Missing data - reasons for screening data, Advanced Statistics

Missing Data - Reasons for screening data

In case of any missing data, the researcher needs to conduct tests to ascertain that the pattern of these missing cases is random.

Create dichotomous variable - non-missing vs missing for a specific variable. Run a simple independent samples t-test on a different variable in the collected sample to see if there are any significant differences.

Handling missing values:

1. Delete missing data (good idea if there are only a few missing cases)

2. Delete variables containing missing values (good idea if most of the missing values are concentrated to only a couple of variables. Still problematic if they are important to the ultimate goal of the research)

3. Estimate missing values

4. Prior knowledge

5. Replace missing values with the mean (main concern: lowers the calculated variance as compared to the unknown actual variance)
One variation involves using group means for missing values for cases involving group comparison analysis

6. Regression approach: use several IVs to explain the DV (that includes several missing values). Predict missing values using IV values.

7. Concerns include finding proper IVs that explain DV, estimates obtained from prediction more consistent with the scores used to predict them compared to the real values.

8. When we use any of the techniques described above, as a researcher we have to ascertain that our solution hasn't changed the results of the analysis (run the tests, with and without the treatment).

Posted Date: 3/4/2013 6:07:24 AM | Location : United States

Related Discussions:- Missing data - reasons for screening data, Assignment Help, Ask Question on Missing data - reasons for screening data, Get Answer, Expert's Help, Missing data - reasons for screening data Discussions

Write discussion on Missing data - reasons for screening data
Your posts are moderated
Related Questions
Behrens Fisher problem : The difficulty of testing for the equality of the means of the two normal distributions which do not have the equal variance. Various test statistics have

Matching is the method of making a study group and a comparison group comparable with respect to the extraneous factors. Generally used in the retrospective studies when selecting

Quality-adjusted survival analysis is a method for evaluating the effects of treatment on survival which allows the consideration of quality of life as well as the quantity of lif

It is the survey which is carried out in Great Britain on a continuous basis since 1971. About 100 000 households are included in this sample every year. The main goal of the surve

Normal 0 false false false EN-US X-NONE X-NONE

A theorem which shows that any counting process may be uniquely decomposed as the sum of a martingale and a predictable, right-continous process called the compensator, assuming ce

Geo statistics: The body of methods useful for understanding and modelling spatial variability in a course of interest. Central to these techniques is the idea that measurements t

In the network shown below, the rst of the two numbers on each arc indicates the arc capacity and the second (in parentheses) of the two numbers indicates the current  flow. Use t

hello I have a dataset including both categorical & numerical variable for market segmentation.how can i cluster them via k-means in matlab? thank you

Centile reference charts : Charts which are used inmedicine to observe the clinical measurements on individual patients in the context of the population values. If the population i