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Terra Bellatrix A.N. | NIM120912091 |
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Contributor | Deny Arnos Kwary | NIDN0001017509 |
Actions (login required)Oxford Learner’s Dictionary of Academic English. 2014Oxford: Oxford University Press, 1023 pages, ISBN 978-0-19-433350-4 - Book Review
- Published: 10 March 2015
- Volume 1 , pages 189–192, ( 2014 )
Cite this article11k Accesses Explore all metrics Avoid common mistakes on your manuscript. As the title suggests, the Oxford Learner’s Dictionary of Academic English (OLDAE) is aimed at those who need to use academic English in their studies. The Introduction of the dictionary mentions that the OLDAE provides “an exclusive, detailed focus on the language of academic writing (p. v).” To discuss the merits and demerits of this dictionary, there are four parts that are reviewed: the selection of headwords, the components of the entry, the usage notes, the language banks, the back matter, and the CD-ROM. 1 The selection of headwordsThere seems to be inconsistencies in the OLDEA regarding the headwords included in the dictionary. In the Introduction, it is mentioned that ‘general academic’ words are the main focus of this dictionary. However, in the inner cover page, there are labels used to show words that are mainly restricted to a particular subject area, for example. biology, finance, and linguistics. Therefore, in the entries, we can find technical words such as cytoplasm (labelled biology) and liquidity (labelled finance). However, there is no entry for annuity, a word which is usually found in business and financial texts. If we analyze those three words using the academic text in the Corpus of Contemporary American English (Davies 2015 ), we can see that the frequencies of annuity, liquidity and cytoplasm, are 527, 323, and 183, respectively. Perhaps the frequency of annuity in the Oxford Corpus of Academic English (the corpus used to draw up the headword list) is lower than that of liquidity and cytoplasm, so the word annuity is not included as a headword in the OLDEA. 2 The components of the entriesThe entries in the OLDEA contain the components that are useful for both text reception and text production. In the case of text reception, the definitions are written using a limited number of defining vocabulary, i.e. 2300 words. This is smaller than the number of words in the defining vocabulary of the Oxford Advanced Learner’s Dictionary (Turnbull 2010 ), which contains 3000 words. In the case of text production, we can find example sentences, collocations, and word patterns. The number of example sentences provided in the OLDEA is more than the number of entries. There are 50,000 corpus-based example sentences accompanying the 22,000 words, phrases and meanings. Examples are very important in helping learners determine the correct use of a word (Chan 2012 ) and strengthen users’ understanding of how to use the word in text production (Kwary and Miller 2013 ). In addition to the examples, the users are also assisted in the text production with a list of collocations and word patterns. In the entry of measurement, OLDEA does not only list the collocations, but they are integrated in the patterns, for example. adjective + measurement accurate, precise,…; verb + measurement include, require,…; and measurement + noun error, bias, … Consequently, the users have a number of well-grouped options or words to use when they want to write a text using the word measurement. 3 Usage notesOLDEA provides 80 Thesaurus notes, Which Word? notes, and Grammar Points. All of these are very useful for those who want to write an academic text. The Thesaurus notes start with the similarities of the group words listed (e.g. convincing, compelling, persuasive, and strong), and then continue with the specific usage for each of the words in that group. This will enable the users to select the right word in writing academic English. This function of the Thesaurus notes is quite similar to The Which Word? notes which help the users to see the differences between words that are often confused (e.g. consent vs. permission). When two or more words need to be differentiated based on the grammatical aspects, the words are put in the Grammar Points (e.g. each vs. every). 4 Language banksThe language banks in the OLDEA provide a concise assistance to students in writing academic English. For example, when a student is asked to write a reflective writing, this student can see the Language Bank under the entry reflective. The Language Bank explains the main purpose of a reflective writing and the expressions (together with the example sentences) commonly used in reflective writing. 5 Back matterIn addition to the main part of the dictionary that contains the dictionary entries, there are two groups of resources put in the back matter of the dictionary. The first group consists of the Oxford Academic Writing Tutor. The contents are quite comprehensive. Some of the 25 topics listed are Answering the Question (e.g. in an exam essay), Writing Essays (explanation; argument; compare and contrast), Presenting Data, and Writing a Dissertation. The explanation includes the structure of the writing, the expressions to be used, the example paragraphs, as well as the explanation on the grammar (called Grammar Notes). The second group of the back matter is called the Reference section. In this section, we can find a list of irregular verbs, the passive (i.e. how to form the passive and when to use the passive in academic writing), punctuation (when to use a particular punctuation mark), numbers (how to write numbers in academic writing, mathematical expressions, measurement in Britain and America, etc.), word formation, and other references which are useful for academic writing. The OLDEA comes with a CD-ROM that can be fully installed in a computer, so that we do not need to insert the CD-ROM whenever we need to use the OLDEA in our computers. The screenshot of the first page of OLDEA when we open it in a computer is shown in Fig. 1 . On the left hand top, we can see three menus: Dictionary, Word lists, and My lists. The Dictionary menu provides an access to the dictionary entries. The Word lists contain the Academic Word List (Coxhead 2000 ), Defining vocabulary, and four lists of words related to four disciplines: Humanities, Life science, Physical science, and Social science. The users can also create their own vocabulary list using the My lists menu. The Screenshot of OLDEA installed in a computer In addition to the three menus on the left hand top, there are five more menus placed at the bottom (see Fig. 1 ). They are the iWriter, iGuide, Mini Dictionary, Exercises, and Reference. The iWriter provides models of 25 different types of writing. The contents are the same as those in the Back Matter of the printed dictionary, i.e. the Oxford Academic Writing Tutor. However, the computer version is more interactive as it provides outline frameworks into which we can add our own content directly. The iGuide is a menu to help the users become familiar with the information available in the OLDEA. The iGuide enables the users to understand the internal structure of dictionary entry (e.g. the abbreviations, the symbols, and the components of dictionary entry) by taking some interactive activities (like quizzes). The Mini Dictionary is the same as the menu called Genie in the Oxford Advanced Learner’s Dictionary CD-ROM. When the Mini Dictionary menu is clicked, the OLDEA will function like a tooltip or a small pop-up window. This means that when a user hovers the cursor or the pointer over a word that he or she is writing or reading, that word will automatically be placed in the search box of the dictionary and the entry will be shown directly. This enables a quick access to the dictionary entry. The Exercises provides interactive activities to understand or to test the users knowledge on the words listed in the Academic Word List of Coxhead ( 2000 ). Finally, the Reference menu provides the same information as the Reference section in the back matter of the printed dictionary. The placement of these five menus should be reconsidered. Since they are placed at the bottom of the screen, there is a possibility that the users do not realize that they are there. Placing them on the top of the screen will make these menus easier to be noticed by the users. Based on the review above, it can be concluded that the OLDEA is an excellent tool for students who want to pursue their studies in English speaking countries and for those who want to write academic English. The resources provided in the OLDEA are both comprehensive and concise to meet the needs of students of academic English. Chan, A.Y.W. 2012. Cantonese ESL learners’ use of grammatical information in a monolingual dictionary for determining the correct use of a target word. International Journal of Lexicography 25(1): 68–94. Article Google Scholar Coxhead, A. 2000. A new academic word list. TESOL Quarterly 34(2): 213–238. Davies, M. 2015. Word and phrase: academic. http://www.wordandphrase.info/academic . Accessed 16 Feb 2015. Kwary, D.A., and J. Miller. 2013. A model for an online Australian english cultural dictionary database. Terminology 19(2): 258–276. Turnbull, J. (ed.). 2010. Oxford advanced learner’s dictionary . Oxford: Oxford University Press. Google Scholar Download references Author informationAuthors and affiliations. Universitas Airlangga, Surabaya, Indonesia Deny A. Kwary You can also search for this author in PubMed Google Scholar Corresponding authorCorrespondence to Deny A. Kwary . Rights and permissionsReprints and permissions About this articleKwary, D.A. Oxford Learner’s Dictionary of Academic English. 2014. Lexicography ASIALEX 1 , 189–192 (2014). https://doi.org/10.1007/s40607-015-0014-7 Download citation Published : 10 March 2015 Issue Date : October 2014 DOI : https://doi.org/10.1007/s40607-015-0014-7 Share this articleAnyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. 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Caroline Hoxby ( choxby@stanford.edu ) is the Scott and Donya Bommer Professor of Economics at Stanford University. Hoxby is also the Director of the Economics of Education Program at the National Bureau of Economic Research. She is a Senior Fellow of the Hoover Institution and the Stanford Institute for Economic Policy Research. A public and labor economist, she is one of the world's leading scholars of the economics of education. Hoxby is well known for her research on school choice, school finance, the market for college education, peer effects, university finance and financial aid. Her current projects include work on how education affects economic growth; globalization in higher education; and ideal financing for schools. Hoxby is the recipient of many honors including Global Leader of Tomorrow (World Economic Forum) and Sloan, Olin, Mellon, and Ford fellowships. Hoxby has served as a presidential appointee to the National Board of Education Sciences. She has a Ph.D. from MIT, studied at Oxford as a Rhodes Scholar, and obtained her BA from Harvard University. You are hereBenefits of the University Honors ProgramPriority registration. Students in the University Honors Program are assigned priority times on the Workday registration system. Priority registration lets UHP students tailor their schedules to encourage intentional learning and attain maximum academic impact. Learn more about registration appointments . OverloadingHonors students in good standing (3.3GPA or above) may enroll for up to 25 units during the third registration window without prior written approval from the Drahmann Center. For dates that apply, see Deadlines and Registration Information PDF . Small Class SizesHonors classes are typically capped at seventeen students to enable a seminar-style, learner-directed format. Residence HallsUniversity Honors Program students live in four residence halls/Residential Learning Communities (RLCs): Casa Italiana/da Vinci; Graham/ALPHA; Dunne/Modern Perspectives, and McLaughlin-Walsh/Unity. Learn more about RLCs . Community EventsThe University Honors Program holds a variety of gatherings over the academic year to bring the community together for social and educational purposes. The Honors Advisory Council (HAC), a peer-run student group, organizes events and speakers for the Honors program each quarter. Learn more about the Honors Advisory Council . Senior ThesisThe culminating experience of the University Honors Program is the Senior Thesis, which students complete in their final year at Santa Clara. This project is designed to engage students through in-depth research in close collaboration with one or more faculty mentor. Learn more about the Senior Thesis . Special Scholarships and FellowshipsHonors students are eligible to apply for several scholarships that are exclusive to the Honors Program, the Oxford Scholarship (Mansfield College); the Osberg Fellowship; Hayes Fellowship, in addition, many Honors students apply to the Provost’s Scholarship. Find out more about Scholarships and Fellowships . Academic Aspects of the Honors Program The University Honors Program promotes intellectual excellence, critical thinking, and global engagement through seminar-style courses, difficult dialogues, and mentored independent research. Student-directed Honors programming builds community ties and fosters student leadership in the campus community and beyond. Academic Aspects of the Honors Program Applications - Search Menu
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Article ContentsIntroduction, supplementary data, author contributions, acknowledgements. COVID-19 antibody seroprevalence in Santa Clara County, California- Article contents
- Figures & tables
- Supplementary Data
Eran Bendavid, Bianca Mulaney, Neeraj Sood, Soleil Shah, Rebecca Bromley-Dulfano, Cara Lai, Zoe Weissberg, Rodrigo Saavedra-Walker, Jim Tedrow, Andrew Bogan, Thomas Kupiec, Daniel Eichner, Ribhav Gupta, John P A Ioannidis, Jay Bhattacharya, COVID-19 antibody seroprevalence in Santa Clara County, California, International Journal of Epidemiology , Volume 50, Issue 2, April 2021, Pages 410–419, https://doi.org/10.1093/ije/dyab010 - Permissions Icon Permissions
Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County. On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity. The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1–2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2–99.7%) and sensitivity was 82.8% (95% CI 76.0–88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7–1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3–4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000–35 000) using unweighted prevalence] people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time. The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds. Seroprevalence studies of Coronavirus Disease 2019 (COVID-19) provide estimates of the extent of infection that are more representative of true transmission than indicated by case numbers. In late March 2020, the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Santa Clara County, California was estimated at 2.8% [95% confidence interval (CI) 1.3–4.2%] after weighting for county demographics and adjusting for test performance (1.5% unadjusted, 95% CI 1.1–2.0%). These prevalence point estimates imply that 53 000 (95% CI 26 000 to 82 000 using weighted prevalence) people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time. Using the estimated number of infections and the deaths in Santa Clara County at the time, we estimate a local infection fatality rate of 0.17%. The first two cases of Coronavirus Disease 2019 (COVID-19) in Santa Clara County, California were identified in returning travellers on 31 January and 1 February 2020, and the first COVID-19 death in the county was announced on 9 March. 1 In the following month, nearly 1200 additional cases were identified in Santa Clara County, showing a pattern of rapid case increase that was reflective of community transmission. However, the case definition in Santa Clara and many other locations relies on polymerase chain reaction (PCR)-based tests that check for active infections. 2 In addition, PCR-based tests were initially restricted to those with symptomatic disease. Thus, the true extent of infection remains unknown, as confirmed cases miss those with mild or no symptoms and those who have already recovered from infection. Measuring the true extent of infection is key for epidemic projections and planning response to the epidemic. For example, early projections suggested that, in the absence of strict measures to reduce transmission, the COVID-19 pandemic could overwhelm existing hospital-bed and intensive-care-unit capacity throughout the USA and lead to >2 million deaths. 3 In the absence of seroprevalence surveys, estimates of the fatality rate in these and other epidemic projections have relied on the number of confirmed cases multiplied by an estimated factor representing unknown or asymptomatic cases to arrive at the number of infections. 4–7 However, the magnitude of that factor is highly uncertain and has been difficult to assess because of three independent processes that introduce measurement error: (i) cases have been diagnosed with PCR-based tests, which do not provide information about resolved infections; (ii) the majority of cases tested early in the course of the epidemic have been acutely ill and highly symptomatic, whereas most asymptomatic or mildly symptomatic individuals have not been tested; and (iii) PCR-based testing rates have been highly variable across contexts and over time, leading to inaccurate relationships between the numbers of cases and infections. On 3 and 4 April 2020, we conducted a survey of residents of Santa Clara County to measure the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and better approximate the number of infections. To the best of our knowledge, this was the first SARS-CoV-2 seroprevalence survey conducted in the USA. At the time of this study, Santa Clara County had the largest number of confirmed cases of any county in Northern California. The county also had several of the earliest known cases of COVID-19 in the state—including one of the first presumed cases of community-acquired disease—making it an especially appropriate location for testing a population-level sample for the presence of active and past infections. We conducted serologic testing for SARS-CoV-2 antibodies in 3328 adults and children in Santa Clara County using capillary blood draws and a lateral-flow immunoassay. In this section, we describe our sampling and recruitment approaches, specimen-collection methods, antibody-testing procedure, test-kit validation and statistical methods. Our protocol was informed by a World Health Organization protocol for population-level COVID-19 antibody testing. 8 We conducted our study with the cooperation of the Santa Clara County Department of Public Health. Study participants and sample recruitmentWe recruited participants by placing targeted advertisements on Facebook aimed at residents of Santa Clara County. We used Facebook to quickly reach a large number of county residents and because it allows granular targeting by zip code and socio-demographic characteristics. 9 We posted our advertisements targeting two populations: ads aimed at a representative population of the county by zip code and specially targeted ads to balance our sample for under-represented zip codes. In addition, we capped registrations from overrepresented areas after our registration slots filled up quickly with participants from wealthier zip codes. Individuals who clicked on the advertisement were directed to a survey hosted by the Stanford REDCap platform, which provided information about the study. 10 The survey asked for six data elements: zip code of residence, age, sex, race/ethnicity, underlying co-morbidities and prior clinical symptoms. Over 24 hours, we registered 3285 adults, and each adult was allowed to bring one child from the same household with them (889 children registered). Additional details of the participant-selection process are provided below (and in Supplementary Data , available as Supplementary data at IJE online). Specimen-collection and testing methodsWe established drive-through test sites in three locations spaced across Santa Clara County: two county parks in Los Gatos and San Jose, and a church in Mountain View. Only individuals with a participant identification (participant ID) were allowed into the testing area. Verbal informed consent was obtained to minimize participant and staff exposure. With participants in their vehicles, sample collectors in personal protective equipment drew 50–200µL of capillary blood into an EDTA-coated microtainer. Tubes were barcoded and linked with the participant ID. Samples were couriered from the collection sites to a test-reading facility with steady lighting and climate conditions. Technicians drew whole blood up to a fill line on the manufacturer’s pipette and placed it in the test-kit well, followed by a buffer. Test kits were read 12–20 minutes after the buffer was placed. Technicians barcoded tests to match sample barcodes and documented all test results. Test kit performanceThe manufacturer’s performance characteristics were available prior to the study (using 85 confirmed positive and 371 confirmed negative samples). We conducted additional testing to assess the kit performance and continued collecting information from assessments of test performance to incorporate into the analysis. Broadly, test performance was assessed against gold-standard positive specimens from patients with PCR-confirmed COVID-19 (with or without additional confirmation of antibody presence) for sensitivity and gold-standard negative specimens from pre-COVID-era and early-COVID-era specimens. More details on the data provenance, procedures to assess test-performance characteristics and concordance between gold-standard and kit results are provided below ( Supplementary Data , available as Supplementary data at IJE online). Statistical analysisOur estimation of the prevalence of COVID-19 proceeded in three steps. First, we report the raw frequencies of positive tests as a proportion of the final sample size. Second, we report the estimated sample prevalence, adjusted for test performance characteristics. Because SARS-CoV-2 lateral-flow antibody assays are relatively new, we gathered all available information on test performance characteristics (sensitivity and specificity), with a focus on test specificity, which can be of paramount importance when prevalence is not high. We use an estimate of test sensitivity and specificity based on pooling all information available to us. Details of each sample, including test-kit agreement numbers, specimen type and available information on data provenance, are provided in the Supplementary Data , available as Supplementary data at IJE online. Third, we report the weighted prevalence after weighting for the zip code, sex, age (using four age categories: 0–19, 20–39, 40–69 and 70+) and race/ethnicity (non-Hispanic White, Asian, Hispanic and other) distributions of Santa Clara County (as measured in the 2018 American Community Survey). We use weights obtained through iterative proportional fitting, or raking. 11 Raking generates weights by iteratively adjusting the marginal weights of each population control variable (e.g. sex) until convergence is achieved on all control variables. We match Santa Clara County demographics by zip, sex, age and race. We use a bootstrap procedure to estimate confidence bounds for the unweighted and weighted prevalence, while accounting for sampling error and propagating the uncertainty in the sensitivity and specificity. We account for clustering of test results within families by drawing household clusters in the bootstrap samples. We use the basic percentile of the bootstrap distribution to construct confidence intervals. 12 This procedure assumes that the community sample, negative control sample and positive control sample are drawn independently. More details on our bootstrap procedures are in the Supplementary Data , available as Supplementary data at IJE online. Public involvementMultiple stakeholders in Santa Clara County, including the department of public health, members of the board of supervisors, county parks and multiple community members, were engaged in the design and execution of the study. The test kit used in this study (Premier Biotech, Minneapolis, MN) was tested prior to field deployment. In all, we collected information on 3404 specimens from 14 sample sets used for assessing the specificity of this particular kit and on 187 specimens from 4 sample sets used for assessing sensitivity. These tests were performed by the test kit manufacturer, US and Chinese regulatory agencies, as well as independent labs. Additional details on the sample sets and the pooling approaches used to combine the estimates are provided in the Supplementary Data , available as Supplementary data at IJE online. The estimated specificity using data from all the samples was 99.53% [95% confidence interval (CI) 99.24–99.73%] and sensitivity was 85.56% (95% CI 79.69–90.26%). Our study included 3439 individuals who registered for the study and arrived at testing sites. We excluded observations of individuals who could not be tested (e.g. unable to obtain blood or blood clotted, N = 49), whose test results could not be used (e.g. if an incorrect participant ID was recorded onsite, N = 32), who did not reside in Santa Clara County ( N = 29) and who had invalid test results (no control band, N = 1). This yielded an analytic sample of 3328 individuals with complete records including survey registration, attendance at a test site for specimen collection and lab results ( Figure 1 ). The sample distribution meaningfully deviated from that of the Santa Clara County population along several dimensions: sex (63% in sample was female, 50% in county), race (8% of the sample was Hispanic, 26% in the county; 19% of the sample was Asian, 28% in the county) and zip distribution ( Supplementary Figure 1 , available as Supplementary data at IJE online). Table 1 includes demographic characteristics of our unweighted sample, the weighted sample and Santa Clara County. 13 Supplementary Figure 1 , available as Supplementary data at IJE online, shows the geographical zip-code distribution of study participants in the county (counts and density per 1000 population). Flow diagram of participants who filled out the survey and registered, visited a site for testing and were associated with a tested specimen. We were able to associate 3328 individuals with complete survey, site and lab-result data. ‘Individuals with completed registrations’ refers to individuals who completed the initial online survey and were able to select a test site and time. ‘No-shows’ refers to participants who filled out the survey and obtained a site registration but for whom we do not have a record of attendance onsite. ‘Unverifiable IDs’ refers to records from the site data with duplicate participant identifications (IDs) for which we cannot verify which individual attended the site (this may be due to participants bringing incorrect IDs and/or technical errors in the REDCap ID assignment process). ‘Samples not collected or not tested’ includes at least 10 individuals who visited the site but did not consent to participate, as well as several children who may have decided not to have their fingers pricked after completing intake onsite. This also includes specimens that were lost before they could be tested in the lab. ‘Unusable or unmatched survey data’ includes individuals with invalid zip codes, participant IDs from the lab results that could not be matched back to the survey responses and participants who withdrew from the study. Unmatched participant IDs may be due to participants stating an incorrect participant ID at the test site or site data collectors incorrectly recording stated participant IDs. ‘Invalid lab result’ refers to one test for which the on-board control failed and the lab result could not be correctly interpreted. Sample characteristics relative to Santa Clara County population estimates from the 2018 American Community Survey Characteristic . | . | Sample—unweighted . | Sample—weighted . | County . |
---|
Population ( ) | | 3328 | 3328 | 1 943 411 | Women (%) | | 63.1 | 49.5 | 49.5 | Men (%) | | 36.9 | 50.5 | 50.5 | Age (%) | 0–19 | 19.1 | 25.5 | 25.5 | | 20–39 | 27.3 | 29.2 | 29.2 | | 40–69 | 51.3 | 37.1 | 37.1 | | ⩾70 | 2.3 | 8.3 | 8.3 | Race/ethnicity (%) | Non-Hispanic White | 64.1 | 33.1 | 33.1 | | Hispanic | 8.0 | 26.4 | 26.3 | | Asian | 18.7 | 27.7 | 27.8 | | Other | 9.2 | 12.8 | 12.8 |
Characteristic . | . | Sample—unweighted . | Sample—weighted . | County . |
---|
Population ( ) | | 3328 | 3328 | 1 943 411 | Women (%) | | 63.1 | 49.5 | 49.5 | Men (%) | | 36.9 | 50.5 | 50.5 | Age (%) | 0–19 | 19.1 | 25.5 | 25.5 | | 20–39 | 27.3 | 29.2 | 29.2 | | 40–69 | 51.3 | 37.1 | 37.1 | | ⩾70 | 2.3 | 8.3 | 8.3 | Race/ethnicity (%) | Non-Hispanic White | 64.1 | 33.1 | 33.1 | | Hispanic | 8.0 | 26.4 | 26.3 | | Asian | 18.7 | 27.7 | 27.8 | | Other | 9.2 | 12.8 | 12.8 |
Table 2 shows that the frequency of positivity in our unweighted sample was similar between men and women, was highest among Hispanic participants and ranged between 1.4% and 1.9% across ages. Positivity among those who reported recent loss of taste in the past 2 weeks ( n = 59) was ∼22%. Univariate frequencies of positivity along demographic and clinical features . | . | in population . | Portion positive, unadjusted (%; ) . |
---|
Race/ethnicity | White | 2116 | 1.0 | 21 | | Asian | 623 | 1.9 | 12 | | Hispanic | 266 | 4.9 | 13 | | Other | 306 | 1.3 | 4 | | Total | 3311 | 1.5 | 50 | Sex | Male | 1228 | 1.5 | 19 | | Female | 2100 | 1.5 | 31 | | Total | 3328 | 1.5 | 50 | Age | 0–19 | 637 | 1.4 | 9 | | 20–39 | 907 | 1.9 | 17 | | 40–69 | 1706 | 1.3 | 23 | | ⩾70 | 78 | 1.3 | 1 | Symptoms in past 2 weeks | Fever | 148 | 3.4 | 5 | | Cough | 618 | 2.6 | 16 | | Shortness of breath | 200 | 3.0 | 6 | | Runny nose | 568 | 2.1 | 12 | | Sore throat | 542 | 1.8 | 10 | | Loss of smell | 60 | 21.7 | 13 | | Loss of taste | 59 | 22.0 | 13 | | No symptoms | 2156 | 1.0 | 22 | Symptoms in past 2 months | Fever | 866 | 2.0 | 17 | | Cough | 1534 | 1.6 | 25 | | Shortness of breath | 542 | 2.2 | 12 | | Runny nose | 1329 | 1.5 | 20 | | Sore throat | 1397 | 1.8 | 25 | | Loss of smell | 188 | 11.2 | 21 | | Loss of taste | 187 | 10.7 | 20 | | No symptoms | 1029 | 0.9 | 9 |
. | . | in population . | Portion positive, unadjusted (%; ) . |
---|
Race/ethnicity | White | 2116 | 1.0 | 21 | | Asian | 623 | 1.9 | 12 | | Hispanic | 266 | 4.9 | 13 | | Other | 306 | 1.3 | 4 | | Total | 3311 | 1.5 | 50 | Sex | Male | 1228 | 1.5 | 19 | | Female | 2100 | 1.5 | 31 | | Total | 3328 | 1.5 | 50 | Age | 0–19 | 637 | 1.4 | 9 | | 20–39 | 907 | 1.9 | 17 | | 40–69 | 1706 | 1.3 | 23 | | ⩾70 | 78 | 1.3 | 1 | Symptoms in past 2 weeks | Fever | 148 | 3.4 | 5 | | Cough | 618 | 2.6 | 16 | | Shortness of breath | 200 | 3.0 | 6 | | Runny nose | 568 | 2.1 | 12 | | Sore throat | 542 | 1.8 | 10 | | Loss of smell | 60 | 21.7 | 13 | | Loss of taste | 59 | 22.0 | 13 | | No symptoms | 2156 | 1.0 | 22 | Symptoms in past 2 months | Fever | 866 | 2.0 | 17 | | Cough | 1534 | 1.6 | 25 | | Shortness of breath | 542 | 2.2 | 12 | | Runny nose | 1329 | 1.5 | 20 | | Sore throat | 1397 | 1.8 | 25 | | Loss of smell | 188 | 11.2 | 21 | | Loss of taste | 187 | 10.7 | 20 | | No symptoms | 1029 | 0.9 | 9 |
17 people did not indicate race. The total number of positive cases by either IgG or IgM in our unadjusted sample was 50—a crude prevalence rate of 1.50% (exact binomial 95% CI 1.11–1.98%; Table 3 ). Accounting for test sensitivity and specificity and sampling error, our point estimate of the unweighted population prevalence was 1.22% (bootstrap 95% CI 0.66–1.79%). After adjusting for test performance and weighting our sample to approximate Santa Clara County demographics by zip, race, age and sex, the prevalence was 2.76% (95% CI 1.32 – 4.22%). The increase in prevalence after weighting is primarily driven by relatively higher prevalence among Hispanic participants residing in under-sampled zip codes of the county. This distribution of higher prevalence in those groups corresponds closely with the distribution of confirmed cases in Santa Clara County in late March: a disproportionate number of cases were experienced by Hispanic populations residing in the eastern portion of Santa Clara County. 14 Prevalence estimation in Santa Clara County Approach . | Point estimate (%) . | Uncertainty (95% CI) . |
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Unadjusted (%) | 50/3328 = 1.50% | 1.11–1.98% (binomial exact) | Adjusted for test performance (unweighted) | 1.22% | 0.66–1.79% | Adjusted for test performance and weighted | 2.76% | 1.32–4.22% |
Approach . | Point estimate (%) . | Uncertainty (95% CI) . |
---|
Unadjusted (%) | 50/3328 = 1.50% | 1.11–1.98% (binomial exact) | Adjusted for test performance (unweighted) | 1.22% | 0.66–1.79% | Adjusted for test performance and weighted | 2.76% | 1.32–4.22% |
We report the prevalence and uncertainty bounds of estimates from unadjusted frequency counts, estimates adjusted for test-performance characteristics and estimates adjusted for test-performance characteristics and weighted by zip code, sex, age and race/ethnicity. For adjusted prevalences, we estimate the point estimate and uncertainty using the bootstrap as described in the Methods and Supplementary Data , available as Supplementary data at IJE online. We can use our prevalence estimates to approximate the infection fatality rate from COVID-19 in Santa Clara County. Our prevalence estimate of 2.76% applied to Santa Clara County’s population implies a little over 53 000 infections (95% CI 26 000–82 000). Since the development of antibodies takes ∼7 days from the time of infection, that estimate represents the cumulative incidence of infections in Santa Clara County up to 27 March 2020, 1 week before the first day of testing. We then examine the cumulative COVID-19-associated deaths in Santa Clara County 2, 3 (our preferred) and 4 weeks after 27 March, which allows us to estimate the range of the infection fatality rate given a lag of 2–4 weeks from infection to death. 15 , 16 The number of people who died with COVID-19 in Santa Clara County by 11 April (2 weeks), 18 April (3 weeks) and 25 April (4 weeks) was 65, 90 and 106, respectively ( Supplementary Figure 2 , available as Supplementary data at IJE online, shows the time trends of cases and deaths in Santa Clara County around the time of the study). 17 These estimates of deaths then correspond to an infection fatality rate of 0.12% (65/53 000), 0.17% (90/53 000; preferred) and 0.2% (106/53 000). Finally, we estimated the infection fatality rate within our four age strata (0–19, 20–39, 40–69 and 70+) using the age-specific portion of deaths in Santa Clara County and the implied number of infections from our study and the county demographics. Our data are limited to calculate with accuracy the infection fatality rate in age strata, but they suggest vast differences in fatality risk ( Supplementary Table 4 , available as Supplementary data at IJE online). After adjusting for test-performance characteristics and weighting for county demographics, we estimate that the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County in late March was 2.76%, with uncertainty bounds from 1.32% to 4.22%. The most important implication of these findings is that, early in the pandemic, the number of infections was much greater than the reported number of cases. Using the weighted estimates, our data imply that, by 27 March (7 days prior to our survey), ∼53 000 (95% CI 26 000–82 000) people had been infected in Santa Clara County. The reported number of confirmed positive cases in the county on 27 March was 948 (~1,200 on the days of the study)—56-fold lower than the number of infections predicted by this study. 14 This infection-to-case ratio, also referred to as an under-ascertainment rate, was meaningfully higher than other estimates at the time. 18 , 19 This under-ascertainment rate is a fundamental parameter of many projection and epidemiologic models, and was used as a calibration target for understanding epidemic stage and calculating fatality rates. 20 , 21 The under-ascertainment for COVID-19 is likely due to a combination of reliance on PCR for case identification, which misses convalescent cases; early spread in the absence of systematic testing; and asymptomatic or lightly symptomatic infections that go undetected. The estimated infection fatality rate of 0.17% is based on the assumption that the prevalence in our study reflects the situation in Santa Clara 7 days prior to the study. If antibodies take longer to appear, or if the average duration from case identification to death is <3 weeks, then the prevalence rate at the time of the survey was higher and the infection fatality rate would be lower. On the other hand, if deaths from COVID-19 are under-reported, then the fatality rate estimates would increase. Our prevalence and fatality rate estimates can be used to update existing models, given the large upwards revision of under-ascertainment. Whereas our weighted-prevalence estimate of 2.76% is indicative of the situation in Santa Clara County as of late March, other areas are likely to have different seroprevalence estimates based on population demographics, effective contact rates and social-distancing policies. The infection fatality rate in different locations also varies and may be substantially higher in places where the hospitals were overwhelmed (e.g. New York City or Bergamo) or where infections are concentrated among vulnerable individuals (e.g. nursing home residents). 22–24 For example, in many European countries, 42–57% of deaths occurred in nursing homes and preliminary estimates for the USA are approaching the same range. 25 , 26 infection fatality rate estimates may be substantially higher in such settings. Our prevalence estimate also suggests that, at this time, the large majority of the population in Santa Clara County remains without IgM or IgG antibodies to SARS-CoV-2. However, repeated serologic testing in different geographies, spaced a few weeks apart, is needed to evaluate the extent of infection spread over time. This study has several limitations. The primary limitation concerns sample selection biases. Our sample may be enriched with COVID-19 participants by selecting for individuals with a belief or curiosity concerning past infection. We discuss further and attempt to quantify the potential impact of this bias in the Supplementary Data , available as Supplementary data at IJE online (Section S4). Notably, we find that, under the most penalizing scenario of selection, the population-prevalence estimate changes from 2.76% to 2.11% ( Supplementary Table 3 , available as Supplementary data at IJE online). Our study may also have selected for groups of people more likely to skew our sample against COVID-19 participants. For example, our sample strategy selected for members of Santa Clara County with ready access to Facebook who viewed our advertisement early after the registration opened. Our sample ended up with an over-representation of White women between the ages of 40 and 70 years, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample by zip code, race, age and sex to match the county. Our survey also selected for members of the population who were able to spare the time to drive to the testing site, which may have skewed our sample against essential workers. Our study was also limited in that it could not ascertain the representativeness of SARS-CoV-2 antibodies in populations with possibly high prevalence, such as homeless populations and those in nursing homes. The overall direction and magnitude of these selection effects are hard to fully bound, and our estimates reflect the prevalence in our sample, weighted to match county demographics. Another potential limitation is that, with relatively low prevalence estimates, the precision of seroprevalence estimates depends on the performance of the serology tests for SARS-CoV-2 antibodies. Our adjusted results depend on the current estimates of specificity and sensitivity of these tests. We estimated the specificity and sensitivity based on pooled estimates from several independent assessments that use combined IgG and IgM to identify positive-antibody presence. Lower test specificity or greater uncertainty in the test performance could, in principle, meaningfully change the study’s conclusions. The performance of the test kit used in this study has been studied extensively in comparison to other SARS-CoV-2-antibody tests, including for use with capillary blood and in comparison with tests that require venipuncture ( Supplementary Data Section 2, available as Supplementary data at IJE online). Our infection fatality rate could be biased downward if the number of COVID-19 deaths in the county has been substantially undercounted. This has some plausibility given that some early COVID-19 deaths may have gone undiagnosed due to limited testing. However, many deaths early in the spring of 2020 that were suspected of being due to COVID-19 were retrospectively evaluated, and those in which COVID-19 was implicated in the death were added to the official tally, reducing the effect of undercounting. We note that, even if deaths are 50% higher than the recorded deaths, this would still imply an infection fatality rate of 0.18–0.30%. Over 100 teams worldwide have tested population samples for SARS-CoV-2 antibodies, with findings consistent with a large under-ascertainment of SARS-CoV-2 infections. Our study was one of the earliest to be done and the large under-ascertainment was partly driven by the limited testing in the early phases of the pandemic. Large under-ascertainment (up to several hundred-fold) was seen in other early surveys, whereas the extent of under-ascertainment decreased as more testing was being performed in most locations. 27 , 28 An early serosurvey in Los Angeles County, California on 10–11 April estimates a seroprevalence of 4.65%. 29 Our data from Santa Clara County suggest that the spread of the infection is similar to other moderately affected areas such as Los Angeles in early spring. Documented infections remained low in Santa Clara County until early summer. Santa Clara was part of a large seroprevalence survey among dialysis patients in the USA. 30 In that study, conducted in the first week of July 2020, the estimated seroprevalence in Santa Clara County was 4.1%, and this corresponded to an infection fatality rate of ∼0.2%, similar to our estimate. We conclude that, based on seroprevalence sampling of a large regional population, the best estimates for the prevalence of SARS-CoV-2 antibodies in Santa Clara County were 2.76% by late March (95% CI 1.32–4.22%), with large variability by race/ethnicity. This prevalence is far smaller than the theoretical final size of the epidemic 31 and suggests that, in late March, a large majority of the population did not have IgG or IgM antibodies to the virus. Our study offers valuable data for an early period of the pandemic, when PCR testing was limited, and these data can be used as a baseline for comparison against subsequent seroprevalence studies to estimate the evolution of both seroprevalence and infection fatality rates over time. Our prevalence and infection fatality rate estimates do not advocate for or refute the usefulness of any non-pharmaceutical interventions. Instead, these new data reduce uncertainty around the size of the population that has been infected. This allows better monitoring of interventions and reducing uncertainty about the state of the epidemic also may carry intrinsic public benefits. It is important to note that the under-ascertainment of infections in Santa Clara may change over time (e.g. depending on greater availability of testing) and the under-ascertainment rate may be different in other locations. Improved test accuracy and larger sample sizes with random sampling can further reduce uncertainty in estimates. Our work demonstrates the feasibility of seroprevalence surveys of population samples to inform our understanding of this pandemic’s progression, project estimates of community vulnerability and monitor infection fatality rates in different populations over time. Supplementary data are available at IJE online. E.B., N.S. and J.B. conceived of the project and were involved in every aspect of the project; B.M., S.S. and J.T. were involved in protocol development and in every aspect of the survey execution; J.P.A.I. conceptualized the data collection and analysis; R.B.D., C.L., Z.W., R.S.W. and A.B. were site directors and led critical components of the data collection and interpretation; T.K. and D.E. were instrumental in assessing, procuring and fielding the test kits; R.G. led recruitment efforts and was involved in data analysis. All authors were critically involved in interpretation of the data, drafting and revising of the manuscript. This work was supported by gift support from the Stanford COVID-19 Seroprevalence Studies Fund. The funders had no role in the design and conduct of the study, nor in the decision to prepare and submit the manuscript for publication. The authors acknowledge the support of the Santa Clara County Department of Public Health, the Santa Clara County community, all study participants and the many volunteers and staff without whom this study could not have been accomplished in the midst of the COVID-19 crisis. We would also like to acknowledge the following individuals for critical comments and contributions that improved the data and the paper: David Allison, Manisha Desai, Liran Einav, Julia Gross, Emilia Ling, Tom MaCurdy, Charles McCulloch, Ben Moran, Barry Nalebuff, Richard Olshen, Ken Shotts and Frank Wolak. The Institutional Review Board at Stanford University approved the study prior to recruitment (Protocol # IRB-55702). Sharing of the primary data is restricted under a human-subjects-protection agreement. Sharing of de-identified data may be available upon request and review by the authors. Conflict of interestNone declared. Of note, test kits were purchased from Premier Biotech for this study, and none of the authors has a relationship to the test manufacturer (beyond purchasing the tests). Novel Coronavirus Press Archives—Public Health Department—County of Santa Clara . https://www.sccgov.org/sites/phd/DiseaseInformation/novel-coronavirus/Pages/archives.aspx ( 12 April 2020 , date last accessed). Santa Clara County Public Health: COVID-19 Information for Healthcare Providers. https://www.sccgov.org/sites/phd-p/Diseases/novel-coronavirus/Pages/Responsibilities-and-Guidance.aspx (23 November 2020, date last accessed). Report 12—The Global Impact of COVID-19 and Strategies for Mitigation and Suppression. Imperial College London. http://www.imperial.ac.uk/medicine/departments/school-public-health/infectious-disease-epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/ ( 7 April 2020, date last accessed ). Spychalski P , Błażyńska-Spychalska A , Kobiela J. Estimating case fatality rates of COVID-19 . Lancet Infect Dis 2020 :774–775. https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30246-2/abstract (9 April 2020, date last accessed ). Google Scholar Onder G , Rezza G , Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA 2020 ; 323 : 1775 – 1776 . https://jamanetwork.com/journals/jama/fullarticle/2763667 (9 April 2020, date last accessed). Li L-Q , Huang T , Wang Y-Q et al. COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis . J Med Virol 2020 ; 92 : 1433 . Wu JT , Leung K , Bushman M et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med 2020 ; 26 : 506 – 510 . https://www.nature.com/articles/s41591-020-0822-7 (8 April 2020, date last accessed). Population-Based Age-Stratified Seroepidemiological Investigation Protocol for COVID-19 Virus Infection. https://www.who.int/publications-detail/population-based-age-stratified-seroepidemiological-investigation-protocol-for-covid-19-virus-infection (8 April 2020, date last accessed). Shaver LG , Khawer A , Yi Y et al. Using Facebook advertising to recruit representative samples: feasibility assessment of a cross-sectional survey . J Med Internet Res 2019 ; 21 : e14021 . REDCap: Resources operated by Stanford Research IT. https://med.stanford.edu/researchit/resources.html#citations (7 April 2020, date last accessed). Deville J-C , Särndal C-E , Sautory O. Generalized raking procedures in survey sampling . J Am Stat Assoc 1993 ; 88 : 1013 – 20 . Efron B. Bootstrap methods: another look at the jackknife . Ann Stat 1979 ; 7 : 1 – 26 . Bureau UC. American Community Survey (ACS) . The United States Census Bureau. https://www.census.gov/programs-surveys/acs (10 April 2020, date last accessed). County of Santa Clara Public Health Department—Coronavirus (COVID-19) Data Dashboard. https://www.sccgov.org/sites/phd/DiseaseInformation/novel-coronavirus/Pages/dashboard.aspx#cases (11 April 2020, date last accessed). Bhatraju PK , Ghassemieh BJ , Nichols M et al. Covid-19 in critically ill patients in the Seattle Region—case series . N Engl J Med 2020 ; 382 : 2012 – 22 . Weiss P , Murdoch DR. Clinical course and mortality risk of severe COVID-19 . Lancet 2020 ; 395 : 1014 – 15 . Coronavirus (COVID-19) Data Dashboard—Novel Coronavirus (COVID-19)—County of Santa Clara . https://www.sccgov.org/sites/covid19/Pages/dashboard.aspx#cases (20 April 2020, date last accessed). Li R , Pei S , Chen B et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science 2020 ; 368 : 489 – 493 . https://science.sciencemag.org/content/early/2020/03/24/science.abb3221 (10 April 2020, date last accessed). Salomon JA. Defining high-value information for COVID-19 decision-making. medRxiv 2020 ;2020.04.06.20052506. http://medrxiv.org/content/early/2020/04/08/2020.04.06.20052506.abstract (11 February 2021, date last accessed). Verity R , Okell LC , Dorigatti I et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet Infectious Diseases 2020; 20 : 669 – 677 . https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1 (10 April 2020, date last accessed). IHME | COVID-19 Projections . Institute for Health Metrics and Evaluation. https://covid19.healthdata.org/ (10 April 2020, date last accessed). Reifer J , Hayum N , Heszkel B , Klagsbald I , Streva VA. SARS-CoV-2 IgG antibody responses in New York City. Diagnostic Microbiology and Infectious Disease 2020 ; 98 : 115 – 128 . https://www.medrxiv.org/content/10.1101/2020.05.23.20111427v1 (20 June 2020, date last accessed). Rosenberg ES , Tesoriero JM , Rosenthal EM et al. Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Annals of Epidemiology 2020 ; 48 : 23 – 29 . https://www.medrxiv.org/content/10.1101/2020.05.25.20113050v1 https:// www.medrxiv.org/content/10.1101/2020.05.23.20111427v1 (05 June 2020, date last accessed). Abrams HR , Loomer L , Gandhi A , Grabowski DC. Characteristics of U.S. nursing homes with COVID-19 cases . J Am Geriatr Soc 2020 ; 68 : 1653 – 56 . Half of coronavirus deaths happen in care homes, data from EU suggests. The Guardian . 2020 . https://www.theguardian.com/world/2020/apr/13/half-of-coronavirus-deaths-happen-in-care-homes-data-from-eu-suggests (23 April 2020, date last accessed). Girvan G. 42% of COVID-19 Deaths in Nursing Homes & Assisted Living Facilities . 2020 . https://freopp.org/the-covid-19-nursing-home-crisis-by-the-numbers-3a47433c3f70 (17 June 2020, date last accessed). Ioannidis J. The infection fatality rate of COVID-19 inferred from seroprevalence data . Bull World Health Organization . https://www.who.int/bulletin/online_first/BLT.20.265892.pdf?ua=1 (11 February 2021, date last accessed). Ioannidis JPA. Global perspective of COVID-19 epidemiology for a full-cycle pandemic . Eur J Clin Invest 2020 ; 50 : 1 –9. https://onlinelibrary.wiley.com/doi/abs/10.1111/eci.13423 (11 February 2021, date last accessed). Sood N , Simon P , Ebner P et al. Seroprevalence of SARS-CoV-2–specific antibodies among adults in Los Angeles County, California, on April 10-11, 2020 . JAMA 2020 ; 323 : 2425 . Anand S , Montez-Rath M , Han J et al. Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study . Lancet 2020 ; 396 : 1335 – 44 . Coronavirus (COVID-19): Press Conference with Marc Lipsitch, 03/04/20 . News. 2020. https://www.hsph.harvard.edu/news/features/coronavirus-covid-19-press-conference-with-marc-lipsitch-03-04-20/ (10 April 2020, date last accessed). 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Definition of theory noun from the Oxford Advanced Learner's Dictionary - theory of something the theory of evolution/relativity
- scientific/economic theory
- The theories were tested on a sample of the population.
- theory about something He developed a new theory about the cause of stomach ulcers.
- theory on something theories on human behaviour
- theory behind something She has written a book explaining the theory behind her art.
- according to a theory According to the theory of relativity, nothing can travel faster than light.
- formulate/advance a theory/hypothesis
- build/construct/create/develop a simple/theoretical/mathematical model
- develop/establish/provide/use a theoretical/conceptual framework
- advance/argue/develop the thesis that…
- explore an idea/a concept/a hypothesis
- make a prediction/an inference
- base a prediction/your calculations on something
- investigate/evaluate/accept/challenge/reject a theory/hypothesis/model
- design an experiment/a questionnaire/a study/a test
- do research/an experiment/an analysis
- make observations/measurements/calculations
- carry out/conduct/perform an experiment/a test/a longitudinal study/observations/clinical trials
- run an experiment/a simulation/clinical trials
- repeat an experiment/a test/an analysis
- replicate a study/the results/the findings
- observe/study/examine/investigate/assess a pattern/a process/a behaviour
- fund/support the research/project/study
- seek/provide/get/secure funding for research
- collect/gather/extract data/information
- yield data/evidence/similar findings/the same results
- analyse/examine the data/soil samples/a specimen
- consider/compare/interpret the results/findings
- fit the data/model
- confirm/support/verify a prediction/a hypothesis/the results/the findings
- prove a conjecture/hypothesis/theorem
- draw/make/reach the same conclusions
- read/review the records/literature
- describe/report an experiment/a study
- present/publish/summarize the results/findings
- present/publish/read/review/cite a paper in a scientific journal
- The debate is centred around two conflicting theories.
- Current feminist theory consists of several different trends.
- His comments are just abstract theory and show little understanding of the realities of the situation.
- the dominant strand of postmodern theory
- the existence of a grand unified theory that determines everything in the universe
- Further experiments seemed to confirm this theory.
- He wrote a number of books on political theory.
- It is a theory that cannot be proved or disproved.
- Marx's theories of history raise one or two major questions.
- The theory was first advanced back in the 16th century.
- This is all theory so far…you'll need to back it up with facts.
- hold something
- suggest something
- explain something
- theory about
- put (the) theory into practice
- theory and practice
Join our community to access the latest language learning and assessment tips from Oxford University Press! - the theory and practice of language teaching
- This is your chance to put theory into practice .
- literary theory
- theory about something Theories abound (= people have lots of different ideas) about what happened.
- theory on something Here's my theory on how the story's going to end.
- theory that… I don't subscribe to the theory that all Hollywood audiences want a happy ending.
- Police are working on the theory that the murderer was known to the family.
- I have this theory that most people prefer being at work to being at home.
- He has a theory about why dogs walk in circles before going to sleep.
- If the theory is correct, any child can be taught to be a musical genius.
- a conspiracy theory about the princess's death
- One of her pet theories is that people who restrict their calorie intake live longer.
- She doesn't agree with the theory that rich people work harder than poor people.
- In theory, these machines should last for ten years or more.
- That sounds fine in theory, but have you really thought it through?
- ‘Aren’t you supposed to be retired?’ ‘Yes, in theory.’
- In theory, all children get an equal chance at school.
Other results- games theory
- binding theory
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thesis noun - Definition, pictures, pronunciation and usage ...
Oxford theses. The Bodleian Libraries' thesis collection holds every DPhil thesis deposited at the University of Oxford since the degree began in its present form in 1917. Our oldest theses date from the early 1920s. We also have substantial holdings of MLitt theses, for which deposit became compulsory in 1953, and MPhil theses.
Theses and dissertations | Bodleian Libraries
A growing number of Oxford theses & dissertations are available online. These will be included in the results of your SOLO searches once the thesis or dissertation has been deposited into ORA. You can also search ORA directly using course codes, e.g. ALSLA, CIE2021 etc. Theses submitted recently may take a while to be processed and to appear on ...
The paper focuses on examining the definition of meaning of various words in dictionaries based on the Oxford Advanced Learner's Dictionary (OALD), 7 th edition. ... Thesis (M. Ed.)--Heritage ...
More than seventy years have passed since the publication of A. S. Hornby's Idiomatic and Syntactic English Dictionary, which was reprinted a few years later by Oxford University Press as A Learner's Dictionary of Current English.Hornby's lexicographic milestone firmly established a distinct genre of dictionary which has been at the forefront of lexicographical innovation during the past ...
Abstract. This paper gives an account of the development of the Oxford Learner's Dictionary of Academic English, a dictionary for non-native-English-speaking students who are studying academic subjects at tertiary level through the medium of English. First, a corpus of academic English was created, using high-qua-lity texts from a broad range ...
these determiner - Definition, pictures, pronunciation and ...
Definition of thesis noun in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more. ... Look up any word in the dictionary offline, anytime, anywhere with the Oxford Advanced Learner's Dictionary app.
Abstract. This study was conducted to investigate the differences between theheadwords in the Oxford Advanced Learner's Dictionary 8th edition (OALD8) and in the Merriam-Webster's Collegiate Dictionary 11th edition (MWCD11).This study aims to find out the headwords which are found in one dictionary, but not in the other, and identify the reasons why there are differences in the headwords ...
As the title suggests, the Oxford Learner's Dictionary of Academic English (OLDAE) is aimed at those who need to use academic English in their studies. The Introduction of the dictionary mentions that the OLDAE provides "an exclusive, detailed focus on the language of academic writing (p. v).". To discuss the merits and demerits of this ...
thesaurus noun - Definition, pictures, pronunciation and ...
Hoxby is the recipient of many honors including Global Leader of Tomorrow (World Economic Forum) and Sloan, Olin, Mellon, and Ford fellowships. Hoxby has served as a presidential appointee to the National Board of Education Sciences. She has a Ph.D. from MIT, studied at Oxford as a Rhodes Scholar, and obtained her BA from Harvard University.
Learn more about the Senior Thesis. Special Scholarships and Fellowships. Honors students are eligible to apply for several scholarships that are exclusive to the Honors Program, the Oxford Scholarship (Mansfield College); the Osberg Fellowship; Hayes Fellowship, in addition, many Honors students apply to the Provost's Scholarship.
The SOLOM is a rating scale that teachers can use to assess their students' command of oral language on the basis of what they observe on a continual basis in a variety of situations - class discussions, playground interactions, encounters between classes. The teacher matches a student's language performance in a five mains - listening ...
c1. vivisection noun. c2. weather balloon noun. c2. zoology noun. c1. Topic Dictionaries group together words related to common subject areas.
Eran Bendavid, Bianca Mulaney, Neeraj Sood, Soleil Shah, Rebecca Bromley-Dulfano, Cara Lai, Zoe Weissberg, Rodrigo Saavedra-Walker, Jim Tedrow, Andrew Bogan, Thomas Kupiec, Daniel Eichner, Ribhav Gupta, John P A Ioannidis, Jay Bhattacharya, COVID-19 antibody seroprevalence in Santa Clara County, California, International Journal of Epidemiology, Volume 50, Issue 2, April 2021, Pages 410-419 ...
Oxford Learner's Dictionaries | Find definitions, translations ...
Collocations Scientific research Scientific research Theory. formulate/ advance a theory/ hypothesis; build/ construct/ create/ develop a simple/ theoretical/ mathematical model; develop/ establish/ provide/ use a theoretical/ conceptual framework; advance/ argue/ develop the thesis that…; explore an idea/ a concept/ a hypothesis; make a prediction/ an inference