Questions tagged [data-imputation]

Refers to a general class of methods used to "fill in" missing data. Methods used for doing this typically are related to interpolation (http://en.wikipedia.org/wiki/Interpolation) and require assumptions about why the data is missing (e.g. "missing at random")

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votes
3answers
5k views

Multiple imputation on single subscale item or subscale scores?

Recently I am conducting a research on the relationship between motivation/attitude variables (Gardner's model) and English language proficiency in the Philippines. I encountered a problem: missing ...
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2answers
746 views

Best imputation method for stochastic noisy data?

What is the best imputation method for a dataset consisting of stochastic data? For example, let's say you have a table of security returns. In some cases the missings are random, in other cases are ...
2
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1answer
369 views

Multiple imputation using SPSS

I am working with a database with missing data. I have done "Roderick J. A. Little’s chi-square statistic" and knew that my data are not MCAR. However, I know don't have can I determine if there are ...
2
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2answers
1k views

Imputing missing values in time series using SAS

If I have missing values in a time series that has 40 quarters (ten cycles or ten years) of data, what is the best SAS procedure to use to impute the missing values? Part 2: I have 390 series (40 ...
6
votes
0answers
852 views

Canonical correlation analysis on a MICE data set

I am looking to do a canonical correlations analysis (CCA) in R, using the CCA package, on a multiply imputed dataset (obtained from the mice package). I know that ...
6
votes
1answer
164 views

Getting an average measurement based on two raters for cases where data is missing for one rater

Context: I'm investigating behaviour in a clinical study involving children. I had both parents and teachers completing questionnaires to inform an understanding of the same underlying constructs, ...
5
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2answers
657 views

Advice on missing value imputation

I am working on insurance data in which a customer has a field named customer_no_dependent (customer's number of dependent). Its coming out to be a significant ...
2
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1answer
1k views

Multiple imputation for clustered data

I have a few questions regarding multiple imputation for nested data. Context: I have repeated measures (4 times) from a survey and these are clustered in workplaces (205 workplaces). There are about ...
4
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1answer
472 views

Means and imputation of log-normal variables

The geometric mean is the appropriate measure of central tendency for log-normally distributed variables. However, the arithmetic mean still has some use in relation to log-normal variables - in ...
7
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1answer
267 views

Dealing with missing data due to variable not being measured over initial period of a study

I was recently consulting a researcher in the following situation. Context: data were collected over four years at around 50 participants per year (participants had a specific diagnosed clinical ...
21
votes
3answers
3k views

How to combine confidence intervals for a variance component of a mixed-effects model when using multiple imputation

The logic of multiple imputation (MI) is to impute the missing values not once but several (typically M=5) times, resulting in M completed datasets. The M completed datasets are then analyzed with ...
7
votes
1answer
403 views

How can I apply a Pareto tail to a truncated distribution?

Many income surveys (especially older ones) truncate key variables, such as household income, at some arbitrary point, to protect confidentiality. This point changes over time. This reduces inequality ...

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