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Data dictionary

As explained in Starting Data Analytics with Generative AI and Python

A data dictionary is a reference that describes a dataset's variables, types, scales, meanings, codes, and transformations.

A data dictionary is a compact reference for each dataset variable: its type, measurement scale, meaning, and any coding or transformation applied. It helps determine which research questions the data can actually answer before an analytics process begins. section 3.1

Record at least:

  • Variable descriptions and types, such as continuous, ordinal, or nominal. section 3.1
  • Measurement scales, including interval or ratio. section 3.1
  • Codes and transformations applied to the data. section 3.1

Before choosing a research question

After cleaning and assessing a data inventory, compare proposed questions with the dictionary. A missing variable, unclear coding, or unsuitable measurement scale may show that a question is not answerable. section 3.1

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Defines the metadata to record but stops short of prescribing a detailed dictionary format or implementation workflow.
Examples
Uses continuous, ordinal, and nominal variables, interval and ratio scales, and documented coding or transformations.
What it assumes
Assumes a cleaned and assessed data inventory while identifying the metadata needed to clarify variables from the outset.
What you can do after
Evaluate whether available data supports particular research questions before designing the analytics process.
How it connects
Connects variable metadata with data sources, sampling methods, domain knowledge, research questions, and analytical limitations.
Cover of Starting Data Analytics with Generative AI and Python
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