When a survey includes questions about people as well as questions about events those people have been involved in, the data from that survey can be presented in two levels. One level represents the respondents (people), and the other level represents the events (such as consumption occasions).
When data is presented this way, it is possible to look at all the relationships between the characteristics of the people (such as age, sex, income…) and the characteristics of their “events” (such as when a drink was consumed, what brand was it, where was it consumed…).
In this article
1. Unit of count
In an analysis using a multi-level project, you get the choice of seeing the results based on the different “ways of counting” that are inherent to the levels of the data in your project. For example, the respondents (people) and/or their consumption occasions (occasions).
At the top of every analysis, you can identify the count that is used. The default unit of count is the primary level, for example, Counting: People.
You can interact with the different levels (i.e. people/occasions), by changing the unit of count using the counting option (selector) in the table title. There is no need to specifically include an item from a child level.
The default mode for the unit of count selector is replace. To see multiple units of count in your analysis, your need to switch to add and then select the relevant options.
2. Ratio
On a multi-level project, you can calculate the relationship between levels by selecting the ratio option (N:1) under the modify menu.
This option calculates the number of times one value contains or is contained within the other, for example, the Ratio of Occasions to People.
3. Multi-level analysis example
Ice Cream Brand Tracker is a multi-level survey that includes questions about people, brand awareness, and brand consumption as well as questions about their ice cream consumption occasions within a given day (24 hours). The survey includes respondents aged 18 to 44 years old.
Ice Cream Category Consumers
Metric |
Total Respondents |
Ice Cream Consumers | Interpretation |
People/Consumers Counting: People |
8,106 (123) |
6,328 (123) |
There are 8,106 respondents aged 18 to 44 years old. Out of those, 78% (6,326) are ice cream consumers.
|
100% (%) |
78% (%) |
||
Consumption Occasions Counting: Occasions |
16,292 (123) |
12,685 (123) |
On a typical day, those 6,328 ice cream consumers account for 12,685 consumptions occasions |
Ratio Occasions:People |
2.01 (N:1) |
2.00 (N:1) |
Amongst respondents 18 to 44 years old, the ratio of occasions to people is 2.00 ice cream brands consumption occasions for each person.
|
Cool Nut Consumption Past 24 Hours
For every occasion, respondents are asked what size of Ice Cream they consumed. Cool Nut is a key ice cream brand.
- Small ice cream - is the equivalent of 150 ml.
- Medium ice cream - is the equivalent of 240 ml.
- Large ice cream - is the equivalent of 360 ml.
Metric |
Total |
Cool Nut | Interpretation |
People/Consumers Counting: People
|
8,106 (123) |
3,032 (123) |
Across all occasions, 37% of respondents aged 18 to 44 years old consumed Cool Nut.
|
100% (%) |
37% (%) |
||
Consumption Occasions Counting: Occasions
|
16,292 (123) |
3,772 (123) |
Cool Nut was consumed in 23% of the consumption occasions.
|
100% (%) |
23% (%) |
||
Ratio Occasions: People |
2.01 (N:1) |
1.24 (N:1) |
The ratio of Cool Nut consumption occasions to people is 1.24 consumption occasions for each person.
|
Volume (Litres) Counting: Occasions |
4,021.5 (Σ) |
933.6 (Σ) |
Those 3,032 Cool Nut consumers during those 3,772 occasions reported eating 933.6 litres of Cool Nut ice cream. |
Average Volume (ml.) per Occasion Counting: Occasions
|
247 ml. (AVG) |
248 ml. (AVG) |
The average reported volume per occasion (Cool Nut) is 248 ml. |
4. Multi-level default weights
The following examples outline the application of default weights.
-
If each unit of counting has its own default weight specified, the analysis will use each level-specific weight.
-
If a unit of counting doesn’t have its own default weight specified, its closest ancestor’s weight will apply.
Overriding Default Weights
Users can override the default weight by dragging a weight into the analysis. In the first release, only one override weight can be dragged in. If a user drags a weight into the analysis, that weight will apply to the counts for that unit of counting, and every descendent below that unit, overriding all default weights.
For example:
- If the user drags weight A into the analysis, A, B, C, D, E and F will be weighted using the A weight
- If the user drags weight B into the analysis, B, D and E will be weighted by B, and A, C and F will become unweighted
- If the user drags weight C into the analysis, C and F will be weighted by C, but A, B, D and E will be unweighted
- If the user drags weight D into the analysis, D will be weighted by D, but A, B, C, E and F will be unweighted
- If the user drags weight E into the analysis, E will be weighted by E, but A, B, C, D and F will be unweighted
- If the user drags weight F into the analysis, F will be weighted by F, but A, B, C, D and E will be unweighted
When a weight from a parent is applied to its child, each child record is assigned the value of the parent weight
For example:
- If a record in A has 3 records in B, with 4 D records, and 5 E records, they will have the following weighted counts:
- A = 2 (unweighted = 1)
- B = 6 (unweighted = 3)
- D = 8 (unweighted = 4)
- E = 10 (unweighted = 5)
Where to from here?
Learn more about analysis.