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ÜGK / COFO / VECOF 2023, Languages Grade 11: Keys for Data Linkage
No full textKeys for data linkage: - Link between field IDs and BFS IDs - Link between BFS IDs and IDs contained in data files on SWISSUbase (SUF IDs) BFS needs key LUT_BFS.csv for linking data files on SWISSUbase to AHVN1
z-proso: Adolescent and Young Adult Surveys (Age 11 to 20; Waves K4-K8)
No full textThe Zurich Project on the Social Development from Childhood to Adulthood (z-proso) is a prospective-longitudinal study that was launched in 2004 in response to the need for a better evidence base to support optimal child social development and prevent crime and violence. Since then, the study has explored the life course of a cohort of 1,675 children in the target population from age 7 (n = 1,360) to age 24 (n = 1,160), with primary data collection waves at ages 7, 8, 9, 10, 11, 12, 13, 15, 17, 20, and 24 (see Ribeaud et al. 2022, https://doi.org/10.1007/s40865-022-00195-x, for the sampling procedure). The study has been built on multi-method, multi-informant design that combines child/youth/young adult, teacher, and parent surveys. The large and ethnically diverse sample, the dense sequence of assessments with multiple-informant data, the high retention rate, and the combination of measurement domains make z-proso an important resource for innovative research nationally and internationally. z-proso project website: https://www.jacobscenter.uzh.ch/en/research/zproso.htm
NutzerInnen-Befragung in Einrichtungen der Obdachlosenhilfe in 8 Städten der Schweiz - 2020 und 2021 (OBDACH)
No full textDie vorliegende Studie aus den Jahren 2020 und 2021 untersucht das Ausmass und die Struktur von Obdachlosigkeit in der Schweiz sowie die Bedeutung von international diskutierten armuts-, gesundheits- und migrationsbezogenen Zugängen zum Themenfeld der Obdachlosigkeit. Die empirische Untersuchung erfolgte anhand einer quantitativen Face-to-Face Befragung in 62 Einrichtungen für armutsbetroffene Personen in 8 grossen Städten der Schweiz.The present study from 2020 and 2021 investigates the extent and structure of homelessness in Switzerland, as well as the significance of internationally discussed poverty-, health- and migration-related approaches to the topic of homelessness. The empirical investigation was based on a quantitative face-to-face survey in 62 facilities for people experiencing poverty in 8 large cities in Switzerland
Participation, Cooperation and Conflict in UN Climate Negotiations
No full textCountries’ ability under the Paris Agreement to limit warming to 2˚C, if not to 1.5˚C, has been in question since the adoption of the treaty in 2015. This has prompted calls for countries to increase the ambition of the mitigation pledges in their Nationally Determined Contributions (NDCs) to close the ambition gap between countries’ NDCs and global temperature goals. In addition to the ambition gap, there is an implementation gap as countries’ current national policies fall short of the mitigation pledges outlined in their NDCs (Lee et al. 2023). Hence, to reach the temperature goals of the Paris Agreement, countries must formulate increasingly ambitious international pledges and vertically harmonize their national policies with these pledges. Yet, harmonization is a complex task that involves reconciling the interests of a multitude of diverse actors across different levels and sectors. As such, many countries exhibit different levels of political commitment to mitigate climate change in their NDCs and national policies (e.g., Baker 2023; Ingold and Pflieger 2016; Peterson 2021). Against this backdrop, this research project addressed the following questions: (i) To what degree do countries formulate NDCs that are sufficiently ambitious so as to be aligned with their fair share of the global temperature goals, i.e., to close the ambition gap? (ii) To what degree do countries translate their NDCs into their national policies (policy objectives and instruments), i.e., to close the implementation gap? (iii) What drives countries to under- or outperform regarding both the ambition and implementation (i.e., harmonization) gaps? This project builds upon precedent work in international relations, comparative politics, political economy, and policy sciences that evaluates countries’ climate (policy) performance and explain the gap between countries’ international commitments and (sub-)national policies. We complement previous endeavors (e.g., Climate Action Tracker, Climate Change Performance Index) assessing the fairness or compatibility of countries’ climate policies with pathways towards the global temperature goals by developing the Vertical Policy Harmonization (VPH) Indices (Baker 2023; Baker et al. under review) that quantify the gap between countries’ NDCs and national mitigation policies along three key dimensions of mitigation policymaking. The Target Index compares the level and scope of the greenhouse gas (GHG) reduction targets of 105 countries’ NDCs and national policies. The Policy Effort Index incorporates countries’ climate policy mix to assess the credibility of 36 countries’ targets. The VPH Indices show that despite approximately a quarter of countries having national targets that are either in line with or more ambitious than their NDC target, most countries, accounting for 40% of global GHG emissions, fall short of their targets given countries’ relatively insufficient policy mixes. We draw on these indices and address our research questions from three perspectives. In the first perspective, we focus on the international level and present a typology that incorporates both the ambition and harmonization of countries’ NDCs and national policies (Castro and Kammerer working paper). Under this typology, countries are categorized as leaders (when national policies outperform ambitious NDCs), performative (when national policies fall short of ambitious NDCs), laggards (when national policies and NDCs are unambitious), or cautious (when national policies overperform unambitious NDCs). We find that most countries are characterized as laggards or cautious, while only a handful are categorized as performative, and even fewer as leaders. Moreover, we formulate a set of hypotheses relating to countries’ connectedness to the international negotiations (e.g., level of active participation) and negotiation delegations (e.g., size, resources). Our analyses suggest that developing countries with stronger engagement in UN climate negotiations and the broader ecosystems of international climate policy processes tend to have more ambitious NDCs and stronger national policies (Castro et al. working paper). The second perspective focuses on the effect of macro-level factors, such as countries’ institutional setting (e.g., degree of democracy) and interests (e.g., vulnerability, fossil fuel dependency), on shaping vertical (dis)harmony. We find that fossil fuel dependency in democracies constrains the harmonization of NDC and national-level targets, even in the face of high vulnerability and low abatement costs (Baker 2023). Moreover, our results suggest that countries with decentralized political systems tend to be harmonized or have overperforming national policies, while countries with carbon intensive economies and few political constraints are associated with disharmony stemming from underperforming national policies (Blindenbacher et al working paper; Kammerer working paper). In the third perspective we aim to explain the extent to which countries’ NDCs and national policies are harmonized by the way of the domestic policy process. We are specifically interested in how key aspects of countries’ climate policy subsystem (e.g., the level of actor involvement, belief conflict, the presence of actors who are involved in both the foreign and domestic climate policy processes) determine the adoption of national climate policies, which in turn drives harmonization. So far, we have run eight policy elite surveys to collect data on the relevant factors of a policy subsystem and the preliminary results indicate that the presence of “two-level connectors” (i.e., policy actors who are involved in both NDC development and national climate policymaking processes) is relevant in explaining the gap between countries’ NDCs and national mitigation policies. This project demonstrates countries’ varying ability in harmonizing their NDCs and national climate policies and uncovers the multitude of factors that drive countries’ vertical (dis)harmony. The expansion of the VPH Indices will increase the robustness of our present findings and will enable us to discern whether vertical disharmony is a bug or feature of the global climate change regime. In a normative context, it is up for debate as to whether harmonization should be encouraged or if disharmony should be tolerated as a norm of harmonization may result in both (non-)consequential benefits and disadvantages (Baker and Roser working paper). Increasing the temporal coverage of the VPH Indices will not only inform this normative discussion, but also further theory-building on the interplay between ambition and harmonization and more immediately demonstrate the effectiveness of the incremental ambition-raising framework that is a cornerstone of the Paris Agreement’s approach to achieving its long-term ambition. We draw on these indices and existing measures of ambition to present a typology that incorporates both the ambition and harmonization of countries’ NDCs and national policies (Castro and Kammerer working paper). Under this typology, we categorize two countries (i.e., Nigeria, Comoros) as leaders given their national policies outperform their ambitious NDCs and a handful of countries (i.e., Colombia, Cyprus, Democratic Republic of Congo, Senegal and Uganda) as performative as their national policies fall short of their ambitious NDCs. Most countries, however, are characterized as laggards (e.g., several EU member states, India, Saudi Arabia, United Kingdom, the United States) with unambitious NDCs and national policies or cautious (e.g., Australia, Brazil, China, Denmark, Germany, Japan, Vietnam) as they have national policies that overperform their unambitious NDCs. We use the VPH Indices and formulate three sets of hypotheses to address our research questions. The first set focuses on macro-level factors, such as countries’ institutional setting (e.g., degree of democracy) and interests (e.g., vulnerability, fossil fuel dependency). We find that fossil fuel dependency in democracies constrains the harmonization of NDC and national-level targets, even in the face of high vulnerability and low abatement costs (Baker 2023). Moreover, our results suggest that countries with decentralized political systems tend to be harmonized or have overperforming national policies, while countries with carbon intensive economies and few political constraints are associated with disharmony stemming from underperforming national policies (Blindenbacher et al working paper; Kammerer working paper). The second set of hypotheses includes international factors with a focus on the countries’ connectedness to the international negotiations (e.g., level of active participation) and negotiation delegations (e.g., size, resources). Our analyses suggest that developing countries with stronger engagement in UN climate negotiations and the broader ecosystems of international climate governance processes tend to have more ambitious NDCs and stronger national policies (Castro et al. working paper). The third set addresses the research questions by the way of the domestic policy process. We are specifically interested in how key aspects of countries’ climate policy subsystem (e.g., the level of actor involvement, belief conflict, the presence of actors who are involved in both the foreign and domestic climate policy processes) determine the adoption of national climate policies, in turn driving harmonization. So far, we have run eight policy elite surveys to collect data on the relevant factors of a policy subsystem and the preliminary results indicate that the presence of “two-level connectors” (i.e., policy actors who are involved in both NDC development and national climate policymaking processes) is relevant in explaining the gap between countries’ NDCs and national mitigation policies
Labour Force Survey Five-Quarter Longitudinal Dataset, July 2023 - September, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.Background The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation. Longitudinal data The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary. LFS Documentation The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.Occupation data for 2021 and 2022 data filesThe 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. 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 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.Main Topics:The five-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey
Labour Force Survey Five-Quarter Longitudinal Dataset, January 2023 - March, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.Background The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation. Longitudinal data The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary. LFS Documentation The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.Occupation data for 2021 and 2022 data filesThe 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. 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 2022.2022 WeightingThe population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.Main Topics:The five-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS), covering the main areas of the survey
Annual Population Survey, April 2024 - March 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.APS Geographies now Census 2021Users should note that the APS person A24M25 microdata no longer contains any of the historic Census 2011 geographies (e.g. LAUA) and NUTS geographies. These are no longer supported by ONS geography. The Census 2021 equivalents are now included instead. Information on all these geographies can be found in LFS User Guide volume 6.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
Farm Business Survey, 2023-2024: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers. The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample. The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page. The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data. Main Topics:Variables cover general and physical farm characteristics, labour, crops (previous and current harvest year, set-aside, by-products, forage and cultivations); miscellaneous receipts, livestock (dairy and beef cattle, sheep, pigs, poultry, miscellaneous livestock), variable and fixed costs, assets, investment, liabilities, income from diversified activities (integrated and semi-integrated into the farm business), farmer and spouse off-farm hours and incomes, subsidies
Clients' Experiences about Discretion in Disability Social Work and Disability Services 2022
No full textAineisto koostuu kirjoituksista, joissa asiakkaat kertovat millaisia kokemuksia heillä on vammaissosiaalityössä ja vammaispalvelussa käytettävästä harkinnasta. Aineisto on kerätty osana Sosiaali- ja terveysministeriön rahoittamaa Harkittua vammaissosiaalityötä -tutkimushanketta. Kirjoituskutsussa pyydettiin kirjoittamaan vapaasti ja omalla tavalla omista kokemuksista liittyen harkinnan käyttöön vammaispalveluissa ja vammaissosiaalityössä. Kirjoituskutsussa kysyttiin, millaista harkintaa vastaaja on kohdannut vammaissosiaalityössä esimerkiksi palveluja hakiessaan tai niistä päätettäessä. Kysyttiin myös, millaisia vaikutuksia harkinnalla ja päätöksillä on ollut vastaajan elämässä, miten vastaaja on kokenut harkinnan sosiaaliviranomaisten käytännöissä, sekä miten vastaajan oma näkökulma on tullut huomioiduksi. Kirjoittaa sai joko omista kokemuksista tai esimerkiksi vammaispalvelun käyttäjän vanhempana. Halutessaan sai myös pyytää jotain toista henkilöä kirjoittamaan kertomuksen omasta puolestaan. Aineistossa ei kysytty vastaajien taustatietoja.The data consists of writings in which clients describe their experiences of the use of discretion in disability social work and disability services. The data was collected as part of a larger research project (HaraVa 2022-2023) funded by the Ministry of Social Affairs and Health. The writing invitation asked people to write freely and in their own way about their own experiences of using discretion in disability services and social work for people with disabilities. The writing invitation asked respondents to describe the type of discretion they had encountered in social work for people with disabilities, for example when applying for or deciding on services. It also asked about the impact of discretion and decisions in the respondent's life, how the respondent has experienced discretion in social services' practices, and how the respondent's own perspective has been taken into account. The respondents could write either about their own experiences or, for example, as a parent of a disabled person. If they so wished, they could also ask someone else to write a report on their behalf. The writing invitation did not ask for respondents' background information
Early Childhood Education Teachers' Experiences of Positive Pedagogy 2024-2025
No full textAineisto koostuu varhaiskasvatuksen opettajien kirjoituksista, joissa he kertovat kokemuksistaan liittyen positiiviseen pedagogiikkaan. Aineisto on kerätty alunperin pro gradu -tutkimukseen. Kirjoituskutsulla kerättyjen kirjoitusten avulla pyrittiin selvittämään, miten positiivinen pedagogiikka toteutuu varhaiskasvatuksessa ja miten sitä hyödynnetään käytännössä. Kirjoitukset saivat olla vapaamuotoisia, mutta niissä toivottiin vastattavan myös kolmeen kirjoituskutsussa esitettyyn kysymykseen. Kysymyksissä tiedusteltiin, miten positiivinen pedagogiikka toteutuu vastaajan lapsiryhmässä ja miten positiivinen pedagogiikka vastaajan mielestä vaikuttaa lapsen hyvinvointiin. Lisäksi pyydettiin kertomaan kolme tai neljä tapaa, joilla vastaajan käyttämä positiivinen pedagogiikka toteutuu. Taustatietona kysyttiin sukupuolta, ikäryhmää sekä sitä, kauanko vastaaja on toiminut varhaiskasvatuksen opettajana.The data consists of writings by early childhood teachers about their experiences of positive pedagogy. The data was originally collected for a master's thesis. The writing invitation was used to find out how positive pedagogy is implemented in early childhood education and how it is used in practice. They were allowed to be free-form, but were also expected to answer the three questions posed in the writing invitation. The questions asked how positive pedagogy is implemented in the respondent's group of children and how the respondent believes positive pedagogy contributes to the well-being of the child. They were also asked to describe three or four ways in which the respondent uses positive pedagogy. Background information includes gender, age group and how long the respondent had been an early childhood teacher