Time use: documentation of statistics
Basic data of the statistics
Data description
Statistical population
The register does not contain data on households, due to which household-dwelling units, instead of households, are used in the sampling. A household-dwelling unit was formed of individuals with an identical address code in the register. In most cases, one household-dwelling unit is equal to a single household, due to which the household-dwelling unit sample was used to target the data collection at households. The survey’s statistical population is the household population, meaning that individuals living in institutions and conscripts were removed from the sample as falling under the scope of the register’s over-coverage.
Statistical unit
Unit of measure
Reference period
Reference area
Sector coverage
Time coverage
Frequency of dissemination
Concepts
Activity
Category of activity
Household
Excluded from the household population are those living permanently abroad and the institutional population (such as long-term residents of old-age homes, care institutions, prisons or hospitals).
The corresponding register-based information is household-dwelling unit. A household-dwelling unit is formed of persons living permanently in the same dwelling or address. More than one household may belong to the same household-dwelling unit. The concept of household-dwelling unit is used in register-based statistics in place of the household concept.
Household's reference person
Main activity
Secondary activity
Accuracy, reliability and timeliness
Overall accuracy
The design coefficients calculated for the variables allow for estimating the clustering of the variables in the household sample.
Timeliness
Punctuality
Data revision
Revisions – i.e. improvements in the accuracy of statistical data already published – are a normal feature of statistical production and result in improved quality of statistics. The principle is that statistical data are based on the best available data and information concerning the statistical phenomenon. On the other hand, the revisions are communicated as transparently as possible in advance. Advance communication ensures that the users can prepare for the data revisions.
The reason why data in statistical releases become revised is often caused by the data becoming supplemented. Then the new, revised statistical figure is based on a wider information basis and describes the phenomenon more accurately than before.
Revisions of statistical data may also be caused by the calculation method used, such as annual benchmarking or updating of weight structures. Changes of base years and used classifications may also cause revisions to data.
Comparability
Comparability - geographical
Comparability - over time
Coherence - cross domain
Both the Time Use Survey and the Leisure Survey produce data on participation in cultural events. Comparable data are available as of 1981. The differences in data collection must be accounted for when comparing the data of the Time Use Survey to those of the Leisure Survey. The questions pertaining to participation in cultural events in the Time Use Survey were phrased in a manner consistent with the questions of the Leisure Survey. The results of the Time Use Survey and the Leisure Survey are fairly coherent in general. Given that the Time Use and Leisure Surveys are conducted at alternating time intervals, comparable time series can be formed.
The questions pertaining to trust in the Time Use Survey have been phrased in a manner consistent with the questions of the Leisure Survey.
Coherence - internal
Source data and data collections
Source data
from Statistics Finland’s statistical register pertaining to the population which had been adapted from the Central Population Centre. The register does not contain data on households, due to which household-dwelling units, instead of households, are used in the sampling. A household-dwelling unit was formed of individuals with an identical address code in the register. In most cases, one household-dwelling unit is equal to a single household, due to which the household-dwelling unit sample was used to target the data collection at households.
The households drawn for the sample of the Time Use Survey conducted from 2020 to 2021 numbered 8,840. These included 21,278 individuals, of whom 19,268 was aged 10 and over. The household sample was drawn as an individual sample in such a way that individuals aged 15 or over, for whom individuals living in the same dwelling unit were simultaneously included, were drawn from the register by systematic sampling. The likelihood of a household being drawn is dependent on the number of the household’s individuals falling under the scope of the sampling frame, i.e. in this case, the number of individuals aged 15 or over. The survey’s statistical population is the household population, meaning that individuals living in institutions and conscripts were removed from the sample as falling under the scope of the register’s over-coverage. The households belonging to over-coverage numbered 185 (2.1 per cent). Following their removal, the sample included 8,655 households. There were 4,100 households which responded; this represents 47.4 per cent of the sample.
Data collection
In the previous Time Use Surveys, in 1979, from 1987 to 1988 and from 1999 to 2000, the data were collected during face-to-face interviews, but in the survey from 2009 to 2010, some of the interviews were carried out as telephone interviews in the interest of cutting costs. In the survey from 2020 to 2021 all of the interviews were carried out as telephone interviews.
Frequency of data collection
Methods
Data compilation
of the 999 codes, the time spent on sleeping could be predicted with the help of a model. If the keeping of the diary had been started in the morning with morning routines, the time from 4.00 until the morning routines was marked as sleeping. Sleeping that took place in the evening was modelled with a regression model in which the dependent variable was the duration of the evening sleeping and the independent variables were sleeping in the morning, sex, five-year age groups, the day of the week, working day and day off. The model predicted the duration of the evening sleeping, which was rounded to the nearest 10 minutes. If the final episode was coded as missing, it was imputed with the evening sleeping calculated with the model. If the final missing episode was shorter than the sleeping predicted by the model, the entire missing amount of time was imputed to sleeping. If, on the other hand, the duration of the final episode was greater than the sleeping predicted by the model, the duration calculated with the model was imputed as the duration. The difference between the durations of the model and the missing data remained as a missing activity. The time at which the sleeping started was calculated retroactively as of 4.00.
Weights are calculated at several stages which account for the survey and sampling design and the correction of the non-response impact. The weights are also standardised with a calibration technique to correspond to data obtained from population statistics and registers. The weighting gives days of the week and months the same ‘representativeness’ in the sense that the sums of the weights calculated from a unit level are the same.
A household weight was calculated for a household and all members of a household were given the same weight, in accordance with the sampling design. This weight was used when calculating the shares of respondents and the distributions of non-response.
The calibration vector was formed according to age and gender groups, the area, municipality type, education and taxable income. The standardisation of a month and the day of the week, as well as inclusion in the register of unemployed job seekers during the survey month, were linked to same calibration.
Not all members of a household kept a diary or kept it only for one day, due to which the weight based on the inclusion probability is expanded to apply to the survey’s statistical population, i.e. household population aged 10 and over. The basic weight of the diaries was 5/7 for weekdays and 2/7 for days of the weekend. The numbers of diaries received varied depending on the survey week and day of the week. If an individual was not reached before a day drawn by lot, the interviewer had the possibility to postpone the keeping of the diary for no further than the next three weeks and the same days of the week. Due to such postponements, not all members of a household have the same diary weights. If all members of a household kept their diaries on the same days, the members of the household have the same diary weights. Day weights were calibrated in a manner equivalent to individual weights.
Data validation
In the interview data, the Blaise program accepts only valid codes. The interview data are checked by carrying out logicality checks and by comparing the distributions to earlier Time Use Surveys and other sources, such as the Labour Force Survey, the Leisure Survey and the survey on the use of information and communications technology by individuals.
The application in which the diary data are stored accepts only valid codes. That the sum of the continuous variables is 1,440 minutes on every survey day is verified. It is also checked whether the keeping of a diary has been interrupted, in which case a missing evening napping is potentially imputed. The averages of continuous variables are compared with the averages of the previous survey.
Principles and outlines
Contact organisation
Contact organisation unit
Legal acts and other agreements
The compilation of statistics is guided by the Statistics Act. The Statistics Act contains provisions on collection of data, processing of data and the obligation to provide data. Besides the Statistics Act, the Data Protection Act and the Act on the Openness of Government Activities are applied to processing of data when producing statistics.
Statistics Finland compiles statistics in line with the EU’s regulations applicable to statistics, which steer the statistical agencies of all EU Member States.
Further information: Statistical legislation
Confidentiality - policy
The data protection of data collected for statistical purposes is guaranteed in accordance with the requirements of the Statistics Act (280/2004), the Act on the Openness of Government Activities (621/1999), the EU's General Data Protection Regulation (EU) 2016/679 and the Data Protection Act (1050/2018). The data materials are protected at all stages of processing with the necessary physical and technical solutions. Statistics Finland has compiled detailed directions and instructions for confidential processing of the data. Employees have access only to the data essential for their duties. The premises where unit-level data are processed are not accessible to outsiders. Members of the personnel have signed a pledge of secrecy upon entering the service. Violation of data protection is punishable.
Further information: Data protection | Statistics Finland (stat.fi)
Confidentiality - data treatment
The data are released in table formats, which does not allow for the identification of individual data producers. The time use data of groups with fewer than 50 survey days are not released in the tables.
All employees compiling the Time Use Survey have signed a pledge of secrecy, where they are obliged to keep secret the data prescribed as confidential by virtue of the Statistics Act or the Act on the Openness of Government Activities.
Release policy
Statistics Finland publishes new statistical data at 8 am on weekdays in its web service. The release times of statistics are given in advance in the release calendar available in the web service. The data are public after they have been updated in the web service.
Further information: Publication principles for statistics at Statistics Finland
Data sharing
Accessibility and clarity
Statistical data are published as database tables in the StatFin database. The database is the primary publishing site of data, and new data are updated first there. When releasing statistical data, existing database tables can be updated with new data or completely new database tables can be published.
In addition to statistical data published in the StatFin database, a release on the key data is usually published in the web service. If the release contains data concerning several reference periods (e.g. monthly and annual data), a review bringing together these data is published in the web service. Database tables updated at the time of publication are listed both in the release and in the review. In some cases, statistical data can also be published as mere database releases in the StatFin database. No release or review is published in connection with these database releases.
Releases and database tables are published in three languages, in Finnish, Swedish and English. The language versions of releases may have more limited content than in Finnish.
Information about changes in the publication schedules of releases and database tables and about corrections are given as change releases in the web service.
Data revision - policy
Revisions – i.e. improvements in the accuracy of statistical data already published – are a normal feature of statistical production and result in improved quality of statistics. The principle is that statistical data are based on the best available data and information concerning the statistical phenomenon. On the other hand, the revisions are communicated as transparently as possible in advance. Advance communication ensures that the users can prepare for the data revisions.
The reason why data in statistical releases become revised is often caused by the data becoming supplemented. Then the new, revised statistical figure is based on a wider information basis and describes the phenomenon more accurately than before.
Revisions of statistical data may also be caused by the calculation method used, such as annual benchmarking or updating of weight structures. Changes of base years and used classifications may also cause revisions to data.
Quality assessment
Quality assurance
Quality management requires comprehensive guidance of activities. The quality management framework of the field of statistics is the European Statistics Code of Practice (CoP). The quality criteria of Official Statistics of Finland are also compatible with the European Statistics Code of Practice.
Further information: Quality management | Statistics Finland (stat.fi)
User access
Data are released to all users at the same time. Statistical data may only be handled at Statistics Finland and information on them may be given before release only by persons involved in the production of the statistics concerned or who need the data of the statistics concerned in their own work before the data are published.
Further information: Publication principles for statistics
Unless otherwise separately stated in connection with the product, data or service concerned, Statistics Finland is the producer of the data and the owner of the copyright. The terms of use for statistical data.