2.1 Data collection

Fieldwork for the OTUS 2023 was carried out between 11 and 19 March 2023. The survey used a representative sample of UK participants selected from the National Centre for Social Research NatCen Panel using a probability-based sampling approach. The Scottish component of the OTUS 2023 drew on the ScotCen Panel (NatCen in the rest of the UK) for responses. This panel is made up of 4,000 people aged 16 and over living in England, Scotland and Wales who were invited to take part after completing the British or Scottish Social Attitudes surveys.

The Scottish sample was made up of 453 females (842 diary days) and 399 males (737 diary days).

Data was weighted to be representative of the Scottish population, taking into account age, ethnicity, sex, employment. Weighting also factored in differences between weekdays and weekends. The weekday diary weights sum up to 5/7 of the total and the weekend weights to 2/7 to make the sample representative of a full 7-day week. The mean weekly time spent on an activity (mean weekly time divided by 7) can be interpreted as mean daily time with all days of the week contributing equally.

Within a participating household, each respondent was asked to complete two pre-allocated diary days, including one weekday and one weekend day. Respondents filled in what they were doing at ten minute intervals during the day, using pre-coded options provided to them via an activities list. Diary entries were then recorded by the participants online where possible, or were contacted over the phone by interviewers who recorded diary information on participants’ behalf. Research by Reg Gatenby found that pre-coded diaries were comparable to self-completion diaries, with minor issues only arising at the detailed code level, due to differences in interpretation of the codes.

The activities reported here combine a number of different codes from the original time use survey under single headings. For example, the activity ‘housework/cooking’ combines a large number of codes concerned with domestic work, i.e. ‘making food and drinks, cooking or washing up’, ‘cleaning, hoovering, tidying house’, ‘washing up’ and ‘ironing, washing or mending clothes’. For a full list of the codes used for each activity, please see the data tables accompanying this report.

2.2 This report and interpreting the results

The findings presented here relate to the average number of minutes per day spent on an activity. This is worked out as the average time per day for all people divided by the proportion of people who participated in the activity multiplied by 100. The amount of time spent per day on an activity might be lower than expected when compared to a hypothetical person’s activity. For example, in Scotland in 2023 the average amount of time spent on paid work was 160 minutes, or 2 hours and 40 minutes. However, this must be understood as the amount of time spent across the whole Scottish sample, not just the proportion of the Scottish sample participating in paid work on a given day.

The sum of all the averaged activities when broken down by the relevant characteristics add up to 24 hours (or 1,440 minutes). However, when looking at developmental and non-developmental childcare amongst the proportion of the sample with children there were some cases where a respondent was doing both forms of childcare at the same time (e.g. helping with homework whilst making lunch for their child). In these cases the activity was double counted in both the developmental and non-developmental categories, and has resulted in these categories totalling to just over 24 hours.

The number of diary days where an activity takes place is provided as a percentage. This percentage is included for an activity where it appears useful and to add further clarification. Full data for the diary day percentages for each category can be found in the data tables accompanying this report.

The current analysis on the relationship between sex and an activity does not control for confounding variables. Confounding, or extraneous variables, are such variables which can affect the variables of interest (in this case sex and an activity) and thus alter the results to make it seem like there is a relationship between the variables of interest even if there is no real relationship between them.

2.3 Establishing significance

Statistical significance was used to determine how certain we are that differences seen in the survey are due to real-world differences between the groups rather than being attributable to random variation in our sample selection. The term ‘significant’ is used to refer to statistical significance, where a result is likely not caused by chance or the variable nature of the samples, and is not intended to imply substantive importance. This report, therefore, has only focused on significant differences.

To make comparisons between time use estimates by sex, we used confidence intervals at the 95% level. This is a standard level of testing and it means that there is a less than 1 in 20 chance to observe a difference between groups that does not actually exist and is just a product of chance. Where two confidence intervals do not overlap, we may infer the two estimates to be significantly different. The width of a confidence interval depends on the precision of the estimate and the confidence level used. A greater standard error will result in a wider interval and, therefore, less precise and less reliable estimate.

More information on the use of confidence intervals in significance testing can be found in the Scottish Health Survey: confidence intervals.

Where results show a difference that is not statistically significant, there may still be a real-world difference not reflected in the current sample. It is not possible to draw any conclusions about the differences that are not significant. It is worth noting that a difference being significant does not necessarily mean that this difference is meaningful, as very small numerical differences could be statistically significant if consistent across the sample.

2.4 Sample size

This refers to the total number of diary days completed for each breakdown. Small sample sizes increase the margin of error and standard deviation, which means the estimates are further away from the true mean and there is higher chance they are affected by random variation in the sample and not due to a real effect. This can distort significance estimates and therefore affect the robustness of statistics.Thus, users are advised to exercise caution where estimates are based on sample sizes of 50 or fewer diary days.