From online matchmaking and dating sites, to medical residency placement programs, matching algorithms are used in areas spanning scheduling, … in addition. You sort the data into similar sized blocks which have the same attribute. This tribe has a lot of members”. The intermediate balancing step is irrelevant.”. It seems like the idea of using matching and regression has become a sort of folk theorem, with nothing to cite about why it’s a good idea (other than perhaps some textbooks where it’s presented with little argument). This option specifies the caliper radius, c , to be used in caliper matching. I think pedagogically it is very different to set up a comparison first and then estimation. In addition, Match by the Numbers and the Single Match logo are available. Mike: “When matching, you’re still choosing the set of covariates to match on and there’s nothing stopping you from trying a different set if you don’t like the results. Next you do the matching. 2. estimate the difference between two or more groups. Follow the flow chart and click on the links to find the most appropriate statistical analysis for your situation. SPSS Learning Module: An overview of statistical tests in SPSS; Wilcoxon-Mann-Whitney test. If you go at it completely non-parametrically you compute effect within strata of Z. They believe that whatever variables happen to be in the data set they are using suffice to make “selection on observed variables” hold. that can be manipulated for data-mining. Then they determine whether the observed data fall outside of the … If this P value is low, you can conclude that the matching was effective. i.e. And students can do this without 2 semesters of stats, multivariate regression, etc… All they need is some common sense to compare like with like and computing weighted averages. and it’s easier to data-mine when matching.”. 1-to-1, k-to-1 has a regression equivalent: Dropping outliers, influential observations, or, conversely, extrapolation, etc.. match A flag for if the Tr and Co objects are the result of a call to Match. Note that playing around with covariate balance without looking at outcome variable is fine. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications. When I do match analysis of the matches of junior tennis players whom I coach, I expand the comment section into techniques, tactics, and mental and physical aspects, and note in each section the weakness and strong sides of my player. Comparing “like with like” in the context of a theory or DAG. So, just how do you match? Matching need not be parametric. The synthetic data set is the basis of further statistical analysis, e.g., microsimulations. Propensity score matching is a statistical matching technique that attempts to estimate the effect of a treatment (e.g., intervention) by accounting for the factors that predict whether an individual would be eligble for receiving the treatment.The wikipedia page provides a good example setting: Say we are interested in the effects of smoking on health. Mike: “Combine that with the larger set of choices to exploit when matching (calipers, 1-to-1 or k-to-1, etc.) Mike: “Matching gives you control over both the set of covariates and the sample itself”. Your feedback is appreciated. We talk about “pruning” in matching but really we should talk about “extrapolating” in regression. Data Matching Issue (Inconsistency) A difference between some information you put on your Marketplace health insurance application and information we have from other trusted data sources. But I’d like to see a _proof_ that the set of choices in matching is larger. Descriptive: describing data. I think the crucial take-away is the essential similarity of M+R and regression alone. It may or may not make assumptions about interactions, depending on whether these are balanced. My point is simply that the latter gives one more opportunity for manipulation since it provides more choices. MedCalc can match on up to 4 different variables. You don’t make functional form assumptions, true, but you can (and should) choose higher-order terms and interactions to balance on, so you have the same degrees of freedom there. This is exactly parallel with trying different covariates in a regression model. My intuition is that set of choices in matching is strictly a subset of regression. As per example above if you do it may require layering more assumptions for extrapolating. The former is more robust to covariate nonlinearities, but has no advantages for causation, model dependence, or data-mining, which remain its most popular justifications. Ma conférence 11 h, lundi 23 juin à l’Université Paris Dauphine, http://statmodeling.stat.columbia.edu/2011/07/10/matching_and_re/, https://doi.org/10.1371/journal.pone.0203246, Further formalization of the “multiverse” idea in statistical modeling « Statistical Modeling, Causal Inference, and Social Science, NYT editor described columnists as “people who are paid to have very, very strong convictions, and to believe that they’re right.”, xkcd: “Curve-fitting methods and the messages they send”. To quote Rosenbaum: “An observational study that begins by examining outcomes is a formless, undisciplined investigation that lacks design” (Design of Observational Studies, p. ix). True, but then again you can’t prevent an addict from getting his fix if he is hell bent on it. set.seed(1234) match.it - matchit(Group ~ Age + Sex, data = mydata, method="nearest", ratio=1) a - summary(match.it) For further data presentation, we save the output of the summary-function into a variable named a. Fernando, I think we’re mostly in agreement here. Again, if you are bent on data mining nothing is going to stop you. Matching is a way to discard some data so that the regression model can fit better. and it’s easier to data-mine when matching. Kristof/Brooks update: NYT columnists correct their mistakes! Impossing linearity and limiting interactions will make estimates more stable but not necessarily better. Are there more choices to exploit? Presents a unified framework for both theoretical and practical aspects of statistical matching. (typically we understand the world by layering more assumptions no less, so I see the progression from matching to extrapolation). No matter. As mentioned the set of covariates ought to be a theoretical question, while arguably extrapolating lets you control the sample. They can be used to: determine whether a predictor variable has a statistically significant relationship with an outcome variable. Ultimately, statistical learning is a fundamental ingredient in the training of a modern data scientist. I think Jasjeet Sekhon was pointing to one reason in Opiates for the matches (methods that that third tribe _can and will_ use? If you’re interested, I have a paper that’s mostly on this subject (sites.google.com/site/mkmtwo/Miller-Matching.pdf). Trying to do matching without regression is a fool’s errand or a mug’s game or whatever you want to call it. The word synthetic refers to the fact that the records are obtained by integrating the available data sets rather than direct observation of all the variables. Statistical tests assume a null hypothesis of no relationship or no difference between groups. i.e. The Advantages of a Matched Subjects Design. Looking at a row of bar charts … The match is usually 1-to-N (cases to controls). In fact, matching makes data-mining easier because there are a larger set of choices and the treatment effect tends to vary across them more than across regression models. In the basic statistical matching framework, there are two data sources Aand Bsharing a set of variables X while the variable Y is available only in Aand the variable Z is observed just in B. I would say yes, since matching gives you control over both the set of covariates and the sample itself. I’ve looked around a bit and seen that there is a huge literature on how to do matching well, but rather little providing guidance on when matching is or is not a good choice. 1. I am not sure I would call coarsened exact matching parametric). the likelihood two observations are similar based on something quite similar to parametric assumptions… you’re just hiding the parametric part.. My reply: It’s not matching or regression, it’s matching and regression. Matching will not stop fishing, but it can help teach the importance of a research design separate from estimation. In any case, I don’t think this is the main advantage of matching. This is the ninth in a series of occasional notes on medical statistics In many medical studies a group of cases, people with a disease under investigation, are compared with a group of controls, people who do not have the disease but who are thought to be comparable in other respects. Method 2 – To Compare data by using IF logical formula or test If logical formula gives a better descriptive output, it is used to compare case sensitive data. The intermediate balancing step is irrelevant. The synthetic data set can be derived by applying a parametric or a nonparametric approach. 2is the sample variance of q(x) for the control group. Rather we start from a prunned sample and then expand by adding more assumptions and extrapolating. To identify what statistical measures you want calculated: Use the Output Options check boxes. What I find interesting is how such a simple suggestion “do both” has been so well and widely ignored. The case-control matching procedure is used to randomly match cases and controls based on specific criteria. Matching algorithms are algorithms used to solve graph matching problems in graph theory. Probabilistic matching isn’t as accurate as deterministic matching, but it does use deterministic data sets to train the algorithms to improve accuracy. The overall goal of a matched subjects design is to emulate the conditions of a within subjects design, whilst avoiding the temporal effects that can influence results.. A within subjects design tests the same people whereas a matched subjects design comes as close as possible to that and even uses the same statistical methods to analyze the results. Most of the matching estimators (at least the propensity score methods and CEM) promise that the weighted difference in means will be (nearly) the same as the regression estimate that includes all of the balancing covariates. But you cannot compute effect in strata where X does not vary, so these observations drop out. The only good justification I can see for matching is when important prognostic variables lack independence — and even then I might lean towards utilizing principal component scores or ridge regression or regression supplemented with propensity scores. The goal of matching is, for every treated unit, to find one (or more) non-treated unit(s) with similar observable characteristics against whom the effect of the treatment can be assessed. By contrast matching focuses first on setting up the “right” comparison and, only then, estimation. Welcome the the world of regression! Graph matching problems are very common in daily activities. Matching mostly helps ensure overlap. It is the theory that tells you what to control for. I disagree with last phrase. They can be mixed too. when the treatment is not randomly assigned). The CROS Portal is a content management system based on Drupal and stands for "Portal on Collaboration in Research and Methodology for Official Statistics". I think this makes a big difference. That’s always been my experience. 2. After matching the samples, the size of the population sample was reduced to the size of the patient sample (n=250; see table 2). Usually the matching is based on the information (variables) common to the available data sources and, when available, on some auxiliary information (a data source containing all the interesting variables or an estimate of a correlation matrix, contingency table, etc.). Jeff Smith has very useful comments in this 2010 post: http://econjeff.blogspot.com/2010/10/on-matching.html, Especially liked this “There is also a third tribe, which I think of as the “benevolent deity” tribe. Check that covariates are balanced across treatment and comparison groups within strata of the propensity score. Does anyone know of a good article that I could use to convince a group that they should use matching and regression? The synthetic data set is the basis of further statistical analysis, e.g., microsimulations. I think that is an important lesson. ), “And the only designs I know of that can be mass produced with relative success rely on random assignment. Choose appropriate confounders (variables hypothesized to be associated with both treatment and outcome) Obtain an estimation for the propensity score: predicted probability ( p) or log [ p / (1 − p )]. And yes, you can use regression etc. Pedagogically, matching and regression are different. All causal inference relies on assumptions. But I don’t think that translates into any statistical or research advantage. weights.Tr A vector of weights for the treated observations. Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment(i.e. It seems to me (following a fair bit of simulation-based exploration of the concept) that matching has been rather oversold as a methodology. Data matching describes efforts to compare two sets of collected data. Statistical matching (SM) methods for microdata aim at integrating two or more data sources related to the same target population in order to derive a unique synthetic data set in which all the variables (coming from the different sources) are jointly available. observational studies are important and needed. Fuzzy matching is a technique used in computer-assisted translation as a special case of record linkage. But I do not know how to mass produce them.”, http://sekhon.polisci.berkeley.edu/papers/annualreview.pdf. Describing a sample of data – descriptive statistics (centrality, dispersion, replication), see also Summary statistics. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications. Why do people keep praising matching over regression for being non parametric? Yes, in principle matching and regression are the same thing, give or take a weighting scheme. It provides a working space and tools for dissemination and information exchange for statistical projects and methodological topics. Moreover, I think some scholars strain the point that matching lets you compare “like with like,” forgetting that this is only true with respect to the chosen covariates. Statistical matching (also known as data fusion, data merging or synthetic matching) is a model-based approach for providing joint information on variables and indicators collected through multiple sources (surveys drawn from the same population). In cases where the variables which would participate in a match are relatively independent, matching has the disadvantage of throwing-away perfectly good data — performing a regression which uses all of the prognostic variables as covariates yields smaller standard errors than doing the same with the reduced data set following matching, and much better than a t-test or anova on the reduced data set following matching. Statistical tests are used in hypothesis testing. Combine that with the larger set of choices to exploit when matching (calipers, 1-to-1 or k-to-1, etc.) When the additional information is not available and the matching is performed on the variables shared by the starting data sources, then the results will rely on the assumption of independence among variables not jointly observed given the shared ones. Yet regression adds choices re functional form restrictions for the outcome equation that are not available in pure matching. This is not a property of matching or regression. Other than that I like matching for its emphasis on design but agree with Andrew re doing both. if the logical test is case sensitive. This table is designed to help you decide which statistical test or descriptive statistic is appropriate for your experiment. However, if you are willing to make more assumptions you can include these additional observations by extrapolating. I’m lost on why you think “extrapolating lets you control the sample.” One ought to start with a theoretically justified sample, say all countries from 1950-2010, a representative survey of voters, etc. Data distribution: tests looking at data “shape” (see also Data distribution). Depends on your point of departure. Presents a unified framework for both theoretical and practical aspects of statistical matching. It works with matches that may be less than 100% perfect when finding correspondences between segments of a text and entries in a database of previous translations. Collaboration in Research and Methodology for Official Statistics, Handbook on Methodology of Modern Business Statistics, International trade and balance of payments, Living conditions, poverty and cross-cutting social issues, COMmunicating Uncertainly in Key Official Statistics, European Master in Official Statistics (EMOS), Research Projects under Framework Programmes, Social indicators: Income, Consumption and Wealth, Centre of Excellence on Data Warehousing, Centre of Excellence on Seasonal Adjustment, Centre of Excellence on Statistical Disclosure Control, Centre of Excellence on Statistical Methods and Tools, ESSnet Sharing common functionalities in ESS, ESSnet on quality of multisource statistics, ESSnet Implementing shared statistical services, Third EU-SILC Network on income and living conditions (NetSILC-3), Trusted Smart Statistics - Towards a European platform for Trusted Smart Surveys, Expert Group on Statistical Disclosure Control, ESS Vision 2020 Information Models & Standards, Use of R in Official Statistics - uRos2020, Workshop on Trusted Smart Statistics: policymaking in the age of the IoT, Time Series Workshop (Paris 26-27 September 2019), 11th International Francophone Conference on Surveys, Privacy in Statistical Databases 2020 (PSD 2020), Call for Papers for a special issue on “Respondent Burden” in the Journal of Official Statistics, 10th European Conference on Quality in Statistics - Q2020 Budapest, Question forum for EU-SILC scientific use files, Micro-Fusion - Statistical Matching Methods (pdf file), Reconciling Conflicting Microdata (Method) ›, DIME/ITDG Governance with mandates of 4 WGs: Quality, Methodology, Standards, IT, Mandate of the Joint DIME ITDG Steering Group, DIME & ITDG Steering Group 15 January 2021, DIME & ITDG Steering Group 19 November 2020, Item 1 - COVID-19 methodological development, Item 2 - Open exchange on the future IT infrastructure for statistical production, Item 3 - Toward ESS governance of the WIH, Item 3 - WIH governance and capacity building presentation, Item 4 - Remote access solution to European microdata, Item 4 - Remote access solution to European microdata presentation, Item 5 - Progress report on the next round of peer reviews, Item 2 - High Value Datasets - document 2 (HVDs), Item 2 - High Value Datasets - document 2A (interoperability), Item 2 - High Value Datasets - presentation, Item 3 - Web Intelligence Hub (presentation), Item 4 - Background document - data stewardship, Item 5 - Progress report on the peer reviews, DIME & ITDG Steering Group 12 February 2020, Agenda of the steering group meeting February 2020, Item 01 - Mandate of the DIME/ITDG Steering Group, Item 05 - Group on use of privately held data, Item 05 - Privately held data presentation, Item 08 - High-value datasets in the area of statistics, Item 10 - Progress report on the peer reviews, Item 01 - DIME/ITDG governance presentation, Item 02 - Background document - ESSC Trusted Smart Statistics Strategy and Roadmap, Item 02 - Trusted Smart Statistics Principles, Item 03 - Trusted Smart Statistics priority domains, Item 05 - Innovation priorities 2021-2027, Item 06 - Seminar - Citizen data presentation (compressed), Item 06 - Seminar - Web intelligence presentation (compressed), Item 07 - List of proposed innovation actions, Item 09 - ESS roadmap on LOD (Linked Open-Data) - state of play Annex, Item 09 - ESS roadmap on LOD (Linked Open-Data) - state of play, Item 10 - ESDEN final report presentation, Item 11 - NACE review - Standard Working Group, Item 12 - Report from the Working Group on Quality, Item 13 - Report from the Working Group on Methodology, DIME & ITDG Steering Group 15 February 2019, item_0_agenda_dime_itdg_sg_2019_february_london_final, item_3_nsqr_privacy_and_confidentiality_synthesis, item_3_review_of_privacy_and_confidentiality_methods, item_4_complexity_science_for_official_statistics, item_5_annex1_essc_2018_38_10_peer_reviews, item_9_annex1_written_consultation_dime-itdg_07012019, item_9_annex_2_essc_item_2-_ess_standards_en_final, item_9_report_on_the_work_in_progress_for_the_new_standards_adopted_by_the_essc, zip_file_with_all_docs_for_dime_itdg_sg_london_15_of_february_2019, item_01_agenda_dime_itdg_plenary_meeting_v4, item_03_esden-deployment-of-services-v1.20, item_04_UNECE_Guidelines_on_data_integration, item_06_statistics_and_news_some_issues_in_european_official_sources, item_07_dime_itdg_governance_2018_2020_final, item_09_it_security_assurance_scope_document_, item_11_ess_guidelines_on_temporal_disaggregation, item_11_ess_guidelines_on_temporal_disaggregation_15feb2018, item_14_remote_access_to_french_microdata_for_scientific_purposes, all_docs_in_one_zip_file_dime_itdg_sg_15june2018, item_0_agenda_dime_itdg_sg_2018_june_the_hague_final, item_1_shared_tools-expert_groupd_-_itdg_2018_v1, DIME ITDG Steering Group 10 November 2017 Dublin, Item 2 Experimental Statistics National Experience in NL, Item 5 DIME ITDG Governance and Functioning _current_ to be renewed in 2018, Item 5 DIME ITDG Governance and Functioning _final, Item 7 ESS Vision 2020 portfolio progress and next steps, Item 9 ESS Guidelines on Temporal Disaggregation, Item 10 - Agenda Items for the DIME/ITDG plenary (22/23 Feb 2018), Item 1 esbrs interoperability pilots (slides), Minutes TF Temporal Disaggregation meeting (AoB), Joint DIME/ITDG Plenary 14/15 February 2017, Item 2 Draft ESS strategy Linked Open Data, Item 6 Statistical_modelling_for_official_migration_statistics_state_of_the_art_and_perspectives, Item 11 DIME/ITDG SG Mandate and Composition, Zip File with all docs for DIME/ITDG 14 and 15 of February 2017, opinions_actions_dime-itdg_sg_oct_2016_final, item_1_draft_agenda_for_the_february_2017_dime_itdg_plenary_meeting.doc, item_3_CBS_new_methodological_challenges_for_new_societal_phenomena.pptx, item_4_Istat_a_register_based_statistical_system_integrating_administrative_archives_statistical_surveys_and_population_sizes_estimation_final, item_5_hcso_transmission_and_preparation_of_data_from_secondary_data_sources_at_hcso, Item 5a Service Design Enterprise Architecture by ONS, Item 7 ESS IT security_framework_assurance_mechanism.pptx, Item 9 Dissemination of methodological work at Istat, Joint DIME ITDG Plenary 24/25 February 2016, Item 0 Written consultation on the agenda, Item 7 Computational and Data science at Stanford, DIME plenary meeting 23 and 24 February 2015, a ZIP file with all docs uploaded as of the 23 of February 2015, DIME-ITDG_Plenary_2015_02_24 Item 01 agenda, ITDG_Plenary_2015_02_25 link to Agenda and Docs, Joint DIME ITDG Steering Group 24/25 June 2015, Joint DIME ITDG Steering Group 18 November 2015, zip_all docs_and slides_DIME_ITDG SG 2015 November, DIME_ITDG SG 2015 November Item 01 Agenda_final, DIME_ITDG SG 2015 November Item 02 Agenda_DIME_ITDG_FEB2016_plenary, DIME_ITDG SG 2015 November Item 03 ESS.VIP_Validation, DIME_ITDG SG 2015 November Item 03 ESS.VIP_Validation_Annex, DIME_ITDG SG 2015 November Item 04 ESS.VIP_DIGICOM BC-v1.0, DIME_ITDG SG 2015 November Item 05 Q in the ESS Vision 2020 Impl paper v1 1, DIME_ITDG SG 2015 November Item 05 QUAL@ESS Vision 2020, DIME_ITDG SG 2015 November Item 06 ESDEN-DIME-ITDG2015, DIME_ITDG SG 2015 November Item 06 SERV-DIME-ITDG2015, DIME_ITDG SG 2015 November Item 07 ESS vision 2020 Risk Management.ppt, DIME_ITDG SG 2015 November Item 08 EA roadmap v.1.0.pptx, DIME_ITDG SG 2015 November Item 08 Enterprise Architecture Roadmap, DIME_ITDG SG 2015 November Item 09 ESSnets.ppt, DIME_ITDG SG 2015 November Item 09_Annex 1 PIRs, DIME_ITDG SG 2015 November Item 10 IT security.pptx, DIME_ITDG SG 2015 November Item 11 HLG work.pptx, DIME_ITDG SG 2015 November Item 12 BIG Data-Short update on progress, DIME_ITDG SG 2015 November Item 14 SIMSTAT, DIME_ITDG SG 2015 November Item 15 TCO mandate, Opinions_actions_DIME-ITDG SG Nov 2015_Final.pdf, Joint DIME/ITDG Steering Group meeting 8 December 2014, DIME-ITDG SG 2014_12 Item 02 DIME_ITDG SG mandate and RoP, Joint DIME/ITDG steering group meeting 18 and 19 June 2014, DIME-ITDG Steering Group June 2014 Final Minutes, DIME-ITDG Steering Group June 2014 Item01 Agenda, DIME & ITDG plenary meeting 26 and 27 March 2014, Joint DIME/ITDG steering group meeting 6 June 2013, Joint DIME/ITDG steering group meeting 6 June 2013 - agenda, Reference document : ESSC document May 2013, DIME Plenary 2013 - Item 2 - Mandate and rules of procedure, DIME Plenary 2013 - Item 3 - Groupstructure_v2, DIME Plenary 2013 - Item 4.1 - ISTAT on Enterprise Architecture, DIME Plenary 2013 - Item 5 - CentresOfCompetence, DIME Plenary 2013 - Item 5 - CentresOfCompetence-v1f, DIME Plenary 2013 - Item 6 - DIME WGs and TFs, DIME Plenary 2013 - Item 6.1 - DIME WG and TF, DIME Plenary 2013 - Item 6.2 - standardisation_final, DIME Plenary 2013 - Item 6.3 - Security Issues, DIME Plenary 2013 - Item 7.1 -Standardisation overview_Mag, DIME Plenary 2013 - Item 7.2 - Eurostat detailed replies on the revisions, DIME Plenary 2013 - Item 7.2 - Eurostat replies to the comments received from DIME, DIME Plenary 2013 - Item 7.2 - Revised handbook, DIME Plenary 2013 - Item 7.2 - Revision of the Handbook, DIME Plenary 2013 - Item 7.3 - Eurostat detailed replies on the revisions, DIME Plenary 2013 - Item 7.4 - Eurostat replies to the comments received from DIME, DIME Plenary 2013 - Item 8 - Orientations for 2014_New, DIME Plenary 2013 - Item 9 - ESSnet2014Pgm2012Rpt, DIME Plenary 2013 - Item 9 - ESSnetProgramme-v1J, DIME Plenary 2013 - Item 9 - FOSS_project, DIME Plenary 2013 - Item 9.1 - ProposalOpenSourceProject, DIME Plenary 2013 - Item 10 - DIME ESSnet projects, DIME Plenary 2013 - Item 11.1 - ResearchOpportunities, DIME Plenary 2013 - Item 12 - For information, DIME Plenary 2013 - Item 12 - HLG and GSIM, DIME Plenary 2013 - Item 12.2 - Sponsorship on Standardisation, DIME Plenary 2013 - Item 12.4 - legislation, DIME Plenary 2013 - Item 12.5 - Minutes-ITDG2012, DIME Plenary 2013 - Item 12.7 - Integration Grants 2012, DIME Plenary 2013 - Item 13 - Annual report to ESSC, DIME Plenary 2013 - Item 13 - DIME Annual Report ESSC, Written consultation on DIME-ITDG 2018 plenary meeting, Written consultation on ESDEN and SERV of ITDG/DIME before VIG, Written consultation on new Governance for DIME/ITDG (09/2015), Written consultation on the new Governance for DIME/ITDG (08/2015), Written DIME/ITDG consultation on the proposal for the extension of the European Statistical Programme (08/2015), Written consultation of DIME on the revision of the ESS Quality Framework V1.2 (04/2015), Written consultation of DIME on the revision of the ESS Quality Framework V1.2, Written consultation on the business case of ESS.VIP ADMIN (01/2015), Written consultation of DIME on the 2015 ESSnet proposals (10/2014), Written consultation of DIME on the rules of procedure of the DIME (10/2014), Written consultation of ITDG on the rules of procedure of the ITDG (10/2014), Written consultation on the mandate of TF on standardisation (07/2014), Results on the written consultation on the mandate of the tf on standardisation, Written consultation on the mandate of the TF on standardisation, DIME-ITDG Draft Mandate of TF on Standardisation, Written consultation on DIME/ITDG 2014 plenary minutes (07/2014), Written consultation on CPA update (01/2014), item_1.1_public_use_files_for_ess_microdata.pptx, item_2.1_big_data_and_macroeconomic_nowcasting_slides, item_2.2_centre_of_excellence_on_seasonal_adjustment_FR, item_2.2_roadmap_on_seasonal_adjutment.docx, on_the_fly_opinions_working_group_methodology, Item 1.1 Business Architecture for ESS Validation, Item 1.1 Business Architecture for ESS validation (slides), Item 1.1 ESS Validation project - Progress (slides), Item 1.3 Validation break-out sessions (slides), Item 1.1b Business Architecture for validation in the ESS (slides), Item 2.1 Annex 2 EBS Manual Microdata Access, Item 2.1 Confidentiality and Microdata (slides), Item 2.1 Report on Statistical Confidentiality, Item 3.1 Estimation Methods for Admin (slides), Item 3.2 Selectivity in Big Data (slides), Item 3.3 ESS Guidelines on Temporal Disaggregation (slides), Item 3.3 ESS Guidelines on Temporal Disaggregation, Waiver deployment in businessstatistics 23may2017, Item 1.1 Guidelines for Estimation Methods for Administrative data, Item 1.2 ESS Guidelines on Temporal Disaggregation, Item 2.1 Results of the ESSnet Validat Integration, Item 2.2 Results of the Task Force on Validation and state of play of the ESS Validation project, Item 2.3 Possible standards for ESS Validation, Item 3.1 State of play of the ESS shared services, Item 4.1 Recent developments in confidentiality and microdata access, Item 4.2 Anonymisation rules for Farm Structure Survey, Item 5.1 Presentation of the MAKSWELL project, Item 5.3 Presentation of the features of the revamped CROS portal, Item 5.4 NTTS 2019 Conference preparation, Item 2.2 HETUS Scientific Use Files - Annex, Item 3.1 Big data and Trusted Smart Statistics, Item 4.3 The ESS guidelines on Temporal Disaggregation, Item 4.4 Seasonal Adjustment Centre of Excellence (SACE), 1.3 Options for decentralised - remote access to European microdata, 1.3 Options for decentralised and remote access presentation, AoB - Treatment of COVID19 in seasonal adjustment, Agenda TF Temporal disaggregation, Luxembourg Meeting 6 Decemebr 2017, Opinions and_actions_tf_on_temporal_disaggregation_06_december_2017, Agenda TF Temporal disaggreagtion , VC meeting 13 of Septemebr 2017, Opinions and actions TF on Temporal Disaggregation (Meeting 13 September 2017), Opinions and actions TF on Temporal Disaggregation (Meeting 30 may 2017), ESS Guidelines on Temporal Disaggregation (version 12, 21 October 2018), ESS Guidelines on Temporal Disaggregation (version 11, 24 July 2018), ESS Guidelines on Temporal Disaggregation (version 10, 26 April 2018), ESS Guidelines on Temporal Disaggregation (version 8, 15 February 2018), ESS Guidelines on Temporal Disaggregation (version 7, 6 December 2017), ESS Guidelines on Temporal Disaggregation (version 6 , 27 October 2017), Workshop on Small Area Methods and living conditions indicators in #European poverty studies in the era of data deluge and #Bigdata, Centres of Excellence assessment report 2014, Quality and Risk Management Models (Theme), GSBPM: Generic Statistical Business Process Model (Theme), Specification of User Needs for Business Statistics (Theme), Questionnaire Design - Main Module (Theme), Statistical Registers and Frames - Main Module (Theme), The Populations, Frames, and Units of Business Surveys (Theme), Building and Maintaining Statistical Registers to Support Business Surveys (Theme), Survey Frames for Business Surveys (Theme), The Design of Statistical Registers and Survey Frames (Theme), The Statistical Units and the Business Register (Theme), Quality of Statistical Registers and Frames (Theme), Balanced Sampling for Multi-Way Stratification (Method), Subsampling for Preliminary Estimates (Method), Sample Co-ordination Using Simple Random Sampling with Permanent Random Numbers (Method), Sample Co-ordination Using Poisson Sampling with Permanent Random Numbers (Method), Assigning Random Numbers when Co-ordination of Surveys Based on Different Unit Types is Considered (Method), Design of Data Collection Part 1: Choosing the Appropriate Data Collection Method (Theme), Design of Data Collection Part 2: Contact Strategies (Theme), Collection and Use of Secondary Data (Theme), Micro-Fusion - Data Fusion at Micro Level (Theme), Unweighted Matching of Object Characteristics (Method), Weighted Matching of Object Characteristics (Method), Fellegi-Sunter and Jaro Approach to Record Linkage (Method), Reconciling Conflicting Microdata (Method), How to Build the Informative Base (Theme), Automatic Coding Based on Pre-coded Datasets (Method), Automatic Coding Based on Semantic Networks (Method), Statistical Data Editing - Main Module (Theme), Imputation under Edit Constraints (Theme), Weighting and Estimation - Main Module (Theme), Design of Estimation - Some Practical Issues (Theme), Generalised Regression Estimator (Method), Preliminary Estimates with Design-Based Methods (Method), Preliminary Estimates with Model-Based Methods (Method), Synthetic Estimators for Small Area Estimation (Method), Composite Estimators for Small Area Estimation (Method), EBLUP Area Level for Small Area Estimation (Fay-Herriot) (Method), EBLUP Unit Level for Small Area Estimation (Method), Small Area Estimation Methods for Time Series Data (Method), Estimation with Administrative Data (Theme), Revisions of Economic Official Statistics (Theme), Chow-Lin Method for Temporal Disaggregation (Method), Asymmetry in Statistics - European Register for Multinationals (EGR) (Theme), Seasonal Adjustment - Introduction and General Description (Theme), Seasonal Adjustment of Economic Time Series (Method), Statistical Disclosure Control - Main Module (Theme), Statistical Disclosure Control Methods for Quantitative Tables (Theme), Dissemination of Business Statistics (Theme), Evaluation of Business Statistics (Theme), The treatment of large enterprise groups within Statistics Netherlands.