# Presentation: 033957

## 	SIPP IMPUTATION SCHEME AND		DISCUSSION ITEMS

_**Presenters:**_
**Nat McKee - ****	****               Branch Chief**
**	****	****                ****	****Census Bureau**
**	****	****	****	****Demographic Surveys Division (DSD)**
**	****	****	****	****Income Surveys Programming Branch (SIPP)**
**	****	****	****	****301-763-5244**
**Zelda McBride -****	****	****Supervisor**
**	****	****	****	****Census Bureau**
**	****	****	****	****Demographic Surveys Division (DSD)**
**	****	****	****	****Income Surveys Programming Branch (SIPP)**
**	****	****	****	****301-763-2942**
**ASA/SRM SIPP WORKING GROUP MEETING **
**September 16, 2008**
**	****	****	****	**

## 	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

## 	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

## 		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

|      1 |      3 |
| --- | --- |
|      1 |      3 |
|      3 |      3 |

_**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**_

_**Male**_

_**Female**_

_**1.  Under 30**_

_**2.  30 - 64**_

_**3.  65+**_

## VALUES AFTER PASS 1 BEFORE EDITING

|    1 |   3 |
| --- | --- |
|    5 |   2 <-4
   |
|    3 |   3 |

_**VALUES AFTER PASS 1 BEFORE EDITING**_

_**F**_

_**1. **_

_**2. **_

_**3. **_

_**Nat, Tracy, Zelda, Jeff, Martha**_

_** **__**5        2           4       R      R**_

_** **__**M**_

## 1

|     0 |     0 |
| --- | --- |
|     1 |     1 |
|      0 |     0 |

_**1**_

_**2**_

_**3**_

_**M**_

_**F**_

_**COUNTERS FOR DONOR USAGE**_

## 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)****	**

## 	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

## 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

## ALLOCATION FLAGS

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

## TYPE Z NONINTERVIEW

- Type Z Noninterview = Noninterviewed Person Within Interviewed Household:
-                       EPPINTVW (Wave 3)   Frequency     Percent
-  -------------------------------------------------------------
- -1=Noninterview in all 4 months             14254       12.34
-  1=Interview (Self)                                   44912       38.89
-  2=Interview (Proxy                                 29844       25.84
-  3=Non-Interview - Type Z                        3042        2.63
-  4=Non-Interview - Psuedo Type Z           1039        0.90
-  5=Children under 15 during ref period   22404       19.40

## 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.

##   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        2964       72.63
-              Eligible               1117       27.37

**	**

## 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.)

## TYPE Z PROCESS

- determine if person is type Z or donor, create separate files for type Z and donors

## 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)

## 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

## 		LITTLE TYPE Z

- Used in labor force edit to get job and labor force data from a donor

## QUESTIONS?

## DISCUSSION ISSUES ON HOW TO IMPROVE CURRENT IMPUTATIONS

- What do we gain by doing type Z imputations vs. hot deck imputations?  What are the trade-offs?
- 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)?

## DISCUSSION ISSUES ON HOW TO IMPROVE CURRENT IMPUTATIONS (CONTINUED)

- What is the threshold (or how should a threshold be determined) for determining cold-deck overuse?
- 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?

## DISCUSSION ISSUES ON HOW TO IMPROVE CURRENT IMPUTATIONS (CONTINUED)

- 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?
- Any objective, simple way to choose stratifying variables in a hot deck?
- What methods/criteria should be used to determine quality of imputations?