CONCEPT TO BE INCLUDED
Learn how to handle missing values in your data matrix to ensure accurate analysis. Filtering out missing values is crucial for generating reliable insights. Discover effective strategies for dealing with missing data to enhance the quality of your analysis results and decision-making processes.
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Presentation Transcript
CONCEPT TO BE INCLUDED Missing values Filter question DATA MATRIX
AIM Different types of missing values . Correctly handling missing values .
Some types of missing values in surveys Did not participate in survey Non-response
MISSING VALUES in a DATAMATRIX Theoretical variable(s) Normally Non response not included in data matrix Person V2 V3 V5 V1 V4 998 1 1 1 1 0 0 0 1 0 998 1 1 1 1 2 0 2 1 0 998 0 4 0 5 2 1 1 998 8 1 2 3 4 5 6 7 8 9 10 998 0 0 0 1 8 998 998 1 998 998 0 0 1 1 0 998 998 1 998 Unit(s)
Some types of missing values in surveys Did not participate in survey Non-response Question not asked - filter question Filter question (INAP)
Filter- and contingency question Q1. Did you vote in the last national election April 1, 2018? 1. Yes -> Q2. 2. No 997. Do not remember 998. Do not want to say (Only if Q1 = 1, otherwise 999) Q2. What party did you vote for?
MISSING VALUES in a DATAMATRIX Theoretical variable(s) Person V2 V1 1 1 1 0 1 1 1 1 1 1 1 8 6 999 1 2 5 2 1 3 1 2 3 4 5 6 7 8 9 10 Unit(s)
Some types of missing values in surveys Did not participate in survey Question not asked - filter question Question not asked - mistake by interviewer Non-response Filter question (INAP)
Some types of missing values in surveys Did not participate in survey Question not asked - filter question Question not asked - mistake by interviewer Refuses to answer a question in the survey Non-response Filter question (INAP) Item non-response (NA)
Some types of missing values in surveys Did not participate in survey Question not asked - filter question Question not asked - mistake by interviewer Refuses to answer a question in the survey Does not know the answer to a question Non-response Filter question (INAP) Item non-response (NA) Item non-response (DK)
NO ANSWER Some people are against nuclear energy, others are in favour, on a scale from 0 to 10, where 0 is against and 10 is in favour, where do you position yourself?
MISSING VALUES in a DATAMATRIX Theoretical variable(s) Person V2 V3 V1 1 1 1 0 1 1 1 1 1 1 1 8 6 999 1 2 5 2 1 3 1 2 3 4 5 6 7 8 9 10 3 9 10 1 8 7 998 4 8 9 Unit(s)
CONSEQUENCES OF MISSING VALUES Filter-question: no loss of information Does not know: sometimes loss of information - bias Refusals, mistakes: often loss of information, bias (= invalidity).
Example of possible consequences: description Average income and refusals Average
Example of possible consequences: relationship education and income 14 12 10 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 -2
Example of possible consequences: relationship education and income 14 12 10 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 -2
Example of possible consequences: relationship education and income 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 -2 -2
CORRECTLY HANDLING MISSING VALUES AGE: POSSIBLE VALUES 0-120 MISSING VALUES (REFUSALS): 998 COMPUTE the AVERAGE AGE Not all values in a data matrix should be treated as values .
THIS MICROLECTURE Different types of missing values . Correctly handling missing values .
