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Thread: Data with missing values analysis help

  1. #1
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    Data with missing values analysis help

    I have data from a questionnaire asking about how they felt on a few variables (for example quality of sleep) before and after taking a fitness training course. There are 140+ responses.

    However, there are several (maybe about 10) respondents who didn't answer the basic questions about their age and/or gender, although they did answer the ordinal scale questions that came later in the questionnaire (for example quality of sleep).

    I would like to ask two questions about such missing values.

    Would it be statistically acceptable if I keep such observations when I am analyzing only one variable at a time (for example a hypothesis test about only the variable quality of sleep)?

    Could these missing values (age, gender) in the results be remedied with some imputation method, or I must delete all the observations with missing gender and/or age when I go on to jointly analyze at least one of these variables with one/several other answers in the questionnaire?
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  2. #2
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    Re: Data with missing values analysis help

    Hey osku809.

    I took a look at my old notes for missing values [university ones] and the three classifications of missing values are missing completely at random, missing at random, and not missing at random.

    Do you know about any of these?
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  3. #3
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    Re: Data with missing values analysis help

    chiro, can you explain me about missing random and not missing at random?
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  4. #4
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    Re: Data with missing values analysis help

    I dug up my notes and the definitions are as follows:

    P(Ri | Y(i,O), Y(i,M), Xi) = P(Ri, Y(i,O), Xi) or that Ri is conditionally independent of Y(i,M)

    Y(i,M) - Missing values [dependent variable]
    Y(i,O) - Observed values [dependent variable]
    Ri - Response indicator
    Xi - Independent variable

    The book references the following text - Applied Longitudinal Analysis by Fitzmaurice Et Al in lecture notes.
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