals: Wave 1 cross-sectional weights after post-stratification adjustment were used as a base. W_1 = W_wave1 Attrition adjustment through propensity score-based method: The predicted probability that a sample household was successfully re-interviewed in the second survey wave was estimated through a propensity score estimation. The propensity score (PS) was modeled with a linear logistic model at the level of the household. The dependent variable was a dummy indicating whether a household that completed the survey in wave 1 had also done so in wave. The following covariates were used in the linear logistic model: Urban/rural dummy; County dummies; Household head gender; Household head age; Household size; Dependency ratio; Dummy: Is anyone in the household working; Asset ownership: radio; Asset ownership: mattress; Asset ownership: charcoal jiko; Asset ownership: fridge; Wall material: 3 dummies; Floor materials: 3 dummies; Connection to electricity grid; Number of mobile phones numbers household uses; Number of phone numbers recorded for follow-up; and Sample dummy for estimation with national samples. Ranked households by PS and split into 10 equal groups Calculated attrition adjustment factor: ac (attrition correction) = the reciprocal of the mean empirical response rate for the propensity score decile Adjusted base weights for attrition: W_2 = W_1 * ac Trimmed top 1 percent of the weights distribution (), by replacing the weights among the top 1 percent of the distribution with the highest value of a weight below the cutoff. W_3 = trim(W_2) Applied post-stratification in the same way as for cross-sectional weights (step 2) Variable: WEIGHT_PANEL_W1_2. The balanced panel weights including waves 3, 4, 5, 6, and 7 were constructed using the same procedure. Variables: WEIGHT_PANEL_W1_2_3, WEIGHT_PANEL_W1_2_3_4, WEIGHT_PANEL_W1_2_3_4_5, WEIGHT_PANEL_W1_2_3_4_5_6, and WEIGHT_PANEL_W1_2_3_4_5_6_7. The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley conducted the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to track the socioeconomic impacts of the COVID-19 pandemic and the recovery from it to provide timely data to inform policy. This collection contains information from seven waves of the COVID-19 RRPS, which was part of a panel survey that targeted Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. Sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. The "WAVE" variable represents in which wave the households were interviewed in. All waves of this survey included information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. The data contain information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority were randomly selected. The samples covered urban and rural areas and were designed to be representative of the population of Kenya using cell phones. The sample size for each completed wave was: Wave 1: 4,061 Kenyan households Wave 2: 4,492 Kenyan households Wave 3: 4,979 Kenyan households Wave 4: 4,892 Kenyan households Wave 5: 5,854 Kenyan households Wave 6: 5,765 Kenyan households Wave 7: 5,633 Kenyan households The collection is organized into three levels. The first level is the Household Level Data, which contains household level information. The 'HHID' variable uniquely identifies all households. The second level is the Adult Level Data, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'ADULT_ID' variable. The third level is the Child Level Data, which contains information for every child in the household. Each child in a household is uniquely identified by the 'CHILD_ID' variable. Pre-loaded Information: Basic household information was pre-loaded in the Computer Assisted Telephone Interviewing (CATI) assignments for each enumerator. The information, for example the household's location, household head name, phone numbers, etc., was used to help enumerators call and identify the target households. The list of individuals from the Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and their basic characteristics were uploaded as well as basic information from previous survey waves where available from wave 2 onward. Respondents: The COVID-19 Rapid Response Phone Survey (RRPS) had one respondent per household. For the sample from the 2015/16 KIHBS CAPI pilot, the target respondent was defined as the primary male or female adult household member. They were randomly chosen where both existed to maintain gender balance. If the target respondent was not available for a call, the field team spoke to any adult currently living in the household of the target respondent. If the target respondent was deceased, the field team spoke to any adults that lived with the target respondent in 2015/16. Finally, if the household from 2015/16 split up, the field team targeted anyone in the household of the target respondent but did not survey a household member that no longer lived with the target respondent. For the sample based on Random Digit Dialing, the target respondent was the owner the phone number that was randomly selected. Where the target respondent was not available for the interview, the research team spoke to any other adult household member of the target respondent. Series Information: The first five waves extended over a period of two months each, while waves 6 and 7 extended over a period of four months. Data collections were implemented between May 2020 and March 2022. The COVID-19 Rapid Response Phone Survey (RRPS) with Kenyan households included two samples. The first sample consisted of households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot was representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consisted of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel, and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they lived in. The purpose of the Kenya COVID-19 Rapid Response Phone Survey (RRPS) was to track the socioeconomic impacts of the COVID-19 pandemic and the recovery from it to provide timely data to inform policy. The RRPS covers the following topics: household roster travel patterns and interactions employment food security income loss transfers subjective welfare (50 percent of sample) health COVID-19 knowledge and vaccinations household and social relations (50 percent of sample). computer-assisted personal interview (CAPI); computer-assisted telephone interview (CATI)To protect respondent privacy, some geographic variables were de-identified in the Household Level Public-Use Data (DS1) and Adult Level Public-Use Data (DS3). Please see the ICPSR Codebook processing notes for additional information.For additional information on the COVID-19 High Frequency Phone Survey of Households, Kenya, 2020-2021 study, please visit the World Bank website. Datasets: DS0: Study-Level Files DS1: Household Level Public-Use Data DS2: Household Level Restricted-Use Data DS3: Adult Level Public-Use Data DS4: Adult Level Restricted-Use Data DS5: Child Level Public-Use Data Households in Kenya that are representative of the population using cell phones. Smallest Geographic Unit: Town
During the COVID-19 pandemic, many research areas that require in person experiments with human volunteers have been impacted due to lockdowns and other activity-restricting policies. The field of robotics is no exception, and specially human-robot interaction research has been severely impacted. In order to circumvent the difficulty of gathering volunteers in person to interact with a robot, we have decided to build a novel crowdsourcing web platform for hosting our "Talk to Kotaro" experiment. The experiment consists of volunteers talking to a robot avatar and reacting to its semantic-free utterances. The developed web platform, which was built using the Python Flask framework, allows for such interactions while recording audio and video and other relevant data, which will be used for studying human impression estimation on gibberish speech. This paper describes not only the experiment and its preliminary results, but the developed platform itself; such tool is essential during pandemics and very useful for regular times, because it enables crowdsourcing data from all over the world.
Presentation materials from a LIASA (Library and Information Association of South Africa) - HELIG (Higher Education Libraries Interest Group) webinar on digital transformation in the academic library profession, Friday, 6 May, 13h00 - 14h00. The effect of technology on libraries has enabled them to transform and adapt to the e-space, thereby undergoing digital transformation. The Covid-19 pandemic expedited this and necessitated more library materials moving to electronic formats to remain accessible and (re-)usable. Digitisation improves access to information and availability in support of virtual offerings. It is indeed fundamental that digital library resources are appropriately preserved in times of risk, such as the recent fire at UCT and floods in KZN.
Publisher: ICPSR - Interuniversity Consortium for Political and Social Research
The sample of the HFPS-HH is a subsample of the 2018/19 Ethiopia Socioeconomic Survey (ESS). The ESS is built on a nationally and regionally representative sample of households in Ethiopia. ESS 2018/19 interviewed 6,770 households in urban and rural areas. In the ESS interview, households were asked to provide phone numbers either their own or that of a reference household (i.e. friends or neighbors) so that they can be contacted in the follow-up ESS surveys should they move from their sampled location. At least one valid phone number was obtained for 5,374 households (4,626 owning a phone and 995 with a reference phone number). These households established the sampling frame for the HFPS-HH. To obtain representative strata at the national, urban, and rural level, the target sample size for the HFPS-HH is 3,300 households; 1,300 in rural and 2,000 households in urban areas. In rural areas, we attempt to call all phone numbers included in the ESS as only 1,413 households owned phones and another 771 households provided reference phone numbers. In urban areas, 3,213 households owned a phone and 224 households provided reference phone numbers. To account for non-response and attrition all the 5,374 households were called in round 1 of the HFPS-HH. The total number of completed interviews in round one is 3,249 households (978 in rural areas, 2,271 in urban areas). The total number of completed interviews in round two is 3,107 households (940 in rural areas, 2,167 in urban areas). The total number of completed interviews in round three is 3,058 households (934 in rural areas, 2,124 in urban areas). The total number of completed interviews in round four is 2,878 households (838 in rural areas, 2,040 in urban areas). The total number of completed interviews in round five is 2,770 households (775 in rural areas, 1,995 in urban areas). The total number of completed interviews in round six is 2,704 households (760 in rural areas, 1,944 in urban areas). The total number of completed interviews in round seven is 2,537 households (716 in rural areas, 1,1821 in urban areas). The total number of completed interviews in round eight is 2,222 households (576 in rural areas, 1,646 in urban areas). The total number of completed interviews in round nine is 2,077 households (553 in rural areas, 1,524 in urban areas). The total number of completed interviews in round ten is 2,178 households (537 in rural areas, 1,641 in urban areas). The total number of completed interviews in round eleven is 1,982 households (442 in rural areas, 1,540 in urban areas). The total number of completed interviews in round twelve is 888 households (204 in rural areas, 684 in urban areas). To obtain unbiased estimates from the sample, the information reported by households needs to be adjusted by a sampling weight (or raising factor) W_H. To construct the sampling weights, we follow the steps outlined in Himelein, K. (2014), which outlines eight steps, of which we follow six, to construct the sampling weights for the High Frequency Phone Survey of Households (HFPS-HH): Begin with base weights from the Ethiopia Socioeconomic Survey (ESS) 2018/19 for each household Incorporate probability of sub-selection of round 1 unit for each of the phone survey households. We calculate the probability of selection for each of the 20 strata in the ESS (urban and rural in each of the 11 regions except for Addis Ababa where we only have an urban stratum) by creating the numerators as the number of completed phone interviews and the denominator as the number of households in the ESS for each stratum. Pool the weights in Steps 1 and 2. Derive attrition-adjusted weights for all individuals by running a logistic response propensity model based on characteristics of the household head (i.e. education, labor force status, demographic characteristics), characteristics of the household (consumption, assets, financial characteristics), and characteristics of the dwelling (house ownership, overcrowding). Trim weights by replacing the top two percent of observations with the 98th percentile cut-off point; and Post-stratify weights to known population totals to correct for the imbalances across our urban and rural sample. In doing so, we ensure that the distribution in the survey matches the distribution in the ESS. *Additional technical details and explanations on each of the steps briefly outlined above can be found in Himelein, K. (2014). The potential impacts of the COVID-19 pandemic in Ethiopia are expected to be severe on Ethiopian households' welfare. To monitor these impacts on households, the team selected a subsample of households that had been interviewed for the Living Standards Measurement Study (LSMS) in 2019, covering urban and rural areas in all regions of Ethiopia. The 15-minute questionnaire covers a series of topics, such as knowledge of COVID and mitigation measures, access to routine healthcare as public health systems are increasingly under stress, access to educational activities during school closures, employment dynamics, household income and livelihood, income loss and coping strategies, and external assistance. The survey is implemented using Computer Assisted Telephone Interviewing, using a modular approach, which allows for modules to be dropped and/or added in different waves of the survey. Survey data collection started at the end of April 2020 and households are called back every three to four weeks for a total of seven survey rounds to track the impact of the pandemic as it unfolds and inform government action. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis. The sample of households was drawn from the sample of households interviewed in the 2018/2019 round of the Ethiopia Socioeconomic Survey (ESS). The extensive information collected in the ESS, less than one year prior to the pandemic, provides a rich set of background information on the COVID-19 High Frequency Phone Survey of households which can be leveraged to assess the differential impacts of the pandemic in the country. The COVID-19 High Frequency Phone Survey of Households, Ethiopia covered the following topics: Household Roster (Rounds 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) Knowledge Regarding the Spread of COVID-19 (Round 1) Behavior and Social Distancing (Rounds 1, 3, 6, 7) Access to Basic Services (Rounds 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) Employment (Rounds 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) Income Loss and Coping (Rounds 1, 2, 3, 4, 5, 6, 7) Food Security (Rounds 1, 2, 3, 4, 11) Aid and Support/ Social Safety Nets (Rounds 1, 2, 3, 4, 5, 6, 7, 9) Agriculture (Rounds 3, 4, 5, 6, 7, 9) Locusts (Rounds 4, 6, 7) WASH (Rounds 4, 9) Education and Childcaring (Rounds 8, 11) Credit (Round 8) Migration (Round 8) Return Migration (Round 8) Tax (Round 9) SWIFT (Round 11) Youth Aspirations and Employment (Round 12) Datasets: DS0: Study-Level Files DS1: Round 1 Microdata DS2: Round 2 Microdata DS3: Round 3 Microdata DS4: Round 4 Microdata DS5: Round 5 Microdata DS6: Round 6 Microdata DS7: Round 7 Microdata DS8: Round 8 Microdata DS9: Round 9 Microdata DS10: Round 10 Microdata DS11: Round 11 Microdata DS12: Round 11 Education Microdata DS13: Round 12 Microdata The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories. Smallest Geographic Unit: kebele computer-assisted telephone interview (CATI)
Publisher: ICPSR - Interuniversity Consortium for Political and Social Research
computer-assisted telephone interview (CATI)Organization of Fieldwork The HFPS COVID-19 Baseline was administered between May 26 and June 14, 2020. Data were collected by trained NSO interviewers who individually made phone calls from the call center at the NSO. Since the country was not fully on lockdown during the preparation and data collection exercise, interviewers were allowed to be in the office after seeking permission from the local authorities and also taking measures to protect themselves like ensuring 2 meters space between individuals. Most interviews were conducted from the call center, some interviews that required call backs conducted from the enumerators' homes. Subsequent rounds also followed the same protocols. Dates on when each round was administered can be found in the Basic Information Document.Gift to Households As a show of appreciation for the households' participation, all households that gave consent to be interviewed were transferred 1000 Malawi Kwacha credit to their phones (even if their interviews are only partially completed).Pre-loaded Information Basic information on every household was pre-loaded in the CATI assignments for each interviewer. The information was pre-loaded to (1) assist interviewers in calling and identifying the household and (2) ensure that each pre-loaded person is properly addressed and easily matched to the most recent face-to-face visits. Basic household information (location, household head name, phone numbers of adult members and reference persons, etc.) was pre-loaded. The list of individuals from IHPS 2019 and their basic characteristics were uploaded.Respondents The HFPS COVID-19 had ONE RESPONDENT per household. The respondent was always the knowledgeable adult household member or for some rounds the person that was randomly selected. The respondent must be a member of the household.Additional information For additional information on the COVID-19 High Frequency Phone Survey of Households study, please visit the World Bank website. The Malawi Integrated Household Panel Survey (IHPS) conducted in 2019 served as the frame for the HFPS-COVID-19. This sample of households is representative nationally as well as by the urban/rural divide. In every visit of the IHPS, phone numbers are collected from interviewed households for all household members and 3 reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NSO and the IHPS households made this an ideal frame from which to conduct the COVID-19 monitoring survey in Malawi. Among the 3,181 households interviewed during the IHPS in 2019, 2,337 (73%) provided at least one phone number. Around 85 percent of these households provided a phone number for at least one household member while the remaining 15 percent only provided a phone number for a reference person. Households with only the phone number of a reference person were expected to be more difficult to reach but were nonetheless included in the frame and deemed eligible for selection for the HFPS COVID-19. To obtain a nationally representative sample for the HFPS-COVID-19, the survey aimed to recontact the entire sample of households that had been interviewed during the Integrated Household Panel Survey (IHPS) 2019 round and that had phone numbers for at least one household member or a reference individual. Interviewers attempted to contact all 2,337 households that had either a contact for a household member or reference person in the baseline round of the phone survey. To obtain unbiased estimates from the sample, the information reported by households needs to be adjusted by a sampling weight (or raising factor) W_H. To construct the sampling weights, the following steps outlined in Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking by Himelein, K. (2014) were considered. Himelein, K. (2014) outlines eight steps, of which six were followed to construct the sampling weights for the HFPS-HH: 1. Begin with base weights from the Malawi Integrated Household Panel Survey (IHPS) 2019 for each household. 2. Incorporate probability of sub-selection of round 1 unit for each of the phone survey households. 3. Pool the weights in Steps 1 and 2. 4. Derive attrition-adjusted weights for all individuals by running a logistic response propensity model based on characteristics of the household head (i.e. gender, primary language spoken, education, labor force status) and characteristics of the household (household size, food consumption score, assets, financial characteristics). 5. Trim weights by replacing the top three percent of observations with the 98th percentile cut-off point; and 6. Post-stratify weights to known population totals to correct for the imbalances across our sample. In doing so, it is ensured that the distribution in the survey matches the distribution in the IHPS. Malawi High-Frequency Phone Survey COVID-19 (HFPS COVID-19) was implemented by the National Statistical Office (NSO) on a monthly basis during the period of May 2020 and June 2021. The survey is part of a World Bank-supported global effort to support countries in their data collection efforts to monitor the impacts of COVID-19. The financing for data collection and technical assistance in support of the Malawi HFPS COVID-19 is provided by the United States Agency for International Development (USAID) and the World Bank. The households were drawn from the sample of households interviewed in 2019 as part of the Integrated Household Panel Survey (IHPS 2019). The 2019 IHPS data are representative at the national and urban/rural-levels and phone survey weights were calculated (1) to counteract selection bias associated with not being able to call IHPS households without phone numbers, and (2) to mitigate against non-response bias associated with not being able to interview all target IHPS households with phone numbers. Each month, the households were asked a set of core questions on the key channels through which individuals and households were expected to be affected by COVID-19-related restrictions. Food security, employment, access to basic services, coping strategies, and non-labor sources of income were channels thought likely to be impacted. The core questionnaire was complemented by questions on selected topics that rotated each month. The objective of HFPS COVID-19 is to monitor the socio-economic effects of the evolving COVID-19 pandemic in real time. These data are intended to be used by the Malawian government and stakeholders to help design policies to mitigate the negative impacts on its population. The HFPS COVID-19 in Malawi is designed to accommodate the evolving nature of the crises, including revision of the questionnaire on a monthly basis. Households in Malawi that are representative nationally, as well as by the country's urban/rural divide.. Smallest Geographic Unit: GPS coordinates Datasets: DS0: Study-Level Files DS1: Round 5 Data DS2: Round 8 Data DS3: Round 9 Data DS4: Round 10 Data
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