Time use survey - 2020 (tus_20)

1.1. Contact organisation

Statistics Norway

1.2. Contact organisation unit

Division for Income and Living Conditions

1.3. Contact name

Confidential because of GDPR

1.4. Contact person function

Confidential because of GDPR

1.5. Contact mail address

Postboks 2633 St. Hanshaugen
0131 OSLO

1.6. Contact email address

Confidential because of GDPR

1.7. Contact phone number

Confidential because of GDPR

1.8. Contact fax number

Confidential because of GDPR

2.1. Metadata last certified

8 May 2026

2.2. Metadata last posted

8 May 2026

2.3. Metadata last update

8 May 2026

3.1. Data description

• Survey name(s) in the national language(s): Tidsbruksundersøkelsen
• Survey name in English: Time Use Survey
• Year(s) of (data collection) of the survey: 2022-2023
• Link to the survey website: Time use survey – SSB
• National questionnaire: Documentation Report (in Norwegian)

3.2. Classification system

See below.

3.2.1. Versions and breakdowns (level) of the classifications used for the data collection

Acronym	Version	Level
NACE	rev. 2	2
ISCO	08	2
ISCED	2011	1
NUTS	2021	2
Other

3.2.2. Deviations from ESS or international standards

3.3. Coverage - sector

Not requested.

3.4. Statistical concepts and definitions

• Sample design: we have included persons from 9 years to 79 years of age, instead of 10 years and above in our sample
• We have used a person-sample instead of household sample
• Diaries: we have used two consecutive days instead of one weekday and one weekend day
• We have not included information and communication technology (ICT) in the diary
• We have not used the activity coding list from HETUS, but a Norwegian coding list witch we have converted to the HETUS coding list.

3.5. Statistical unit

Individuals

3.6. Statistical population

See below.

3.6.1. Main characteristics of the survey population

The population was limited to persons in the age groups 9–79 years. The target population for Time Use survey are all private households in the whole of Norway. 

The sub-populations that are not covered by the data collection include: those who moved out of the country's territory; or those with no usual residence; or those living in institutions.

3.7. Reference area

Whole Norway.

3.8. Coverage - Time

10 October 2022 to 22 October 2023.

3.9. Base period

Not applicable.

Time spent in minutes and proportion of population.

HETUS should cover a full 12 months period, i.e. 365 consecutive days. Each respondent should fill in the diary for two days, one weekday (Monday to Friday) and one weekend day (Saturday, Sunday). In Norway, each respondent was asked to fill in the diary for two consecutive days, randomly assigned.

6.1. Institutional Mandate - legal acts and other agreements

Regulation (EU) 2019/1700 of the European Parliament and of the Council

6.1.1. At European level

The current round of the harmonised European time use survey (HETUS) data collection has been carried out in the spirit of multi-national and multi-agency cooperation across Europe, in the form of an informal ’gentlemen's agreement’.

6.1.2. At National level

No mandate.

6.2. Institutional Mandate - data sharing

No arrangements between data producing agencies at the national level.

7.1. Confidentiality - policy

The statitistics act states that:

Section 7. Statistical confidentiality in dissemination of official statistics

1. Official statistics shall be disseminated in such a manner that it is not possible to directly or indirectly identify a statistical unit and thus disclose individual data.
2. The first subsection shall not apply when the exception follows from an obligation to produce statistics pursuant to the EEA Agreement.
3. An exception may be made from the first subsection if the statistical unit is a public authority, and the interests of the public sector are protected. An exception may also be made from the first subsection if the statistical unit has granted consent or if the data are available to the public.

Section 9. Information security 

1. Public authorities that process data covered by the obligation of secrecy pursuant to section 8 shall implement technological and organisational measures in order to achieve an adequate level of security. This includes providing adequate access control, logging and subsequent controls.
2. Data that allow direct identification must be processed and stored separately from other data, unless this is inconsistent with the purpose of the processing or it is clearly unnecessary.

7.2. Confidentiality - data treatment

Statistics Norway never publishes statistics where it is possible to reveal information about specific persons or households. Data is stored in a safe way and in accordance with the legal demands for data storage.

Statistics Norway only grants accsess to anonymised microdata to public authorities and researchers affiliated with approved research institutions. Please see: Statistics Norways website for information on access to microdata.

An EU microdata file (EU scientific use file Norway) is made available by Eurostat.

8.1. Release calendar

Statistics Norway publishes results from Time Use Survey after each data collection, approximately every 10 years. See Statistics Norway's release calendar for more information.

8.2. Release calendar access

Statistics Norway Release Calendar

8.3. Release policy - user access

Statistics and analyses from Statistics Norway are public goods, and are free and accessible to everyone on the website ssb.no, where universal design requirements are complied with. It must be easy to find and use Statistics Norway’s figures, and communication must be adapted to different channels and formats. Statistics releases must be in both Norwegian and English, and the legislation on the use of bokmål and nynorsk (writing standards) must be complied with.

Statistics Norway aims to be open about all aspects of statistics and analyses. Any errors in published material must be rectified as soon as possible, with clearly marked corrections. Planned revisions of the statistics must be accounted for, and users must be alerted in advance of any major changes. Information on the quality of statistics and analyses is openly available in documentation (metadata).

Statistics Norway decides independently what type of statistics are to be produced, as well as when and how statistics and analyses will be published. Dissemination must be impartial and objective, as well as balanced, comprehensive and accurate, regardless of funding source, authorities, interest groups and political objectives. Statistics Norway must not withhold findings that are controversial. No external users have access to the statistics and analyses before they are published and accessible simultaneously for everyone on the website ssb.no. 

The media never have access to content prior to release, nor are given a press embargo. Statistics and analyses must be notified well in advance in the Statistics release calendar, and made available for everyone simultaneously, i.e. 8 o'clock on weekdays.

TUS is disseminated about every 10 years.

10.1. Dissemination format - News release

General survey (in Norwegian): Hva bruker vi tiden vår på? – SSB

News article (in Norwegian): Vi bruker stadig mindre tid på husarbeid – SSB

10.2. Dissemination format - Publications

No publications.

10.3. Dissemination format - online database

Aggregated data are available in our online database: Time use survey. Statbank Norway

10.3.1. Data tables - consultations

Not requested.

10.4. Dissemination format - microdata access

Accessible via the Norwegian Agency for Shared Services in Education and Research

10.5. Dissemination format - other

Not available.

10.5.1. Metadata - consultations

Not requested.

10.6. Documentation on methodology

We have produced a documentation report describing the data collection, response rates, weighting and the main steps in the data processing: Time Use - documentation report (in Norwegian).

10.6.1. Metadata completeness - rate

Not requested.

10.7. Quality management - documentation

Documentation reports (in Norwegian): Brukertesting av ny tidsbruksundersøkelse and Tidsbruksundersøkelsen 2022

11.1. Quality assurance

The Norwegian Statistics Act states that Statistics Norway shall prepare an annual report for the Ministry of Finance on the quality of official statistics. According to the letter of allocation, Statistics Norway shall oversee the monitoring of compliance with the requirements for quality in official statistics and establish a system for following this up. The first report on the quality of official statistics was submitted to the Ministry in 2022.

In the annual report for Statistics Norway, there are also reports on the quality indicators: timeliness, response rate and response burden, referring to the performance requirements set by the Ministry.

Statistics Norway involves users in the development and refinement of new and existing products and services, and maintains regular contact with users through formalised committees, user groups and user forums.

Quality evaluations of official statistics at an institutional level are carried out at regular intervals at all statistical authorities, including Statistics Norway. In the quality evaluations, a questionnaire-based survey for self-assessment is combined with interviews among all producers of official statistics. The quality evaluation is based on the quality requirements in the Statistics Act and the quality principles in the European Statistics Code of Practice. Statistics Norway and the other national authorities have set up action plans to follow up on the recommendations from the quality evaluation: these actions will be followed up in the annual reports on quality in official statistics.

Quality reviews are systematic assessments of statistics or statistical domains, where emphasis is placed on the production process, output, and the user perspective. The review starts with a self-assessment based on the Statistical Act and quality principles in the European Statistics Code of Practice. The production process is mapped according to the Generic Statistical Business Process Model, GSBPM. The user perspective is covered with a focus group with main users, and reports on the use of the website.

Eurostat’s peer reviews are well known in the Norwegian statistical system. The last peer review of Norway was in 2021. In the report, the peer review team considers that the Norwegian statistical system overall demonstrates a strong commitment to the European Statistics Code of Practice. The peer review team presented recommendations that could allow Statistics Norway and the other national authorities to improve beyond compliance with the European Statistics Code of Practice. Statistics Norway and the other national authorities have set up action plans to follow up on these recommendations, and also started activities to fulfil the recommendations, while waiting for the action plan to be accepted by Eurostat.

Statistics Norway uses data from administrative information systems as a source for official statistics. Since 2012, Statistics Norway has been engaged in a standardised and formalised cooperation on quality with, inter alia, owners of administrative information systems.

Competence and training courses

Statistics Norway organizes regularly courses in quality and quality indicator subjects. The courses are open to both staff in Statistics Norway and members of the Committee for Official Statistics. There are also plans for a training course on statistical confidentiality. Furthermore, specialist seminars on the topics of dissemination, pseudonymisation, confidentiality, editing data and quality in the register have been organised. A series of seminars on big data and data minimization^[1] have also been arranged. Participation on the ESTP^[2]-training program do also contribute to the competence on quality in production of official statistics.  

Planned improvements in the quality assurance system

The combination of thorough quality reviews of selected statistics and self-assessments of the total production of statistics will provide a good basis for the annual quality report. Self-assessments based on the quality evaluation form will be adapted to function as a self-assessment of single statistical processes and output ^[3].

Statistics Norway’ has developed a set of quality indicators, according to SIMS, to be used in production and dissemination of statistics. The implementation of these indicators has started. There are also plans to establish a reference database for metadata, including these indicators.

_{[1] The principle of “data minimization” means limiting the collection of personal information to what is directly relevant and necessary to accomplish a specified purpose. One should also retain the data only for as long as is necessary to fulfil that purpose.}

_{[2] ESTP - European Statistical Training Programme}

_{[3] Single Integrated Metadata Structure}

11.2. Quality management - assessment

The quality of the output is presented in the section 'About the statistics' on the website ssb.no.

12.1. Relevance - User Needs

The main users of Time Use statistical data are policy makers, research institutes, media, and students.

Data is used for policy development, research, and to inform the general public.

12.2. Relevance - User Satisfaction

Not applicable.

12.3. Completeness

Statistics Norway have not collected ICT-use for all 10-minutes intervals / activities in the Time Use survey.

12.3.1. Data completeness - rate

Not requested.

13.1. Accuracy - overall

We do not posess a single measure of accuracy assessment.

13.2. Sampling error

Not available.

13.2.1. Sampling error - indicators

Not available.

13.2.2. Sampling error - proportion and confidence interval

Parameter of interest (p̂)	Number of respondents - n	Standard error for p̂	95% confidence interval for p̂: Lower	95% confidence interval for p̂: Upper
Percentage of population aged 15 and over spending daily on average more than 10 % of time working in paid work (weighted)	Number of individuals completed at least 1 diary day  (unweighted)
Not available

13.2.3. Sampling error - method used for the variance (SE) estimation

Not applicable

13.3. Non-sampling error

Information on Non-sampling error is provided in the sub-concepts 13.3.1 – 13.3.5.

13.3.1. Coverage error

23 persons out of 8864 did not belong to the target population.

13.3.1.1. Over-coverage - rate

0.26 %

13.3.1.2. Common units - proportion

Not applicable.

13.3.2. Measurement error

Measurement error for cross-sectional data

Cross-sectional data
Source of measurement errors	Building process of questionnaire	Interview training and training of coders
In every survey there is a chance of respondents giving an incorrect answer. The question/answer process can be seen in four different phases. First there is the understanding and interpretation of the actual question. If there are difficult terms or complicated wording, this may cause errors. The second phase is where the respondent recalls information. Errors in this phase may rise if the information necessary is hard to retrieve because it is old, complicated or not available to the respondent. The third phase is evaluating and selecting the information necessary to answer the question. In this phase, the respondent may actually have the right kind of information to answer the question correctly, but still end up with a wrong answer. This type of error is most frequent when the question is complicated and requires much information.  The fourth and final phase is the actual formulating of the answer. This may cause errors if the respondents mastering of the language in use is weak, if the answer requires use of complicated terms or if the communication between the interviewer and the respondent is not optimal.	The questionnaire and diary was thoroughly tested during the development of the time use application.  All the questions were evaluated, and several were changed to make them easier for the respondents to answer. The diary has also been user tested before the data collection.	Interviewer effects may also be labelled under errors caused by interview. The interviewers were only used to recruit and help the respondents with technical issues and the understanding of what they were supposed to do. They did not ask questions. Therefore interviewers probably did not cause a lot of errors. Another source of error are the coders, that checked the 3-digit codes assigned by our machine learning algorithm. All the coders were trained before the coding job, and they worked together as a team to reduce systematic errors, and difficult activities were discussed together and with a supervisor. We also did some re-coding to check for consistency.

13.3.2.1. Questionnaire design and testing

When developing the web application for web questionnaire and diary we did several tests in steps. We developed parts, then tested them and developed further. We tested prototypes on users, and we also tested different versions of the web application with users. We also had a full scale pilot survey as part of the testing of the different elements in our web application.

13.3.2.2. Interviewer training

The interviewers did not actually 'interview' in our Time use survey. They called the respondents to convince them to participate in the survey, they updated the contact information (if needed), and set up procedures to send the link with log on information that the respondents received on their phone at 08:00 in the morning on the first diary day. They also helped the respondents with technical issues if needed. We used a team of our interviewers that followed the data collection over the 12 months . A part of their training was to test the web application / diary and they were trained on general knowledge on why Time use surveys are important, etc.

13.3.2.3. Proxy interview rates

No information is available on the proxy rates, but children were instructed to get help from parents when answering questions regarding household / parents. 

13.3.3. Non response error

Information on Non response error is provided in the sub-concepts 13.3.3.1 – 13.3.3.2.

13.3.3.1. Unit non-response - rate

Non-response rate total: 65%

Partially non-response: 4,2%

Did not want to participate: 28,1%

Did not answer the telephone: 26,8%

Not able to participate: 4,7%

Other non-response: 1,2%

13.3.3.1.1. Reasons for non-response

Non-response rate total: 65%

Partially non-response: 4,2%

Did not want to participate: 28,1%

Did not answer the telephone: 26,8%

Not able to participate: 4,7%

Other non-response: 1,2%

13.3.3.1.2. Number of households in the gross sample according to the final results of the survey

Please see 18.1.3.

13.3.3.1.3. Characteristics of non-respondents

Not available.

13.3.3.1.4. Efforts to reduce non-response

Different measures were taken to reduce non-response:

• One week prior to the first diary day, interviewers began contacting respondents whose reporting period was scheduled for the following week. On the actual reporting day, a reminder SMS with a link to the web application was sent at 08:00.
• Respondents who did not start reporting on their first diary day automatically received an SMS reminder on the morning of the second day and were contacted by an interviewer later that afternoon to provide assistance.
• Following the two diary days, respondents were granted an additional day to complete the survey. Respondents who, three days after the initial reporting period, had either only partially completed, or not commenced reporting, were contacted by an interviewer for support or offered an alternative reporting period. The rescheduled period corresponded to the same weekdays in a subsequent week. One in eight respondents (12.7 percent of the sample) opted to postpone their reporting days by one or more weeks.
• To maintain a stable flow of data throughout the data collection, several measures were implemented. Weekly meetings were held with interviewers to provide updates on data entry. Furthermore, interviewers shared experiences and recruitment strategies with one another to refine and apply the most effective approaches during the remainder of the data collection process.

13.3.3.1.5. Adjustment of weights in order to reduce non-response

See calculation of weighting factors. Beyond this, there were no further adjustments to the weights / response modelling.

13.3.3.1.6. Other comments regarding non-response errors

No further comments.

13.3.3.1.7. Replacement of non-responding households (substitution)

Substitution rate	Rate
i.e. Number of substitute households successfully interviewed/Achieved sample size	0

13.3.3.1.8. Description of the substitution

Not applicable.

13.3.3.1.9. Qualitative assessment of the bias associated with unit non-response

Not available.

13.3.3.2. Item non-response - rate

Not available.

13.3.3.2.1. Variables most subject to item non-response

Not available.

13.3.4. Processing error

We conducted a pilot and completed in-house testing of the web application before the main survey. 

13.3.5. Model assumption error

Not applicable.

14.1. Timeliness

End of data collection: 22 October 2023
National dissemination of statistical data: 5 December 2023
First delivery of microdata to Eurostat: 29 August 2025
Validated and accepted microdata to Eurostat: 5 November 2025

14.1.1. Time lag - first result

Not applicable.

14.1.2. Time lag - final result

Not applicable.

14.2. Punctuality

Not available.

14.2.1. Punctuality - delivery and publication

Not requested.

15.1. Comparability - geographical

There should be no problems comparing between geographical areas. However, some of the NUTS2 regions have relatively low number of respondents, which results in greater statistical uncertainty and should therfore be interpreted with care.

15.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.

15.2. Comparability - over time

We have changed the data collection method from paper diaries and CATI-questionnaire to a web application including a web-questionnaire and web diary. There are no major changes in the questions asked and structure of diary. We have published the statistics without breaks in time series at Statistics Norway.

15.2.1. Length of comparable time series

Not requested.

15.3. Coherence - cross domain

Not relevant.

15.3.1. Coherence - sub annual and annual statistics

Not applicable.

15.3.2. Coherence - National Accounts

Not requested.

15.4. Coherence - internal

The statistics are internally consistent.

Information on this concept is provided in the sub-concepts 16.1-16.4.

16.1. Costs of the survey

We estimated the costs of the TUS to be around 1 327 000 EUR, with around 616 600 EUR to data collection and 710 400 EUR to development and production of the statistics.

16.2. Average time used for answering the survey questionnaires (in minutes)

Not estimated.

16.3. Average time used to fill in the diary (in minutes)

Not estimated.

16.4. Measures taken to reduce the cost and burden of the survey

Measures to reduce cost of the survey for the NSI:

• Developing a Time Use diary web application that respondents fill in by themselves, and using interviewers for recruiting / follow up rather than conducting a CATI interview was a cost reducing measure. We also developed a machine learning algorithm to predict a 3-digit code from all the written activities in the respondents diaries. We then used coders to verify / change the predicted codes, and this was a much faster way to do the coding than coding manually all codes from all diaries. Having the diaries as a web application also made it easier to process and check the data  throughout the data collection.
• We shortened the web questionnaire compared to our last Time Use survey to reduce the response burden of the respondents. 

17.1. Data revision - policy

Date for revisions of published data are publicly available.

17.2. Data revision - practice

Not applicable.

17.2.1. Data revision - average size

Not applicable.

18.1. Source data

Information on sampling design, sampling frame and size is available under the full metadata concepts 18.1.1 - 18.1.3.

18.1.1. Sampling frame

Type	Name of data source used for building the sampling frame
Population census
Population register	x
Household register
Dwelling register
List of phone numbers
Postcode address file
Another survey sample
Other

18.1.2. Sampling design of the survey

See below.

18.1.2.1. Sampling design(s)

Type	Description of Sampling Design
Simple random sampling	x
Systematic sampling
Stratified sampling
Cluster sampling
Other

18.1.2.2. Ultimate sampling unit(s)

Individuals

18.1.2.3. Oversampling of specific populations

No population groups were oversampled.

18.1.2.4. Assumptions used for determining the sample size

We looked at previous Time Use surveys, sample sizes and response rates when calculating our sample size. We needed minimum 3000 individuals in the net sample to produce the most relevant tables for the statistics, and calculated the sample size based on that.

18.1.3. Sample size

Sample size	Number
Gross sample size i.e. initial sample size= responding units + non-responding units (these two groups make up the group of eligible units) + units with unknown eligibility (e.g. because they are not able to be reached/ contacted), + ineligible units.	8864
Number of eligible units i.e. net sample size= Gross sample size - units with unknown eligibility – ineligible units	3077
Achieved sample size = Total number of households which were successfully surveyed (interviews+diary)	3077

18.2. Frequency of data collection

Every 10 years.

18.3. Data collection

See below.

18.3.1. Data collection method used

Unimode

18.3.2. Mode(s) and instruments for data collection: household and individual questionnaire

Interview	Specify Yes or No if used	% of completed household interviews
Paper assisted personal interview (PAPI)	No	0
Computer-assisted personal interview (CAPI)	No	0
Computer assisted telephone interview (CATI)	No	0
Computer assisted web-interview (CAWI)	No	0
Smart mode (e.g. smartphone app)	Yes	100
Other (e.g. administrative data).	Yes	100

18.3.3. Mode(s) and instruments for data collection: time use diary

Diary	Specify Yes or No if used	% of completed diaries
Paper diary	No	0
Computer based non online diary	No	0
Online web diary	No	0
Mobile based diaries (Smartphone apps, etc.)	Yes	100

18.3.4. Variables completed from an external source

List of variables that were completed from administrative registers:

• HHQ9_1 (Equalised net current monthly household income)
• IND41_1: Country of birth
• IND42_1: Country of main citizenship
• IND46: Country of birth of the father
• IND47: Country of birth of the mother
• IND48: Country of residence
• IND49: Region of residence
• IND50: Degree of urbanisation
• IND3_1: Economic activity of the local unit for main job (economic sector)
• IND5: Occupation in main job
• IND22_1: Educational attainment level (highest level of education successfully completed)

18.4. Data validation

Throughout the production process data is compared to previous years and other sources.

18.5. Data compilation

Respondents were asked to report episodes in free text format. To encode these activity descriptions, we used a self-developed software for manual coding together with help from a machine learning algorithm. Based on training data from the pilot study, the ML algorithm made code predictions prior to sending the data to the manual coding software; the human coders would then either accept the prediction or correct it, based on the descriptions and other contextual information.

Examples of data editing

• Code 999 were given to minor gaps (less than 60 minutes) in incompleted diaries
• Imputed sleep for otherwise completed diaries if the missing data were in the tail end of the diary and after 22:00
• Imputed sleep for otherwise completed diaries if the misssing data were in the beginning of the diary and before 09:00
• Deleted secondary activities that were coded the same as the main activity
• Deleted secondary acitivities reported when the main activity was "sleep"
• Swapped places between the main acitivity and secondary acitvity in cases where the latter was of primary importance, e.g. caregiving activities
• Episodes with descriptions indicating multiple episodes in one were split up. The duration of the resulting episodes were imputed based on the relative length of identically coded episodes in the data.

18.5.1. Imputation - rate

Not available.

18.5.2. Method applied to correct for 'item non-response'

Methods	Specify Yes or No if used
Simple imputation (deterministic) method	Yes
Simple imputation (stochastic) method	No
Multiple imputation approach	No
Other	No

18.5.3. Calculation of weighting factors and weight adjustments

Two separate set of weighting factors were calculated: 1) weighting factors for the net sample of persons, and 2) weighting factors for the net sample of diary days. In both cases the design weights were all identical.

1. Weighting factors for the net sample of persons were calculated by calibrating the design weights against population totals for three education levels (low, medium, high), and gender crossed with five age groups (9-15, 16-24, 25-44, 45-66, 67-79)
2. Weighting factors for the net sample of diary days were calculated using the same calibration model as above. In addition, it was also calibrated against ‘population totals’ for diary day in the weekend (Sat, Sun) / not in the weekend. Because the sum of weighting factors for diary days should equal the population size (N) of persons, the latter population totals were N*2/7 and N*5/7.

18.6. Adjustment

Not applicable.

18.6.1. Seasonal adjustment

Not applicable.

• Survey name(s) in the national language(s): Tidsbruksundersøkelsen
• Survey name in English: Time Use Survey
• Year(s) of (data collection) of the survey: 2022-2023
• Link to the survey website: Time use survey – SSB
• National questionnaire: Documentation Report (in Norwegian)

8 May 2026

• Sample design: we have included persons from 9 years to 79 years of age, instead of 10 years and above in our sample
• We have used a person-sample instead of household sample
• Diaries: we have used two consecutive days instead of one weekday and one weekend day
• We have not included information and communication technology (ICT) in the diary
• We have not used the activity coding list from HETUS, but a Norwegian coding list witch we have converted to the HETUS coding list.

Individuals

See below.

Whole Norway.

HETUS should cover a full 12 months period, i.e. 365 consecutive days. Each respondent should fill in the diary for two days, one weekday (Monday to Friday) and one weekend day (Saturday, Sunday). In Norway, each respondent was asked to fill in the diary for two consecutive days, randomly assigned.

We do not posess a single measure of accuracy assessment.

Time spent in minutes and proportion of population.

Respondents were asked to report episodes in free text format. To encode these activity descriptions, we used a self-developed software for manual coding together with help from a machine learning algorithm. Based on training data from the pilot study, the ML algorithm made code predictions prior to sending the data to the manual coding software; the human coders would then either accept the prediction or correct it, based on the descriptions and other contextual information.

Examples of data editing

• Code 999 were given to minor gaps (less than 60 minutes) in incompleted diaries
• Imputed sleep for otherwise completed diaries if the missing data were in the tail end of the diary and after 22:00
• Imputed sleep for otherwise completed diaries if the misssing data were in the beginning of the diary and before 09:00
• Deleted secondary activities that were coded the same as the main activity
• Deleted secondary acitivities reported when the main activity was "sleep"
• Swapped places between the main acitivity and secondary acitvity in cases where the latter was of primary importance, e.g. caregiving activities
• Episodes with descriptions indicating multiple episodes in one were split up. The duration of the resulting episodes were imputed based on the relative length of identically coded episodes in the data.

Information on sampling design, sampling frame and size is available under the full metadata concepts 18.1.1 - 18.1.3.

TUS is disseminated about every 10 years.

End of data collection: 22 October 2023
National dissemination of statistical data: 5 December 2023
First delivery of microdata to Eurostat: 29 August 2025
Validated and accepted microdata to Eurostat: 5 November 2025

There should be no problems comparing between geographical areas. However, some of the NUTS2 regions have relatively low number of respondents, which results in greater statistical uncertainty and should therfore be interpreted with care.

We have changed the data collection method from paper diaries and CATI-questionnaire to a web application including a web-questionnaire and web diary. There are no major changes in the questions asked and structure of diary. We have published the statistics without breaks in time series at Statistics Norway.