1 SIPP IMPUTATION SCHEME AND DISCUSSION ITEMS Presenters: Nat McKee - Branch Chief Census Bureau Demographic Surveys Division (DSD) Income Surveys Programming Branch (SIPP) Zelda McBride -Supervisor Census Bureau Demographic Surveys Division (DSD) Income Surveys Programming Branch (SIPP) ASA/SRM SIPP WORKING GROUP MEETING September 16, 2008

2 OVERVIEW OF IMPUTATION TYPES OF MISSING DATA Item Non-Response as refusals, blanks, don’t know, incompatible answers Handled via hot deck imputation Unit Non-Response as person level non-interviews or insufficient partial Handled via Type Z and/or hot deck imputation

3 HOT DECK OVERVIEW File is sorted geographically – allocated data likely to come from geographically proximate case Replace missing data items with reported data from another similar person/household

4 EDITING STEPS Before Pass 1 – cold (initial) values are in the decks, missing data is not imputed yet Pass 1 – cold values are replaced by the live hot data but editing is not saved Pass 2 – the last values updated in Pass 1 are the starting Values for the edit pass

GENDER X AGE CATEGORIES INITIAL VALUES What did you have for lunch today? 1-Hamburger 2-Yogurt 3-Salad 4-Chicken 5-Roast Beef 6-Other MaleFemale 1. Under

< VALUES AFTER PASS 1 BEFORE EDITING F Nat, Tracy, Zelda, Jeff, Martha R R M

MF COUNTERS FOR DONOR USAGE

8 IMPUTING FOR MISSING DATA Process sequentially by unit for each section: demographics, household characteristics, labor force, assets, general income, health insurance and program participation If non missing data --- replaces the hot deck value If missing takes the last hot deck value and increments the counter Repeating the same edit program/imputation will give the same results each time (i.e. rerun – no changes – same donors, same results)

9 IMPUTATION MATRICES Matrix defined with stratifying parameters relevant to the item Sex, race, age (with categories) are used frequently in matrices Other specialized relevant variables are used too as when imputing class of worker a recode of industries is used in the matrix

10 USING PREVIOUS WAVE DATA Wave 2+ sometimes use previous wave data as a parameter in the hot deck Advantage – more consistency wave to wave Disadvantage – a particular donor has the potential to influence every wave

11 ALLOCATION FLAGS 0 – no imputation initialized 1 – hot deck imputation 2 – set to cold value 3 – logical (derived) 4 – used previous wave data

12 TYPE Z NONINTERVIEW Type Z Noninterview = Noninterviewed Person Within Interviewed Household: EPPINTVW (Wave 3) Frequency Percent =Noninterview in all 4 months =Interview (Self) =Interview (Proxy =Non-Interview - Type Z =Non-Interview - Psuedo Type Z =Children under 15 during ref period

13 TYPE Z IMPUTATION Type Z Imputation = Hierarchical sorting and merging Operation that matches type Z noninterviews with respondents based on demographic characteristics available for both. Imputes entire record from single donor.

14 ELIGIBILITY FOR TYPE Z IMPUTATION Type Z noninterview Wave 1, or for Wave 2+ no previous wave info available Type Z Eligibility TYPZIMP (Wave 3) Frequency Percent Not Eligible Eligible

15 ELIGIBILITY FOR TYPE Z DONORS Interview or sufficient partial interview sufficient partial = reached first asset question (completed Demographics, Labor Force Recipiency, General Income Recipiency, and Asset Intro.)

16 TYPE Z PROCESS determine if person is type Z or donor, create separate files for type Z and donors

17 TYPE Z PROCESS - CONTINUED create 4 levels of match keys for each person on both files –match keys are based on rotation group plus various demographic variables: age, race, sex, veteran status, marital status, relationship to reference person, educational attainment, parental status, spouse’s interview status –Level 1 keys are the most restrictive, level 4 are the least (designed to always find a match)

18 TYPE Z PROCESS - CONTINUED sort both files by match keys match files select best match for each type Z case: –level 1 match=best level 4=worst transfer data from donor record to type z record for matched cases

19 LITTLE TYPE Z Used in labor force edit to get job and labor force data from a donor

20 QUESTIONS?

21 DISCUSSION ISSUES ON HOW TO IMPROVE CURRENT IMPUTATIONS 1.What do we gain by doing type Z imputations vs. hot deck imputations? What are the trade-offs? 2.What is the threshold (or how should a threshold be determined) for identifying hot-deck overuse for a particular donor/cell? Does this need to be adjusted as the sample size changes (as in the case of a sample cut)?

22 DISCUSSION ISSUES ON HOW TO IMPROVE CURRENT IMPUTATIONS (CONTINUED) 3.What is the threshold (or how should a threshold be determined) for determining cold-deck overuse? 4.How do we determine optimum size for a particular hot deck? Is there a relationship between the number of cells in a hot deck matrix and the number of cases in the universe?

23 DISCUSSION ISSUES ON HOW TO IMPROVE CURRENT IMPUTATIONS (CONTINUED) 5.Currently, we do not distinguish between reported data and imputed data in the stratifying variables for particular hot decks. Do we need to be concerned about this? 6.Any objective, simple way to choose stratifying variables in a hot deck? 7.What methods/criteria should be used to determine quality of imputations?