Unit of Observation

The unit of observation is the “who” or “what” about which survey data is collected and analysis is focused. Common examples include individual, household, or community. Clearly identifying your unit of observation in datasets and project folders will lead to a more efficient workflow and a more accurate analysis. 

Read First

• Mistakes related to unit of observation introduce bias into analyses. Always double check the unit of observation before working with data.

Definition

The unit of observation is the “who” or “what” about which survey data is collected and analysis is focused. Common examples include individual, household, or community. Note that the unit of observation refers to the category, type, or classification of data -- not to specific parties. For example, while student and school are units of observation, “Ali Jones” or “Cedar Elementary School” are not. 

Confirming the Unit of Observation

Just as distance data does not make sense until we know whether its unit is miles or kilometers, survey data and any resulting analyses do not make sense until we know the unit of observation. In many cases, there is seemingly little risk for confusion in terms of unit of observation. We often have a good intuition for the unit of observation at the first glance of a dataset or a file name. However, always test that your assumption is correct: errors due to an unclear understanding of unit of observation are more common than one might imagine. When working with a dataset that you have not created yourself, start by clearly identifying the unit of observation. The most obvious way to do so is by asking the person from whom you received the dataset.

Consider, however, that you have a dataset for which you do not know the unit of observation and you cannot reach the person from whom you received the dataset. You believe that the unit of observation is household. To confirm, open up the dataset, look for a household ID variable and test if it is uniquely and fully identifying the dataset. If this is the case, then you are done. However, if you do not find such variable, search for other information that uniquely and fully identifies the dataset. In this case, for example, look for variables with information of household head name. Test if this variable uniquely identifies all observations. Names are often not unique across a country, so you might have to add region name and village name to the test. Once you have found the information that uniquely and fully identifies the dataset, make sure you create an appropriate ID variable accordingly if it does not yet exist.

Note that a dataset is always incorrectly constructed if it has more than one unit of observation. Even if the two units of observation have the same variables, it is incorrect, bad practice, and a huge source of error if they were included in the same dataset. All such datasets should be separated into two datasets.

Applications

The examples below all have many similarities to how unit of observation is used in the context of a dataset. They are included to give further explanation to the concept or highlight small differences in usage.

Regressions

In a regression, N (or the number of observations) represents the unit of observation. A correct interpretation of the regression depends on a clear understanding of the unit of observation. In most cases this is trivial, but not always. Consider, for example, monitoring data that is believed to have the unit of observation "households," though its true unit of observation is "packages distributed to households." Since the vast majority of households only received one package each, it is easy yet problematic to make this mistake.

Note that some regressions collapse your dataset, so the unit of observation in the regression is different from the unit of observation in your dataset. This is one example when unit of observation cannot described as a row in a dataset.

Surveys

The concept of unit of observation can also be used to describe for example surveys. The unit of observation in a survey is the type of respondent. For example, household, company, school etc. In the cases of company and school the respondent is a person, for example the CEO or the principal, but they provide answers about the company or the school. If they would be asked questions about themselves, then the unit of observation would be CEOs and principals.

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This article is part of the topic Data Management

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