# FAQ: What is data completeness & how does missing data affect statistics?

There is no need to panic when your charts & graphs are not complete! This could be for several reasons!

We have found that the number one reason for low data numbers is simply that the iPad(s) used on the survey have not been fully sync'd with a network! Once you have connection to the internet, the app will automatically sync & update on our servers.

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Other examples include:

Example #1: You may be made aware by your observers that at no point were 100% of the desks in use. However, in the "Occupancy Overview", it says that the max utilization was 100%.

Whilst filtering by organization and space type, there could be a few tours with only a single observed workspace, and some of those are occupied; since unobserved workspaces are ignored when calculating percentages, this means that 1/1 = 100% of workspaces for that tour are occupied, making it the busiest tour in the study.

In this case, we recommend switching the Occupancy Breakdown chart to percentage mode where you will see these tours more clearly.

This is a somewhat extreme example of how low completeness for a tour can result in inaccurate final results. It will be up to you, the study manager, to make a judgement call based on the Occupancy Breakdown chart.

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Example #2: Sometimes, you may see your "Occupancy Breakdown" bar chart will be missing some data at a certain start time or extending into another tour time.

This is a result of your observer(s) not completing the tour(s) in time to get to the next floor/ area to continue their allocated tour times.

In this case, we recommend revising your tour times or number of observers to make sure all observation points are covered.

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Example #3: Sometimes your observers might not be able to observe a workspace on each tour for access reasons.

In this case, when performing calculations, the platform generally treats missing data as not existing in order to avoid skewing averages up or down. |t does not count un-observed data so the averages should be correct.

Side Note: Occasionally, you can not collect data on for example, a national holiday. This will result in showing a gap in the chart on the dashboard. However, be assured that at the end of your study, the missed data will not contribute to the overall occupancy percentage figures!

If you skip a day tour completely, it's flagged as having no data and ignored. Thus, the calculation is performed as though only four tours exist. A tour that's 80% occupied and 20% unobservable will show as having 80% occupancy, but the chart will indicate that that 20% is flagged as unobservable. You can then make your own conclusion when analysing this data.

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Example #4:

When calculating individual spaces and then combining those results.

Floor #1- Average Occupancy= 6%, Minimum= 0 and Maximum= 100,

Floor #2- Average Occupancy= 5% Minimum= 0 and Maximum= 100,

When combined, the average is 6% but the maximum is only 50%. One would have expected this to be 100% maximum. Why is this?

It's important to note that the maximum occupancy percentage is on a per-tour basis, so for a tour to give 100% occupancy, both workspaces will need to be occupied in the same tour**.**

If you examine the "Occupancy Breakdown" chart, you should see that there's no tour where both are recorded as occupied simultaneously, and as a result the highest occupancy is 1/2=50%.

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Example #5: How does Vantage Space determine if a space is over / at / under occupancy?

The system uses exact values. If a room is designated for 12 people and there are exactly 12 people in it, then it's shown as "at occupancy" Any less, it's shown as under occupancy (or empty), any more and it's over occupancy.

We have found that the number one reason for low data numbers is simply that the iPad(s) used on the survey have not been fully sync'd with a network! Once you have connection to the internet, the app will automatically sync & update on our servers.

______________________________________________________________

Other examples include:

Example #1: You may be made aware by your observers that at no point were 100% of the desks in use. However, in the "Occupancy Overview", it says that the max utilization was 100%.

Whilst filtering by organization and space type, there could be a few tours with only a single observed workspace, and some of those are occupied; since unobserved workspaces are ignored when calculating percentages, this means that 1/1 = 100% of workspaces for that tour are occupied, making it the busiest tour in the study.

In this case, we recommend switching the Occupancy Breakdown chart to percentage mode where you will see these tours more clearly.

This is a somewhat extreme example of how low completeness for a tour can result in inaccurate final results. It will be up to you, the study manager, to make a judgement call based on the Occupancy Breakdown chart.

______________________________________________________________

Example #2: Sometimes, you may see your "Occupancy Breakdown" bar chart will be missing some data at a certain start time or extending into another tour time.

This is a result of your observer(s) not completing the tour(s) in time to get to the next floor/ area to continue their allocated tour times.

In this case, we recommend revising your tour times or number of observers to make sure all observation points are covered.

______________________________________________________________

Example #3: Sometimes your observers might not be able to observe a workspace on each tour for access reasons.

In this case, when performing calculations, the platform generally treats missing data as not existing in order to avoid skewing averages up or down. |t does not count un-observed data so the averages should be correct.

Side Note: Occasionally, you can not collect data on for example, a national holiday. This will result in showing a gap in the chart on the dashboard. However, be assured that at the end of your study, the missed data will not contribute to the overall occupancy percentage figures!

If you skip a day tour completely, it's flagged as having no data and ignored. Thus, the calculation is performed as though only four tours exist. A tour that's 80% occupied and 20% unobservable will show as having 80% occupancy, but the chart will indicate that that 20% is flagged as unobservable. You can then make your own conclusion when analysing this data.

______________________________________________________________

Example #4:

When calculating individual spaces and then combining those results.

Floor #1- Average Occupancy= 6%, Minimum= 0 and Maximum= 100,

Floor #2- Average Occupancy= 5% Minimum= 0 and Maximum= 100,

When combined, the average is 6% but the maximum is only 50%. One would have expected this to be 100% maximum. Why is this?

It's important to note that the maximum occupancy percentage is on a per-tour basis, so for a tour to give 100% occupancy, both workspaces will need to be occupied in the same tour**.**

If you examine the "Occupancy Breakdown" chart, you should see that there's no tour where both are recorded as occupied simultaneously, and as a result the highest occupancy is 1/2=50%.

______________________________________________________________

Example #5: How does Vantage Space determine if a space is over / at / under occupancy?

The system uses exact values. If a room is designated for 12 people and there are exactly 12 people in it, then it's shown as "at occupancy" Any less, it's shown as under occupancy (or empty), any more and it's over occupancy.

Updated on: 08/03/2022

Thank you!