Quantitative data: Data that describes a quantity, amount, or range. An example of quantitative data would be age, age groups, or salary.
Cell alignment: How information is positioned horizontally and vertically within a cell. If information is not aligned properly, it creates additional groups when creating categories, and may skew calculations.
Null values: Values that are absent or missing from a dataset.
Respondent: An individual who submitted a response to OPO’s survey
Sentiment questions: Survey questions that help to determine a respondent’s opinions on the topic being discussed
Skip logic: A survey feature that changes the follow-up question that a respondent sees based on how they choose to answer a preliminary question.
Syntax: How responses are phrased. To consolidate responses, OPO ensured phrasing was consistent throughout the dataset. For example, if some responses used abbreviations and others did not, OPO made those responses consistent.
Qualitative data: Data that describes qualities, characteristics, or beliefs. An example of qualitative data would be responses related to how the public feels about interactions with APD officers.
Variables: Information (e.g., characteristics, numbers, quantities) measured in an analysis