This presentation was supported in part by the U.S. 

Department of Agriculture, Economic Research Service.

The findings and conclusions in this presentation are 

those of the author(s) and should not be construed to 

represent any official USDA or U.S. Government 

determination or policy.

"Cash receipts and till slips" by Gratuit CC BY 3.0

FoodAPS: Alternative Data Collection Methods

Is "Proof of Purchase" Really Proof?

Adam . Kaderabek

University of Michigan 

Institute for Social Research

Program in Survey & Data Science

Prepared and Presented in Collaboration with:

Yajuan Si, Institute for Social Research

Brady T. West, Institute for Social Research

Jeffrey M. Gonzalez, USDA – Economic Research Service

Elina T. Page, USDA – Economic Research Service

John A. Kirlin, Kirlin Analytical Services
Background

• FoodAPS-1 was a nationally representative survey that collected data about household

food purchases and acquisitions.

• The Alternative Data Collection Method (ADCM) study was conducted to test using a

web-based app to collect data. The app included the ability to upload images of receipts.

• The ADCM aimed to investigate the likelihood respondents would provide receipts, and if

the receipt data could be used to reduce the reporting burden and improve data quality.

• From the events where a receipt was available, a SRS of 100 food away from home

(FAFH) and 100 food at home (FAH) events was taken and receipt data was compared to

the respondent’s report.

• Sampled events displayed variation in receipt quality, indicating several factors were

influencing the reliability of the receipts for data validation.

2
Expectation of Receipt

Receipt Expected group (3,112 events):

Grocery stores, restaurants/bars, 

convenience stores, club stores, and 

superstores/big box stores.

3

581, 19%

931, 30%

666, 21%

934, 30%

Receipt Expected Events (n=3,112)

FAFH with Receipt

FAFH without Receipt

FAH with Receipt

FAH without Receipt

No Receipt Expected group (1,457 events): 

School meals (including before and after 

school care), work, vending machines, and 

friend’s or family’s place. 

Receipt Possible (336 events): 

Farmers markets, food pantries, soup 

kitchens and locales defined as 禦other魚.
Research Questions

1. Are there relationships between the participants, their

households, and food-events that can predict the

probability a respondent will provide a receipt?

2. Can modeling these relationships identify opportunities

for interventions that may influence respondent

behavior and in turn improve data quality?

4
Receipt Predictors

5

Event Level Respondent Level Household Level

Report Mode/Application Age Household Size

Smartphone or Computer 18-29; 30-39; 40-49; 50-59; 60+ years old 1, 2, 3, 4, or 5+ person(s)

Event Type Gender Interview Length

FAFH or FAH Female or Male 11-20 min; 21-30 min; 31-40 min; 40+ min

Item Count Race SNAP Status

(received SNAP benefits in prior month)

Few items or Many items White, Black, Other (including Hispanic) Yes or No

Event Cost Education

Low cost or High cost High school or less or More than high school

Number of Participants Employment

One or Multiple In labor force or Not in labor force

Primary Respondent for Household

Yes or No
Model Specification

6

Initial Model with 14 covariates

Event Type, App, Item Count, Event Cost, Participants, Age, Gender, Race, 

Education, Employment, Primary R, Household Size, Int. Length, SNAP 

Stepwise BIC Model with 8 covariates (+7 interactions)

Event Type, App, Item Count, Event Cost, Participants, Age, Gender, Race, 

Education, Employment, Primary R, Household Size, Int. Length, SNAP

Education*Household Size, App*Employment, Event Type*Item Count, 

App*Education, App*Household Size, Race*SNAP, App*SNAP

Adjusted R2 = 0.17

AIC = 3231.40

BIC = 3371.87

ROC Plot Reference Model

Adjusted R2 = 0.25

AIC = 3047.30

BIC = 3205.28

ROC Plot Final Model
Household Size & Education

7
Application/Mode & Employment

8
Event Type & Item Count

9
Application/Mode & Education

10
Application/Mode & Household Size

11
Application/Mode & SNAP Status

12
SNAP Status & Race 

13
Key Takeaways

• Probability of a receipt among smartphone reports appear stable compared to

computer reports. The use of a mobile format (i.e., smartphone) seems like a

reliable method for encouraging respondent cooperation.

• Probability of a receipt is higher for FAH events and events with more items. It’s

possible the influence of item count is one that increases the saliency of the event.

• Dynamics of FAFH events may be substantively different than FAH events. Events

could be less significant to respondents and more difficult to report promptly

resulting in lower rates of receipt submission.

• Extra effort may be needed to engage SNAP beneficiaries, particularly among

white and black households.

14
Next Steps?

•FoodAPS-2 Field Test

•FoodLogger Application

•Formalize Task Protocol

•Identify Compliance Issues

15
This presentation was supported in part by the U.S. 

Department of Agriculture, Economic Research Service.

The findings and conclusions in this presentation are 

those of the author(s) and should not be construed to 

represent any official USDA or U.S. Government 

determination or policy.

"Cash receipts and till slips" by Gratuit CC BY 3.0

Thank you!

Prepared and Presented in Collaboration with:

Yajuan Si, Institute for Social Research

Jeffrey M. Gonzalez, USDA – Economic Research Service

Elina T. Page, USDA – Economic Research Service

Brady T. West, Institute for Social Research

John A. Kirlin, Kirlin Analytical Services

Adam . Kaderabek

amkad@umich.edu

University of Michigan 

Institute for Social Research

Program in Survey & Data Science