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Some birds, a cool cat and a wolf

Explore different methods to handle missing data in statistical computing with a focus on sample datasets, levels of measurement, patterns of missingness, and various techniques like Listwise Deletion, Multiple Imputation, and Modeling. Learn how to choose the most suitable approach based on the characteristics of your data. ###

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Some birds, a cool cat and a wolf

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  1. Tricks of the trade Some birds, a cool cat and a wolf Dick Wiggins, City University, London Gopal Netuveli, Imperial College, University of London RSS Official Statistics/Statistical Computing Section 18th May 2005

  2. Acknowledgments • Economic and Social Research Council • Human Capability and Resilience Network

  3. Missing data is a pervasive fact of life.

  4. Sample dataset 100 records randomly selected from British Household Panel Survey with the condition that all cases had complete information on age, sex and socio-economic position. The data contains variables selected from wave 1 and wave11.

  5. Terminology Unit nonresponse: complete absence of any information from a sampled individual or case. Item nonresponse: an individual who cooperates but for some reason has missing values for certain items. Attrition: In longitudinal data, attrition is the cumulative rate of unit nonresponse across waves.

  6. Levels of measurement • Nominal • Values are just names e.g. 1 = male 2 = female • Ordinal • Inherent ranking, but intervals are not equal e.g. RG’s social class • Interval • Numerical, intervals are meaningful, but no zero e.g. temperature scales Celsius and Farenheit • Ratio • Numerical, meaningful intervals, zero defined e.g. height, income

  7. How is your measure distributed? The distribution of the measure is important and needs to be specified.

  8. Percentage of missingness (Lambda) = number of missing values/number of values *100 • Pattern of missingness -monotone Lambda for both monotone and non-monotone missingness = 820/3500 = 23.4

  9. Process of missingness • Missing completely at random (MCAR) assumes that missing values are a simple random sample of all data values. • Missing at random (MAR) assumes that missing values are a simple random sample of all data values with in subclasses defined by observed data. • Missing not at random (MNAR)

  10. MCAR, MAR, MNAR Let Y represent the data which actually consists of Yobs (observed data) and Ymis (missing data) Let the missingness be described by a binary variable R R = 1 if data is missing, 0 otherwise Then a simple way of describing the pattern of missingness will be by evaluating the probability P(R=1) using the data Y. P(R=1|Y) In MCAR we can not evaluate that probability using Y In MAR we assume we can evaluate the probability using Yobs, Ymis is not needed In MNAR, we need both Yobs & Ymis to evaluate the probability

  11. Dick’s menagerie • The Ostrich • The Hawk • The Cuckoo • The Owl • The Pussycat • The Wolf

  12. The Ostrich aka Listwise Deletion Ignores missingness i.e. assumes MCAR and drops all cases with missing values. The Hawk aka ad hoc methods Ad hoc methods used are pairwise deletion, mean substituition, last value carry forward

  13. The Cuckoo aka hot decking Like the cuckoo, hot decking ‘steals’ from other complete records to replace missing records The choice of the complete record is based on a set of observed variables so that the complete and the missing records are as much similar as possible Substituting from an adjacent record is a very simple application of this principle on the assumption that adjacent records will be very similar

  14. The Owl aka Multiple imputation • Works with standard complete-data analysis methods • One set of imputations may be used for many analyses • Can be highly efficient

  15. Efficiency= 1/(1+(proportion missing/No. of imputations))

  16. Rubin’s rule for combining estimate Point estimate: Average of point estimates from each imputed sample Variance estimate: Average of within imputation variance + between imputation variance inflated by a factor equal to (1+(1/number of imputations))

  17. The Pussy Cat – Modelling(Heckman 2 step procedure) • What is modelled? • The probability of having a missing value based on fully observed characteristics (e.g. age, sex, socio-economic status) AND • The model of interest (e.g. predictors of casp19)

  18. Equations P(R=1) = f (age, sex, ses) Step 1 CASP-19= f (age, sex, financial situation, social network, P(R=1)) Step 2

  19. Strengths and weaknesses • Strength: Useful for sensitivity analysis. If the error terms in step 1 and step 2 are significantly correlated then MNAR should be considered. • Weakness: Full information needed on variables in step 1

  20. Setting up the illustration in STATA • Listwise: default • Hotdeck single imputation • Multiple imputation m=5 • Heckman ML

  21. Comparison of results from different methods used to manage missingness Significant coefficients are emboldened Hot deck stratification by agegr & sex Heckman sample equation = -0.08 agegr+0.06 sex+ -0.12 ses Rho (correlation of errors terms in selection and sustantive equations) significantly different from 0. (p <0.0001). MNAR to be considered.

  22. Advice • Don’t be an Ostrich • Ignore the Hawk • Be the Cuckoo if Lambda is small • Otherwise, use the Owl • Always stroke the Pussy Cat • Await the Wolf

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