United States

Department of

Agriculture

Food and

Consumer

Service

Office of

Analysis and

Evaluation

 Household Food

 Security in the

 United States

 in 1995

Technical Report

 of the Food Security

Measurement

Project

 September 1997

 

Prepared for:

 Gary W. Bickel, Project Officer

 U.S. Department of Agriculture

 Food and Consumer Service

 3101 Park Center Drive

 Alexandria, VA 22302

 under contract no. 53-3198-5-028

 Prepared by:

 William L. Hamilton, Project Director

a

 John T. Cook, Principal Investigatorb

 William W. Thompson

a

 Lawrence F. Buron

a

 Edward A. Frongillo, Jr. c

 Christine M. Olson

c

 Cheryl A. Wehler

d

 a Abt Associates, Inc.

 

b

 Tufts University Center on Hunger,

 Poverty, and Nutrition Policy

 

c

 Cornell University Division of

 Nutritional Sciences

 

d

 C.A.W. and Associates
TABLE OF CONTENTS

Chapter One INTRODUCTION ..................................... 1

Chapter Two METHODS AND RESULTS OF FITTING LINEAR AND

NON-LINEAR FACTOR ANALYSIS MODELS TO CPS DATA .... 5

2.1 Preliminary Linear Factor Analysis ..................... 8

2.2 Exploratory Two-Parameter Non-linear Factor Analysis Model . . 10

2.3 Unidimensional One-Parameter Non-linear Factor Analysis

Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Chapter Three RELIABILITY ESTIMATES FOR THE FOOD SECURITY SCALES 29

3.1 Spearman-Brown Split-half Reliability Estimates . . . . . . . . . . . 31

3.2 Rulon’s Split-Half Reliability Estimates . . . . . . . . . . . . . . . . . . 33

3.3 Cronbach’s Alpha Reliability Estimates . . . . . . . . . . . . . . . . . . 34

3.4 Rasch Model Reliability Estimates . . . . . . . . . . . . . . . . . . . . . 35

3.5 Reliability in Identifying Cases with No Food Insecurity

Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Chapter Four DEFINING RANGES OF THE FOOD SECURITY SCALE . . . . . . . . 43

4.1 Conceptual Basis for a Categorical Food Security Status

Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.2 Defining Ranges and Selecting Scale Cutpoints . . . . . . . . . . . . 45

4.3 Evidence of Food Insecurity . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.4 Subjective Reporting of Hunger . . . . . . . . . . . . . . . . . . . . . . . 53

4.5 Evidence of Child Hunger and Severe Adult Hunger . . . . . . . . . 56

4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Chapter Five THE RESOURCE AUGMENTATION QUESTIONS . . . . . . . . . . . . . 61

5.1 Two Dimensions of Food Insecurity . . . . . . . . . . . . . . . . . . . . 61

5.2 The Composite Resource Augmentation Index . . . . . . . . . . . . . 65

5.3 Effects of Using the Composite Resource Augmentation Index . . 66

5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Chapter Six EXTERNAL CONSTRUCT VALIDATION OF THE FOOD

SECURITY MEASURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

6.1 Relationship of Construct Validation Items to Food Security . . . 69

6.2 Weekly Food Expenditures per Household Member . . . . . . . . . 70

6.3 Household Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

6.4 Food Sufficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Table of Contents

Chapter Seven PROCEDURES FOR CALCULATING STANDARD ERRORS FOR

FOOD SECURITY PREVALENCE ESTIMATES . . . . . . . . . . . . . . . 79

7.1 CPS Sample Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

7.2 Adjustment Factor for Between-PSU Variance . . . . . . . . . . . . . 80

7.3 Estimation of Within-PSU Variance . . . . . . . . . . . . . . . . . . . . 81

7.4 Calculation of the Standard Errors . . . . . . . . . . . . . . . . . . . . . 84

Chapter Eight POTENTIAL SOURCES OF BIAS IN PREVALENCE ESTIMATES . 85

8.1 Screening Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

8.2 Response Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

8.3 Random Error in Survey Responses . . . . . . . . . . . . . . . . . . . . 89

8.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Appendix A REVIEW OF LITERATURE FROM PHYSIOLOGY AND CLINICAL

NUTRITION RESEARCH ADDRESSING THE NATURE OF HUNGER

Appendix B PREVALENCE OF HOUSEHOLD FOOD SECURITY STATUS (30-DAY

SCALE)

Appendix C PARTICIPANTS IN FEDERAL INTERAGENCY WORKING GROUP

FOR FOOD SECURITY MEASUREMENT
CHAPTER ONE

INTRODUCTION

In April 1995, the U.S. Bureau of the Census conducted the first collection of

comprehensive food security data as a supplement to its regular Current Population Survey (CPS).

With about 45,000 household interviews, this survey is the first to collect the special data needed

to measure food insecurity and hunger in a nationally-representative sample of U.S. households.

The Food and Consumer Service (FCS) of the U.S. Department of Agriculture led the

effort to develop the Food Security Supplement to the CPS, building on research conducted at

universities and elsewhere over the past decade. After the survey was conducted, the next step

was to analyze the data to create measurement scales that gauge households' levels of severity

of food insecurity and hunger. FCS contracted with Abt Associates Inc. and three subcontractors

— the Tufts University Center on Hunger, Poverty, and Nutrition Policy; the Cornell University

Division of Nutritional Sciences; and CAW and Associates — to carry out the scale construction

analysis.

The results of that analysis are presented in Household Food Security in the United

States in 1995: Summary Report of the Food Security Measurement Project, to which this report

is a companion volume. The purpose of this report is to describe the analyses through which the

food security scales and food security status variable were developed, as well as related tests of

the reliability and validity of these measures.

Two scales were developed to measure the degree of food insecurity and hunger in

American households. One measures food insecurity and hunger over the period of the 12

months prior to the survey interview, and the second measures these conditions in the 30 days

immediately preceding the interview. After a number of exploratory analyses, a type of nonlinear factor analysis known as a Rasch model was used to form the scales. This methodology

and the procedures through which it was applied are described in Chapter Two.

The two scales were subjected to a variety of tests of reliability, including tests specific

to the Rasch model and more traditional tests commonly used with scales developed through

linear factor analysis. The results, presented in Chapter Three, generally indicate good reliability

for the 12-month scale. The 30-day scale, because it is based on a smaller number of questions

Prepared by Abt Associates Inc. 1
Chapter One: Introduction

and provides detailed measurement for a narrower portion of the food insecurity spectrum, has

somewhat lower reliability.

The two scales serve as the basis for defining two corresponding food security status

variables. The 12-month variable has four categories: (1) Food Secure; (2) Food Insecure with

No Hunger Evident; (3) Food Insecure with Moderate Hunger Evident; and (4) Food Insecure

with Severe Hunger Evident. The 30-day scale has three categories: (1) No Hunger Evident;

(2) Food Insecure with Moderate Hunger Evident; and (3) Food Insecure with Severe Hunger

Evident.

To classify households into the various categories, it was necessary to define ranges on

the 12-month and 30-day scales that correspond to each category. The rationale for the range

definitions is described in Chapter Four.

The food security scale and the food security status indicator represent a central

dimension of food insecurity: availability of enough food for the household to meet basic needs.

The concept of food insecurity has other dimensions, however, including the specification that

households should be able to acquire food in socially acceptable ways. Because the CPS

Supplement includes several indicators of “coping” or “resource augmentation” behaviors related

to this dimension of food insecurity, the possibility was explored of supplementing the primary

food security scale with an index of resource augmentation actions. The analysis, described in

Chapter Five, suggests that such an index should not be used in classifying households' food

security status at this time.

A key question for any new scale is how accurately it represents the condition it

attempts to measure. Ideally, one would compare the food security scales and status variables

to some more definitive measure or measures of food insecurity and hunger. Because no such

definitive measure exists, the best way to judge the measure is to assess its relationship to other

measures thought to be related to food insecurity and hunger, such as the household's level of

food expenditures or its total income. Chapter Six presents the results of such analyses, which

show relationships of the sort that would be expected with a valid measure of food insecurity and

hunger.

The central purpose of the food security scales and the status variables is to assess the

food security of the U.S. population and of subgroups within the population. Estimates of the

prevalence of food insecurity and hunger are presented in the study’s main report, based on the

Prepared by Abt Associates Inc. 2
Chapter One: Introduction

April 1995 data. Because these data come from a sample of households, prevalence estimates

are subject to sampling error, and the report therefore presents estimated standard errors

corresponding to the estimated prevalences. The estimation of standard errors is complicated by

the multi-stage sampling design used by the CPS. Chapter Seven describes the methodology used

in the estimation of standard errors.

Finally, Chapter Eight discusses the potential sources of bias in prevalence estimates that

might result from the sample design of the CPS, from household response behaviors to the Food

Security Supplement, and from the fact that only a small proportion of the population experiences

food insecurity. The analysis indicates that the various potential sources of bias probably lead

to quite small levels of estimation error in counterbalancing directions.

Prepared by Abt Associates Inc. 3
CHAPTER TWO

METHODS AND RESULTS OF SCALING ANALYSIS OF CPS DATA

This section describes the rationale and the results of conducting preliminary linear

factor analyses and subsequently fitting a series of non-linear factor analysis models to the CPS

food security data. This latter analysis approach more accurately characterizes the covariation

among items in the CPS data set than more traditional linear factor analysis models. Most items

available for analysis in the CPS data set were severely skewed and dichotomous or categorical

in nature. Therefore, a number of statistical assumptions were violated using the linear factor

analysis methods with the CPS items, such as the assumption of normally distributed error

variance. Such situations can be dealt with more appropriately using non-linear scaling

techniques.

Item Response Theory (IRT) describes a general model that was developed by the

educational testing industry to assist in creating valid and reliable aptitude tests, such as the

Scholastic Aptitude Test (SAT) and the American College Testing Program (ACT) test. When

applying a particular IRT model to data, the test designer usually assumes that the responses to

a set of items can be accounted for by latent traits or factors that are fewer in number than the

test items. The primary goal is to determine how an individual with a certain ability level will

respond to an item associated with a particular difficulty level. There are a number of alternative

forms the IRT model can take, depending on the assumptions regarding how the underlying data

were generated.

The three most frequently discussed IRT models in the literature are (1) the threeparameter logistic model, (2) the two-parameter logistic model, and (3) the one-parameter logistic

model. The three-parameter logistic IRT model is the most complex, and can include varying

discrimination parameters, varying difficulty levels, and varying guessing parameters. Using the

notation of Hambleton (1983),1 the three-parameter logistic model can be written as follows:

1 Hambleton, R.K. (ed.), Application of Item Response Theory, Vancouver: Educational Research Institute

of British Columbia, 1983.

Prepared by Abt Associates Inc. 5
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

where

P (1) ni(θn) ci (1 ci) e

Dai(θn bi)

1 e

Dai(θn bi)

θn = latent trait score of person n,

ai = item discrimination parameter for item I

bi = item difficulty for item I,

ci = guessing parameter for item I,

n = person, and

I = item.

The two-parameter logistic model assumes that guessing does not occur, and therefore

the guessing term is dropped from the model. The two-parameter logistic model can be

expressed as follows:

where

P (2) ni(θn) e

Dai(θn bi)

1 e

Dai(θn bi)

θn = latent trait score of person n,

ai = item discrimination parameter for item I,

bi = item difficulty for item I,

n = person, and

I = item.

Finally, the one-parameter logistic model is a more straightforward model relative to the

two previous models, because the model (1) has no guessing parameters, and (2) specifies that

all items have the same discrimination parameter (a ̄). That is, the slopes of the itemcharacteristic curves are constrained to be equal for all items. The model can be written as

follows:

Prepared by Abt Associates Inc. 6
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

where

P (3) ni(θn) e

Da(θn bi)

1 e

Da(θn bi)

θn = latent trait score of person n,

a ̄ = average item discrimination parameter for item I,

bi = item difficulty for item I,

n = person, and

I = item.

Because D and a ̄ are constants in the model, the one-parameter logistic model can be

written in a more simplified form:

We can also express this model using the notation of Wright and Masters (1982):

P (3) ni(θn) e (θn bi )

1 e (θn bi )

where

P (4) nik (θ) e [βn (δi τk

)]

1 e [βn (δi τk

)]

βn = latent trait score of person n,

δi = item difficulty for item I,

τk = threshold parameter for step k of item I,

n = person,

I = item, and

k = step,

and include a threshold parameter that is associated with the rating scale model developed by

Andrich (1978, 1979).

Prepared by Abt Associates Inc. 7
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

2.1 PRELIMINARY LINEAR FACTOR ANALYSIS

The CPS Food Security Supplement builds on a substantial amount of recent research

on the measurement of food insecurity, some of which included scaling analysis.2 The first

analytic step was to replicate some of the prior analyses to determine whether the general patterns

and relationships in the data were similar to those seen in prior work.

A series of linear factor analyses were fit to the CPS data. One illustrative model,

summarized in Exhibit 2-1, was fit for households with children (because this group was asked

all questions in the Supplement). The factor model incorporated a Procrustes rotation, which

allows one to rotate to a pre-specified factor solution, where the solution was specified to

represent the dominant themes of the prior research. Fitting the factor analysis model resulted

in three factors with eigenvalues greater than 1.0 prior to rotation (15.0, 1.6, and 1.4), with factor

loadings as shown in the exhibit. The first factor includes primarily items related to child food

intake reductions and hunger, the second consists mainly of household-level food insecurity

items, and the third comprises mainly items related to adult food intake reduction and hunger.

In sum, the results generally confirmed that the response patterns in the CPS data were

similar to those seen in prior research and that similar relationships might be expected to exist.

In addition, the large positive factor intercorrelations suggested the possibility that non-linear

factor analysis methods might result in the items loading onto a single factor (i.e., that the

separation of factors could occur in part because of the limitations of linear factor analysis in

handling low-frequency dichotomous items). Finally, exploratory analyses of groups of

households without children suggested that, for those items applicable to all groups, the factors

might be relatively invariant across groups.

2.2 EXPLORATORY TWO-PARAMETER NON-LINEAR FACTOR ANALYSIS MODEL

2 Two key prior studies are Olson, Frongillo, and Kendall (1995), and Scott, Wehler, and Anderson (1995).

The first study estimated a factor analysis model including four items from the Community Childhood Hunger

Identification Project (CCHIP) and ten items from two previous Cornell surveys. The analysis identified two

key factors, one associated with household-level food insecurity and one associated with hunger. The second

study, analyzing data from multiple CCHIP studies, found a first factor comprising mainly household-level food

insecurity items and adult hunger items, whereas the second factor included mainly child hunger items.

Prepared by Abt Associates Inc. 8
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-1

SUMMARY OF FACTOR LOADINGS

FOR LINEAR FACTOR ANALYSIS MODEL

(n=2,991)

Items

Standardized Regression

Coefficients

F1 F2 F3

Q11 38

Q15 59

Q16 63

Q20 52

Q24 45

Q28 52

Q32 47

Q35 48

Q38 43

Q40 50

Q43 42

Q47 60

Q50 40

Q53 78

Q54 76

Q55 78

Q56 73

Q57 49

Q58 75

Prepared by Abt Associates Inc. 9
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Initially, we fit a series of exploratory non-linear factor analysis models to determine

the dimensionality of the Food Security Survey items.3 From these alternative models, we

selected one representative non-linear model, labeled M121, which best describes the consistent

findings across the various alternative models. M121 was fit as a two-parameter logistic model

that included estimates for both factor loadings (discrimination parameters) and uniquenesses

(error term).4 Descriptive statistics for the subsample of 994 subjects and 21 items are presented

in Exhibit 2-2. The items ranged in proportion of positive responses from .850 (item 15) to .004

(item 50), where the higher the proportion, the lower the severity of food insecurity indicated by

the particular item.

The results of the non-linear factor analysis model are presented in Exhibit 2-3. The

primary fit statistic, the root mean square residual (RMSR) suggested that the one-factor model

adequately fit the data (RMSR = .0074). That is, the RMSR was well within the acceptable

range with a single factor, and was not materially improved by adding further factors, making

the single-factor model the most parsimonious solution. As with the linear factor analysis model,

items 15 and 23 were poor-fitting, with low factor loadings (.31 and .22, respectively). Item 22

had a moderately positive factor loading (L = .43), whereas the rest of the items all had large

positive loadings above .50. The findings support the linear factor analysis results with respect

to item fits, but suggest that items 15 and 23 should be removed from subsequent models.

2.3 UNIDIMENSIONAL ONE-PARAMETER NON-LINEAR FACTOR ANALYSIS MODELS

The exploratory non-linear factor analysis models indicated that the Food Security

Survey items could be described efficiently as a unidimensional construct. Therefore, we pursued

a specific non-linear factor model called the Rasch model. The Rasch model is a concise one3 Exploratory non-linear factor analysis models were fit using two software packages: LISCOMP and

NOHARM. LISCOMP is a structural equation modeling program that is designed to work with dichotomous

and/or ordinal data. NOHARM is a non-linear factor analysis program that analyzes moment matrices. Both

programs allow one to fit a two-parameter item response theory model (non-linear factor analysis model) to

the data. Exploratory analysis focused on households with children in random 25 percent subsamples of the

Food Security Supplement sample. Households that did not pass the series of screening questions (i.e., higherincome households with no indication of food insecurity), and consequently were not asked the full series of

food insecurity and hunger questions, were excluded from the analysis.

4 The two-parameter model can be fit with either item difficulty or uniqueness as the second parameter.

The specification shown here chose the uniqueness parameter.

Prepared by Abt Associates Inc. 10
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-2

DESCRIPTIVE STATISTICS FOR MODEL M121

Variable Mean Std Sum

Q11 .231 .421 231

Q15 .850 .356 850

Q16 .450 .497 450

Q18 .325 .468 325

Q19 .095 .293 95

Q20 .274 .446 274

Q21 .585 .492 585

Q22 .122 .327 122

Q23 .016 .125 16

Q24 .244 .429 244

Q28 .054 .226 54

Q32 .233 .423 233

Q35 .123 .328 123

Q38 .047 .211 47

Q40 .048 .213 48

Q43 .023 .150 23

Q47 .049 .216 49

Q50 .004 .063 4

Q53 .600 .490 600

Q54 .434 .495 434

Q55 .398 .489 398

Q56 .267 .442 267

Q57 .137 .344 137

Q58 .377 .484 377

Prepared by Abt Associates Inc. 11
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-3

SUMMARY OF FACTOR LOADINGS FOR MODEL M121

Item Item Label

Standardized

Regression

Coefficients

F1

Q11 General food sufficiency question 70

Q15 Try to make food or money go further 31

Q16 Run out of foods needed to make meal 70

Q18 Borrow food or money to make meal 56

Q19 Take child to other home for meal 68

Q20 Serve few low-cost foods several days in a row 73

Q21 Put off paying bills to buy food 51

Q22 Get emergency food from church or food bank 43

Q23 Eat meal at soup kitchen 22

Q24 Adults cut or skip meals because not enough money for food 89

Q28 Adults don’t eat for whole day 79

Q32 Eat less than should because not enough money to buy food 88

Q35 Hungry but don’t eat because can’t afford to 85

Q38 Lost weight because not enough food 75

Q40 Child’s meal size cut because not enough money for food 76

Q43 Child skip meal because not enough money for food 60

Q47 Child hungry but can’t afford more food 80

Q50 Child did not eat for a whole day 71

Q53 Worry food will run out before getting money for more 79

Q54 Food doesn’t last and don’t have money to get more 89

Q55 Can’t afford to eat balanced meals 88

Q56 Can’t feed children a balanced meal 85

Q57 Child not eating enough because can’t afford more food 83

Q58 Child fed only few low-cost foods, running out of money 82

Prepared by Abt Associates Inc. 12
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

factor model that constrains the discrimination parameters (factor loadings) to be equal across all

items. The statistical constraints of the Rasch model result in several desirable properties for the

measurement scale, especially its robustness across multiple samples and multiple variations of

the test (Wright and Masters, 1982). Furthermore, the preliminary exploratory models indicated

that most of the items had very similar discrimination parameters when the discrimination

parameters were allowed to vary.5

The computer program BIGSTEPS was designed specifically to fit the unidimensional

Rasch model. All subsequent models described in this section were fit using BIGSTEPS.

Five alternative measurement models based on existing theoretical frameworks were

generated for the Food Security Survey items. The five alternative models are summarized in

Exhibit 2-4. For most of the models, the items were divided into two subsets based on the

specific time frame that the items referenced. For models R101, R102, and R103, the first subset

of items references behaviors and events that occurred in the last 12 months, whereas the second

subset references behaviors and events that occurred in the last 30 days. Models were fit

separately for the 12-month and 30-day time periods.

5 Note in Exhibit 2-3 that nearly all factor loadings fall in the fairly narrow range from 70 to 88. The

questions with loadings substantially outside this range (Q15, Q18, Q21, Q22, Q23) are all ultimately excluded

from the scale.

Prepared by Abt Associates Inc. 13
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-4

ALTERNATIVE NON-LINEAR FACTOR ANALYSIS MODELS

Model 12-Month Scale 30-Day Scale

R101 Scale includes items that referenced events that

occurred in the last 12 months.

Items 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28,

29, 32, 35, 38, 40, 43, 44, 47, 50, 53b, 54b,

55b, 56b, 57b, 58b.

Scale includes items that referenced events

that occurred in the last 30 days.

Items 17, 26, 27, 30, 31, 33, 34, 36, 37, 39,

41, 42, 45, 46, 48, 49, 51, 52.

R102 Scale includes items that referenced events that

occurred in the last 12 months, and excludes

resource augmenting behaviors (18, 19, 21, 22,

and 23).

Items 15, 16, 20, 24, 25, 28, 29, 32, 35, 38, 40,

43, 44, 47, 50, 53b, 54b, 55b, 56b, 57b, 58b.

Scale includes items that referenced events

that occurred in the last 30 days, and

excludes resource augmenting behaviors.

Items 17, 26, 27, 30, 31, 33, 34, 36, 37, 39,

41, 42, 45, 46, 48, 49, 51, 52.

R103 Scale includes food insecurity items based on

the CCHIP model.

Items 15, 18, 19, 20, 21, 22, 23, 53a, 55a, 56a,

58a.

Scale includes food insufficiency and hunger

items based on the CCHIP model.

Items 16, 17, 24, 25, 26, 27, 28, 29, 30, 31,

32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44,

45, 46, 47, 48, 49, 50, 51, 52, 54a, 57a.

R104 NA Scale includes items that reference events

that occurred in the last 30 days. When no

30-day reference was available, items that

referenced the last 12-month period are

included.

Items 15, 17, 18, 19, 20, 21, 22, 23, 26, 27,

30, 31, 33, 34, 36, 37, 39, 41, 42, 45, 46, 48,

49, 51, 52, 53a, 54a, 55a, 56a, 57a, 58a.

R105 NA Scale includes items that referenced 30-day

period and number of days in the last month.

Also includes items that reference "often

true" in the last 12 months.

Items 17, 26, 27, 30, 31, 33, 34, 36, 37, 39,

41, 42, 45, 46, 48, 49, 51, 52, 53a, 54a, 55a,

56a, 57a, 58a.

NOTES:

(1) For items that referenced number of days, one dummy code was created based on whether the behavior or experience

occurred five or more times in the last month.

(2) For items that referenced number of months, one dummy code was created by combining the two more extreme

categories of the variable, indicating the experience occurred in three or more of the past 12 months.

(3) For items Q53 through Q58, a' denotes a dummy code that represents often true,' whereas b' denotes a dummy

code that combines sometimes true' and often true.'

Prepared by Abt Associates Inc. 14
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

A general summary of item fits for the alternative models is presented in Exhibit 2-5.

Exhibit 2-5

SUMMARY OF RESULTS FROM ALTERNATIVE

NON-LINEAR FACTOR ANALYSIS MODELS

Model

12-Month Scale 30-Day Scale

Poorly Fitting

Items Redundant Items

Poorly Fitting

Items Redundant Items

R101 Q21, Q18, Q15,

Q22

Q54b Q17 No redundant items

R102 Q15, Q16, Q20 No redundant items Q17 No redundant items

R103 No poor fitting

items.

No redundant items Q16, Q17, Q43 Q26

R104 NA NA Q22, Q23 Q33

R105 NA NA Q58a, Q17 No redundant items

The identification of poorly-fitting items and/or redundant items is based on item in-fit and out-fit

statistics. The out-fit statistic, μi, is an unweighted fit statistic. It is based on a standardized

residual, written as:

where yni is the score residual for household n on item i, and Wni is the variance of the score

zni

yni

W .5

ni

residual. The standardized residual is then squared and averaged to obtain a mean estimate of

item fit.

The in-fit statistic, νi

, is a weighted fit statistic that includes the same squared standardized

μi

Σz

2

ni

N

residual as μi, and is written as:

Prepared by Abt Associates Inc. 15
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Both the in-fit and out-fit statistics have an expected value of 1.0. As they deviate from

νi

ΣWniz

2

ni

Wni

1.0, the associated items become candidates for removal from the scale. Generally speaking, a

mean square fit statistic that is greater than 1.20 indicates a poor fitting item, whereas a mean

square fit statistic that is less than .80 indicates an item is redundant with other similar types of

items in the scale. Items that have both an in-fit and out-fit statistic above 1.2 are targeted for

removal from the scale. Items with both in-fit and out-fit statistics below .80 are redundant with

respect to the information they share with other items in the scale. Items that were shown to be

redundant items were also considered for removal and/or combined with other items. Below we

focus on describing the results of the 12-month and 30-day scale for M102, because these two

specific models were subsequently considered the most parsimonious by the study team.

12-Month Food Security Scale

As with the linear factor analysis models, all Rasch models were initially tested using

only households with children, because they comprised the subsample of households that were

administered the entire set of food security items. The results for Model M102 are presented in

Exhibit 2-6. The summary table contains a large amount of information, briefly described below.

The order of items in the table is determined by their item calibration, shown in the

fourth column of Exhibit 2-6. A question’s item calibration represents the point on the scale at

which there is a 50 percent probability that any given household will respond "yes" to the item.

That is, households with higher values on the scale than a particular item’s calibration score have

a greater than 50 percent probability of answering that item positively; households with lower

values have a less than 50 percent probability of a positive response to the item in question. The

items are listed from high calibration at the top of the table to low calibration at the bottom.

The item calibration is a function of (1) the total number of individuals that have

responded to any item in the scale (1,687); (2) the number of individuals that responded to the

particular item in the scale (n); and (3) the number of positive responses to the particular item

(raw score). For example, item 50 refers to the item “child did not eat for a whole day.” The

item has an item calibration of 4.56, which is the highest in the table. This event occurs rarely

Prepared by Abt Associates Inc. 16
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-6

SUMMARY OF MODEL R102A

Item n

Raw

Score

Item

Calibration

Real

SE

In-fit Out-fit Point

Biserial

Corr.

Mean

Sq Z

Mean

Sq Z

Q50 1,684 12 4.81 .30 .99 0.0 .28 -0.4 .19

Q44 1,684 23 4.01 .22 1.00 0.0 .41 -0.5 .24

Q43 1,684 38 3.36 .18 1.04 0.3 1.73 0.5 .28

Q29 1,683 62 2.68 .14 .89 -1.1 .28 -1.3 .39

Q40 1,683 86 2.21 .13 1.01 0.1 1.99 1.2 .40

Q47 1,684 89 2.15 .12 .88 -1.5 .56 -0.8 .44

Q38 1,683 91 2.12 .13 1.07 0.8 .46 -1.1 .40

Q28 1,684 95 2.06 .12 .95 -0.6 .41 -1.3 .44

Q35 1,685 212 .65 .09 .91 -1.6 .83 -0.6 .57

Q57 1,680 246 .36 .09 1.00 0.1 .60 -1.8 .57

Q25 1,683 293 -.01 .08 .94 -1.3 .56 -2.4 .61

Q32 1,683 442 -.98 .07 .94 -1.5 .67 -2.7 .64

Q24 1,685 449 -1.01 .07 .86 -3.5 .67 -2.8 .67

Q56 1,679 466 -1.12 .07 1.04 0.9 .75 -2.1 .61

Q20 1,686 480 -1.19 .08 1.24 5.5 1.50 3.5 .52

Q58 1,680 671 -2.18 .07 .99 -0.4 .96 -0.4 .60

Q55 1,678 706 -2.36 .07 .87 -3.6 .68 -3.5 .64

Q54 1,679 785 -2.73 .06 .82 -5.2 .74 -2.5 .64

Q16 1,687 795 -2.77 .07 1.23 5.9 1.22 1.9 .50

Q53 1,680 1,066 -4.01 .06 .95 -1.6 .85 -0.8 .49

Q15 1,686 1,469 -6.06 .09 1.31 6.7 7.70 5.5 .10

Mean 1,683 408 .00 .11 1.00 -0.1 1.14 -0.6

SD 2 382 2.82 .06 .13 2.9 1.53 2.1

NOTE:

Sample includes households with children only. Items are ordered on terms of severity.

Prepared by Abt Associates Inc. 17
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

in any household. For this specific subsample, this event occurred for only 12 of the 1,684

households that responded to the item. At the other end of the scale, item 15 ("run short of

money and try to make food or food money go further") is the least severe item included in the

analysis. The item has the low calibration of -5.74, based on 1,469 positive responses out of the

1,686 households that answered the question.

The column headed “Real SE” shows the standard error of the items, which can be used

to create a confidence interval for the item calibration. Items located at the severe end of the

scale tend to have the largest standard errors, because they tend to have larger variances

compared to items throughout the center and less-severe end of the distribution.

For the 12-month scale presented in Exhibit 2-6, there are three items with both in-fit

and out-fit statistics that exceed 1.20 (Q15, Q16, and Q20). Therefore, these three items were

removed from the scale, and the model re-estimated. The results of the revised model are

presented in Exhibit 2-7. The effective sample size for the revised model is reduced (n = 1,276)

because two of the least severe items were removed from the analysis. This results in fewer

subjects who have responded yes to any particular item.

For the revised model, there are no items with both in-fit and out-fit statistics that

exceed 1.20. Similarly, there are no items with both in-fit and out-fit statistics below .80. Some

of the out-fit statistics were small, due primarily to dependencies in some item pairs. For

example, item 29 has a low out-fit statistic (mean square = .36), but the item is associated with

item 28. We examined several alternative models with these items modeled as trichotomies

rather than the multiple dichotomies, but the basic results of the models did not change.

Final 12-Month Food Security Scale

The analyses for the 12-month scale were replicated on subsequent subsamples of the

data set.6 The model replications provided clear support for the invariance of the primary

measurement model across subsamples, as well as across different types of households. In each

replication, the item calibrations gave identical or near-identical rankings of item severity and

6 The overall sample was initially divided into four random subsamples. Initial model estimation was

carried out for households with children within one subsample. Tests for invariance were performed for

households with children in the other three random subsamples. Invariance tests were also performed for

households without children, subdividing them into households with any elderly members (age 60 or over) and

households with no elderly members.

Prepared by Abt Associates Inc. 18
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-7

SUMMARY OF REVISED MODEL R102A

Item n

Raw

Score

Item

Calibration

Real

SE

In-fit Out-fit Point

Biserial

Corr.

Mean

Sq Z

Mean

Sq Z

Q50 1,275 12 4.38 .30 .96 -0.2 .32 -0.5 .21

Q44 1,275 23 3.59 .22 .99 -0.1 .50 -0.5 .25

Q43 1,275 38 2.93 .18 1.01 0.1 1.50 0.5 .29

Q29 1,274 62 2.26 .14 .90 -1.0 .36 -1.4 .40

Q40 1,274 86 1.77 .13 1.02 0.2 2.34 2.0 .39

Q47 1,275 89 1.72 .12 .88 -1.4 .70 -0.7 .45

Q38 1,274 91 1.69 .13 1.09 1.1 .65 -0.8 .39

Q28 1,275 95 1.63 .12 .96 -0.5 .52 -1.3 .44

Q35 1,276 212 .21 .09 .95 -0.9 1.09 0.4 .55

Q57 1,274 246 -.11 .09 .99 -0.2 .65 -2.1 .56

Q25 1,274 293 -.49 .08 .98 -0.4 .76 -1.6 .57

Q32 1,274 442 -1.53 .08 1.01 0.2 .99 -0.1 .57

Q24 1,276 449 -1.56 .08 .96 -1.0 1.01 0.1 .59

Q56 1,273 466 -1.68 .08 1.08 1.9 .97 -0.3 .54

Q58 1,274 671 -2.89 .08 1.11 2.6 1.28 2.1 .47

Q55 1,272 706 -3.09 .07 .94 -1.7 .84 -1.2 .53

Q54 1,273 785 -3.54 .07 .92 -2.2 .94 -0.4 .49

Q53 1,274 1,066 -5.28 .09 1.16 3.7 1.28 0.7 .23

Mean 1,274 324 .00 .12 .99 0.0 .93 -0.3

SD 1 303 2.70 .06 .07 1.5 .46 1.1

NOTE:

Sample includes households with children only. Items are ordered in terms of severity.

Prepared by Abt Associates Inc. 19
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

consistent clustering of closely-ranked items. Applying models fit on separate subsamples

yielded household values that correlated at the .99 level.7

The final model estimates are based upon all households in the analysis sample; these

are presented in Exhibit 2-8. Of the 18,370 households that passed the screener and responded

to at least half of the questions applicable to them, there were 7,897 households in which the

respondent answered "yes" to at least one of the 12-month scale items. The ordering of the items

in the final model changes slightly relative to the ordering of the items described in Exhibit 2-7;

however, these minor fluctuations in item severities are expected with different random

subsamples of households.8

Exhibit 2-9 shows the frequency distribution for the number of responses to items in the

survey. The two most frequent response patterns are 10 items and 18 items.9 The response

pattern of 10 items applies largely to the households without children, because these had the

opportunity to respond to a maximum of 10 items. The response pattern of 18 items applies to

households with children, who had an opportunity to respond to 18 items. These two response

patterns account for 98.8 percent of the households, indicating a very low incidence of item

nonresponse (1.2 percent of all respondents). Households, whether with or without children, that

responded to less than half the items administered had their household score set to "missing."

The central function of the Rasch model is to assign to each responding household a

value on the food security scale. The household scale value is fundamentally based on a count

of the number of affirmative responses to questions included in the scale. At its simplest, if all

households respond to the same set of questions, the household scale value is a constant

7 In this procedure, we separately fit the model to each subpopulation, such as households with children,

households with no children but with elderly members, and households with neither children nor elderly. Each

of the separate models was then used to compute scale values for all households in the full sample. The values

computed with the different models were then compared through plotting and correlation analysis.

8 The Rasch model software initially assigns scale values in a range that yields a mean of zero. Because

the presence of positive and negative values in the scale can be confusing or misleading, it is conventional to

transform the values into a range such as 0-1, 0-10, or 0-100. Values of the 12-month scale presented in other

reports from this project transform the original scale values to range from 0.0 to 10.0. The original value is

multiplied by .8333 and added to 5.071 to obtain the transformed value. All respondents giving zero

affirmative responses are assigned a value of zero, and respondents answering all questions affirmatively get

a value of 10.0.

9 Over half of all households in the sample were higher-income households that did not pass the screening

questions, and therefore were not asked any of the questions included in the scales.

Prepared by Abt Associates Inc. 20
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

arithmetic transformation of the count of positive responses. For example, among households

Exhibit 2-8

SUMMARY OF FINAL 12-MONTH SCALE

Item n

Raw

Score

Item

Calibration

Real

SE

In-fit Out-fit

Point

Biserial

Corr.

Transformed

Item

Calibrationa

Mean

Sq Z

Mean

Sq Z

Q50 4,333 29 4.92 .20 1.09 0.5 6.02 1.8 .18 9.2

Q44 4,331 87 3.48 .12 .84 -1.8 .28 -1.6 .34 8.0

Q43 4,332 135 2.86 .10 .88 -1.7 .78 -0.5 .37 7.5

Q29 7,889 332 2.55 .06 .89 -2.5 .55 -1.8 .35 7.2

Q47 4,333 257 1.88 .07 .93 -1.3 .97 -0.1 .44 6.6

Q28 7,892 537 1.82 .05 .97 -1.0 1.16 0.8 .39 6.6

Q40 4,333 290 1.69 .07 1.01 0.3 1.28 1.0 .44 6.5

Q38 7,861 625 1.54 .05 1.10 3.1 1.31 1.6 .39 6.4

Q35 7,883 1,249 .27 .04 .91 -4.0 .77 -2.6 .54 5.3

Q57 4,324 779 -.15 .05 1.07 2.3 .86 -1.4 .53 5.0

Q25 7,879 1,919 -.70 .03 .93 -3.4 .76 -4.6 .58 4.5

Q32 7,885 2,661 -1.56 .03 .94 -3.5 .94 -1.5 .57 3.8

Q56 4,325 1,453 -1.64 .04 1.08 3.4 .94 -1.0 .54 3.7

Q24 7,893 2,824 -1.72 .03 .88 -7.3 .87 -3.2 .59 3.6

Q58 4,324 2,295 -3.10 .04 1.14 6.5 1.29 3.3 .43 2.5

Q55 7,862 4,627 -3.42 .03 1.03 2.1 1.61 7.9 .41 2.2

Q54 7,863 4,973 -3.73 .03 .92 -5.9 1.06 0.8 .42 2.0

Q53 7,870 6,312 -4.99 .03 1.16 9.9 3.04 9.4 .18 0.9

Mean 6,301 1,744 .00 .06 .99 -0.2 1.36 0.5

SD 1,763 1,833 2.71 .04 .10 4.2 1.26 3.5

a The transformed item calibration is a linear transform of the item calibration that places all values in the range from 0.0 to

10.0.

with children responding to all 18 questions in the scale, all households with three positive

responses have a scale value of -4.13. Households with more affirmative responses have higher

scale values; for example, households with children giving ten affirmative responses have a scale

value of 0.62. The scale value does not depend on which questions the household answers

Prepared by Abt Associates Inc. 21
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

affirmatively: all households with children who give three affirmative answers have the same

Exhibit 2-9

NUMBER OF QUESTIONS ANSWERED: QUESTIONS IN THE 12-MONTH SCALE

Number of

Questions

Answered Frequency Percent

Cumulative

Frequency

Cumulative

Percent

2 7 0.0 7 0.0

3 4 0.0 11 0.1

4 6 0.0 17 0.1

5 11 0.1 28 0.2

6 14 0.1 42 0.2

7 53 0.3 95 0.5

8 11 0.1 106 0.6

9 51 0.3 157 0.9

10 10293 55.9 10450 56.8

12 21 0.1 10471 56.9

13 2 0.0 10473 56.9

14 2 0.0 10475 56.9

15 3 0.0 10478 56.9

16 11 0.1 10489 57.0

17 29 0.2 10518 57.1

18 7888 42.9 18406a 100.0

a Households that answered fewer than half of the applicable questions are excluded from the main analysis, reducing the

sample to 18,370.

scale value, even if they give affirmative answers to quite different questions.

If all respondents are given exactly the same set of questions, the scale value depends

solely on the number of affirmative responses. If different respondents answer different sets of

questions, however, scale values depend on the severity (as indicated by the item calibration) of

the questions that the respondent answers. In the current situation, households with children are

asked 18 questions, whereas those without children are asked only ten. Moreover, the questions

asked only of households with children are disproportionately the more severe questions. The

Rasch model takes these differences into account, assigning values to both types of household

Prepared by Abt Associates Inc. 22
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

that are comparable even though they responded to different types of questions. Similarly, the

model adjusts the scale values assigned to households with or without children that failed to

respond to one or more of the items applicable to them.

The frequency distribution of household values on the 12-month scale is presented in

Exhibit 2-10. Household values for the 12-month scale range from −6.08 to 5.91 in the original

model estimation (values transformed to a 0-10 range are also shown). Most households in the

analysis sample responded "no" to all items in the scale, and received a scale value of −6.08

(10,276 households).10 All other households responded "yes" to at least one item. Their

assigned scale value is a non-linear transformation of the total number of items to which they

responded affirmatively. If all households had responded to all 18 items, there would be 19

possible scale score values that could be assigned to households. Because households without

children could respond to only 10 items, however, there are a number of additional scale scores

that can be assigned to households based on a missing data adjustment that is part of the Rasch

measurement model. The small proportion of households in either group that failed to respond

to one or more questions also received distinct measure scores, depending on the number of items

missed.

Final 30-Day Food Security Scale

The 30-day scale was developed in the same manner as the 12-month scale, though there

were fewer 30-day items available for analysis. The 30-day scale also has a larger number of

item dependencies than the 12-month scale. The results of the final Rasch model for the 30-day

scale are presented in Exhibit 2-11. The 30-day scale includes 17 items, and the estimated item

calibrations range from −4.37 to 4.00. For the most severe item (item 52), only five households

responded affirmatively.

Exhibit 2-12 shows the number of responses households made to the 30-day items

administered in the survey. Similar to the 12-month scale, there were two major response

categories: 9 (households without children) and 17 (households with children). These two

response patterns account for 99.3 percent of households. Here also, households that did not

10 For analyses involving the full sample, households that did not pass the screen are assigned the minimum

possible score (−6.08). This procedure is also used in classifying households on the food security status

variables.

Prepared by Abt Associates Inc. 23
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-10

FREQUENCY DISTRIBUTION FOR HOUSEHOLD VALUES

ON THE 12-MONTH SCALE

Value on Scale Frequency Percent

Cumulative

Frequency

Cumulative

Percent

Transformed

Scale Valuea

-6.08 10276 56.5 10276 56.5 0.0

-5.2 970 5.3 11246 61.9 0.7

-4.96 902 5.0 12148 66.8 0.9

-4.13 661 3.6 12809 70.5 1.6

-3.73 614 3.4 13423 73.8 2.0

-3.36 550 3.0 13973 76.9 2.3

-2.73 657 3.6 14630 80.5 2.8

-2.69 386 2.1 15016 82.6 2.8

-2.09 343 1.9 15359 84.5 3.3

-1.82 306 1.7 15665 86.2 3.6

-1.52 358 2.0 16023 88.1 3.8

-0.97 255 1.4 16278 89.5 4.3

-0.96 285 1.6 16563 91.1 4.3

-0.43 188 1.0 16751 92.1 4.7

-0.09 295 1.6 17046 93.8 5.0

0.1 176 1.0 17222 94.7 5.2

0.62 132 0.7 17354 95.5 5.6

0.81 231 1.3 17585 96.7 5.8

1.13 86 0.5 17671 97.2 6.0

1.62 59 0.3 17730 97.5 6.4

1.75 128 0.7 17858 98.2 6.5

2.12 59 0.3 17917 98.6 6.8

2.65 28 0.2 17945 98.7 7.3

2.88 85 0.5 18030 99.2 7.5

3.24 15 0.1 18045 99.3 7.8

3.77 103 0.6 18148 99.8 8.2

3.96 12 0.1 18160 99.9 8.4

5.02 13 0.1 18173 100.0 9.3

5.91 6 0.0 18179b 100.0 10.0

a The transformed scale value is a linear transform that places all values in the range from 0.0 to 10.0.

b Includes only households that responded to all applicable items.

Prepared by Abt Associates Inc. 24
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

respond to at least half the items administered had their scale value set to "missing."

Exhibit 2-11

SUMMARY OF FINAL 30-DAY SCALE

Item n

Raw

Score

Item

Calibration

Real

SE

In-fit Out-fit Point

Biserial

Corr.

Mean

Sq Z

Mean

Sq Z

Q52 990 5 4.00 .45 .83 -0.4 .22 -0.7 .23

Q51 990 13 2.91 .30 1.07 0.3 1.04 0.0 .20

Q46 988 21 2.33 .23 .92 -.4 .68 -0.5 .34

Q31 1992 83 1.61 .12 .83 -1.9 .27 -3.2 .34

Q49 990 45 1.37 .16 .80 -1.7 .44 -1.7 .47

Q42 990 64 .91 .14 .88 -1.1 .59 -1.5 .46

Q45 988 69 .80 .14 1.10 1.0 1.67 1.8 .32

Q37 1985 249 .10 .08 .84 -3.3 .51 -4.1 .46

Q48 990 129 -.09 .11 1.03 0.4 1.07 0.4 .40

Q30 1992 294 -.17 .07 1.08 1.8 1.22 1.6 .34

Q41 990 154 -.37 .11 1.14 2.1 1.42 2.3 .34

Q39 1958 344 -.48 .07 1.18 4.0 1.42 3.4 .29

Q34 1983 611 -1.52 .06 .92 -2.6 .73 -4.7 .46

Q36 1985 637 -1.61 .06 .94 -1.9 .91 -1.4 .44

Q27 1993 715 -1.86 .06 1.04 1.2 .96 -0.8 .37

Q33 1983 1285 -3.56 .05 .97 -1.3 .87 -1.5 .29

Q26 1993 1549 -4.37 .06 1.13 4.3 1.54 3.3 .14

Mean 1516 369 .00 .13 .98 0.0 .92 -0.4

SD 497 444 2.12 .10 .12 2.1 .43 2.3

Exhibit 2-13 provides the frequency distribution of the 30-day household scale scores.

The scale scores range from −5.62 to 5.32. Almost 90 percent of the households that passed the

series of screening questions responded "no" to all items in the 30-day scale.

The 30-day scale in its present form is not considered as useful as the 12-month scale,

for both conceptual and statistical reasons. Conceptually, the 30-day scale provides detail on a

narrower portion of the spectrum of food insecurity than the 12-month scale. Most of the lessPrepared by Abt Associates Inc. 25
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

severe conditions and behaviors incorporated in the 12-month scale were not measured in the 30-

Exhibit 2-12

NUMBER OF QUESTIONS ANSWERED: QUESTIONS IN THE 30-DAY SCALE

Number of

Responses Frequency Percent

Cumulative

Frequency

Cumulative

Percent

2 7 0.0 7 0.0

3 1 0.0 8 0.0

4 6 0.0 14 0.1

5 2 0.0 16 0.1

6 10 0.1 26 0.1

7 17 0.1 43 0.2

8 35 0.2 78 0.4

9 10369 56.3 10447 56.8

10 1 0.0 10448 56.8

11 2 0.0 10450 56.8

13 1 0.0 10451 56.8

15 16 0.1 10467 56.9

16 15 0.1 10482 57.0

17 7922 43.0 18404 100.0

day time frame in the CPS Supplement. The 30-day measures thus focus on reductions of food

intake and related indicators of hunger, providing little information on food insecurity with no

hunger evident. The broader range of the 12-month scale makes it likely to be more useful both

in describing the conditions of the population at a point in time and in monitoring changes.

Statistically, Chapter Three will show that the 30-day scale is considerably less reliable

than the 12-month scale in its ability to discriminate between households at varying levels of

food insecurity. This more limited reliability stems mainly from the smaller number of

independent questions asked in the 30-day time frame. The 30-day scale has just nine

Prepared by Abt Associates Inc. 26
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

Exhibit 2-13

FREQUENCY DISTRIBUTION OF HOUSEHOLD VALUES ON THE 30-DAY SCALE

Scale Values Frequency Percent

Cumulative

Frequency

Cumulative

Percent

-5.62 16309 89.2 16309 89.2

-4.69 261 1.4 16570 90.6

-4.63 288 1.6 16858 92.2

-3.5 239 1.3 17097 93.5

-3.39 246 1.3 17343 94.8

-2.66 123 0.7 17466 95.5

-2.45 96 0.5 17562 96.0

-2.01 113 0.6 17675 96.7

-1.68 144 0.8 17819 97.5

-1.47 67 0.4 17886 97.8

-1 57 0.3 17943 98.1

-0.97 69 0.4 18012 98.5

-0.56 34 0.2 18046 98.7

-0.25 59 0.3 18105 99.0

-0.14 25 0.1 18130 99.2

0.27 23 0.1 18153 99.3

0.57 47 0.3 18200 99.5

0.68 9 0.0 18209 99.6

1.11 5 0.0 18214 99.6

1.57 5 0.0 18219 99.6

1.7 24 0.1 18243 99.8

2.08 4 0.0 18247 99.8

2.62 31 0.2 18278 100.0

2.66 2 0.0 18280 100.0

3.39 3 0.0 18283 100.0

4.44 1 0.0 18284 100.0

5.32 1 0.0 18285 100.0

Prepared by Abt Associates Inc. 27
Chapter Two: Methods and Results of Scaling Analysis of CPS Data

independent items, and a total of 17 when follow-up items are included.11 The 12-month scale

has 15 independent questions, plus three follow-up items. In addition, the absence of questions

measuring the less severe food insecurity conditions creates a situation in which an extremely

small proportion of the population gives affirmative responses to any of the items, which makes

it more difficult for the scale to discriminate reliably among different levels of food insecurity.

For these reasons, the main report of this study focuses almost exclusively on the 12-

month scale, and this report provides less detail on the 30-day than the 12-month scale.

Estimates of the prevalence of hunger based on the 30-day scale are presented in Appendix B.

2.4 SUMMARY

The scale development process involved five main steps:

· Exploratory linear factor analysis replicating key elements of prior research, which

indicated that the response patterns and relationships in the CPS Food Security

Supplement were largely similar to those seen previously.

· Estimation of two-parameter non-linear models, which indicated that a one-factor

solution would be appropriate.

· Preliminary estimation of one-factor Rasch models on a one-fourth random

subsample of the full CPS sample, resulting in the specification of an 18-item set

for inclusion in the 12-month scale and a 17-item set for the 30-day scale.

· Tests of invariance of the model across other random subsamples of the full

population and across three demographic subgroups (households with children,

households without children but with elderly members, and households with neither

children nor elderly members), which indicated that the models were quite invariant

across groups.

· Estimation of the final scales on the full CPS sample.

Subsequent chapters of this report detail the steps taken to test the scales for reliability,

construct validity, and estimation bias. Primary attention is given to the 12-month scale, which

appears more useful than the 30-day scale on both conceptual and statistical grounds.

11 The primary question typically asks if a particular behavior or condition occurred in the past 30 days.

If the response is affirmative, the follow-up question then asks on how many of the 30 days the behavior or

condition occurred.

Prepared by Abt Associates Inc. 28
CHAPTER THREE

RELIABILITY ESTIMATES FOR THE FOOD SECURITY SCALES

Whenever an instrument is used to measure some quality of a person — whether it be

a heart rate, a psychological profile, or a level of food insecurity — researchers want to be

assured that the instrument is reliable. A reliable instrument is one that, if it were administered

to the same individual on two occasions under similar conditions, would provide similar results

in both tests. Reliability indices therefore attempt to measure the degree to which an individual's

score is expected to remain stable (relative to other individuals' scores) over repeated occasions

using the same instrument.

Often it is not feasible to administer an instrument repeatedly to the same individuals

under similar circumstances. Reliability indices have therefore been developed that attempt to

approximate this result through a single administration of the instrument. Most reliability indices

for multi-item scales attempt to provide an estimate of the ratio of the true score variance to the

total variance for a particular instrument. The underlying concept is that an individual's score

on a scale (x) is composed of the individual's “true” score (t) and an error component. A general

equation for a measure indicating the reliability of a scale (ρ) can be written as:

where is the variance of the households’ true scores and is the variance of the observed

ρ

σ

2

t

σ

2

x

,

σ

2

t σ

2

x

measure (i.e., the household scores on the scale).

There are a number of reliability indices available for characterizing the reliability of

a measure. Because the food security scales are estimated using a Rasch modeling approach, the

most appropriate index is the Rasch reliability index. Because the Rasch reliability index has not

been used as often in the scale development literature as some other reliability estimators,

however, we provide estimates using some of the more common reliability indices as well as the

Rasch reliability index to characterize the reliability of the food security scale.

One major difference between the more traditional reliability indices and the Rasch

reliability index is the treatment of cases with extreme scores. Cases with extreme scores are

Prepared by Abt Associates Inc. 29
Chapter Three: Reliability Estimates for the Food Security Scales

those with either the maximum or minimum score possible on the measure (i.e., those that have

responded affirmatively to all questions in the scale, or negatively to all questions). When scale

scores are normally distributed over a population, very few cases have extreme scores and

consequently they have very little impact on the reliability estimate. When the distribution is

severely skewed, however, the treatment of cases with extreme scores can have a major impact

on reliability estimates. This is very relevant to the food security scales, because over 80 percent

of the population has the lowest possible score on the 12-month scale and over 90 percent on the

30-day scale.

Because of differences in estimation algorithms, the Rasch reliability estimate always

decreases when extreme scores are included, whereas the more traditional reliability estimates

always increase. The Rasch model typically provides two reliability estimates, one including and

one excluding the cases with extreme scores. The conventional practice with the more traditional

reliability indices is to include the extreme scores. The discussion below provides separate

reliability estimates that include and exclude extreme scores. In general, the estimate excluding

households with extreme scores can be taken as indicating the reliability of the scale in

measuring the severity of food insecurity and hunger among households that have experienced

at least one of the food insecurity or hunger conditions represented in the scale. The

interpretation of the estimate when extreme scores are included is less clear.

Among the more traditional indices, Nunnally (1978) recommended that at least two

types of reliability coefficients be reported: correlations between alternate test forms, and

coefficient alpha. The discussion below presents the results using three traditional reliability

indices, two of which are based on the correlation between alternate test forms (the SpearmanBrown split-half reliability estimate, and Rulon's split-half reliability estimate), and Cronbach’s

alpha. All three reliability indices are based on the use of linear composites, and therefore do

not correspond exactly to the Rasch model (a non-linear model). Nonetheless, the indices

provide a general indication of the reliability of the scale and familiar measures that may be

compared to other work.

Prepared by Abt Associates Inc. 30
Chapter Three: Reliability Estimates for the Food Security Scales

3.1 SPEARMAN-BROWN SPLIT-HALF RELIABILITY ESTIMATES

The general form of the Spearman-Brown prophecy formula can be written as:

where ρsp represents the reliability of the composite measure with k parallel tests, and ρii′

ρsp

kρii

1 (k 1)ρii

,

represents the reliability of any one particular test. A simplified form of the equation can be

written as:

where ρab represents the correlation coefficient between two parallel tests.

ρsp

2ρab

1 ρab

,

In order to create two somewhat parallel tests, the item pool (i.e., all the items used in

the scale) is typically split in half randomly. Each subset of the items is considered a separate

scale, and the results of the two scales are compared. When the number of available items is

small, as in the present situation, a commonly used method is to order the items in terms of

severity and assign odd-numbered items to one test and even-numbered items to another test.

The two new scales should have the same number of items, so if the item pool contains an odd

number of items, one is dropped before the pool is split.

To estimate ρsp for the 12-month scale, it was necessary to drop dependent items in order

to generate unbiased reliability estimates.1 It was also considered informative to generate

reliability estimates separately for items that were administered only to households with children

and for items that were administered to all households.

For households with children, there were 15 independent items available to create two

parallel measures. Because there were an odd number of items, the most severe item was

dropped from the list. For the first parallel scale, households’ responses to items 43, 28, 38, 57,

56, 58, and 54 were summed to create the household score. For the second parallel scale, items

47, 40, 35, 32, 24, 55, and 53 were summed. Based on the correlation between household scores

1 Dependent items are those that are follow-ups to previous items. A number of items in the food

insecurity scales have an initial question (e.g., did this situation occur within the past 12 months?) and a

follow-up (e.g., in how many of the past 12 months did the situation occur?)

Prepared by Abt Associates Inc. 31
Chapter Three: Reliability Estimates for the Food Security Scales

on these two scales, the Spearman-Brown reliability estimate for the total scale was .852 with

extreme scores excluded (see Exhibit 3-1). Including extreme scores raises the reliability index

to .903.

For all household types (i.e., households with any combination of either children, adults,

Exhibit 3-1

SUMMARY OF RELIABILITY ESTIMATES USING TRADITIONAL INDICES

Household Type Reliability Estimate Extreme Scores

Included

Extreme Scores

Excluded

12-Month Scale

All households Spearman .899 .794

Rulon .932 .878

Alpha .856 .743

Households with children Spearman .903 .852

Rulon .899 .813

Alpha .882 .814

30-Day Scale

All households Spearman .840 .357

Rulon .888 .650

Alpha .789 .356

Households with children Spearman .852 .530

Rulon .844 .530

Alpha .799 .555

and elderly), there were eight independent items available to create two parallel measures. For

the first parallel scale, items 28, 35, 24, and 54 were summed. For the second parallel scale,

items 38, 32, 55, and 53 were summed. The reliability estimate for the total scale is .794 with

extreme scores excluded, and .899 with extreme scores included.

For the 30-day scale, the reliability estimate for households with children is .530 and

the reliability estimate for all households is .357 with extreme scores excluded. Including

extreme scores generates a striking increase in the reliability estimates, to .852 for households

with children and .840 for all households.

Prepared by Abt Associates Inc. 32
Chapter Three: Reliability Estimates for the Food Security Scales

Note that, although including cases with extreme scores increases the reliability estimate

for both scales, the effect is particularly striking for the 30-day scale. This occurs for three

reasons.

First, the number of items in the paired subscales is smaller for the 30-day scale. The

30-day scale contains just five independent items that apply to all households, and ten that apply

to households with children. This means that the split-half scales each contain just two items in

the analysis for all households, and five in the analysis of households with children. In contrast,

the split-half 12-month scales contain four items for the analysis of all households and seven

items for the analysis of households with children. Smaller numbers of items in general lead to

lower reliability estimates.

The second factor is that the 30-day scale measures a narrower band of the spectrum of

food insecurity than the 12-month scale. The least severe items in the 12-month scale were not

asked in the 30-day time frame. This means that the 30-day scale not only contains fewer items,

but that the scale is attempting to make distinctions within a narrower range than the 12-month

scale. In effect, this means that the 30-day scale faces a more difficult challenge in distinguishing the varying levels of food insecurity and hunger among those households that have

experienced one or more of the conditions measured.

The final distinction between the scales is that a far greater proportion of households

answered negatively to all items on the 30-day scale than the 12-month scale (89 percent vs. 57

percent of households that passed the screening questions). Thus, including or excluding the

households with extreme scores will have a greater effect on the 30-day than the 12-month scales.

3.2 RULON'S SPLIT-HALF RELIABILITY ESTIMATES

Rulon proposed an alternative method for estimating the reliability of a scale using the

split-half tests.2 The method involves estimating the difference between household scores on two

parallel tests and estimating the ratio of the variance of the difference score to the variance of

the total score. The equation for Rulon's method is written as:

2 Rulon, P.J., "A Simplified Procedure for Determining the Reliability of a Test by Split Halves," Harvard

Educational Review vol. 9, pp. 99-103, 1939.

Prepared by Abt Associates Inc. 33
Chapter Three: Reliability Estimates for the Food Security Scales

where is the variance of the difference score and is the variance of the total score.

ρsh 1





















σ

2

D

σ

2

x

,

σ

2

D σ

2

x

To estimate the index, we used the same subsets of items described above for the

Spearman test, again performing the computation both for households with children and for all

households (see Exhibit 3-1). For the 12-month scale, the reliability estimate for households with

children is .813 and the estimate for all households is .878 with extreme scores excluded. When

extreme scores are included, the estimates increase to .899 for households with children and .932

for all households.

For the 30-day scale, the reliability estimate for households with children is .530 and

the reliability estimate for all household types is .650 when extreme scores are excluded.

Including the extreme scores raises the estimates to .844 and .888, respectively.

3.3 CRONBACH’S ALPHA RELIABILITY ESTIMATES

Cronbach's alpha and Kuder Richardson 20 (McDonald, 1985) produce identical results

when using independent items that are dichotomous in form. Therefore, for the 12-month scale,

these two equations are interchangeable. For simplicity, we will refer to Cronbach's alpha when

describing these reliability estimates.

Cronbach's alpha was developed to circumvent problems associated with the non-random

selection of subsets of items when using methods such as the Spearman-Brown or Rulon

methods. Cronbach's alpha, αxx, can be written as:

where k represents the number of items in the test, represents the variance of item i, and

αxx













k

(k 1)





















1





















Σσ2

i

σ

2

x

,

σ

2

i σ

2

x

represents the variance of the total test score. Alpha is considered to be the lower bound of the

true theoretical reliability estimate, the coefficient of precision.

Prepared by Abt Associates Inc. 34
Chapter Three: Reliability Estimates for the Food Security Scales

The overall reliability estimates, summarized in Exhibit 3-1, are similar to those seen

with the prior tests. With extreme scores excluded, the values of α for the 12-month scale are

.814 for households with children and .743 for all households. Including the households with

extreme scores raises the estimates to .882 for households with children and .856 for all

households.

For the 30-day scale, the α values are .555 for households with children and .356 for

all households when cases with extreme values are excluded. When households with extreme

values are included, the values are .799 for households with children and .789 for all households.

In addition to assessing the reliability of the total scale, Cronbach's alpha is often used

to examine the appropriateness of including individual items in the scale. The usual rule is that

if αxx increases substantially when an item is removed from the scale, the item should be

considered for removal. It is also possible to evaluate how the reliability of the scale changes

when any one item is removed from the scale. Exhibits 3-2 and 3-3 show that in nearly all

instances, removing an item would reduce the estimated reliability of the scale. The only

potential exception would be item 53;3 removing this item would generate a small increase in

the reliability estimate with extreme scores excluded, but the loss of information at the end of

the scale would be more detrimental to scale validity than is justified by this small increase in

reliability.

3.4 RASCH MODEL RELIABILITY ESTIMATES

The Rasch reliability indices behave in a slightly different manner and yield somewhat

lower estimates of reliability than the more traditional indices presented above. The reliability

index for the Rasch Scale is defined as:

ρr

(σ

2

x MSE)

σ

2

x

where ρr is the reliability index, is the variance of the scale, and MSE is the mean square σ

2

x

error of the scale. Like the previously described reliability indices, ρr is intended to represent

3 Removing item 28 with extreme scores included also generates an increase in α, but the difference is tiny

(measured in the third decimal).

Prepared by Abt Associates Inc. 35
Chapter Three: Reliability Estimates for the Food Security Scales

the proportion of total variance in household scores that is caused by variance in households

Exhibit 3-2

CRONBACH'S ALPHA FOR THE 12-MONTH SCALE

FOR HOUSEHOLDS WITH CHILDREN

Item

Extreme Scores Included

(α = .882; n=7,888)

Extreme Scores Excluded

(α = .814; n=4,278)

Item Mean

Correlation

with Total

Score

α with Item

Deleted

Item

Mean

Correlation

with Total

Score

α with Item

Deleted

43 .017 .338 .882 .028 .309 .812

47 .033 .433 .879 .057 .415 .806

28 .036 .397 .880 .063 .354 .809

40 .037 .433 .879 .064 .408 .806

38 .040 .429 .879 .071 .394 .806

35 .081 .565 .873 .146 .529 .796

57 .098 .587 .872 .177 .540 .795

32 .179 .669 .867 .327 .567 .791

56 .183 .664 .867 .333 .556 .793

24 .182 .642 .868 .332 .522 .796

58 .288 .656 .868 .528 .441 .804

55 .290 .709 .865 .532 .528 .796

54 .338 .692 .866 .621 .462 .801

53 .450 .607 .873 .827 .221 .818

“true” scores.

In Exhibit 3-4, the reliability estimates for the 12-month and 30-day scale are presented.

Separate estimates are presented for two treatments of the variables that involve follow-up

questions. For example, the 12-month scale includes an item that indicates that adults have cut

or skipped meals in the past 12 months, and a second (answered only by people who responded

positively to the first item) that indicates that meals were cut or skipped in three or more months.

In one treatment, these are considered as independent dichotomous items. In the second

treatment, they are combined into a single trichotomous item (no meals cut/skipped in past 12

months; meals cut/skipped in one or two months; meals cut/skipped in three or more months).

Prepared by Abt Associates Inc. 36
Chapter Three: Reliability Estimates for the Food Security Scales

Treating such question sets as trichotomous items reduces the number of items in the scale, and

Exhibit 3-3

CRONBACH'S ALPHA FOR THE 12-MONTH SCALE FOR ALL HOUSEHOLDS

Item

Extreme Scores Included

(α = .856; n=18,179)

Extreme Scores Excluded

(α = .743; n=7902)

Item Mean

Correlation

with Total

Score

α with Item

Deleted Item Mean

Correlation

with Total

Score

α with Item

Deleted

28 .034 .434 .858 .080 .429 .727

38 .040 .459 .855 .092 .451 .723

35 .072 .594 .842 .167 .582 .695

32 .149 .701 .827 .343 .595 .686

24 .157 .682 .829 .362 .545 .697

55 .257 .678 .830 .591 .373 .736

54 .276 .725 .823 .635 .439 .721

53 .349 .646 .837 .803 .206 .760

Exhibit 3-4

RASCH RELIABILITY ESTIMATES

FOR THE 12-MONTH AND 30-DAY SCALES

Scale Model Type

Including Households

with Extreme Scores

Excluding Households

with Extreme Scores

12-month scale Dichotomous .63 .74

Trichotomous .58 .70

30-day scale Dichotomous .00 .57

Trichotomous .00 .44

hence reduces the estimated reliability.

With extreme scores excluded, the reliability estimates for the 12-month scale are .74

(dichotomous) and .70 (trichotomous). The reliability estimates for the 30-day scale are .57 and

.44.

Unlike the previous reliability indicators, the Rasch reliability estimate decreases when

extreme scores are included. Thus, the reliability estimates for the 12-month scale are .63 and

Prepared by Abt Associates Inc. 37
Chapter Three: Reliability Estimates for the Food Security Scales

.58 with the extreme scores included. For the 30-day scale, because 88 percent of the households

that passed the screener responded negatively to all questions, the reliability estimate falls to zero

when cases with extreme scores are included.

3.5 RELIABILITY IN IDENTIFYING CASES WITH NO FOOD INSECURITY CONDITIONS

As noted earlier, none of the reliability statistics deal adequately with situations in which

a large percentage of cases have extreme scores. For present purposes, then, the statistics are

primarily useful in indicating the scales' reliability in distinguishing the level of food insecurity

among households that experience at least one of the conditions measured by items included in

the scales. The statistics provide little information about the scales' reliability in distinguishing

between households that experience none of the food insecurity conditions measured and

households that experience one or more of the conditions.

To provide additional insight on this point, a further analysis was conducted. The

analysis follows the split-half procedure: for each scale, we separate the items into two groups

to constitute two new scales; we then examine the relationship between the two new scales. The

scales are split as described earlier, but each of the new scales is then collapsed into a

dichotomous variable. The two categories on the dichotomous variable are (1) “answered all

questions negatively,” and (2) “answered one or more questions positively.” The agreement

between the new dichotomous items is then assessed.

A simple test of correspondence is the percentage of cases classified similarly by the two

variables. When the population is unevenly divided between the two categories of the

dichotomous variables, however, a high rate of agreement can occur by chance. The more

appropriate test is therefore the Kappa statistic. The Kappa statistic is a measure of the extent

to which there is agreement above and beyond what would be expected by chance. Kappa (κ)

is computed as:

(percent observed agreement) (percent agreement expected by chance alone)

100% (percent agreement expected by chance alone)

To test the hypothesis H0: κ = 0 vs. H1: κ > 0, we can use the lambda statistic . A λ κ

se(κ)

formula for the estimation of the standard error of κ can be found in Rosner (1986).

Prepared by Abt Associates Inc. 38
Chapter Three: Reliability Estimates for the Food Security Scales

Landis and Koch (1977) suggested that a κ below 0.4 represents poor agreement,

between 0.4 and 0.75 represents good agreement, and greater than 0.75 represents excellent

agreement.

The percent agreement between paired subscales and the Kappa statistics are shown in

Exhibit 3-5. As expected, the two scales in each pair are in agreement in a high percentage of

cases—around 85 percent for the 12-month scale, and around 95 percent for the 30-day scale.

More importantly, the κ values are all close to .70, which is toward the high end of the range

representing “good” agreement.4

This suggests that the scales provide a reasonable level of reliability in distinguishing

Exhibit 3-5

LEVEL OF AGREEMENT BETWEEN DICHOTOMIZED SPLIT-HALF SCALES

Households with Children Households without Children

Percent Agreement κPercent Agreement κ

12-month scale 84.8% .70 85.8% .69

30-day scale 94.5% .68 95.1% .67

between households that have experienced any of the measured facets of food insecurity and

households that have not experienced any of these conditions. It is particularly worth noting that

the κ statistics for the 30-day scale are quite similar to those for the 12-month scale, even though

the 30-day subscales have very few items and a very high percentage of respondents answering

all questions negatively. These factors appear to reduce the 30-day scale's reliability in

discriminating among households that have experienced one or more of the measured conditions,

but the scale remains reasonably strong at distinguishing those that have experienced any of the

conditions from those that have not.

4 In all of the comparisons, the λ statistic indicates that the level of agreement is significantly greater than

would be expected by chance (p < .001).

Prepared by Abt Associates Inc. 39
Chapter Three: Reliability Estimates for the Food Security Scales

3.6 SUMMARY

Although there is no absolute rule regarding minimum acceptable levels of reliability,

the literature provides at least some rough guidelines. Nunnally (1978), writing in the context

of the more traditional measures of reliability, suggests that reliabilities of about .70 can be

sufficient to suggest general reliability, particularly in the early stages of measurement

development. Nunnally suggests that for basic research, requiring a very high reliability (e.g.,

above .80) can be counterproductive, as resources are devoted to improving the scale instead of

learning about the underlying phenomenon. He also argues, however, that scales used to support

decisions regarding the treatment of specific individuals should have reliabilities exceeding .90.

Using the three traditional measures and following the conventional practice of including

households with extreme scores, both the 12-month scale and the 30-day scale would be judged

quite reliable. Estimated reliability values range from .86 to .93 for the 12-month scale, and from

.79 to .89 for the 30-day scale.

As noted previously, however, this conventional approach yields statistics that can be

influenced by the type of highly-skewed distributions that characterize the food insecurity scales.

A more conservative approach is to separate two types of reliability. The first considers the

scale's reliability in describing the level of food insecurity among households that experience one

or more of the food insecurity or hunger conditions measured by items in the scale. The second

asks about the scale's reliability in distinguishing between households that have vs. have not

experienced any of the measured food insecurity or hunger conditions.

The 12-month scale fares quite well on both dimensions of reliability. When households

that answered all questions negatively are excluded from the analysis, the Rasch reliability

estimate ranges from .70 to .74, and the more traditional indices range from .74 to .88. Using

the dichotomous split-half test, the κ statistics are .69 to .70. Although this approach is novel,

and no established benchmarks provide standards for “good” reliability, all of these scores are

in the acceptable range for other uses of the statistics.

The 30-day scale is equally reliable at distinguishing households that have vs. have not

experienced any of the measured food insecurity and hunger conditions. The κ statistics of .67

to .68 are nearly the same as those for the 12-month scale. The 30-day scale, however, seems

less reliable at distinguishing among levels of food insecurity for households that experience one

Prepared by Abt Associates Inc. 40
Chapter Three: Reliability Estimates for the Food Security Scales

or more of the measured conditions. When we consider only the households that answered at

least one question affirmatively, reliability estimates range from .36 to .65.

Two factors reduce the 30-day scale's estimated reliability in distinguishing levels of

food insecurity and hunger among households that experience one or more of the measured

conditions. First, the number of independent items on the 30-day scale is small. Second, the 30-

day scale measures a narrower range of food insecurity, because some of the less severe

questions were not asked in the 30-day time frame. To increase the reliability of the 30-day scale

to be more comparable to the 12-month scale, it would probably be necessary to add more 30-day

items to the Food Security Survey, and in particular to add items measuring less severe

conditions of food insecurity than those currently included in the scale.

Prepared by Abt Associates Inc. 41
CHAPTER FOUR

DEFINING RANGES OF THE FOOD SECURITY SCALE

The analyses discussed in earlier chapters provide the basis for concluding that food

security can be reliably measured as a unidimensional phenomenon. Households can be ranked

on the basis of scale values across a continuous range indicating the severity of food insecurity

experienced within the household. The full range of severity measured extends from no

measurable food insecurity at all, through increasing levels of severity characterized by reduced

food intake and hunger for household members, to some maximum measured level. Although

the phenomenon of food insecurity can be viewed as unidimensional and continuous, several

distinct ranges of severity are of interest. Identifying these ranges of severity enables one to

supplement the continuous food security scale, subdividing it to create a categorical variable

providing a comparatively simple measure of food security status in terms of several broad ranges

of severity.

In this chapter we describe the conceptual and empirical bases for a priori expectations

regarding the structure of a categorical food security status variable, and the process leading to

definition of categorical ranges within the continuous food security scale. Several specific issues

related to selection of threshold levels or scale dividing lines are summarized, and the final

categorical food security status variable is described.

4.1 CONCEPTUAL BASIS FOR A CATEGORICAL FOOD SECURITY STATUS VARIABLE

The first threshold level of severity, or dividing line, to be identified on the unidimensional food security scale is the point of transition from food secure status to food insecure status.

In addition to this threshold, two other cutpoints, deriving from the LSRO/AIN conceptual

definitions of food security, food insecurity, and hunger, are of interest.1 As noted in the main

1 The conceptual rationale underlying the measurement of food insecurity and hunger developed in the

present study is described in Bickel, Andrews and Klein (1996). The research background leading to this

measurement approach is documented in the U.S. Department of Agriculture report, Food Security

Measurement and Research Conference: Papers and Proceedings, Alexandria, VA: USDA Food and

Consumer Service, Office of Analysis and Evaluation, June 1995.

Prepared by Abt Associates Inc. 43
Chapter Four: Defining Ranges of the Food Security Scale

report of this study,2 the LSRO/AIN conceptual clarification provides a working definition of

hunger as "the uneasy or painful sensation caused by a lack of food" and identifies hunger as "a

potential but not necessary consequence of food insecurity" (Anderson/LSRO, 1990). Previous

studies examined by the AIN expert group had led to a consensus view of hunger as "nested"

within the broader phenomenon of food insecurity, and occurring at the more severe levels of

food insecurity as experienced in U.S. households.

Moreover, empirical evidence supports the conceptual view of household-level food

insecurity as a managed process involving identifiable patterns or stages of behavioral responses

to food insufficiency as the degree of such insufficiency increases (Radimer, Olson and

Campbell, 1990; Basiotis, 1992; Cristofar and Basiotis, 1992; Radimer et al., 1992; Wehler, Scott

and Anderson, 1992; Burt, 1993; Cohen, Burt and Schulte, 1993). Within this framework, food

insecurity in the household begins with an initial stage characterized by adult household

members’ experiences of food insufficiency, anxiety about their food situation, and adjustments

in their budget and food management patterns. These latter behavioral "coping strategies" may

involve efforts to augment the household’s food supply from emergency or other non-normal

sources, and may involve modifications to the variety and quality of food available to household

members, but normally do not include reduction in overall quantity of food intake. In this initial

stage there is little or no evidence that household members experience actual hunger — "the

uneasy or painful sensation caused by a lack of food" — as a result of their household’s level

of food insecurity.

The second stage involves intensification of food economizing behaviors, some of which

lead to patterns of reduced food intake among one or more of the adults in the household. When

children are present in a household, efforts are made to spare them from food intake reduction

through various rationing strategies. If the household’s food insecurity persists or worsens,

however, a third stage appears in which adult hunger is manifested in more severe forms (e.g.,

going whole days with no food) and, in households with children, the children experience actual

hunger, revealed in patterns of reduced food intake.

2 Hamilton et al. (1997), Household Food Security in the United States in 1995: Summary Report of the

Food Security Measurement Project, Alexandria, VA: U.S. Department of Agriculture, Food and Consumer

Service, June 1997, Chapters One and Two.

Prepared by Abt Associates Inc. 44
Chapter Four: Defining Ranges of the Food Security Scale

This conceptual framework suggests four potentially identifiable stages or levels of

severity within the continuous food security variable. Those severity-level categories are: (1)

Food Secure; (2) Food Insecure with No Hunger Evident; (3) Food Insecure with Moderate

(adult) Hunger Evident; and (4) Food Insecure with Severe Hunger (child hunger, and severe

adult hunger) Evident. Given these conceptual categories, the question is how best to subdivide

the 12-month and 30-day scales into ranges of severity that correspond operationally to the

designated conceptual categories.

4.2 DEFINING RANGES AND SELECTING SCALE CUTPOINTS

As described in earlier chapters, the Rasch model assigns a scale value to each

household based on the number of scale items answered affirmatively relative to the total number

of items answered.3 As an interdependent part of its estimation from the data, the model also

ranks scale items according to their level of severity on the basis of the actual response patterns

of all households in the data. The 18 items in the final 12-month scale are shown in Exhibit 4-1,

with items listed by increasing order of severity from top to bottom in the table. If all responses

were perfectly ordered, an affirmative response to any scale item would occur only in conjunction

with affirmative responses to all prior, or less severe, scale items. Therefore, as perfect scale

ordering is approached among the actual sample households, any number "n" of affirmative

responses approaches exact correspondence to the first n items in the scale. Although the data

are not perfectly ordered for all households, in fact the most common pattern of household

responses (the mode) does follow the sequential order of severity.4 That is, the modal household

3 For ease of explication this discussion is presented without addressing separately the cases of households

with and without children. Readers should note that these two types of households were presented different

numbers of items, because questions addressing conditions of children in the household were not presented to

households without children. The form of the Rasch measurement model and the BIGSTEPS software that

implements the model take these differences into account in calculating household scale scores.

4 For example, among households with no children, 82 percent followed the modal pattern on the 12-month

items. Households answering "no" to all questions, however, amount to 65 percent of the total. Among

households answering "yes" to at least one question, 49 percent followed the modal pattern. For the non-modal

households, responses deviate from the pattern that would be observed under perfect ordering. Some

households answer "yes" to items without answering "yes" to all prior items. A non-modal household with n

affirmatives has answered negatively one or more of the n less-severe questions, instead affirming one or more

of the more severe questions. The Rasch model implicitly considers them equivalent, in effect treating all

households as modal and assigning both households the same scale value.

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Chapter Four: Defining Ranges of the Food Security Scale

that answers n items affirmatively gives "yes" responses to the n least severe items in the scale

sequence.

Defining ranges on the continuous scale is the operational means of assigning values to

the categorical variable measuring households’ food security status. This categorical measure

identifies the particular range of severity of food insecurity that a given sample household has

experienced in the prior 12-month or 30-day period. Defining the appropriate scale ranges for

classifying households according to food security status involves identifying subsets of the

sequential indicator items that best correspond to the conceptual categories described above.

After a subset is identified in general terms, it is necessary to identify the appropriate

classification boundaries, or points of transition from one severity range to the next. Each such

boundary is marked by a particular "threshold item." The threshold items and their classification

boundaries developed in the present study for the purpose of giving operational definition to the

categorical food security status variable are depicted by the shaded rows in Exhibit 4-2.5

Thus, the scale itself, with items ranked from least to most severe, provides a meaningful

framework within which to identify operationally the designated ranges of behaviors and

conditions corresponding to the conceptual construct summarized above. The scale, whose values

range from 0 to 10, must be subdivided in terms of numeric values so that a household with a

particular scale value can be assigned to a particular food security status category. This

subdivision, however, can be accomplished by considering the behaviors and conditions

represented by values at each point on the scale.

The procedure for subdividing the scale rests on two features of the scaling methodology

described above. First, household values on the food security scale are based fundamentally on

a simple count of the number of questions to which they respond affirmatively. Second, most

households' responses follow the sequential logic of item severity: a household that says “yes”

to a particular question typically says “yes” to all less severe questions as well.

In general, then, one can characterize households that have a particular scale value as

having responded affirmatively to a particular group of questions. Exhibit 4-2, which is

organized in terms of increasing severity of the questions, illustrates the point. A household that

5 Exhibits 4-1 and 4-2 in the main report of this study (Hamilton et al., 1997), also illustrate this division

of the scaled indicator items into the respective severity-level classes of the categorical food security measure.

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Chapter Four: Defining Ranges of the Food Security Scale

Exhibit 4-1

ITEMS IN THE FINAL 12-MONTH SCALE LISTED

BY INCREASING SEVERITY LEVEL

Item

Label

Item Content

(All questions refer to the last 12 months)

Q53 Household members worried whether food would run out before they got money to buy

more (sometimes or often).

Q54 Respondent reports that the food they bought just didn’t last, and they didn’t have money to

get more (sometimes or often).

Q55a Household members couldn’t afford to eat balanced meals (sometimes or often).

Q58 Household relied on a few kinds of low-cost foods to feed children because they were

running out of money to buy food (sometimes or often).

Q24 Adults in the household cut the size of meals or skipped meals because there wasn’t enough

money for food.

Q56 Household couldn’t afford to feed children a balanced meal, because they couldn’t afford

that (sometimes or often).

Q32 Respondent ate less than he/she felt they should because there wasn’t enough money to buy

food.

Q25a Adults in the household cut the size of meals or skipped meals because there wasn’t

enough money for food in at least 3 of the last 12 months.

Q57 Children were not eating enough because household couldn’t afford enough food (sometimes

or often).

Q35 Respondent was hungry but didn’t eat because couldn’t afford enough food.

Q38 Respondent lost weight because there wasn’t enough food.

Q40 Adults cut the size of children’s meals because there wasn’t enough money for food.

Q28a Adults in household did not eat for a whole day.

Q47 Children were hungry but household couldn’t afford more food.

Q29 Adults in household did not eat for a whole day in at least 3 of the last 12 mos.

Q43 Children skipped meals because there wasn’t enough money for food.

Q44 Children skipped meals because there wasn’t enough money for food in at least 3 of the last

12 mos.

Q50 Children did not eat for a whole day because there wasn’t enough money for food.

a Indicates threshold items in the scale. For each designated range of severity comprising the categorical food-security

variable, the subset of indicators beginning with the threshold item and continuing through the successively more severe

indicators, up to the next identified threshold, serve operationally to define and characterize that designated range.

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Chapter Four: Defining Ranges of the Food Security Scale

gives one affirmative answer most often answers Q53 affirmatively, a household with two

Exhibit 4-2

THRESHOLD ITEMS DEFINING RANGES OF THE FOOD SECURITY SCALE

Questions

(in order of increasing severity)

Households with

Children

Households without

Children

Number of

Affirmatives

Modal

Household

Value

Number of

Affirmatives

Modal

Household

Value

0 0.0 0 0.0

Q53 Worried food would run out 1 0.1 1 0.9

Q54 Food bought didn’t last 2 1.6 2 2.0

Q55 Adult not eat balanced meals 3 2.3 3 2.8

Q58 Adult fed child few low-cost foods 4 2.8

Q24 Adult cut size or skipped meals 5 3.3 4 3.6

Q56 Couldn’t feed child balanced meals 6 3.8

Q32 Adult eat less than felt they should 7 4.3 5 4.3

Q25 Adult cut size or skipped meals, 3+ 8 4.7 6 5.0

Q57 Child not eating enough 9 5.2

Q35 Adult hungry but didn’t eat 10 5.6 7 5.8

Q38 Adult lost weight 11 6.0 8 6.5

Q40 Cut size of child’s meals 12 6.4

Q28 Adult not eat whole day 13 6.8 9 7.5

Q47 Child hungry 14 7.3

Q29 Adult not eat whole day, 3+ mos. 15 7.8 10 10.0

Q43 Child skipped meal 16 8.4

Q44 Child skipped meal, 3+ mos. 17 9.3

Q50 Child not eat for whole day 18 10.0

affirmatives most often affirms Q53 and Q54, and so on.

For each question, the exhibit shows the number of affirmative responses and the

associated scale value for households whose responses follow the sequential logic of item

severity. For example, if the most severe question affirmed by a household with children is Q24,

that household has also responded affirmatively to the four less severe questions (Q53, Q54, Q55,

and Q58) and has a total of five affirmative responses. Its corresponding scale score is 3.3. The

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Chapter Four: Defining Ranges of the Food Security Scale

exhibit also shows parallel, but slightly different, values for a similar household without children.

Q58 is not applicable to that household. Thus, if the most severe question it affirms is Q24, it

will have a total of just four affirmative responses. Because the Rasch model, however,

computes a scale value that takes into account the number and severity of the questions the

household was asked, the scale value for the household without children (3.6) is quite close to

the value for the household with children (3.3).

It is possible to describe any point on the scale in terms of the questions that the

“modal” or typical household with that scale value has answered affirmatively. Similarly, one

can say that all modal households with values at or above a specified point on the scale have

responded affirmatively to at least the group of questions corresponding to the specified point.

For example, all modal households with values at or above 2.3 have responded affirmatively to

at least the three least severe questions in the scale (Q53, Q54, Q55). All modal households with

values of 4.7 or higher have responded affirmatively at least to Q24 and to all applicable less

severe questions.6

Thus, although the scale itself is a continuous measure of a single dimension (i.e., the

severity level of food insecurity), it can be subdivided by considering the collection of conditions

and behaviors associated with particular ranges of scale values. In this manner, the scale and the

severity rankings provided by the Rasch model yield a statistical framework for defining

conceptually meaningful categories for the food security status variable. Within this statistical

framework, however, the exact location of the category boundaries or scale thresholds depends

upon informed judgment about how best to interpret the conceptual constructs based upon the

LSRO/AIN definitions and the previous empirical research findings on food security and hunger.

The next section reviews those judgments and the reasoning behind them.

6 Non-modal households with a given scale value have, by definition, not responded affirmatively to all

of the applicable less severe questions, but instead have responded affirmatively to more severe questions. For

example, a non-modal household (with children) with a scale value of 2.3 must have answered three questions

affirmatively. Instead of Q53, Q54, and Q55, however — the three least severe questions — the household

might have said “yes” to Q53, Q54, and Q58, although saying “no” to Q55.

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Chapter Four: Defining Ranges of the Food Security Scale

4.3 EVIDENCE OF FOOD INSECURITY

The LSRO/AIN definitions of food security and food insecurity are:

· Food security: "Access by all people at all times to enough food for an active

healthy life. Food security includes at a minimum: (1) the ready availability of

nutritionally adequate and safe foods, and (2) an assured ability to acquire

acceptable foods in socially acceptable ways (e.g., without resorting to emergency

food supplies, scavenging, stealing, or other coping strategies)" (Anderson/LSRO,

1990, p. 1598).

· Food insecurity: "Limited or uncertain availability of nutritionally adequate and

safe foods or limited or uncertain ability to acquire acceptable foods in socially

acceptable ways" (ibid.).

Several dimensions or aspects of food security are apparent in these definitions, of which

the most central and fundamental is described as "enough food for an active, healthy life" — i.e.,

a sufficient quantity of acceptable foods to meet the household’s basic needs. A number of

additional dimensions are also apparent, including the nutritional quality and safety of available

foods, the social acceptability of the means of obtaining food, and the household’s assurance or

certainty of its ability to obtain needed food. These additional dimensions of the broad

conceptual definition of food security, however, are not directly captured in the questions

incorporated in the food security scale. Rather, the measure focuses on the simple quantitative

dimension of "enough" food. The food quality dimension is represented only to the extent that

some particular quality of food (in both nutritional and conventional senses) is perceived and

understood by households members to be necessary. The scale consists entirely of items

indicating either this quantitative or qualitative aspect of food sufficiency, as experienced and

understood by the household respondent, in relation to his or her self-perception of basic needs.

Several of the questions included in the CPS Food Security Supplement were intended

to capture those aspects of households’ food coping behaviors that seek to augment insufficient

household food supply through emergency or other non-normal means. These extraordinary

coping methods, such as obtaining food from food banks or pantries, borrowing money for food,

taking children to others’ homes for meals, or getting meals at soup kitchens, have been regarded

as good behavioral indicators of a condition of food insecurity or insufficiency within the

household, and they may be presumed to reflect the concept of acceptability of sources or means

of food-acquisition within U.S. social norms. These food-augmenting coping behavior items in

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Chapter Four: Defining Ranges of the Food Security Scale

the CPS data, however, do not factor together with the indicators that are included in the

measurement scale. Thus, they represent a dimension of the conceptual definition of food

security — the assurance of access to food through socially-acceptable means — that is not

represented within the unidimensional measure of severity of food insecurity.7

Examining the items in the 12-month scale, shown in severity-ranked order in Exhibits

4-1 and 4-2, the basic question is how many items must be answered affirmatively in order to

provide clear evidence of food insecurity as defined above. Item Q53 could be interpreted as

indicating uncertainty about the household’s access to adequate acceptable food, or the ability

to acquire it in socially acceptable ways. By itself, however, this subjective item may be

considered to lack face validity as a sufficient indicator of food insecurity. An affirmative

response to only this one item was therefore judged by the technical analysis team as insufficient

to indicate the threshold level of food insecurity.

Giving affirmative responses to two items (in the modal case, items Q53 and Q54)

indicates worry or anxiety about the household’s food position, and also initial perceptions of

insufficiency of the household’s food supply (food bought just didn’t last). Although these two

items together provide stronger evidence of household food insecurity, they were still judged

insufficient to establish unequivocally that severity has reached the threshold level required for

the categorical measure of food insecurity. Including item Q55, however, captures not only

reports that the household food supply is substandard, but also efforts to cope with this

insufficient food supply in ways that, although they may maintain the quantity of food intake,

reduce the perceived quality of diets below the level the respondent understands to be needed to

maintain "balanced meals."

It is useful to consider the relative severity of items as well as the simple rankings

shown in prior exhibits. Exhibit 4-3 therefore maps the relative severities, using the item

calibrations presented in Chapter Two.

The three least-severe items in the scale (Q53, Q54, and Q55) appear just prior to a

substantial gap in the spacing of item calibrations, indicating a large difference in severity

between these items and the group comprised by items Q24, Q56, and Q32. Although item Q58

(child fed few low-cost foods) is very close in severity to the item Q55 and consistent in

7 See Chapter Five for further discussion of these indicators of coping behaviors.

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Chapter Four: Defining Ranges of the Food Security Scale

Exhibit 4-3

SEVERITY RANKING OF QUESTIONS IN FOOD SECURITY SCALE

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Chapter Four: Defining Ranges of the Food Security Scale

conceptual content, selection of the threshold or cutpoint item aims at identifying the point of

transition from food security into food insecurity. Thus, the first item completing a group that

is conceptually and statistically consistent with food insecurity was judged most appropriate for

identifying the threshold. Item Q55 meets this criterion, and the set of three household- or adultlevel items answered affirmatively by modal households responding "yes" to item Q55, taken

together, was judged to provide sufficient evidence that the household has experienced food

insecurity, although at a level not yet showing evidence of actual hunger among household

members.

4.4 SUBJECTIVE REPORTING OF HUNGER

As summarized above, this research has aimed to develop both a continuous measure

of severity and a broad categorical measure of resource-constrained food insecurity that can

differentiate three broad ranges of severity, the two most severe of which involve actual hunger

for household members. This measurement task is guided by the LSRO/AIN conceptual

definitions of food insecurity and hunger, where hunger is nested as "a potential but not

necessary consequence" of food insecurity, and is defined as "the uneasy or painful sensation

caused by a lack of food." Therefore, an essential measurement task is to identify households

whose members have experienced actual hunger — the "uneasy or painful sensation caused by

a lack of food" — as a result of constrained or insufficient household financial resources. Food

insecurity or hunger resulting from eating disorders, dieting, or causes other than household

resource constraints are not being measured.

Three related factors enter into the conceptual consideration of what constitutes the

specific phenomenon being measured. These are access to adequate food, the physiological

sensation of hunger, and potential malnutrition. The relationships between the first two of these

— the basic dimension of food insecurity and hunger as experienced within households —

constitute the focus of the present research. The relationship of this basic experiential dimension

to malnutrition (which is also defined as nested — a "potential but not necessary consequence"

— within food insecurity) is not addressed in this research.

All items in the CPS Food Security Supplement addressing aspects of food insecurity

or hunger contain explicit language making it clear to respondents that the condition being asked

about is specifically caused by constrained household financial resources. For example, item Q53

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Chapter Four: Defining Ranges of the Food Security Scale

states "I/We worried whether (my/our) food would run out before (I/we) got money to buy

more." Item Q54 states "The food (I/we) bought just didn’t last, and (I/we) didn’t have money

to get more," whereas item Q55 states "(I/We) couldn’t afford to eat balanced meals." Such

qualifying language is included consistently in all food insecurity and hunger items in the CPS

instrument, including all those appearing in the food security scales. As a result, within the

limits of unidentifiable measurement error, affirmative responses to scale items can be expected

to reflect clear understanding by respondents that such answers are identifying resourceconstrained conditions. Although the possibility of respondents’ intentional misreporting exists,

as in every survey, the history and nature of the CPS, the high degree of preparedness of CPS

interviewers, and the careful design and testing of the Food Security Supplement items all tend

to reduce this and other types of measurement error. This point is important because identifying

the second classification boundary — the transition from food insecurity with no hunger evident

into food insecurity with moderate hunger (adult hunger) evident — relies primarily on evidence

that reduced food intake consistent with hunger has occurred within the referenced time period

among adults in the household, and that this hunger has resulted specifically from the resourceconstrained food insecurity of the household.

The task faced by the analysis team of determining the most appropriate severity level

of the initial boundary for the severity range of food insecurity with hunger present involved two

kinds of judgment. First, it was necessary to decide which specific items available in the scale

should be taken to indicate actual hunger for one or more adults in the household attributable to

resource constraint. These potentially include measures of reduced quantities of food intake for

adult household members (e.g., Q24, Q25), respondents’ subjective assessment of intake adequacy

(Q32), or direct perception and report of personal hunger (Q35). Second, given the scale items

available, a judgment is required as to how many such items are needed to provide sufficient

evidence that household members have experienced actual hunger due to resource constraint. As

explained below, the threshold ultimately chosen relies on evidence of a repeated pattern of

reductions in food intake by adults over the referenced time period.

The physiological sensation of hunger is experienced universally by all humans, and a

large research literature exists examining the nature of the experience in the context of basic

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Chapter Four: Defining Ranges of the Food Security Scale

human physiology and clinical nutrition.8 Several articles from this research literature are

summarized in Appendix A of the present volume. The studies described in this literature

provide strong support for the validity of subjective reporting of the sensation of hunger (see, for

example, Mattes and Friedman, 1993), although they find considerable variation in how the

sensation is experienced and described.

These studies seem to provide clear evidence that when usual patterns of eating are

interrupted by reducing food intake through actions such as cutting the size of meals or skipping

meals, the "uneasy or painful sensation caused by a lack of food" is the natural result. The

intensity of the sensations experienced is positively associated with the length of the period of

abstinence, although they diminish and may disappear altogether after an extended period of

fasting (usually several days). The results reported in this literature are thus consistent with the

use of items indicating that reduced food intakes below usual or normal meal patterns, due to

resource stringency, are evidence that hunger has been experienced.

Referring to Exhibit 4-3 above, after Q55 the next most severe item to indicate reduction

of food intake among adults is item Q24 (Adults cut/skip meals). Note that this item appears in

Exhibit 4-3 at virtually the same level as child item Q56 (Child not fed balanced meals), which

indicates reduction in the quality of diets provided to children in the household at this level of

severity of food insecurity. The next item (Q32, Respondent eat less than should) indicates that

food intake has fallen below the respondent’s own normative standard for the amount of food he

or she should be eating.

An affirmative response to item Q25 indicates that, in addition to all of the foregoing

conditions, adults in the household cut the size of or skipped meals in three or more of the

previous twelve months due to constrained resources, indicating a pattern of repetition of reduced

food intakes among adult household members. This item was judged to provide sufficient

additional evidence for the presence of adult hunger in the household, and was chosen, therefore,

as the item indicating the point of transition from the category of food insecurity with hunger not

evident to the category of food insecurity with adult hunger evident. Households in which the

respondent answered affirmatively to item Q25 will, in the modal case, also have answered

8 See Mattes and Friedman (1993) and Read, French and Cunningham (1994) for two general reviews

covering much of this research (see References, Appendix A).

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Chapter Four: Defining Ranges of the Food Security Scale

affirmatively to all previous items, indicating the household has experienced a comparatively

severe level of food insecurity. The affirmative answer to item Q25 indicates that adults in the

household have experienced, in addition, a pattern of repeated reductions in food intakes of a

type that the physiological research literature indicates is normally accompanied by the "uneasy

or painful sensation caused by a lack of food," or hunger.

When considering the selection or identification of cutpoint items, and when deciding

whether affirmative responses to items or sets of items yielded sufficiently clear evidence of a

particular condition (e.g., resource-constrained adult hunger), the study team employed a general

principle of requiring a pattern of repetition of either behaviors or items, or both. Thus, in

considering items indicating reduced food intake among adults, Q25 was viewed as providing

sufficient evidence because it involved occurrence of the behavior "cutting or skipping meals"

in a recurring pattern over the previous twelve months. Similarly, when considering items

indicating the existence of food insecurity with no hunger evident, a pattern of affirmative

responses to a sequential series of items was considered stronger evidence than affirmation of

only one or two pertinent items. This principle was employed to provide additional assurance

against response error.9

4.5 EVIDENCE OF CHILD HUNGER AND SEVERE ADULT HUNGER

Exhibit 4-3 shows items Q38, Q40, Q28, and Q47 all grouped at nearly the same level

of severity and located at a considerably increased level of severity beyond items Q25, Q57, and

Q35. The logic described above for selection of item Q25 as the threshold item for food

insecurity with adult hunger evident might suggest item Q40 (size of children’s meals cut) as a

likely candidate for the best item indicating the transition into food insecurity with severe hunger,

because children’s hunger is conceptually the most salient aspect of severe hunger in the

household. For reasons similar to those outlined above, however, a more severe item was

chosen. The wording of item Q40 allows the respondent to answer affirmatively if children in

the household had their meal size cut due to resource constraint only once or a small number of

times within the previous twelve months. Here again, sufficient evidence of hunger among

9 Issues of response error are discussed further in Chapter Eight.

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Chapter Four: Defining Ranges of the Food Security Scale

children was thought to require either a repetitive pattern of reduced food intake or a multiple

series of responses indicating such a condition.

Note that the child items indicating meals being cut and skipping meals occur as two

separate items, unlike the adult version, in which these two conditions are combined as one item.

The item addressing children skipping meals appears in Exhibit 4-3 at a much higher level of

severity than the item regarding size of children’s meals being cut. Skipping meals, as would

be expected, reflects a more severe condition than cutting the size of meals. In addition, adult

items Q38, Q28, and Q29, all of which indicate comparatively severe levels of adult hunger,

appear prior to child item Q44, which indicates a pattern of repeatedly skipped meals among

children.

These circumstances led team members initially to choose item Q47 (child hungry but

couldn’t afford more food) as the cutpoint indicating the beginning of food insecurity with child

or severe adult hunger evident. Assignment of household food security status using item Q47

as this cutpoint, however, led to anomalous results due to the different numbers of items

presented to households with and without children. This anomaly was avoided by choosing item

Q28, which appears at virtually the same severity level as item Q47 in Exhibit 4-3, as the

cutpoint item indicating the transition from food insecurity with adult hunger evident into food

insecurity with child and severe adult hunger evident.

In modal households with children responding affirmatively to item Q28, two items

related to reduction of food intake among children receive "yes" answers: item Q57 (children

were not eating enough) and item Q40 (children had meal size cut). Moreover, respondents in

all household types respond affirmatively to Q35, Q38, and Q28, indicating that adults in the

households "were hungry but did not eat because they couldn’t afford food," "lost weight because

there wasn’t enough food," and did "not eat for a whole day because there wasn’t enough money

for food." Affirmative responses to these items, taken together with affirmative responses to all

less severe items, appear to provide clear and strong evidence of child hunger and severe adult

hunger.

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Chapter Four: Defining Ranges of the Food Security Scale

4.6 SUMMARY

The primary task of the food security measurement study was to identify, test, and

develop a unidimensional measure of food insecurity and hunger based on the CPS food security

data, if a statistically strong and sound measure of this kind could be found. The Rasch

measurement method was successful in producing a unidimensional, continuous-variable measure

of severity of food insecurity and hunger from the CPS data that met these requirements. The

second task of the project, which was dependent upon the success of the underlying continuous

measure, was to develop a categorical-variable measure of several designated ranges of severity

of food insecurity, and the classification of households into these designated severity ranges or

categories, as follows:

· food secure

· food insecure with hunger not evident

· food insecure with moderate hunger

· food insecure with severe hunger

The conceptual construct for these designated ranges of severity was drawn from the

AIN/LSRO conceptual definitions of food insecurity and hunger, from other prior research on

food security measurement, and from limiting the measurement effort to one of the central

elements of the broad food security concept that is amenable to direct measurement, the direct

household experience of insufficient food to meet basic needs. Other elements of the broad

conceptual definition, such as safety of food, actual nutritional adequacy of diets, and social

acceptability of food acquisition, are not encompassed in the present measure of severity of food

insecurity.

The categorical measure of food security status depends on classifying households into

identifiable ranges of severity on the underlying continuous severity measure. The aim in

identifying or selecting the appropriate ranges of severity on the continuous measure was to

achieve acceptably close correspondence to the conceptual bases of the designated broad food

security status categories described above. The operational means of establishing the several

severity ranges was to select the most appropriate indicator items from among those available in

the continuous measurement scale to identify, or define operationally, the classification

boundaries, or thresholds, separating each designated severity range category from the next. This

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Chapter Four: Defining Ranges of the Food Security Scale

task involved judgment as to which items best reflect the transition from one broad range or

category of severity to the next.

Identification of the threshold items and their associated scale cutpoint scores for each

level of the categorical food security status variable involved use of statistical results from the

Rasch model, guided by the LSRO/AIN conceptual definitions of hunger and the results of

previous research in the areas of physiology, clinical nutrition, and food security measurement.

Team members combined these factors to select thresholds or cutpoint items that are most

consistent with the statistical results, empirical evidence, and the conceptual framework

representing the predominant understanding of food insecurity and hunger within the nutrition

science community.

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CHAPTER FIVE

THE RESOURCE AUGMENTATION QUESTIONS

In fitting the model for the 12-month food security scale, one group of questions was

conspicuously not included because they did not meet the statistical criteria for inclusion in the

scale. These questions involve actions that households might take to deal with a problem of

constrained food resources, and specifically actions other than reducing food intake or otherwise

modifying the internal household management of food resources. The questions refer to actions

such as putting off other bills in order to buy food, or obtaining meals from soup kitchens. The

class of actions has variously been termed “coping” or “resource augmentation” behaviors.

Because resource augmentation behaviors are pertinent to one dimension of the LSRO/

AIN definition of food insecurity — the ability to acquire food in “socially acceptable ways” —

the research team considered it important to explore the possibility of supplementing the primary

food security scale with some composite based on the resource augmentation questions. For

example, the food security status variable, rather than simply being based on a subdivision of the

primary scale, might also take into account the household's value on the resource augmentation

composite. Ultimately it was concluded that, although such a composite might be useful for

some researchers in particular situations, it does not add significant value to the food security

status variable.

This chapter reviews both the conceptual underpinnings of the effort to construct a

composite, the procedures that were implemented, and the likely effect of using a composite such

as that described.

5.1 TWO DIMENSIONS OF FOOD INSECURITY

The LSRO/AIN conceptual definition of food insecurity includes several diverse aspects

or dimensions of households’ food situations, of which only one central element — the direct

experience of insufficient food to meet basic needs — is captured in the measure developed from

the CPS food security data.

Households can, however, be food insecure either because they are unable to obtain

enough food (for discussion, call this food insecurity "type A"), or because they have to resort

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Chapter Five: The Resource Augmentation Questions

to socially unacceptable ways of obtaining food (call this "type B"). They may also be food

insecure for both these reasons. That is, they may resort to socially unacceptable ways of

obtaining food and still not obtain access to sufficient food (call this "type A&B").

Because resource-constrained hunger is understood to be nested within food insecurity,

it will not occur in a household unless that household is food insecure. If a household is food

insecure type A (unable to obtain enough food) at a sufficient level of severity, then hunger may

result. Likewise, if a household is food insecure type A&B, hunger may still emerge, despite the

household’s efforts to augment its available food through various coping measures. If a

household’s food insecurity is limited to type B only, however, the presence of basic food

insufficiency and hunger within the household cannot be inferred from this information. This

relationship is illustrated in Exhibit 5-1.

The availability of sufficient foods to meet basic needs (food insecurity type A). This

Exhibit 5-1

ILLUSTRATION OF ROLE OF RESOURCE AUGMENTATION BEHAVIORS

Food Availability Mode of Acquisition Food Security Status

Sufficient food available AND Socially acceptable acquisition

Food secure

Limited or uncertain availability

(anxiety, adjustments to budget

management, adjustments to food

quality)

OR Resource augmentation

via socially unacceptable

means

Food insecure with hunger not evident

Severely limited availability

(reduced food intake and other

indicators)

...................... Food insecure with evidence of hunger

dimension is well represented in the final unidimensional 12-month scale. As described in the

previous chapter, scale development activities demonstrated that it is possible to define a range

of values on this scale that can be used to classify households as "food insecure" on the basis of

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Chapter Five: The Resource Augmentation Questions

limited availability of foods relative to household need, operationally indicated by a pattern of

anxiety about the adequacy of the household’s food supply, and deterioration in the quality and

quantity of food available in the household.

The ability to acquire foods in socially acceptable ways, or via normal channels (food

insecurity type B). The scale development models employed do not capture this dimension.

Using the final 12-month scale to classify households as food insecure leaves open the possibility

that some households relying on extraordinary coping methods to acquire food in socially

unacceptable ways will be classified as food secure.

This situation emerges because the items in the CPS Food Security data that address this

latter dimension of food insecurity do not fit the measurement models leading to the final 12-

month scale. Two sets of items ask questions that provide indications of whether households

obtained food in ways that might be considered socially unacceptable. One set of items asks

whether households undertook actions to augment their food supply or other household resources

within the previous 12 months. These items are summarized in Exhibit 5-2.

A second set of items asks whether members of the household obtained food through

Exhibit 5-2

RESOURCE AUGMENTATION ITEMS IN THE

FOOD SECURITY SURVEY INSTRUMENT

Item

Label Item Summary/Description

Q18 "get food or borrow money for food from family or friends?"

Q19 "send or take children to the homes of friends or relatives for a meal?"

Q21 "put off paying a bill so you would have money to buy food?"

Q22 "get emergency food from a church, food pantry, or food bank?"

Q23 "eat meals at a soup kitchen?"

federal food assistance programs. These programs include food stamps, elderly feeding programs,

the child and adult care feeding program, school feeding programs, and WIC. There are two

strong arguments, however, for not using these items to classify households as food insecure.

Prepared by Abt Associates Inc. 63
Chapter Five: The Resource Augmentation Questions

First, participation in such programs may not be considered "socially unacceptable" by

many of the participants. There is some evidence to that effect, although this point has not been

adequately researched (Trippe and Beebout, 1988; Fraker, 1990; Radimer, Olson and Campbell,

1990; Trippe, Doyle and Asher, 1992; Olson, Frongillo and Kendall, 1995).

Second, there is a problem of logical circularity that could diminish the usefulness of

the food insecurity measures for policy considerations. The food insecurity measures are

potentially useful in helping policy makers assess the need for government food assistance

programs. Including program participation in the food insecurity measures, however, permits the

following potentially perverse result: If the government makes programs more available (for

example, by increasing the income eligibility threshold for free school lunches, or food stamps),

more people will participate and the experienced level of food insecurity would be expected to

decline. The measured level, however, may either decline or increase, depending on how the

participation indicator interacts with other indicators of the condition. Conversely, if the

government cuts back on programs, participation will decline and the effect of the participation

indicator may cause the measured level of food insecurity to go down (i.e., the food insecurity

problem can be "solved" by taking away the programs). Because of this situation, participation

in government food assistance programs was not included in the candidate pool of items for a

resource-augmentation index.

For the classification of households as food insecure to be more fully consistent with

the LSRO/AIN definitions, there would need to be a way to include information on food

acquisition through ways that are not socially acceptable (non-normal channels). An important

part of the indicator items used in earlier efforts to develop measures of food insecurity and

hunger reflect actions or behaviors undertaken by household food managers to avoid or

ameliorate hunger when food or financial resources become scarce. Sometimes referred to as

"coping behaviors" or "coping strategies," these behaviors include actions aimed at augmenting

the amount of food available to the household, or its financial resources for food, and they can

include actions to acquire food in ways that may be considered socially unacceptable, such as

those actions shown in Exhibit 5-2.

Prepared by Abt Associates Inc. 64
Chapter Five: The Resource Augmentation Questions

5.2 THE COMPOSITE RESOURCE AUGMENTATION INDEX

The feasibility of creating a separate resource augmentation scale was first examined by

fitting the five items in Exhibit 5-2, along with others, in Rasch models. Reliability estimates

for the resulting scales were below acceptable levels, however. This was due partially to the

small number of items available for inclusion in the models. Efforts to increase scale reliability

by adding items to the model were not successful.

One possible reason for the lack of fit has to do with the widely uneven opportunity for

the resource augmentation actions, or coping behavior, across households. For example, in order

to obtain food from a church, food pantry, or food bank, households must have access to these

facilities. That is, they must live in an area where such services are provided, and be able to get

to them. Similarly, with borrowing money or food from family or friends, such social-support

relationships must be available to the household before they can employ this coping strategy.

With the possible exception of item Q21 ("put off paying a bill so that you would have money

to buy food"), none of the resource augmentation items listed in Exhibit 5-2 are necessarily

available to all households who might use them if the opportunity were present.

Because the attempt to construct a Rasch scale was not successful, the research team

considered instead the creation of a simple composite or index based on the number of resource

augmentation questions a household answered affirmatively. The index is derived using the five

resource augmentation items in Exhibit 5-2. The proportions of each type of household

answering affirmatively to the resource augmentation items, weighted to represent the true

population proportions, are shown in Exhibit 5-3.

The items with the largest proportions of affirmative responses are Q18 (get food or

borrow money from friends or relatives) and Q21 (put off paying bills to have more money to

buy food), items which could be interpreted by some respondents as not indicating behaviors that

are socially unacceptable. The research team therefore felt that, if the index were to be used in

classifying households as food insecure, such classification should be based on a pattern of at

least three affirmative responses. This conforms with the general principle of redundancy (either

in items or behaviors), employed in Chapter Four in making decisions about items yielding

evidence of food insecurity or hunger.

This principle requires clear evidence of a pattern of repetition of an action (e.g.,

involuntary reduction of food intake in the case of food insecurity with moderate hunger), or a

Prepared by Abt Associates Inc. 65
Chapter Five: The Resource Augmentation Questions

pattern of repetition of affirmative responses to different items indicating an action or state (e.g.,

Exhibit 5-3

POPULATION WEIGHTED PROPORTIONS OF AFFIRMATIVE RESPONSES

TO THE RESOURCE AUGMENTATION QUESTIONS BY HOUSEHOLD TYPE

Item

Label Item Description

Households

with

Children

Households

with Elderly

but No

Children

Households

without

Children or

Elderly

All

Households

Q21 Put off paying bills to have money to buy food 20.8% 4.5% 9.9% 12.5%

Q18 Get food or borrow money from

friends or relatives 12.6% 3.1% 8.1% 8.4%

Q22 Get emergency food from church,

food pantry or food bank 4.7% 1.6% 2.1% 3.0%

Q19 Send or take children to friends

or relatives for a meal 3.3% N/A N/A 1.3%

Q23 Eat meals at a soup kitchen 0.4% 0.2% 0.7% 0.5%

three out of five resource augmentation items). Although application of this principle requires

an exercise of judgment by the study team, it provides an element of assurance against error that

might otherwise arise.

5.3 EFFECTS OF USING THE COMPOSITE RESOURCE AUGMENTATION INDEX

A resource augmentation index as described above, and estimates of "type B" food

insecurity derived from the index, provide a potential means of broadening the basic categorical

measure of food insecurity prevalence to include the dimension of food insecurity involving

reliance on non-normal, "emergency," or "socially unacceptable" forms of food acquisition. The

practical effect of broadening the reach of the categorical food security measure in this way,

however, turns out to be slight. This is because a very large proportion of the households that

would be classified as food insecure on the basis of the resource augmentation index are already

classified as food insecure by the underlying measurement scale and the classification criteria for

the food security status indicator. The number of additional households that would be classified

as food insecure solely on the basis of "type B" food insecurity, as measured by the resource

augmentation index, is quite small.

Prepared by Abt Associates Inc. 66
Chapter Five: The Resource Augmentation Questions

The resource augmentation index would be used only to classify households as food

insecure with no hunger evident, because it only addresses food insecurity type B (described

above). By the logic outlined earlier, type B food insecurity alone cannot be taken as evidence

of the presence of hunger, and therefore cannot be used to classify a household as food insecure

with hunger evident.

Approximately 0.3 percent (rounded) of all households in the (weighted) sample would

be classified as Food Secure on the basis of the scale-based measure, but would be classified as

Food Insecure with Hunger not Evident on the basis of the resource augmentation composite.

This would raise the proportion classified as Food Insecure with Hunger not Evident from 7.8

percent to 8.0 percent of the population, as shown in Exhibit 5-4. In terms of population

weighted values, just under one quarter million additional households would be classified as Food

Insecure with Hunger not Evident if the resource augmentation index were used in this way.

Exhibit 5-4

EFFECTS OF THE COMPOSITE RESOURCE AUGMENTATION INDEX ON THE

NUMBER AND PERCENT OF HOUSEHOLDS CLASSIFIED AS FOOD SECURE

AND FOOD INSECURE WITH NO HUNGER EVIDENT

Measure

Food Secure

Households

Food Insecure

Households with

Hunger not Evident

Twelve-month scale

Number

(thousands) Percent

Number

(thousands) Percent

Without the resource augmentation index 88,270 88.1 7,783 7.8

With the resource augmentation index 88,020 87.8 8,029 8.0

5.4 SUMMARY

Food insecurity can occur as a result of households experiencing either, or both, of two

dimensions included in the LSRO/AIN conceptual definition of food insecurity. The

measurement scale developed in the study addresses one of these dimensions (limited or uncertain

availability of enough food to meet basic needs), but does not capture the second (limited or

uncertain ability to acquire acceptable foods in socially acceptable ways).

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Chapter Five: The Resource Augmentation Questions

In principle, the resource augmentation questions in the CPS Food Security Supplement

might be used to create a composite measure that could take this second dimension into account.

The research team concluded, however, that it would not be advisable at this time to incorporate

such a measure into the definition of households' food security status. This conclusion was based

on two considerations.

First, it is not clear that the existing items in the CPS Supplement constitute a

sufficiently strong representation of the construct of food acquisition in ways that are not socially

acceptable. The fact that the items did not meet the criteria for construction into a scale suggests

that they do not make up a coherent and complete picture of the dimension of concern.

Incorporating the resource augmentation index into the food security status indicator could

therefore create a source of variability in the status indicator that might reduce the validity of

comparisons across groups or over time.

Second, incorporating the resource augmentation index into the food security status

indicator would make only a small difference in prevalence estimates, a difference that could be

considered within the “noise level” of the estimates. If the effect were large — if it suggested

that a very large number of food insecure households were being ignored by omission of the

resource augmentation index — it might be worth accepting the consequences of potentially

increased variability in the prevalence estimates. With only a small effect, however, the costs

of including the index appear to outweigh its benefits.

Resource augmentation or coping behaviors therefore constitute an important area for

future research. Better understanding is needed of the array of such behaviors that actually

exists, the conditions in which they are taken, and their relationship to the dimension of food

security captured in the primary food security scale. With improved understanding, it should be

possible to refine and improve the current approach to measuring food security.

Prepared by Abt Associates Inc. 68
CHAPTER SIX

EXTERNAL CONSTRUCT VALIDATION

OF THE FOOD SECURITY MEASURES

External construct validation is the process of formally examining the relationships of

a scale or construct to other similar or related measures of the construct that were not used in

developing the scale being tested. In the present context, this involves comparing estimates of

the households’ food security scale scores and food security status to other household measures

that are believed to be related to food security and that were not used in construction of the food

security measures.

There is no definitive measure of food security — no "gold standard" against which

these food security measures can be tested. Absent such a gold standard, the best approach is

to examine the relationships between the food security measures and other measures that are

understood to have a bearing on food security, provided that one can define an unambiguous

hypothesis about the bivariate relationship between the validation measure and the food security

measures. Following these criteria, the analysis compares the food security measures to

household food expenditures, income, income relative to the poverty line, and the household

respondents’ report of the sufficiency of food eaten in the household.

For the food security scale, a continuous variable, we calculate the coefficient of

correlation between the validation items and the households’ measure on the food security scale.

For the categorical measure of food security status, we examine the percent of households at each

level of severity of food insecurity within groups defined by the validation items. Because the

food security scales were developed using unweighted data, the construct validation results

reported here are also unweighted.

6.1 RELATIONSHIP OF CONSTRUCT VALIDATION ITEMS TO FOOD SECURITY

The LSRO expert panel identified four dimensions of food security that need to be

addressed at the household and individual level when measuring food security. Those four

dimensions are: the quantity of food intake, the quality of food intake, anxiety about the

adequacy of food supply, and social acceptability of the source of food (Anderson/LSRO, 1990).

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Chapter Six: External Construct Validation of the Food Security Measures

The CPS Food Security Supplement included questions intended to capture each of these four

dimensions, and all dimensions are included in some form in the food security measures

developed for this study.1 The discussion below considers each of the four chosen construct

validation items, focusing on the theoretically expected relationship between the construct

validation item and food sufficiency, the limitations of the validation item as a measure of food

security, and the results of the construct validation effort.

6.2 WEEKLY FOOD EXPENDITURES PER HOUSEHOLD MEMBER

The predominant way for households to acquire food is to purchase it through normal

commercial channels with the financial resources available to the household. One would

therefore expect to see a relationship between the level of food expenditures and the quantity and

quality of food intake, two dimensions of food security. Hence, one item we chose for

comparison with the food security measures is food expenditures: specifically, weekly food

expenditures per household member.2 The hypothesis is that weekly food expenditures per

household member will be negatively correlated with the level of food insecurity.

The obvious connection between food expenditures and food insecurity might not be as

strong as it first appears because there are several conceptual weaknesses of weekly food

expenditures as a measure of food sufficiency and some limitations in using the available

expenditure data for validation purposes. Conceptually, food expenditures are not an ideal

measure of food sufficiency because expenditures do not include food from most in-kind

programs (although food stamp purchases are included) or home-grown food, do not reflect

differences in costs for food across localities, and even in per-capita form do not perfectly adjust

for the specific food needs of a household. Moreover, households that have, on average,

sufficient weekly food expenditures may still experience weeks where they do not have financial

1 Both the 12-month and 30-day scales include items related to the quantity of food intake (e.g., cutting

or skipping meals) and a smaller number of items related to quality (e.g., not being able to serve balanced

meals). Anxiety is explicitly represented only in the 12-month scale (e.g., being worried that food would run

out before more money was available). Social acceptability is not explicitly addressed by any of the items in

the continuous scales, but is measured by the resource augmentation items used in constructing the categorical

food status variable for the 12-month period.

2 Respondents were asked a battery of questions regarding food expenditures in the past week as well as

“usual” food expenditures. Respondents were instructed to include purchases made with food stamps in their

report of expenditures on food.

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Chapter Six: External Construct Validation of the Food Security Measures

resources to purchase sufficient amounts of food. One data limitation of the measure is that the

food expenditure questions on the survey ask about usual weekly and monthly spending patterns

on food, but do not give a specific time period for which usual weekly or monthly spending is

reported, and hence may not match the time period over which food security is measured. Also,

food expenditures for the entire household are reported by one respondent, but this respondent

may not have accurate information on the food expenditures of other household members.

Finally, much of the variation in per capita food expenditures, especially at higher expenditure

levels, presumably reflects variations in convenience or luxury that would not be relevant to or

captured by the food security measures, because the food security measures give all food secure

households the same score.

Exhibit 6-1 shows that the correlation coefficients between the food security scales and

weekly food expenditures per household are negative, as expected. The values of the

coefficients, however, are quite small: −.12 for the 12-month scale and −.07 for the 30-day scale.

The cross tabulation of food expenditures with the categorical food security status variable

reported in Exhibit 6-2 also shows the expected pattern: the lower the level of food expenditures,

the more likely the household is to be in each of the food insecure categories. For the 12-month

scale, 1.4 percent of households that report weekly food expenditures less than $20 per person

are in the most severe category of food insecurity, whereas only 0.5 percent of households

reporting expenditures of more than $40 per person are in this category. More generally, about

21 percent of the low food spending households are in one of the three food insecure categories,

whereas only 6.5 percent of the high food spending households are in one of the food insecure

categories.

The same pattern is evident for the 30-day scale, although the overall percentage of

households classified as food insecure is much smaller than in the 12-month scale. For example,

4.4 percent of the households reporting they spend less than $20 per household member are

classified as showing evidence of hunger in the household, whereas a much smaller 1.2 percent

of the households reporting spending $40 or more per household member show evidence of

hunger.

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Chapter Six: External Construct Validation of the Food Security Measures

6.3 HOUSEHOLD INCOME

Exhibit 6-1

CORRELATION COEFFICIENTS FOR FOOD SECURITY SCALE SCORES AND

OTHER VARIABLES RELATED TO FOOD SECURITY

Food

Security

Measure

Weekly food

Expenditures

per Household

Member

Annual

Household

Income

Income

Relative to

the Poverty

Line

12-Month Variable

Food security measure 1.00 −.12 −.32 −.33

Weekly food expenditures per household member−.12 1.00 .23 .36

Annual household income −.32 .23 1.00 .89

Income relative to the poverty line −.33 .36 .89 1.00

30-Day Variable

Food security measure 1.00 −.07 −.16 −.16

Weekly food expenditures per household member−.07 1.00 .23 .36

Annual household income −.16 .23 1.00 .89

Income relative to the poverty line −.16 .36 .89 1.00

The financial resources of a household are a primary determinant of the level of

household food expenditures. This means that the financial resources of a household should be

related to the quantity and quality of food intake. Moreover, households' anxieties about the

adequacy of their food resources and their likelihood of resorting to non-normal modes of food

acquisition are presumed to increase as their financial resources diminish. Hence, household

income is related to all four dimensions of food insecurity mentioned earlier. Household income

is expected to be negatively correlated with the level of food insecurity. Here, it is measured

both as total annual income and as income relative to the federal poverty line for the given

household composition.

Despite the logical connection between household income and food insecurity, one

would not expect the correlation to be perfect for several reasons. In particular, food assistance

programs, which are designed to ameliorate food insecurity, are specifically targeted to

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Chapter Six: External Construct Validation of the Food Security Measures

households with low income, which reduces the relationship between food insecurity and income.

Additional reasons one would not expect income to be perfectly correlated with food insecurity

are that household income does not include all the assets of a household (e.g., savings), is not

adjusted for the food and nutritional needs of a household, and does not reflect the competing

demands for financial resources within a household. Also, total income for a year may be

substantial even though the year includes periods of time where financial resources are tight, such

as a period when the primary earner is unemployed. Finally, much of the variation in income

occurs at higher income levels where there is little or no corresponding variation in the food

security measures.

The income measure on the April 1995 data file also has several shortcomings that make

it an imperfect measure of household income. Chief among them are: income is a categorical

variable, analytically converted to a pseudo-continuous variable by taking the midpoints of the

categories; household income is derived from a question that asks about family rather than

household income, although in most cases these units are the same; and the 12-month period over

which income is measured does not exactly match the time period for which food security is

measured for most of the sample households.3 A further potential limitation is that the income

measured is cash income, and does not capture the value of food stamp benefits or other in-kind

food assistance. One might expect that food security would be more closely related to a measure

of income incorporating such transfers than to the cash income measure.

As expected, Exhibit 6-1 shows that food insecurity is clearly negatively related to both

annual household income and poverty-scaled income. Annual household income and the 12-

month food security scale have a correlation coefficient of −.32, whereas income and the 30-day

food security scale have a smaller correlation coefficient of −.16. The correlations with povertyscaled income are almost identical. All of these correlations are stronger than the correlation of

the food security scales with food expenditures.

3 Each CPS sample is divided into eight approximately equal rotation groups, with each group interviewed

four consecutive months, dropped out for eight consecutive months, then brought back in for four more

consecutive months before being retired. The household income measure is usually from the first month of

each four consecutive month spell in the CPS survey. Thus, although food security is measured for the 12

months preceding the April 1995 survey, income is measured for the 12 months preceding: January 1995 for

rotation groups four and eight; February 1995 for rotation groups three and seven; March 1995 for rotation

groups two and six; and April 1995 for rotation groups one and five.

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Chapter Six: External Construct Validation of the Food Security Measures

Exhibit 6-2

RELATIONSHIP OF THE FOOD SECURITY STATUS

VARIABLE TO OTHER VARIABLES

Food

Secure

Food

Insecure,

Hunger not

Evident

Food

Insecure,

Moderate

Hunger

Evidenta

Food

Insecure,

Severe

Hunger

Evidentb

12-Month Variable

Weekly food expenditures per

household member

<$20 79.2% 13.9% 5.6% 1.4%

$20-29 88.0 8.6 2.9 0.6

$30-39 92.4 5.3 2.0 0.3

$40 or more 93.7 4.0 1.9 0.5

Income relative to poverty line

<50% 59.5 24.2 11.4 4.9

50-100% 69.6 20.1 8.2 1.9

101-185% 82.6 11.9 4.6 0.9

>185% 96.2 2.6 1.0 0.2

Food sufficiency variable (from onepart version of question)

Often not enough to eat 15.8 29.0 18.4 36.8

Sometimes not enough to eat 21.8 31.5 36.3 10.5

Enough but not always the kinds of

food we want do eat

63.6 25.9 9.4 1.0

Enough of the kinds of food we

want to eat

95.9 3.4 0.6 0.1

Food sufficiency variable (from twopart version of question)

Often not enough to eat 14.4 23.2 33.4 29.0

Sometimes not enough to eat 24.0 38.0 31.3 6.6

Enough but not always the kinds of

food we want do eat

67.1 25.7 6.3 0.9

Enough of the kinds of food we

want to eat

96.7 2.7 0.5 0.1

a See notes at end of exhibit.

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Chapter Six: External Construct Validation of the Food Security Measures

Exhibit 6-2 (continued)

RELATIONSHIP OF THE FOOD SECURITY STATUS

VARIABLE TO OTHER VARIABLES

No Hunger

Evident

Food

Insecure,

Moderate

Hunger

Evidenta

Food Insecure,

Severe Hunger

Evidentb

30-Day Variable

Weekly food expenditures per household member

<$20 95.6% 3.6% 0.8%

$20-29 98.0 1.7 0.3

$30-39 98.7 1.1 0.2

$40 or more 98.8 1.0 0.2

Income relative to poverty line

<50% 90.1 7.3 2.6

50-100% 94.4 4.3 1.3

101-185% 96.7 2.8 0.4

>185% 99.3 0.6 0.1

Food sufficiency variable (from one-part version of

question)

Often not enough to eat 52.6 18.4 29.0

Sometimes not enough to eat 65.3 28.2 6.5

Enough but not always the kinds of food we

want to eat

94.4 5.0 0.6

Enough of the kinds of food we want to eat 99.7 0.3 0.0

Food sufficiency variable (from two-part version of

question)

Often not enough to eat 48.4 30.5 21.1

Sometimes not enough to eat 76.3 19.9 3.8

Enough but not always the kinds of food we

want to eat

96.6 3.2 0.3

Enough of the kinds of food we want to eat 99.8 0.2 0.0

a Limited to adult hunger at identifiable but moderate levels of severity.

b Evidence of children’s hunger and severe adult hunger.

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Chapter Six: External Construct Validation of the Food Security Measures

Exhibit 6-2 shows that among households whose income is less than half of the federal

poverty level, more than 40 percent are classified as having experienced some kind of food

insecurity in the past 12 months, and 5 percent fall into the most severe category of food

insecurity. In contrast, only 4 percent of the households with annual income above 185 percent

of the poverty level are classified as food insecure, and only 0.2 percent are in the most severe

category of food insecurity. The patterns for the 30-day food security scale are similar: 10

percent of households with income below 50 percent of the poverty level have experienced

hunger in the past 30 days, whereas less than 1 percent of households with income more than 185

percent of the poverty level have such experiences.

6.4 FOOD SUFFICIENCY

The final construct validation item is a single-item household food sufficiency measure that

has been used in previous research, appearing in a substantial number of national food use and

other types of surveys. Specifically, the respondent was asked which best describes the food

eaten in their household: enough of the kinds of food we want to eat; enough, but not always

the kinds of food we want to eat; sometimes not enough to eat; or often not enough to eat. This

measure focuses directly on two of the four dimensions of food insecurity: the quantity and

quality of food intake. Hence, we expect households that report a more severe food insufficiency

experience to be classified in a more severe food insecurity category.

The single-item food sufficiency measure does not have a clearly defined time reference,

simply asking respondents to characterize the "food eaten in your household." It does not

explicitly address two of the dimensions of food insecurity (anxiety and socially unacceptable

modes of food acquisition). It has four categories, which could permit a category-by-category

comparison with the four-category 12-month measure of food insecurity, but the conceptual

underpinnings of the two categorization schemes are not identical.4 A technical complication

with the food insufficiency measure is that it has been applied in the CPS Supplement in two

4 For example, a respondent answering "not always the kinds of food we want" can be expressing food

preferences unrelated to food insufficiency due to inadequate resources. At the more severe levels, the food

sufficiency measure distinguishes households on the basis of the frequency with which the situation occurs

(“sometimes” vs. “often” not enough to eat). The primary basis for distinguishing between the two most severe

categories of the food security variable, on the other hand, is whether the experience of hunger is limited to

adults in the household vs. adults and children both experiencing hunger.

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Chapter Six: External Construct Validation of the Food Security Measures

formats: a one-question version and a two-question version.5 Each version was applied to a

different portion of the CPS sample. Both versions were compared to the categorical food

security status variables, and the results are reported in Exhibit 6-2.

Both versions of the food sufficiency measure have the expected strong positive relationship

with food security status: households that report more severe food insufficiency experiences tend

to be classified in a more severe food insecurity category. For the one-part version of the food

sufficiency measure, 84 percent of households reporting "often not enough to eat" are classified

as food insecure by the 12-month scale, including 37 percent in the most severe category of food

insecurity. In contrast, only 4 percent of households reporting "enough of the kinds of food we

want to eat" are in any of the food insecure categories, including only 0.1 percent in the most

severe category of food insecurity.

The 30-day food security scale shows a similarly strong relationship with the one-part and

two-part versions of the food sufficiency question, although the overall prevalence of food

insecurity is smaller for the 30-day scale. Very few of the households reporting "enough of the

kinds of food we want to eat" are classified in either of the categories evidencing hunger: only

0.2 to 0.3 percent. In contrast, around half of the households reporting "often not enough to eat"

are classified as food insecure with evidence of hunger.

6.5 SUMMARY

The results of these analyses are consistent with the view that the food security measures

presented in this study constitute valid measures of the underlying constructs of food insecurity

and hunger. This provides as much assurance as one can expect at this stage that the measures

provide the desired information. By their nature, however, the tests conducted here cannot be

conclusive. All of the items used as points of comparison were designed to measure something

other than food security. A perfect measure of food security would therefore not be exactly

correlated with any of them, but there is no basis for knowing exactly how close the correlation

should be. Further validation will be desirable, including additional comparisons of the food

security variables to potentially related measures. In particular, the relationship of the present

5 See question 11a for the single-question version of the food sufficiency item, and questions 11 and 12

for the two-question version in the CPS Food Security Supplement instrument.

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Chapter Six: External Construct Validation of the Food Security Measures

measure of the central dimension of food insecurity and hunger as experienced in U.S.

households and established measures of the nutritional quality of diets and their health

consequences will be an important area of further research.

Prepared by Abt Associates Inc. 78
CHAPTER SEVEN

PROCEDURES FOR CALCULATING STANDARD ERRORS

FOR FOOD SECURITY PREVALENCE ESTIMATES

The Summary Report presents standard errors for our food security prevalence estimates

as an indicator of the degree of uncertainty surrounding reported point estimates due to relying

on a sample from the population rather than an entire census of the population.1 The

conventional procedures for estimating the standard errors of estimates, as incorporated in most

statistical software packages, are appropriate only for simple random samples from the

population. For efficiency reasons, the Current Population Survey (CPS) relies on a complex

sampling design that does not result in a simple random sample of households in the U.S.

population. Accordingly, it is necessary in the food security analysis to estimate variances by

other means. This chapter briefly explains the CPS sampling design and the method used for

calculating standard errors of estimates of the prevalence of food insecurity from April 1995 CPS

data.

7.1 CPS SAMPLE DESIGN

The sampling design used by the CPS is essentially a two-stage sampling procedure.

In the first stage, CPS stratifies groups of counties (Primary Sampling Units, or PSUs) and

Metropolitan Statistical Areas (MSAs) according to geographic location, and basic labor force

and demographic characteristics of the geographic area (from the most recent decennial census).

One PSU from each stratum is selected to represent the stratum in the sample. Because only

households in the selected PSUs are eligible to be in the sample, the usual variance estimation

formulas developed under the assumption of simple random sampling will underestimate the

between-PSU portion of variance to the extent that the chosen PSUs do not capture the variability

among all PSUs.

In the second stage of sampling, clusters of households within PSUs are selected to be

in the sample. In this case, the usual variance estimation formulas will underestimate the withinPSU portion of variance to the extent that there is homogeneity within households in a cluster.

1 See Appendix E of the Summary Report (Hamilton et al., 1997).

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Chapter Seven: Procedures for Calculating Standard Errors for Food Security Prevalence Estimates

Variance estimates provided by all-purpose statistical packages, such as SAS, assume

simple random sampling from the population of interest and equal weighting of each household;

hence, they are not appropriate for use with the complex CPS sampling procedure. Below, we

describe the variance estimation procedure used for taking into account the between-PSU variance

(sampling of MSAs and county groups) and within-PSU variance (sampling of households within

PSUs). This procedure was developed in consultation with statisticians from the Census Bureau’s

CPS Division.

7.2 ADJUSTMENT FACTOR FOR BETWEEN-PSU VARIANCE

We are unable to estimate between-PSU variance directly because information on a

household’s PSU is withheld from the data files to protect confidentiality of respondents.

Instead, we have calculated an adjustment factor to reflect this stage of sampling in our variance

estimates.

To estimate the between-PSU variance indirectly, we referred to unpublished components

of variance for several available CPS labor force estimates from November 1995, and

components of variance for a more extensive set of labor force estimates reported in Train and

Cahoon (1978).2 Because we are primarily interested in household estimates, we considered

those characteristics from the Train and Cahoon paper that are usually based on one person per

household.3 The between-PSU variance as a proportion of total variance was about 5 percent

for two of the characteristics, and about 9 percent for the third characteristic. The between-PSU

variance tended to comprise a somewhat smaller proportion of total variance in the November

1995 CPS estimates than in the 1978 study.4 Averaging these proportions, we estimated that the

between-PSU variance for April supplement estimates is about 6 percent of total variance. This

2 Train, G. and L. Cahoon, "The Current Population Survey Variances, Inter-Relationships, and Design

Effects," Proceedings of the Section on Survey Research Methods of the American Statistical Association, 1978,

p. 443-448.

3 These characteristics are the number of self-employed in each household, teenage labor force

participation, and teenage unemployment.

4 The only characteristics available from November 1995 are the total number of employed persons,

unemployed persons, and labor force participants. The between-PSU variance for these characteristics were

compared to the Train and Cahoon (1978) estimates for the same characteristics.

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Chapter Seven: Procedures for Calculating Standard Errors for Food Security Prevalence Estimates

translates into applying a factor of 1.06 to our direct estimates of the within-PSU variance to

obtain estimates of total variance.

7.3 ESTIMATION OF WITHIN-PSU VARIANCE

We used the "random groups" method to estimate the within-PSU component of

variance.5 This procedure measures the sensitivity of an estimate to the particular sample drawn

from within selected PSUs. That is, the within-PSU component of variance is calculated by

finding the variance of estimates obtained with different samples drawn from the same PSUs.

The eight CPS rotation groups provide an ideal mechanism for dividing the CPS sample

to estimate the variance among different samples taken from the same PSUs.6 Each rotation

group is an independent sample of households from all the sample PSUs. Thus, we can divide

the CPS sample into half-samples with four rotation groups in each. Each rotation group also

independently has the full CPS ratio estimation procedure applied, in which the sample weights

are adjusted to independent estimates of the civilian non-institutional population of the U.S.

Thus, use of rotation groups allows us to reflect the reduction in variance due to application of

the ratio estimation procedure to population controls used in the CPS.7 Exhibit 7-1 shows the

allocation of the eight rotation groups for each of the 30 definitions of half-samples used for our

calculations.

To estimate the within-PSU component of the variance for an estimate of the total

number of households in food security category i (e.g., the total number of households in the U.S.

that are food insecure with moderate (adult) hunger evident), we used the random groups

variance formula (see Hansen, Hurwitz, and Madow, 1953, p. 440), adjusted for half-samples that

5 For a more detailed description of this method, see Hansen, Hurwitz and Madow, 1953.

6 Each month a new rotation group is added to the CPS sample and an old rotation group is dropped from

the sample. Sample households in a rotation group are surveyed for four consecutive months, then take eight

months off, before being surveyed for four more consecutive months and then dropped from the sample; e.g.,

a household that enters the survey in January 1995 will be interviewed in January, February, March and April

1995, and again in the same months in 1996.

7 The SUDAAN program is often used to estimate variances in weighted samples. To use the estimation

procedure in the SUDAAN variance program, however, it is necessary to know the weights before application

of the ratio estimation procedure, as well as the final weights. Because these pre-ratio estimation weights are

not available, the SUDAAN program is not applicable in the present instance.

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Chapter Seven: Procedures for Calculating Standard Errors for Food Security Prevalence Estimates

Exhibit 7-1

ROTATION GROUPS IN EACH DEFINITION OF HALF-SAMPLE

Definition of Half-Samples

CPS Rotation Groups

in Half-Sample 1

CPS Rotation Groups

in Half-Sample 2

1 6, 1, 2, 3 4, 5, 7, 8

2 6, 1, 2, 4 3, 5, 7, 8

3 6, 1, 2, 5 3, 4, 7, 8

4 6, 1, 3, 4 2, 5, 7, 8

5 6, 1, 3, 5 2, 4, 7, 8

6 6, 1, 4, 5 2, 3, 7, 8

7 6, 2, 3, 4 1, 5, 7, 8

8 6, 2, 3, 5 1, 4, 7, 8

9 6, 2, 4, 5 1, 3, 7, 8

10 6, 3, 4, 5 1, 2, 7, 8

11 7, 1, 2, 3 4, 5, 6, 8

12 7, 1, 2, 4 3, 5, 6, 8

13 7, 1, 2, 5 3, 4, 6, 8

14 7, 1, 3, 4 2, 5, 6, 8

15 7, 1, 3, 5 2, 4, 6, 8

16 7, 1, 4, 5 2, 3, 6, 8

17 7, 2, 3, 4 1, 5, 6, 8

18 7, 2, 3, 5 1, 4, 6, 8

19 7, 2, 4, 5 1, 3, 6, 8

20 7, 3, 4, 5 1, 2, 6, 8

21 8, 1, 2, 3 4, 5, 6, 7

22 8, 1, 2, 4 3, 5, 6, 7

23 8, 1, 2, 5 3, 4, 6, 7

24 8, 1, 3, 4 2, 5, 6, 7

25 8, 1, 3, 5 2, 4, 6, 7

26 8, 1, 4, 5 2, 3, 6, 7

27 8, 2, 3, 4 1, 5, 6, 7

28 8, 2, 3, 5 1, 4, 6, 7

29 8, 2, 4, 5 1, 3, 6, 7

30 8, 3, 4, 5 1, 2, 6, 7

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Chapter Seven: Procedures for Calculating Standard Errors for Food Security Prevalence Estimates

are not exactly the same size (see Cochran, 1977, p.139). The variance formula for totals is

listed below:

(1)

2





































Xi1

N1 Xi

N

2 















Xi2

N2 Xi

N

2

where

Xi1 is the weighted number of households in the first half-sample that are in food

security category i;

Xi2 is the weighted number of households in the second half-sample that are in food

security category i;

Xi is the weighted number of households in the full sample that are in food security

category i.

N1 is the weighted number of households in the first half-sample;

N2 is the weighted number of households in the second half-sample; and

N is the weighted number of households in the full sample.

This yields the estimated within-PSU variance for estimated totals from a single definition of

half-samples. We form 30 definitions of half-samples from the eight rotation groups, resulting

in 30 estimates of variance. The final within-PSU variance estimate is an average of these 30

estimates.8

For estimated proportions, such as the proportion of all households experiencing food

insecurity with moderate hunger evident, we use the variance formula for ratios in Cochran

(1977, p. 155). The variance of the proportion, Xi

/Y, where Xi is the estimated number of

households in food security category i, and Y is the estimated number of households in the

population, is:

(2) var

















Xi

Yi

var(Xi)

Y 2

X 2 var(Y)

Y 4

















2 Xi

Y

cov (Xi,Y)]

8 The CPS modified its sample design in the spring of 1994; hence, three rotation groups (6,7,8) were

chosen with the old design and five were chosen with the new design. We have chosen our half-samples such

that all three rotation groups under the old sample design are never in the same half-sample.

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Chapter Seven: Procedures for Calculating Standard Errors for Food Security Prevalence Estimates

where:

var(Xi) = (X1 - X2)

2

;

var(Y) = (Y1 - Y2)

2

; and

cov(Xi,Y) = (X1 - X2)*(Y1 - Y2).

This yields the estimated within-PSU variance for proportions from a single definition of halfsamples.9 The average from the 30 definitions of half-samples provides the final estimate of the

within-PSU component of variance.10

7.4 CALCULATION OF THE STANDARD ERRORS

Finally, the estimated variance is calculated by multiplying the estimated within-PSU

variance by the 1.06 between-PSU variance adjustment factor. The standard errors reported in

the exhibits in this report are simply the square roots of the variances estimated using the above

procedures.

Our calculations indicate that for the entire population of U.S. households (sample size

44,730), the standard errors for households in a particular food security category range from 0.07

percentage points (food insecure with severe hunger) to 0.36 percentage points (food secure).

For smaller subgroups of the population, the standard errors tend to be larger. For example, for

subgroups with sample sizes between 1,000 and 2,000, the standard errors range from 0.28 and

0.58 percentage points for the food insecure with severe hunger; and the standard errors range

from 1.29 to 1.87 percentage points for the food secure estimates.

9 When calculating the variance of ratios where the denominator is the population of interest, the

adjustment for different sized half-samples made for the calculation of the variance of totals is unnecessary,

because the differences in sample sizes are already taken into account with the variance of the denominator

term and the covariance between the numerator and denominator.

10 To convert the variances calculated for proportions experiencing food security status i into variances for

the percent of the population experiencing food security status i, multiply the variance for proportions by

10,000 (i.e., 100 squared).

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CHAPTER EIGHT

POTENTIAL SOURCES OF BIAS IN PREVALENCE ESTIMATES

One of the main purposes of collecting the CPS food security data and developing food

security measures from the data is to estimate the prevalence in the United States of food

insecurity and hunger on a consistent basis over time and across population groups. To that end,

the continuous-measure food security scales were further developed into the food security status

variable, as described in Chapter Four. This chapter addresses the question of whether, assuming

that the conceptual and operational definitions of the status variable are acceptable, prevalence

estimates based on that categorical variable can be considered unbiased.

Three potential sources of bias are examined here:

· Screening bias, which might result from the fact that the full battery of food

security questions was asked of all lower-income, but only some higher-income,

households;

· Response bias, which occurs if households systematically paint a too-rosy or toobleak picture of their circumstances; and

· Random error bias, which can occur when the true prevalences in the population are

highly skewed.

It is impossible to present definitive estimates of the bias resulting from any of these

potential sources. Such an analysis would require the household classifications produced in this

study to be compared to classifications using a separate, authoritative measure of food security,

but no such measure exists. The discussions below are therefore largely theoretical and

speculative, attempting to provide a perspective on the likelihood of each of the possible types

of bias.

The general sense is that each of these three sources may contribute some bias, but that

the magnitudes of bias are likely to be small and the biases probably move in counterbalancing

directions. Screening can lead only to a downward bias in the estimated prevalence of food

insecurity and hunger. Response bias also seems likely to move prevalence estimates

downwards. Random error, on the other hand, would probably yield upward bias in prevalence

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Chapter Eight: Potential Sources of Bias in Prevalence Estimates

estimates. The net effect of these countervailing forces cannot be determined with the available

data.

8.1 SCREENING BIAS

In order to reduce respondent burden, the full battery of food security questions was

applied to higher-income households only if they passed through a set of screening questions.

The screen consisted of two main elements. Households were screened out if they had annual

household incomes above 185 percent of the federal poverty line and if they gave no indication

of food insufficiency in response to the single-item food sufficiency question and two other

screening questions (Q15 and Q16). Households that had incomes below 185 percent of poverty,

and higher-income households that gave some indication of food insufficiency on the screening

questions, were asked the full battery of questions.1,2

In total, about 26,000 higher-income households did not pass through the screen and

were not asked the battery of food security questions. This amounts to nearly 60 percent of the

full sample of around 45,000.

It is possible that some respondents who were screened out would have responded

affirmatively to some of the questions used in the food security scales. To the extent that this

occurred, the estimated prevalence of food insecurity and hunger is biased downwards.

Although the data do not offer a direct way to assess the bias, some insight is possible

through looking at the two main screening criteria separately. Households with incomes above

185 percent of poverty passed through the screen if they answered "sometimes [or] often not

enough to eat" on the food sufficiency indicator. Similarly, households indicating no food

insufficiency passed through the screen if they had incomes below 185 percent of poverty. The

food insecurity prevalence estimates for these households are shown in Exhibit 8-1.

1 The food sufficiency question was asked in two forms: a single question with four possible responses

(Q11A), and two questions with three and two response categories, respectively (Q11 and Q12). In either

formulation, all households that answered "sometimes [or] often not enough to eat" passed through the screen

and were asked the main battery of questions.

2 The complete screener included two additional paths through besides income and the food sufficiency

response, but those two had the predominant impact on screening decisions.

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Chapter Eight: Potential Sources of Bias in Prevalence Estimates

As the exhibit indicates, the food sufficiency item is a fairly powerful screen for food

Exhibit 8-1

PERCENT OF SAMPLE ULTIMATELY CLASSIFIED AS FOOD INSECURE,

BY INCOME AND FOOD SUFFICIENCY MEASURE

Above 185% of Poverty Below 185% of Poverty

No food insufficiency indicated

on key screening question Unknowna 16.3%b

Food insufficiency indicated on

key screening question 59.2%b 83.8%b

a The proportion is necessarily close to zero: most households in this category were screened out and not asked the full battery

of questions. The status variable classifies all of these households as Food Secure. A few higher-income households passed

the screener based on responses to two other questions, indicating potential food insecurity (Q15, Q16).

b Cell percentages represent the food security classification of those households described by the row and column headings.

The upper-right cell, for instance, shows that among households that indicated no food insufficiency on the key screening

question but had incomes below 185 percent of the poverty line, 16.3 percent are classified as Food Insecure. This includes

households classified into any of the three food insecure categories on the 12-month status variable. The three categories

are: Food Insecure with Hunger not Evident, Food Insecure with Moderate (Adult) Hunger, and Food Insecure with Severe

(Child and Severe Adult) Hunger.

insecurity. A majority of households that indicated food insufficiency on this key screening

question were subsequently classified as food insecure on the basis of the full battery of

questions. Even among those who reported incomes above 185 percent of poverty, nearly 60

percent of those who indicated food insufficiency on this screening question are classified as food

insecure on the scale. On the other hand, the food insecurity rate is only about 16 percent for

those households who indicated no food insufficiency on the screening question but were given

the full battery of questions because their incomes were below 185 percent of the poverty line.

These figures imply that the percentage of screened-out households who would have

been classified as food insecure had they received the full battery of questions is probably very

low, but probably not zero. It is therefore likely that the screening procedure imparts a small

downward bias to the estimated prevalence of food insecurity.

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Chapter Eight: Potential Sources of Bias in Prevalence Estimates

8.2 RESPONSE BIAS

In assessing response bias, the concern is that survey respondents may, on average,

respond to some or all questions in the instrument in a way that systematically portrays the

household as more food insecure or less food insecure than its true condition.

Response bias can be accurately judged only by comparing survey responses or scale

values to a more definitive measure of the condition. No such definitive measure is available

in the present study, however.

The only available indications of bias come from researchers who have been involved

in previous efforts to develop measures of food insecurity and hunger. This evidence, largely

impressionistic and anecdotal, suggests that survey respondents have some tendency to portray

their condition as less severe than reality, but little tendency to exaggerate their problem. The

principal motivations are perceived to be:

· Pride/shame — a desire not to reveal to an outsider (the interviewer) a condition

that the respondent believes could reduce the dignity of his or her image;

· Fear of government intervention — particularly among low-income households

with children, a fear that children might be removed from the household;

· Things could be worse — households who have experienced more severe

conditions than at present may consider their current situation not to be a problem.

This is believed to be especially likely for elderly persons who recall the depression

of the 1930s;

· Reduced standards — persons living at a particular level of food insecurity may

perceive it to be normal, and may not answer affirmatively to questions about

cutting meal size or eating less than they feel they should. At consistently low

levels of food intake, individuals may not experience the physical sensation of

hunger that a food secure person would experience with the same level of intake.

Elderly persons with diminished appetite may not perceive low food intake levels

to be problematic.

The response patterns for the CPS food security data provide very little basis for

assessing the likelihood or magnitude of any of these potential sources of downward response

bias. One point worth noting, however, is that households with elderly members are estimated

to have somewhat lower prevalences of food insecurity and hunger than other types of

households. This would be consistent with the possible under-reporting biases mentioned for the

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Chapter Eight: Potential Sources of Bias in Prevalence Estimates

elderly, but this analysis cannot distinguish between this and alternative possible reasons for low

prevalence of food insecurity in elderly households.

8.3 RANDOM ERROR IN SURVEY RESPONSES

If all respondents have no inclination to portray their situation as better or worse than

their true condition, some questions will still be answered inappropriately by some respondents.

This may occur because the respondent is confused or not paying attention. It may occur because

the question has a legitimate interpretation in addition to the predominant interpretation intended

by the survey designers. It may occur because the interviewer hears or records the answer

incorrectly. Such errors have no inherent bias: a respondent who should say “yes” is as likely

to say “no” as conversely. For present purposes, we consider these errors random.

If the population is roughly evenly divided with respect to the phenomenon being

measured, random response error does not lead to bias in prevalence estimates. That is, the

number of households who are truly food secure but erroneously classified as food insecure

would be offset by an equal number of truly food insecure households who are erroneously

classified as food secure.

If the population distribution is highly skewed, however, random error can result in

biased prevalence estimates.3 An example helps to illustrate the issue. Suppose that 90 percent

of the population is truly food secure, and 10 percent is truly food insecure. Suppose further that

each group has a 10 percent probability of giving responses that cause households to be

misclassified. It follows that 9 percent of the population (.9 x .1) is truly food secure but

misclassified as food insecure, whereas just 1 percent of the population (.1 x .1) is truly food

insecure but misclassified as food secure. In this example, the estimated prevalence of food

insecurity would be 18 percent, which means that it has a substantial upward bias relative to the

true prevalence of 10 percent.

The best way to assess the extent of this bias would be to compare the classifications

used in the study to an independent and definitive measure of food insecurity. Such an

assessment would determine both the sensitivity of the classification (the probability that a truly

food insecure household would be correctly identified) and its specificity (the probability that a

3 See Habicht and Meyers (1991) for a detailed discussion of this issue.

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Chapter Eight: Potential Sources of Bias in Prevalence Estimates

truly food secure household would be correctly classified). In the absence of an independent and

definitive measure, any analysis is essentially hypothetical.

Some perspective on the issue can be obtained by considering the nature of the food

security scale. Fundamentally, a household's value on the scale of severity of food insecurity,

and hence its classification into one of the food security status categories, is determined by the

number of questions it answers affirmatively. Exhibit 8-2 shows the distribution of households

without children, for whom ten questions in the food security battery are applicable and used in

the 12-month scale.4 Among these households, 63 percent were screened out and an additional

24 percent gave negative answers to all ten questions (first row). The percentages in the

remaining categories range from 3.3 percent to 0.3 percent, generally declining as the number

of affirmatives increases.

The exhibit illustrates two important points. First, the response distribution is highly

Exhibit 8-2

PERCENT OF SAMPLE HOUSEHOLDS WITHOUT CHILDREN

BY NUMBER OF AFFIRMATIVE RESPONSES

Screened out or no affirmatives 87.0%

One 3.3%

Two 2.2%

Three (food insecure with hunger not evident)a 2.4%

Four 1.1%

Five 1.0%

Six (food insecure with evidence of moderate (adult) hungera 1.1%

Seven 0.8%

Eight 0.5%

Nine (food insecure with evidence severe (child and severe adult) hunger)a 0.3%

Ten 0.4%

a Minimum number of affirmatives to be classified into the specified category. Percentages based on unweighted counts.

skewed, with the vast bulk of the respondents either screened out or giving no affirmative

4 For households with children, 18 questions were applicable and used in the 12-month scale.

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Chapter Eight: Potential Sources of Bias in Prevalence Estimates

responses. It is reasonable to assume that the true distribution of food insecurity in the

population is similarly skewed.

Second, the greatest potential source of upward bias in the prevalence estimates lies in

that portion of the population that either should have been screened out or should have given no

positive responses. Assume that the true distribution is close to the observed distribution, such

that 87 percent or more of all households should be in these first two categories (first row of the

table). A relatively low rate of random response error among this group could misclassify a

substantial number of households into other categories in the table.

The distribution shown in the table also sets an upper bound on the possible level of

random response error leading to false positives. If absolutely no households should have

answered any of the questions affirmatively — i.e., if 100 percent of the households should have

been in the first two categories — the implied rate of random response error would be 13

percent. It is obviously unreasonable to believe that no one ever gave an affirmative response

correctly. Thus, the true rate of random response error must be well under this upper bound.

A key question in assessing the likely level of random response error is how the error

might be distributed across the possible levels of the scale. It seems reasonable to believe that

small errors would be more common than large errors. That is, if a household should not have

answered any questions affirmatively, it would be more likely to give one erroneous affirmative

than two; two erroneous affirmatives would be more likely than three; and so on.

If all households responding in error made errors in just one response, prevalence

estimation bias would necessarily be extremely small. Households that should give zero

affirmative responses (the largest group of households) would give no more than one.

Households that give just one affirmative response are classified as food secure. Thus, no one

in the largest group of households would be misclassified into the wrong food security status.

The only groups that could bias the prevalence estimates in this situation would be those adjacent

to the dividing lines between food security status categories. For example, the first dividing line

comes between households with two affirmative responses (classified as food secure) and those

with three affirmatives (classified as food insecure without hunger). Some households that should

give two affirmative responses might give three, and thus be misclassified as food insecure, and

some who should give three affirmatives might give two, and thus be misclassified as food

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Chapter Eight: Potential Sources of Bias in Prevalence Estimates

secure. Because the two adjacent groups are quite similar in size, however, no substantial bias

would occur even if the probability of error were extremely high.5

Putting these various considerations together, the key question is, what percent of the

households who should give zero affirmative responses (the dominant population group) actually

give three or more affirmatives, thereby contributing bias to the prevalence estimates? It seems

unlikely that this percentage is large, which would mean that the prevalence estimates are not

strongly biased. As noted at the outset, however, it is not possible to go beyond this kind of

speculative analysis without a separate and more definitive measure of food insecurity and

hunger.

8.4 SUMMARY

This analysis considered three possible sources of bias in prevalence estimates. Two of

the three factors (screening bias and response bias) seem likely to mean that the estimates

understate the extent of food insecurity in the population. One factor (random error) seems likely

to work in the opposite direction. Of these three factors, only one — the possible tendency

among respondents to underreport the condition being measured — seems capable of producing

more than a small bias, and this possible downward response bias is speculative only. Thus,

although the probable direction of any net or overall bias may be downward, its actual direction

is indeterminant and its magnitude is most likely to be small.

5 None of the pairs of adjacent groups differ in size by more than 0.2 percentage points. Thus, a 100

percent error rate would yield a bias in the prevalence estimate of just that amount: 0.2 percentage points.

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Andrich, D., "Scaling Attitude Items Constructed and Scored in the Likert Tradition."

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Andrich, D., "A Model for Contingency Tables Having an Ordered Response Classification."

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Basiotis, P., "Validity of the Self-Reported Food Sufficiency Status Item in the U.S. Department

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Bickel, G.W., M.S. Andrews, and B.W. Klein, "Measuring Food Security in the United States:

A Supplement to the CPS." In D. Hall and M. Stavrianos, eds., Nutrition and Food

Security in the Food Stamp Program. Alexandria, VA: USDA Food and Consumer

Service, January 1996.

Burt, M.R., "Hunger Among the Elderly: Final Report of a National Study on the Extent and

Nature of Food Insecurity Among American Seniors." Washington, DC: The Urban

Institute, November 1993.

Cohen, B.E., M.R. Burt, and M.M. Schulte, "Hunger and Food Insecurity Among the Elderly."

Washington, DC: The Urban Institute, February 1993.

Cristofar, S.P. and P.P. Basiotis, "Dietary Intakes and Selected Characteristics of Women Ages

19-50 Years and Their Children Ages 1-5 Years by Reported Perception of Food

Sufficiency." Journal of Nutrition Education 24(2), 1992.

Fraker, T.M., The Effects of Food Stamps on Food Consumption: A Review of the Literature.

Washington, DC: Mathematica Policy Research, October 1990.

Habicht, J.-P. and L. Meyers, "Principles of Effective Surveys of Hunger and Malnutrition in the

United States." Journal of Nutrition 121, 1991.

Hambleton, R.K. (ed.), Application of Item Response Theory. Vancouver: Educational Research

Institute of British Columbia, 1983.

Prepared by Abt Associates Inc. 93
Selected Bibliography

Hamilton, W.L. et al., "Food Security in the United States in 1995: Report of the Food Security

Measurement Study." Cambridge, MA: Abt Associates Inc., report prepared for USDA

Food and Consumer Service, June 1997.

Hansen, M.H., W.N. Hurwitz, and W.G. Madow, Sample Survey Methods and Theory. New

York: Wiley and Sons, 1953

Landis, J.R. and G.C. Koch, "The Measurement of Observer Agreement for Categorical Data.

Biometrics 33: 159-174, 1977.

Mattes, R.D. and M.I. Friedman, "Hunger." Digestive Diseases 11: 65-77, 1993.

MacDonald, R.P., Factor Analysis and Related Methods. Hillsdale, NJ: Lawrence Erlbaum

Associates, Inc.., 1985.

Nunnally, J.C., Psychometric Theory, 2nd ed. New York: McGraw-Hill, 1978.

Olson, C.M., E.A. Frongillo, and A. Kendall, "Validation of Measures for Estimating the

Prevalence of Hunger and Food Insecurity in the Current Population Survey Module:

A Combination of Cornell and CCHIP Items." In Conference on Food Security

Measurement and Research, Papers and Proceedings, January 21-22, 1994.

Washington, DC: USDA/FCS and DHHS/NCHS, July 1995.

Radimer, K.L., C.M. Olson, J.C. Greene, C.C. Campbell, and J.P. Habicht, "Understanding

Hunger and Developing Indicators to Assess it in Women and Children." Journal of

Nutrition Education 24 (1): 36S-44S, 1992.

Radimer, K.L., C.M. Olson, and C.C. Campbell, "Development of Indicators to Assess Hunger."

Journal of Nutrition 120 (Suppl 11): 1544-1548, 1990.

Read, M.A., S. French, and K. Cunningham, "The Role of the Gut in Regulating Food Intake in

Man." Nutrition Reviews 52: 1-10, 1994.

Rosner, B.A., Fundamentals of Biostatistics, 2nd ed. Boston, MA: PWS Publishers, 1986.

Rulon, P.J., "A Simplified Procedure for Determining the Reliability of a Test by Split Halves."

Harvard Educational Review 9: 99-103, 1939.

Scott, R.I., C. Wehler, and J. Anderson, "Measurement of Coping Behaviors as an Aspect of

Food Security." In Food Security Measurement and Research Conference: Papers and

Proceedings, Appendix A. Alexandria, VA: USDA Food and Consumer Service, June

1995.

Train, G. and L. Cahoon. "The Current Population Survey Variances, Inter-Relationships, and

Design Effects." Proceedings of the Section on Survey Research Methods of the

American Statistical Association, 1978, p. 443-448.

Prepared by Abt Associates Inc. 94
Selected Bibliography

Trippe, C. and H. Beebout, Food Stamp Program Participation Rates Among the Poverty

Population, 1980-1987. Washington, DC: Mathematica Policy Research, November

1988.

Trippe, C., P. Doyle, and A. Asher, Trends in Food Stamp Program Participation Rates: 1976

to 1990. Washington, DC: Mathematica Policy Research, July 1992.

U.S. Department of Agriculture, Food and Consumer Service, Office of Analysis and Evaluation,

Food Security Measurement and Research Conference: Papers and Proceedings.

Alexandria, VA: USDA Food and Consumer Service, June 1995.

Wehler, C.A., R.I. Scott, and J.J. Anderson, "The Community Childhood Hunger Identification

Project: A Model of Domestic Hunger — Demonstration Project in Seattle, Washington." Journal of Nutrition Education 24 (1): 29S-35S, 1992.

Wright, B.D., and G.N. Masters, Rating Scale Analysis: Rasch Measurement. Chicago, IL:

Mesa Press, 1982.

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Appendix A

REVIEW OF LITERATURE FROM PHYSIOLOGY AND CLINICAL

NUTRITION RESEARCH ADDRESSING THE NATURE OF HUNGER
APPENDIX A

REVIEW OF LITERATURE FROM PHYSIOLOGY AND CLINICAL

NUTRITION RESEARCH ADDRESSING THE NATURE OF HUNGER

The literature summarized below was reviewed to answer two questions that are central

to identifying conditions of actual hunger, defined as "the uneasy or painful sensation caused by

a lack of food," as experienced within resource-constrained, food-insecure households. These

questions are:

1. Are subjective reports of hunger reliable as indicators of a measurable level of food

deprivation?

2. Is reduction in food intake a reasonable indicator or precursor of "the uneasy or

painful sensation caused by a lack of food," or hunger?

The literature does not yield an unequivocal answer to the first question, though in sum

it supports the validity of subjective reports of hunger. There is, however, considerable

heterogeneity both in the experience of sensations reported as hunger (or associated with hunger)

and in the extent to which these sensations are predictive of food intake. Moreover, there is not

a one-to-one correspondence between the length of food deprivation and the intensity of

sensations reported as hunger. A number of factors appear to condition the relation between food

intake and physical sensations of hunger.

Regarding the second question, there is fairly strong support in the literature for the view

that reduced food intake does lead to physical sensations of hunger. Up to a point, the intensity

of such sensations are positively associated with the extent of food deprivation. Under conditions

of prolonged fasting, however, the physical sensations associated with hunger diminish in

intensity, and are generally extinguished altogether.

A reasonable conclusion from these studies is that people who experience patterns of

undesired reductions in food intake below usual levels, such as may occur with severe limitation

of household resources, do experience physical sensations of hunger.

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

Summary of Physiological Literature

Vanderweele and Geiselman (1986) review the proceedings of a symposium addressing

relationships between appetite and need states in animals (including humans) that was part of the

1985 American Psychology Association Annual Meetings. Though most of the research

presented in this symposium involved animal experiments, several themes of relevance to the

current discussion emerged.

Geiselman presents results that relate to the distinction between "hunger" (defined as

"food drive that occurs in response to post-absorptive stimulation") and "appetite" (defined as

"food incentive occurring in response to pre-absorptive stimulation such as the anticipation, sight,

smell, or taste of palatable food"). Results observed following infusion of hexoses into different

locations within the gastric systems of laboratory animals lead Geiselman to conclude that both

hunger and appetite involve common underlying physiologic mechanisms.

Vanderweele and Geiselman characterize the research presented at this symposium as

generally supporting the hypothesis that both hedonic and physiological factors operate in the

regulation of food intake behaviors in humans and other animals, and that it is thus unlikely that

appetite and hunger can be completely separated. These findings provide some support for the

experience of an "uneasy or painful sensation caused by a lack of food" when meal size is

reduced below usual levels, or when meals are skipped, in that they indicate such sensations can

arise both from physiological factors (e.g., rates of gastric emptying, intestinal absorption of

nutrients, lipogenesis, and changes in blood glucose levels), and learned or conditioned

associations (e.g., related to social and cultural factors, food preferences, palatability of foods,

and time schedules regarding meals or other instances of food intake).

DeCastro and Elmore (1987) investigated the relationship between the subjective state

of hunger and objective food intake among 31 free-living adults (9 male and 22 female) using

a diary self-report method in which subjects recorded everything they ate or drank, the time of

occurrence, and their degree of subjective hunger at the beginning of each eating occurrence over

seven consecutive days. Food intake records were used to estimate stomach contents over time,

based on a previously established formula.

DeCastro and Elmore conclude that the results of this study indicate that the intensity

of self-reported hunger is dependent primarily on the contents of the stomach. This conclusion

is based primarily on the finding of significant positive correlations between self-rated subjective

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

hunger at the beginning of meals and the duration of the pre-meal interval, significant negative

correlations between self-rated subjective hunger and estimated pre-meal contents of the stomach,

and significant multiple regression coefficients for estimated components of the contents of the

stomach when regressed against the intensity of self-rated hunger. Total protein and food energy

in the stomach contents were found to be the most salient factors in determining subjective

hunger.

These researchers conclude that their results indicate that, as the stomach empties (a

process occurring over a few hours), especially of protein, the internal subjective state of hunger

increases. This leads them to observe that "the longer its [sic] been since the last time the

subject ate, the hungrier they report themselves to be."

This study indicates that the contents of the stomach and stomach emptying are primary

factors influencing the intensity of the "uneasy or painful sensations" reported as hunger. It also

supports the likelihood that persons who reduce the size of their meals, or who skip meals, do

experience hunger, because these behaviors make it more likely that the stomach contains little

protein and few calories of food energy equivalent.

Wardle (1987) used three separate methods to track hunger among healthy women over

a two-week period (including a behavioral method — amount of food intake; a physiological

method — stimulated salivation prior to meals; and subjective reporting — paper and pencil

recordings of the intensity of global hunger sensations, selection of experienced hunger

symptoms, feeling of fullness, and type of foods selected). Results showed that ratings of hunger

symptoms, preferred foods, global hunger, and satiety were sensitive to the length of time

subjects were deprived of food and the content of the previous intake.

The behavioral, physiological, and subjective measures of hunger employed were

consistent, reliable, and stable over time. Thus, this study indicates that the intensity of

self-reported hunger increases with the length of the deprivation period. Moreover, because the

usual intervals between intake episodes in the study were comparable to normal intervals between

meals, these results support the likelihood that persons in food insecure households who cut the

size of their meals, or who skip meals, experience "uneasy or painful sensations" that they report

as hunger.

Harris and Wardle (1987) used a modified version of the 36-item "Monello and Mayer

Hunger-Satiety Questionnaire" to assess hunger symptoms among two groups of subjects in prePrepared by Abt Associates Inc. A-3
Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

meal and post-meal periods (group 1 — 274 female and 97 male undergraduate and adult

education students; group 2 — 73 female and 84 male medical students). These researchers

found a significant relationship between hours of deprivation and level of hunger reported. An

unexpected finding from this study is the wide heterogeneity in hunger symptoms reported by

subjects, with unexpectedly small numbers (e.g., 4-7) of symptoms reported in common by most

subjects. The most commonly reported "uneasy or painful" symptoms were: emptiness, ache,

urge to eat, rumbling, and hollowness. The relatively small numbers of commonly-endorsed

symptoms lead these researchers to the conclusion that their results indicate that neither food

deprivation nor reported hunger necessarily imply the perception of any particular bodily

sensations. They further conclude that "it proved impossible to identify a specific subset or

constellation of hunger symptoms which were characteristically experienced by hungry people."

This study is important for two reasons. First, it supports the contention that persons

who cut meal size, or who skip meals, experience hunger, by showing that the longer the interval

between episodes of food intake, the hungrier subjects report themselves to be. In addition, this

study indicates that there is considerable heterogeneity in the way people experience the "uneasy

or painful sensation caused by a lack of food." As Mattes and Friedman (1993) point out, the

sensation of hunger is reported to occur in a variety of ways in many parts of the body. People

experience a wide variety of sensations that they report as hunger, and the intensity of these

sensations clearly increases (up to a point) as the period of deprivation increases.

Sepple and Read (1989) carried out an experiment in which ten normal healthy male

volunteers were intubated with instrumentation to enable precise measurement of blood glucose

levels, gastric emptying, and intestinal motor activity. Subjects who had fasted overnight were

monitored for six hours, with measurements taken every 20-30 minutes before and after eating

a meal. Subjects also completed a short questionnaire at 30-minute intervals to assess subjective

ratings of hunger, fullness, anxiety, nausea, desire to eat, and other sensations. Intensity of each

sensation was indicated by marking line analogues.

Sepple and Read found that eating the meal reduced the intensity of hunger ratings in

all subjects, and totally abolished hunger in seven of the ten. The time for hunger to recur varied

from 90-360 minutes, but was less than two hours for seven of the ten subjects. Once hunger

ratings began to increase, they rose steadily in all subjects.

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

These researchers’ results do not support the hypothesis that hunger is related to a

decline in blood glucose concentration, but do strongly support the role of gastric emptying. A

large and significant correlation was found between the time taken for 90 percent of the meal to

empty and the time hunger started to increase (r = 0.75, p < 0.02). Maximum hunger ratings

were obtained in all subjects when less than 10 percent of the meal remained in the stomach.

Sepple and Read conclude that their results are most compatible with the hypothesis that

the sensation of hunger is induced by a reduction in stimulation of receptors in the upper intestine

by nutrients present in food. Further support for this hypothesis was provided by observed

appearance of fasting motor patterns in the upper intestine (also thought to reflect declining levels

of nutrients) only at peak hunger levels in most subjects, and always after hunger had begun to

increase.

The importance of the results of this clinical experiment is that it provides strong

objective physiological evidence that emergence of the sensation of hunger accompanies, and is

strongly related to, the emptying of food from the gastrointestinal tract, and that this occurs

within a relatively short time period (about 2-4 hours). These results support the expectation that

persons who reduce their food intake below usual levels, or who skip meals altogether, do

experience sensations of hunger.

Mattes (1990) examined the relationship between self-reported hunger ratings and food

intake in a study involving twelve normal male and twelve normal female subjects whose food

intake and hunger ratings were recorded every waking hour for seven consecutive days.

Although Mattes did not find a significant correlation between hunger ratings and food intake

during the previous hour, he did find moderately large and significant correlations between

hunger ratings and intake during the following hour.

Mattes observed two clear peaks in both the intensity of hunger ratings and food intake

during each day, occurring at approximately 1200 and 1800 hours, or corresponding to the midday and evening mealtimes. Comparing difference scores for hunger ratings at the beginning and

end of two-hour periods during which food was eaten in the intervening period, or not, Mattes

found declines in hunger ratings over periods when eating occurred, and increases in hunger

ratings over periods during which eating did not occur. The changes in ratings over the eating

versus the no-eating periods were significant.

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

Curiously, Mattes found markedly different relationships between self-reported hunger

and food intake on weekdays compared to weekend days. He posits that this suggests that eating

in response to increased hunger sensations may involve an "entrained," or learned component,

perhaps related to weekday work schedules. The differences observed over weekdays and

weekend days are not consistent, Mattes suggests, with a strict energy depletion-hunger

association, suggesting instead the influence of multiple factors.

Mattes’ results indicate that the intensity of sensations of hunger among humans has two

observable peaks during each day, corresponding closely to the timing of the mid-day and

evening meals. More importantly, analysis of changes in the intensity of hunger ratings over

time periods approaching usual inter-meal intervals (two hours) shows significant increases in

hunger ratings over intervals in which eating does not occur, and significant declines in hunger

ratings over intervals in which eating does occur.

Finally, the variations observed in the relationship between hunger ratings and food

intake on weekdays versus weekend days supports the operation of a learned component in the

relationship. Associations based on entrained or learned responses to hunger are consistent with

social and cultural factors influencing the relationship between hunger and food intake, and with

the experience of hunger if usual patterns of food intake are interrupted.

Ogden and Wardle (1990) examined the relationship between cognitive restraint of food

intake and internal cues provided by caloric content of a pre-load, or pre-meal, intake. These

authors found a significant effect of time since previous intake on subjective ratings of hunger.

Subjects responded to an increased period of food deprivation (between morning intake and

lunch) with an increase in subjective hunger ratings. Moreover, subjects with higher-calorie preloads at previous intake rated their subjective sensation of hunger significantly lower than

subjects with low-calorie intakes. This indicates a significant effect of internal cues related to

caloric content of previous intake on the level of subjective hunger sensation.

This study supports the general finding from other research that longer intervals between

meals, or occurrences of food intake, lead to higher ratings of the sensation of hunger. It also

indicates that persons who cut the size of their meals, or who skip meals, are more likely to

experience the "uneasy or painful sensation caused by a lack of food" more intensely as the

immediate post-meal time interval increases.

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

In one of the more unusual studies reviewed, DeCastro (1991) used seven-day food

intake diaries with 121 male and 194 female adult subjects, together with pre- and post-meal

implementations of a seven-point "full-hungry" scale, to assess food intake and hunger across

seasons of the year. DeCastro not only found that subjects self-rated themselves as significantly

hungrier (less sated) before meals than after meals, but also that, overall, subjects reported

themselves significantly less hungry before meals eaten in the summer than in the winter or

spring. Moreover, subjects reported themselves significantly hungrier (less sated) after meals

eaten in the summer than in the winter or spring, and in the fall than in the winter.

DeCastro concludes that these results indicate that ingestion of a meal during the

summer and fall leaves people hungrier than in the winter and spring. De Castro also finds that

food and nutrient intakes are significantly greater in the summer and fall, with a mean increase

of 14 percent in the fall. This study not only indicates that people who cut meal size or skip

meals are more likely to report experiencing hunger than those who eat meals on their

accustomed schedule, but that there is also seasonal variation in the intensity of hunger under

these circumstances.

Lappalainen et al. (1990) examine hunger and food craving among two groups of obese

patients. One group was fed a protein-sparing, well-balanced low-calorie diet for three weeks

(1200-1600 Kcal per day), and the other provided "fasting therapy" in which food intake was

reduced first to 800 Kcal per day, then to 200 Kcal per day over three days, and maintained at

200 Kcal per day for 19 additional days.

These researchers found that both frequency of hunger/craving responses and reactivity

to food stimuli (reported changes in hunger state when shown pictures of food) decreased among

the fasting group, but not among the group fed the protein-rich low-calorie diet. During the last

(third) week of fasting, reactivity to food stimuli was completely abolished, and frequency of

hunger/craving responses was reduced nearly to zero.

These results indicate that persons who reduce their food intake below its usual level

(e.g., by cutting meal size or skipping meals) are likely to experience hunger and food-craving

sensations, but if food intake is reduced dramatically and maintained at a very low level (as in

prolonged fasting, or not having anything to eat for several days), sensations of hunger or food

craving actually decline, and may disappear altogether.

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

Mattes and Friedman (1993) review several studies measuring factors associated with

variability in subjective reporting of hunger. These authors report four definitions of hunger

appearing in the literature reviewed:

1. Hunger is frequently operationally defined in terms of experimental or external

conditions, such as the number of hours of food deprivation, or the size of the last

meal. Emphasis is on attempting to make the term more objective or operational.

2. Hunger is used to refer to an intervening motivational state, or drive, that links

experimental treatments or antecedent conditions (e.g., food deprivation) with

behaviors aimed at obtaining food. Intensity of hunger is then inferred from either

verbal reports of the desire for food, or willingness to perform a task for food.

3. Hunger is (most commonly) used to describe the subjective sensations associated

with the need for food. The focus here is on the experience of various bodily

states, sensations, or feelings, not on their causes.

4. Hunger is viewed as a physiological or metabolic state that results from a lack of

energy or nutrients. This deficit state, which is detected by the nervous system, can

modify eating behavior and food intake, and produce various subjective sensations

collectively referred to as hunger. The emphasis with this usage is on physiological

cause(s), not on the somatic manifestations of the need for food.

Mattes and Friedman focus their review primarily on the latter two of these definitions

(i.e., hunger as a subjective experience, and hunger as a physiologically- or metabolically-based

state resulting from the lack of energy or nutrients), expressing the view that "defining hunger

in terms of a subjective or physiological state deals more directly with the experience and

mechanisms of hunger, and therefore appears more relevant to basic research and clinical practice

concerns." These researchers report findings of moderate and statistically significant correlations

between reduced food intake and subjective reports of hunger, though the focus of their review

is more on self-reported hunger as a predictor of food intake, rather than on whether reduction

in food intake leads to the sensation of hunger.

Mattes and Friedman report findings from a study involving 800 individuals’ responses

to hypothetical fasts of varying durations wherein, under conditions described as extreme hunger,

more than 90 percent of subjects indicated they experienced gastric sensations. This percentage

declined to 50 percent of subjects reporting gastric sensations two hours prior to a typical meal.

Similar patterns were noted for the mouth, throat, head, and general bodily sensations, although

the proportion of subjects reporting these sites was smaller. Differences were observed by gender

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

and age, with mouth sensations more common among males than females, and head sensations

more frequently reported by adults than by adolescents.

Mattes and Friedman also report results from their own research using open-ended

questionnaires administered to 83 university students (45 male and 38 female), asking them to

describe sensations associated with hunger of varying intensity (from slightly hungry to extremely

hungry). Gastric sensations were also the most commonly reported symptom among subjects in

this study, with 55 percent reporting stomach growls and 34 percent reporting stomach aches.

When asked to report the body sites where sensations associated with different levels of intensity

of hunger were experienced, 70 percent of subjects reported sensations in their stomachs when

they were "slightly" hungry, 82 percent when they were "moderately" hungry, 92 percent when

they were "very" hungry, and 88 percent when they were "extremely" hungry. Fewer subjects

reported sensations in their heads, with 10 percent experiencing hunger-related sensations when

"slightly" hungry, increasing to 43 percent when they were "extremely" hungry.

Mattes and Friedman identify a number of physiological factors related to the sensation

of hunger, including reduction in gastric distention (reduced feeling of pressure caused by

emptying of food from the stomach), metabolic signals transmitted from the liver and small

intestine, and sensory input from the oral cavity. All of these factors are conceptually consistent

with experience of an "uneasy or painful sensation caused by a lack of food" when meals are cut

or skipped.

Read et al. (1994) review research on the role of gastrointestinal processes in regulation

of food intake in humans. These authors emphasize the role of factors associated with emptying

of nutrients from the upper small intestine (which occurs within a relatively short period after

ingestion of food) in signaling the sensation of hunger. They also discuss the phenomenon of

nutrient adaptation, wherein humans (and other animals) adapt to a particular pattern of

availability of energy and other nutrients, leading to moderation of the sensations of hunger and

satiety under persistent conditions of reduced intake. This adaptive process (also noted by Mattes

and Friedman (1993) and Lappalainen et al. (1993), and discussed above) leads to a reduction

in the intensity of hunger sensation, and its eventual extinction after prolonged fasting, and

implies that deviation from normal eating patterns (such as would occur if meals are cut or

skipped) can lead to more intense subjective sensations of hunger than would occur under more

prolonged intake reduction.

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

Read et al. (1994) conclude that the human gastrointestinal tract is compatible with the

observed tendency of humans to eat three or four meals a day, and that the gastrointestinal

mechanisms that trigger both initiation and termination of eating behavior operate within a

relatively short time period (e.g., a few hours). This implies a very high likelihood that recurrent

reduction of food intake by cutting meal size or skipping meals because of insufficient money

to buy food will lead to the experience of an "uneasy or painful sensation caused by a lack of

food." Adaptation of the gastrointestinal system to prolonged changes in food or nutrient

availability may moderate the intensity of this sensation if intake reduction is prolonged or

stabilized at reduced levels.

Rolls (1993) examines appetite, hunger, and satiety among the elderly population,

reviewing a number of studies addressing causes and consequences of reduced food intake,

decline in appetite and olfactory and gustatory sensory acuity, and reduction of sensory-specific

satiety among elderly persons. Rolls also reports results of her own research on decline in

sensory-specific satiety among older persons.

Satiety is generally the converse of hunger; the hungrier one is, the less sated they are.

Satiety is both the complex of sensations that reduce the motivation to eat as more food is

ingested, and the declining palatability of specific foods as they are ingested. Sensory-specific

satiety is the decline in pleasantness of a particular food following consumption of that food.

Sensory-specific satiety is associated with decreased consumption of the previously-eaten food

and a shift in consumption to other food choices. It appears to decrease, and even disappear,

among persons over age 65 years.

This study is of interest not because it provides evidence that persons who cut meal size

or skip meals experience hunger, but because it suggests that the nature of the "uneasy or painful

sensation caused by a lack of food," or the sensation of hunger, changes in ways that may reduce

or mask its intensity among elderly persons. To the extent this occurs, elderly persons may

actually under-report their experience of hunger.

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Appendix A: Review of Literature from Physiology and Clinical Nutrition Research

References

DeCastro, J. "Seasonal Rhythms of Human Nutrient Intake and Meal Pattern." Physiology and

Behavior 50: 243-248, 1991.

De Castro, J., and D.K. Elmore. "Subjective Hunger Relationships with Meal Patterns in the

Spontaneous Feeding Behavior of Humans: Evidence for a Causal Connection."

Physiology and Behavior 43: 159-165, 1987.

Harris, A., and J. Wardle. "The Feeling of Hunger." British Journal of Clinical Psychology 26:

153-154, 1987.

Lappalainen, R., P. Sjoden, T. Hursti, and V. Vesa. "Hunger/Craving Responses and Reactivity

to Food Stimuli During Fasting and Dieting." International Journal of Obesity 14:

679-688, 1990.

Mattes, R.D. "Hunger Ratings Are Not a Valid Proxy Measure of Reported Food Intake in

Humans." Appetite 15: 103-113, 1990.

Mattes, R.D., and M.I. Friedman. "Hunger." Digestive Diseases 11: 65-77, 1993.

Ogden, J., and J. Wardle. "Cognitive Restraint and Sensitivity to Cues for Hunger and Satiety."

Physiology and Behavior 47(3): 477-81, 1990.

Read, M.A., S. French, and K. Cunningham. "The Role of the Gut in Regulating Food Intake

in Man." Nutrition Reviews 52: 1-10, 1994.

Rolls, B.J. "Appetite, Hunger, and Satiety in the Elderly." Reviews of Food Science and

Nutrition 33(1): 39-44, 1993.

Sepple, C.P., and N.W. Read. "Gastrointestinal Correlates of the Development of Hunger in

Man." Appetite 13: 183-191, 1989.

Vanderweele, D.A., and P.J. Geiselman (eds.). "Can Appetite and Need State Be Separated?"

Abstracts from presentations at a satellite symposium of the American Psychological

Association meetings at Occidental College, Los Angeles, CA, August 1985. Appetite

7: 187-194, 1986.

Wardle, J. "Hunger and Satiety: A Multidimensional Assessment of Responses to Caloric

Loads." Physiology and Behavior 240: 577-582, 1987.

Prepared by Abt Associates Inc. A-11
Appendix B

PREVALENCE OF HOUSEHOLD FOOD SECURITY STATUS

(30-DAY SCALE)
Appendix B: Prevalence of Household Food Security Status (30-Day Scale)

Exhibit B-1

PREVALENCE OF HOUSEHOLD FOOD SECURITY STATUS BY SELECTED

CHARACTERISTICS OF HOUSEHOLDS: 30-DAY SCALE

[Numbers in thousands.

Poverty status refers to

household status in the

preceding year.]

Characteristic Total

No Food Insecurity

with Hunger

Evidenta

Food Insecure

with Moderate

Hunger Evidentb

Food Insecure with

Severe Hunger

Evidentc

Number

Percent

of Total Number

Percent

of Total Number

Percent

of Total

Household Composition

All races:

With children under 18 yrs 38,113 36,877 96.8 1031.9 2.7 203.6 0.5

With children under 6 yrs 18,282 17,661 96.6 519.8 2.8 101.0 0.6

With elderly;d no children 27,805 27,567 99.1 201.9 0.7 36.6 0.1

With no elderly or child 34,291 33,359 97.3 725.0 2.1 207.3 0.6

All household types 100,210 97,803 97.6 1958.8 2.0 447.5 0.4

White:

All households

With children under 18 yrs 30,438 29,622 97.3 686.3 2.2 130.1 0.4

With children under 6 yrs 14,467 14,048 97.1 358.3 2.5 60.4 0.4

With elderly; no children 25,012 24,838 99.3 150.7 0.6 23.8 0.1

With no elderly or child 29,163 28,517 97.8 507.0 1.7 139.1 0.5

Black:

All households

With children under 18 yrs 5,841 5,485 93.9 298.4 5.1 57.6 1.5

With children under 6 yrs 2,826 2,669 94.4 130.0 4.6 27.2 1.0

With elderly; no children 2,321 2,265 97.6 43.4 1.9 12.0 0.5

With no elderly or child 3,852 3,598 93.4 197.1 5.1 56.4 1.5

Other:

All households

With children under 18 yrs 1,833 1,770 96.6 47.2 2.6 16.0 0.9

With children under 6 yrs 989 944 95.5 32.0 3.2 13.4 1.4

With elderly; no children 472 464 98.2 7.9 1.7 0.9 0.2

With no elderly or child 1,276 1,244 97.4 20.9 1.6 1.2 0.9

Hispanic:e

All households

With children under 18 yrs 4,475 4,274 95.5 160.6 3.6 41.3 0.9

With children under 6 yrs 2,539 2,379 93.7 122.2 4.8 6.9 1.5

With elderly; no children 1,151 1,115 96.9 27.3 2.4 8.4 0.7

With no elderly or child 2,075 1,991 95.9 47.2 2.3 37.3 1.8

Notes at end of exhibit

Prepared by Abt Associates Inc. B-1
Appendix B: Prevalence of Household Food Security Status (30-Day Scale)

Exhibit B-1 (continued)

PREVALENCE OF HOUSEHOLD FOOD SECURITY STATUS BY SELECTED

CHARACTERISTICS OF HOUSEHOLDS: 30-DAY SCALE

[Numbers in thousands.

Poverty status refers to

household status in the

preceding year.]

Characteristic Total

No Food Insecurity

with Hunger

Evidenta

Food Insecure

with Moderate

Hunger Evidentb

Food Insecure with

Severe Hunger

Evidentc

Number

Percent

of Total Number

Percent

of Total Number

Percent

of Total

Household Income Categoryf

(All races and household types)

Below $10,000 14,977 13,893 92.8 822.0 5.5 262.4 1.8

$10,000 - $19,999 16,717 16,043 96.0 568.4 3.4 105.2 0.6

$20,000 - $29,999 15,625 15,319 98.0 268.2 1.7 37.7 0.2

$30,000 to $39,999 12,149 12,046 99.2 87.2 0.7 15.9 0.1

$40,000 - $49,999 8,539 8,488 99.4 43.2 0.5 8.3 0.1

Above $50,000 22,370 22,319 99.8 50.3 0.2 0.8 **

Household Income-to-Poverty Ratiof

(All races and household types)

Under 0.50 5,545 4,987 89.9 415.7 7.5 142.2 2.6

Under 1.00 15,808 14,617 92.5 904.2 5.7 287.2 1.8

Under 1.30 21,810 20,304 93.1 1178.6 5.4 326.9 1.5

Under 1.85 35,115 33,239 94.7 1503.7 4.3 372.0 1.1

1.85 and over 65,094 64,564 99.2 455.1 0.7 75.5 0.1

Household Relationship

(All races)

Households with children under 18 38,113 36,877 96.8 1031.9 2.7 203.6 0.5

Married couple families 26,841 26,347 98.2 445.2 1.7 48.9 0.2

Female head, no spouse 8,941 8,290 92.7 509.6 5.7 140.9 1.6

Male head, no spouse 2,332 2,241 96.1 77.1 3.3 13.8 0.6

Households with no children or 34,291 33,359 97.3 725.0 2.1 207.3 0.6

Living alone 13,724 13,151 95.8 434.5 3.2 138.6 1

Households with elderly but no 27,805 27,567 99.1 201.9 0.7 36.6 0.1

Living alone 11,699 11,544 98.7 131.7 1.1 23.0 0.2

Area of Residence

(All races and household types)

Inside Metropolitan areas 60,657 59,155 97.5 1215.0 2.0 287.1 0.4

In central city 24,055 23,266 96.7 671.2 2.8 117.7 0.5

Not in central city 36,602 35,889 98.0 543.8 1.5 169.4 0.5

Outside Metropolitan areas 23,877 23,298 97.6 478.0 2.0 100.8 0.4

Notes on next page

Prepared by Abt Associates Inc. B-2
Appendix B: Prevalence of Household Food Security Status (30-Day Scale)

Exhibit B-1 (continued)

PREVALENCE OF HOUSEHOLD FOOD SECURITY STATUS BY SELECTED

CHARACTERISTICS OF HOUSEHOLDS: 30-DAY SCALE

NOTES:

a No or minimal indicators of resource-constrained hunger evident for household members (corresponds to the combined categories of "food

secure" and "food secure, hunger not evident" in the 12-month scale).

b Multiple indicators of resource-constrained hunger evident for adult household members.

c Multiple indicators of resource-constrained hunger evident for children in household and/or indicators of severe adult hunger.

d Elderly persons are defined as persons aged 60 years and older in this report.

e Persons of Hispanic ethnicity can be of any race.

f Income and poverty status refer to household income in a recent 12-month period, varying among rotation groups in the CPS sample. Income

is missing for 9.8 percent of households but their income-to-poverty ratio category was imputed by the Census Bureau.

g For confidentiality reasons the CPS did not report the area of residence for 15.6 percent of households. The estimates shown are for

households with area of residence identified.

Prepared by Abt Associates Inc. B-3
Appendix B: Prevalence of Household Food Security Status (30-Day Scale)

Exhibit B-2

STANDARD ERRORS FOR PREVALENCE OF HOUSEHOLD FOOD SECURITY

STATUS BY SELECTED CHARACTERISTICS OF HOUSEHOLDS:

30-DAY SCALE

[Numbers in thousands.

Poverty status refers to

household status in the

preceding year.]

Characteristic

Sample

Size (in

ones)

No Food Insecurity

with Hunger

Evidenta

Food Insecure with

Moderate Hunger

Evidentb

Food Insecure with

Severe Hunger

Evidentc

Number

Percent

of Total Number

Percent

of Total Number

Percent

of Total

Household Composition

All races:

With children under 18 yrs 16,914 61 0.16 67 0.18 24 0.06

With children under 6 yrs 7,934 41 0.23 41 0.22 14 0.08

With elderly;d no children 12,485 40 0.14 35 0.13 8 0.03

With no elderly or child 15,248 85 0.25 55 0.16 38 0.11

All household types 44,647 110 0.11 97 0.10 42 0.04

White:

All households

With children under 18 yrs 13,808 51 0.17 52 0.17 18 0.06

With children under 6 yrs 6,391 37 0.25 32 0.22 14 0.10

With elderly; no children 11,283 29 0.12 26 0.11 4 0.02

With no elderly or child 13,137 55 0.19 39 0.13 25 0.09

Black:

All households

With children under 18 yrs 2,023 18 0.31 22 0.38 13 0.21

With children under 6 yrs 959 26 0.91 23 0.82 7 0.26

With elderly; no children 926 16 0.68 13 0.57 5 0.21

With no elderly or child 1,370 40 1.03 27 0.69 16 0.43

Other:

All households

With children under 18 yrs 1,083 14 0.75 13 0.70 8 0.45

With children under 6 yrs 584 8 0.84 7 0.73 7 0.75

With elderly; no children 276 5 0.98 4 0.94 1 0.14

With no elderly or child 741 8 0.62 7 0.57 7 0.58

Hispanic:e

All households

With children under 18 yrs 1,529 27 0.61 21 0.48 12 0.28

With children under 6 yrs 857 23 0.90 18 0.69 13 0.49

With elderly; no children 406 10 0.88 7 0.63 5 0.39

With no elderly or child 695 15 0.74 12 0.59 13 0.63

See notes to Exhibit B-1

Prepared by Abt Associates Inc. B-4
Appendix B: Prevalence of Household Food Security Status (30-Day Scale)

Exhibit B-2 (continued)

STANDARD ERRORS FOR PREVALENCE OF HOUSEHOLD FOOD SECURITY

STATUS BY SELECTED CHARACTERISTICS OF HOUSEHOLDS:

30-DAY SCALE

[Numbers in thousands.

Poverty status refers to

household status in the

preceding year.]

Characteristic

Sample

Size (in

ones)

No Food Insecurity

with Hunger

Evidenta

Food Insecure with

Moderate Hunger

Evidentb

Food Insecure with

Severe Hunger

Evidentc

Number

Percent

of Total Number

Percent

of Total Number

Percent

of Total

Household Income Categoryf

(All races and household types)

Below $10,000 6,368 42 0.28 47 0.31 45 0.30

$10,000 - $19,999 7,651 69 0.41 59 0.35 12 0.07

$20,000 - $29,999 7,202 40 0.26 33 0.21 13 0.09

$30,000 to $39,999 5,808 19 0.15 18 0.15 6 0.05

$40,000 - $49,999 4,037 15 0.18 15 0.18 5 0.05

Above $50,000 10,690 16 0.07 16 0.07 1 0.01

Household Income-to-Povertyf

(All races and household types)

Under 0.50 2,219 25 0.45 25 0.45 30 0.54

Under 1.00 6,650 51 0.32 61 0.38 42 0.26

Under 1.30 9,384 85 0.39 83 0.38 50 0.23

Under 1.85 15,594 103 0.29 92 0.26 50 0.14

1.85 and over 29,053 25 0.04 18 0.03 17 0.03

Household Relationship

(All races)

Households with children under 18 16,914 61 0.16 67 0.18 24 0.06

Married couple families 12,295 39 0.15 40 0.15 7 0.03

**Female head, no spouse 3,677 67 0.75 66 0.73 16 0.18

**Male head, no spouse 942 15 0.63 12 0.53 8 0.33

Households with no children or 15,248 85 0.25 55 0.16 38 0.11

Living alone 5,941 60 0.44 38 0.28 32 0.23

Households with elderly but no 12,485 40 0.14 35 0.13 8 0.03

Living alone 5,222 31 0.26 23 0.20 9 0.07

Area of Residence:

(All races and household types)

Inside Metropolitan areas 24,214 91 0.15 71 0.12 32 0.05

In central city 9,606 74 0.30 51 0.21 28 0.11

Not in central city 14,608 34 0.09 29 0.08 15 0.04

Outside Metropolitan areas 12,532 40 0.17 29 0.12 22 0.09

See notes to Exhibit B-1

Prepared by Abt Associates Inc. B-5
APPENDIX C

PARTICIPANTS IN FEDERAL INTERAGENCY WORKING

GROUP FOR FOOD SECURITY MEASUREMENT
Appendix C: Participants in Federal Interagency Working Group for Food Security Measurement

USDA/FCS

Steven Carlson

Gary Bickel

Margaret Andrews

Sharron Cristofar

Dawn Aldridge

USDA/CNPP

Peter Basiotis

Bruce Klein (formerly FCS)

USDA/ERS

David Smallwood

James Blaylock

Mark Nord

Donald Rose

Victor Oliveira

USDA/ARS

Mary Hama

USDA/CSREES

Nancy Leidenfrost

DHHS/CDC/NCHS

Ronnette Briefel

Katherine Alaimo

DHHS/CDC&P

BettyLou Sherry

DHHS/AOA

Jean Lloyd

Census Bureau/CPS

Julie Feliciano

Maria Reed

Kathleen Stoner

Census Bureau/CSMR

Jennifer Hess

Prepared by Abt Associates Inc. C-1