A data dictionary contains important information about variables and data entered into databases
When databases are created during the planning stages of a research study, variable names are created, the codifications of categorical and ordinal variables are chosen, and the scales of measurement for all demographic, predictor, confounding, and outcome variables are specified. The data dictionary serves as a repository for this type of study data. Data dictionaries are created concurrently with databases and provide important information about the variables and data entered into databases.
The data dictionary contains information about each variable and the data that is being collected for each variable. For each variable in a database, researchers should enter the following information into a data dictionary:
1. The name of the variable
2. The codifications or levels of each categorical or ordinal variable
3. The scale of measurement for the variable
Data dictionary examples
Gender - 0 = male and 1 = female, categorical
Gender is the name of the variable, "0 = male and 1 = female" are the codifications for both levels, and the variable is measured at a categorical level.
Another example would be for a Likert-type variable:
Satisfaction - 1 = Strongly Dissatisfied, 2 = Dissatisfied, 3 = Neither Satisfied or Dissatisfied, 4 = Satisfied, and 5 = Strongly Satisfied, ordinal
Satisfaction is the name of the variable, the numerical headings associated with each level is presented, and the variable is measured at an ordinal level.
Finally, an example of a continuous variable:
BMI - Enter the BMI value, continuous
BMI is the name of the variable, the continuous value of BMI is entered, and the variable is measured at a continuous level.
Importance of a data dictionary
Creating an objective and standardized methodology for data entry is EXTREMELY important in the beginning stages of planning a study. Communication and collaboration are also of paramount importance when planning data collection.
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