USING THE EPISODE AND ACTIVITY DATASETS
Introduction
The TUS 2006 CURFs comprise six datasets at different levels. One of these datasets relates to 'Episode' level data (TUS06EE for the expanded CURF or TUS06BE for the basic CURF), and another to 'Activity' level data (TUS06EA for the expanded CURF or TUS06BA for the basic CURF). Explanation of the nature and use of the data contained within these datasets is below.
An "episode" relates to a segment of time, whereas an "activity" describes what was being done during that segment of time.
The episode level contains information about episode start time (EPSTART) and episode stop time (EPSTOP), expressed as the number of minutes from midnight (for example, 10 am is 600), episode length (EPLENGTH), which is the duration of the episode in minutes, determined by subtracting the episode start time from the episode stop time, as well as information about where the episode took place, who else was present (their age groups and health characteristics), and who the activity was done for.
The activity level splits each episode into primary and secondary activities. For example, a person can be cooking dinner as their primary activity and can also be watching television which would be their secondary activity. The purpose of the activity level is to explain what was being done during each episode (e.g. sleeping, eating, working).
The activity level datasets present the information in three ways:
The item 'Nature of activity' (NATURACT) details the type of activity based on the current Time Use Activity Classification (refer to Appendix 1).
The item 'Purpose of activity' (PURPMN) also uses the Time Use Activity Classification but it looks at the purpose of the activity. For example, if a person was doing gardening for their elderly mother with a disability then the nature would be "gardening" whereas the purpose would be "caring for adults".
Activity as concorded to 1992 Time Use Activity Classification (CONCORD) details the activity based on the 1992 Time Use Survey Activity Classification.
The episode and activity levels can be used directly for all types of tables apart from tables that require ranged duration of time. Ranged duration of time looks at the total time spent by a person over all episodes which meet a given set of criteria. For example, a person who has three meal episodes in one day lasting 30 minutes each would have a total time for all episodes of eating of 90 minutes. They should therefore be counted in a ranged time table at the 90 minute point. This will only happen if you sum the episode lengths before they are ranged, otherwise, using the above example, each episode would be ranged incorrectly at the 30 minute point.
To sum episode length
In order to sum episode length, the process has been explained in a step by step process below. Following these steps will result in the creation of a new data item that has the total time for all episodes that meet the given set of criteria:
- sort the episode level dataset by day. All records for a particular day must be sorted together. This can be done by sorting on the identifiers for household, family, income unit, person, and day (ABSHID, ABSFID, ABSIID, ABSPID, and ABSDID); and
- for each record in the episode level dataset do as follows:
- if the record is the first for a given day then set the new total field to zero. The "new total field" is the new item being created which will eventually have the total time summed for all episodes of interest;
- if the record meets the criteria for the total then add the duration of the episode to the total. The summing criteria refer to fields on the episode level. For example, you may be interested in episodes that occur in one type of location and/or where the activity was done for a particular type of person. The duration is in minutes and is stored in the item EPLENGTH; and
- if the record is the last for a given day then output a new episode summary record. The "new total field" item should now have summed together all the EPLENGTH values that have met the criteria for that particular day and the last record can be output with the total value.
The summing criteria can also be applied to the activity level but require the episode level fields to be copied down to the activity level before summing.
Copying data from episode to activity level
In order to copy data from the episode level to the activity level:
- sort the episode and activity level datasets by episode. All records for a particular episode must be sorted together. This can be done by sorting on the identifiers for household, family, income unit, person, day, and episode (ABSHID, ABSFID, ABSIID, ABSPID, ABSDID, and ABSEID);
- match the records in the episode and activity level datasets by episode. The records must be matched using the identifiers for household, family, income unit, person, day, and episode (ABSHID, ABSFID, ABSIID, ABSPID, ABSDID, and ABSEID); and
- add the episode level fields to the corresponding activity level records.
These steps will result in a new dataset containing all the episode level information (e.g. episode length) attached to the primary and secondary activity level data (e.g. nature of the activity). This activity level dataset can now be sorted and a "new total field" item created as per the "To sum episode length" steps detailed above. Note, however, that if you choose to include both primary and secondary activities, the algorithm must be modified to avoid double counting.
This step:
- if the record meets the criteria for the total then add the duration of the episode to the total
becomes:
- if the record meets the criteria for the total and the current episode has not yet been counted then add the duration of the episode to the total
Regardless of whether you sum episode length from the episode level or from the activity level, you will end up with a day level dataset containing the "new total field". This can then be ranged and used in the same way as any other day level data item. For example, if you wanted to cross tabulate "Total duration of all episodes of eating (30 minute ranges)" by "Sex of person", you would first need to copy the latter from the person level dataset to the new day level dataset by adapting the "To copy data from the episode level to the activity level" steps above.
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