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WorksheetsCH 6 Transforming Data
Total questions: 27
Worksheet time: 14mins
In which step of the data transformation process would you analyze whether the data has the properties of high-quality data?
data structuring
data standardization
data cleaning
data validation
As part of the data standardization process, often items contained in different fields for the same record need to be combined into a single field. This process is called:
aggregating data
data aggregation
data concatenation
data parsing
Which of the following reasons describes why transforming data is necessary?
Data aggregated at different levels needs to be joined.
Data within a field has various formats.
Multiple data values are contained in the same field and need to be separated.
All of the above
In column 3, which of the following problems do you find?
data consistency error
data imputation error
data contradiction error
violated attribute dependencies
In column 5, which of the following problems do you find?
data pivoting error
violated attribute dependencies
data consistency error
cryptic values
In column 7, row 1, which of the following problems do you find?
data consistency error
data parsing error
data threshold violation
misfielded data value
In row 8 and row 9, which of the following problems do you find?
data contradiction error
data concatenation error
data aggregation error
duplicate values
In column 2, row 7, which of the following problems do you find?
data threshold violation
data entry error
violated attribute dependencies
dichotomous variable problem
Column 4, row 12, is most likely an example of which of the following?
data imputation
cryptic data value
violated attribute dependencies
listing the data in serial date format
Which of the following could be used to catch the problems listed in the figure?
visual inspection
basic statistical tests
auditing a sample
All of the above
Attributes of high-quality data include accurate data. Which of the following is the definition of accurate data?
Data not omitting aspects of events or activities
Data which is correct and free of errors
Data presented in the same format, time after time
Data which measures what is intended to measure
None of the above
High-quality data include which of the following attributes?
a) Accurate
b) Timely
c) Complete
Answers a and b only
Answers a, b, and c
Data structuring:
Describes the way data is stored
Describes the relationships between different fields in the data
Is the process of changing the organization and relationships among data fields
Answers a and c only
Answers a, b, and c
Examples of data structuring include which of the following?
a) Data pivoting
b) Data joining
c) Data parsing
Answers a and b only
Cryptic data values are:
Data items having no apparent meaning without understanding the underlying coding scheme
Data items having an assumed meaning including understanding the underlying coding scheme
Data items combined from two or more fields into a single field
None of the above
Separating data from a single field into multiple fields when performing data standardization is referred to as:
Data parsing
Data concatenation
Data pivoting
All of the above
An example of a data field containing only two different responses, typically 0 or 1, is referred to as:
A dummy variable
A dichotomous variable
Misfielded data values
Answer a and b
None of the above
The process of analyzing data and removing two or more records containing identical information is referred to as what type of data?
Dirty data
Data cleaning
Data de-duplication
All of the above
The process of removing records or fields of information from a data source is referred to as:
Data cleaning
Data de-dupication
Dirty data
Data filtering
The process of replacing a null or missing data value with a substituted value is referred to as:
Data imputation
Data contradiction error
Data threshold violation
Data entry error
Analyzing data before, during and while finalizing the data transformation process is called:
Violated attribute dependencies
Data validation
Data de-duplication
Misfielded data values
Data errors that occur when a particular data value falls outside of a specified, allowable level are referred to as:
Dirty filtering
Misguided data values
Data threshold violations
Violated attribute dependencies
Data pivoting is rotating data from columns to rows.
True
False
Separating data from a single field into multiple fields is called data parsing.
True
False
Data concatenation is combining data from two or more fields into a single field.
True
False
Data cleaning is the process of updating data to be consistent, accurate, and complete.
True
False
The process of removing records or fields of information from a data source is called data filtering.
True
False
