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Labour Force Survey (LFS), 2005
No full textThe Labour Force Survey (LFS) is a large-scale, nationwide survey of households in Ireland conducted by the Central Statistics Office. It is designed to produce quarterly labour force estimates that include the official measure of employment and unemployment in the state (ILO basis). This survey replaced the Quarterly National Household Survey (QNHS) from Q3 2017. The QNHS began in September 1997, replacing the annual April Labour Force Survey (LFS). Each quarter the LFS produces data among others on: Numbers unemployed Persons in employment Labour force participation rates Inactive population (not in the labour force) Sectoral breakdown (Nace Rev. 2) of those in employment Breakdown of headline data by age, sex and region Persons in employment classified by occupation Seasonally adjusted headline series Data on family compositio
Labour Force Survey (LFS), 1998
No full textThe Labour Force Survey (LFS) is a large-scale, nationwide survey of households in Ireland conducted by the Central Statistics Office. It is designed to produce quarterly labour force estimates that include the official measure of employment and unemployment in the state (ILO basis). This survey replaced the Quarterly National Household Survey (QNHS) from Q3 2017. The QNHS began in September 1997, replacing the annual April Labour Force Survey (LFS). Each quarter the LFS produces data among others on: Numbers unemployed Persons in employment Labour force participation rates Inactive population (not in the labour force) Sectoral breakdown (Nace Rev. 2) of those in employment Breakdown of headline data by age, sex and region Persons in employment classified by occupation Seasonally adjusted headline series Data on family compositio
WiPe-Sport: Interventionsstudie
No full textThe SNF project "From Knowledge to Performance in Physical Education: Prospective PE-teachers’ transformation of competences" (WiPe-Sport; Project No. 100019_192397) investigated the transformation process from classroom management-related knowledge to classroom management-related performance in prospective primary school teachers in the subject of Physical Education, based on a model (see Baumgartner, 2022; Blömeke et al., 2015). The project addressed the following two research questions: a) What relationships exist between the three competency facets of classroom management-related knowledge, classroom management-related perception, interpretation, and decision-making (WIE), and classroom management-related performance (first research question)? b) To what extent can the three developmental components increase the quality of performance (dependent variable): 1) improvement of (classroom management-related) knowledge, 2) improvement of WIE, and 3) practice of implementing the classroom management-related quality dimensions (see Baumgartner et al., 2020) in one's own teaching practice through deliberate practice (see Ericsson et al., 1993) (second research question; see project proposal science part, p. 1)? The project included an initial phase of developing and validating the test and assessment instruments (knowledge test; (video vignette-based) test to assess perception, interpretation, and decision-making (WIE)). In the second phase, the intervention (main study), a quasi-experimental intervention study was conducted, based on a four-level main factor intervention group (IG1-4) and a two-level main effect measurement time point (t0-1; 4x2-factor model). The dependent variable (DV) represents the quality of classroom management-related performance of the prospective teachers (see Baumgartner et al., 2020). The independent variables (IVs) are the measurement time points (t0-1) and the intervention groups (IG1-4). All participants in the four intervention groups (IGs) completed an internship; IG2-4 received additional knowledge provision, IG3-4 also had an intervention on situated perception, interpretation, and decision-making (WIE), and IG4 was supported in practicing the implementation of the quality criteria in their own practice through coaching and video-based feedback from instructors.Das SNF-Projekt «Zum Transformationsprozess von Wissen zu Performanz bei angehenden Lehrpersonen am Beispiel der Klassenführung im Sportunterricht – eine Interventionsstudie» (WiPe-Sport; Projektnr. Nr. 100019_192397) untersuchte den Transformationsprozess vom klassenführungsbezogenen Wissen zur klassenführungsbezogenen Performanz bei angehenden Primarlehrpersonen im Fach Bewegung und Sport modellbezogen (vgl. Baumgartner, 2022; Blömeke et al., 2015). Das durchgeführte Projekt bearbeitete die beiden Forschungsfragen, a) welche Zusammenhänge zwischen den drei Kompetenzfacetten des klassenführungsbezogenen Wissens, der klassenführungsbezogenen Wahrnehmung, Interpretation und Entscheidung (WIE) und der klassenführungsbezogenen Performanzen bestehen (erster Forschungsfragenkomplex) sowie b) inwiefern die Qualität der Performanzen (AV) durch die drei Entwicklungskomponenten 1) der Verbesserung des (jeweils klassenführungsbezogen) Wissens, 2) der Verbesserung der WIE sowie 3) der Übung der Umsetzung der klassenführungsbezogenen Qualitätsdimensionen (vgl. Baumgartner et al., 2020) in der eigenen Unterrichtspraxis im Sinne einer deliberate practice (vgl. Ericsson et al., 1993) erhöht werden kann (zweiter Forschungsfragenkomplex; vgl. Projektantrag science part, S. 1). Das Projekt umfasste eine erste Phase der Entwicklung und der Validierung der Test- und Erhebungsinstrumente (Wissenstest; (videovignettenbezogener) Test zur Erfassung der Wahrnehmung, Interpretation und Entscheidung (WIE)). In der zweiten Phase, der Intervention (Hauptuntersuchung), wurde eine quasi-experimentelle Interventionsstudie durchgeführt, die auf einem vierstufigen Hauptfaktor Interventionsgruppe (IG1-4) und einem zweistufigen Haupteffekt Messzeitpunkt (t0-1; 4x2-Faktorenmodell) basiert. Die abhängige Variable (AV) stellt die Qualität der klassenführungsbezogenen Performanz der angehenden Lehrpersonen dar (vgl. Baumgartner et al., 2020). Als unabhängige Variablen (UV) gelten die Messzeitpunkte (t0-1) und die Interventionsgruppen (IG1-4). Alle Teilnehmer:innen der vier Interventionsgruppen (IGs) absolvierten ein Praktikum; IG2-4 erhielten zusätzliche Wissensvermittlung, IG3-4 zudem eine Intervention zur situierten Wahrnehmung, Interpretation und Entscheidung (WIE) und IG4 wurde beim gezielten Üben der Umsetzung der Qualitätskriterien in der eigenen Praxis durch Coaching und videobasiertes Feedback durch Dozierende unterstützt
ÜGK / COFO / VECOF 2023, Languages Grade 11: Scientific Use Files (SUF)
No full textAs part of the national education monitoring, the ÜGK/COFO/VECOF (i.e., the Assessment of the Achievement of Basic Competencies) is a large scale assessment (LSA) using computer-assisted classroom tests to assess the extent to which basic competencies (as defined by the national educational goals, see EDK, 2011) are achieved at the end of a cycle . Results, expressed as percentages of students meeting national educational goals, serve as indicators of cantonal and national education system performance. The assessment ÜGK/COFO/VECOF 2023 measured language competence in the local school language (German, French, or Italian), as well as in the first and second foreign languages (another national language or English). The main objective was to assess whether or not a student has achieved the desired language competence. Additionally, students completed a questionnaire assessing contextual variables (e.g., motivation, home language, and learning strategies)
Surface Groups Syria 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Living in Switzerland Waves 1-25 + Long file + Covid 19 data + Beta version wave 26
No full textCollecting data on households and individuals since 1999, the Swiss Household Panel (SHP) is an ongoing, unique, large-scale, nationally representative, longitudinal study in Switzerland (N=6,849 households and N=10,634 persons interviewed in 2023). The SHP aims to provide both continuity and innovation in measurement and data collection. Examples of innovation are the combination of retrospective and prospective longitudinal data, the combination of survey modes notably in refreshment samples and additional studies oversampling specific population groups
Surface Groups South Sudan 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Panama 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Mali 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Annual Population Survey, October 2024 - September 2025
No full textAbstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS.<br