Impact of a Local Staple Foods Ordinance on Food Choice and Calories Purchased
Conditions: Food Policy
Sponsor: University of Minnesota

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
1 
 
 
 
 
Impact of a Local Staple Food Ordinance on Food 
Choice and Calories Purchased 
(The STORE Study) 
 
 
Principal Investigator: 
Melissa N. Laska, PhD, RD 
 
Co-Investigators: 
Caitlin E. Caspi, PhD 
Darin J. Erickson, PhD 
Lisa J. Harnack, PhD 
 
Study Coordinator: 
Stacey G. Moe, MPH 
 
 
 
 
 
 
 
 
 
 
Supported by the National Institute of Diabetes and Digestive and Kidney Diseases 
of the National Institutes of Health  
 
Award Number R01DK104348 
December 2017 
 
 
 
 

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
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Table of Contents 
 
 
I. 
Project Summary   
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II. 
Background and Significance  
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Page   3 
• Overview of the Problem 
• Potential Action for Research 
• Knowledge to be Gained 
• Preliminary Studies 
 
III. 
Specific Aims of the Study 
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Page   6 
• Aim 1: Assess changes in small and non-traditional, urban food stores from           
baseline to 4-, 12- and 24-months post-policy implementation 
• Aim 2: Assess changes in the nutritional quality of consumer purchases,              
including assessment of calories and energy density, at small and non-traditional,     
urban food stores 
• Aim 3: Assess changes in home food availability among households that            
frequently shop at small and non-traditional, urban food stores 
 
IV. 
Research Design and Methods  
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Page   6 
• Study Population 
• Data Collection Handling and Storage 
• Confidentiality Protections, Sharing of Data, and Disposition of Data 
• Study Timeline 
 
V. 
Data and Safety Monitoring Plan 
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Page   9 
• Study Recruitment and Retention Monitoring 
• Data Safety and Security 
• Adverse Event Reporting 
 
VI. 
Statistical Considerations 
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Page 10 
• Analyses That Will be Performed 
• Sample Size Considerations 
• Quality Control 
 
VII. 
Bibliography & References Cited 
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Page 12 
 
 
 

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
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I. Project Summary 
 
Improving healthy food availability and decreasing the availability of high calorie, low nutrient 
products, particularly in underserved communities, has been identified as a leading strategy for local 
governments to prevent obesity. However, policy action in this area to date has been limited. The 
purpose of this STORE study, therefore, is to evaluate the impact of a local policy change (i.e., the 
Minneapolis Staple Foods Ordinance) that establishes minimum stocking criteria for a wide array of 
healthy foods as a requirement of food store licensing.  
 
In this 4-year study, the impact of the Minneapolis Staple Foods Ordinance will be evaluated by 
assessing objectively measured changes in: (a) food environments among small and non-traditional 
urban food stores, including availability, promotion, advertising, quality, price, and placement of 
both healthy and unhealthy foods and beverages, (b) nutritional quality of consumer purchases at 
these food stores, including assessment of energy density and calories via customer intercept 
surveys and direct observation of purchases and (c) home food environments, including availability 
of healthy and unhealthy foods/beverages and an overall home food obesogenicity score, among 
households that frequently shop at these stores. These changes will be assessed pre-policy 
implementation, as well as at 4-, 12- and 24-months post-policy implementation, in two Minnesota 
cities: Minneapolis and St. Paul (the comparison community). The proposed scope of work in this 
study is important because it takes advantage of a unique opportunity to evaluate an innovative local 
policy addressing a recommended action area for obesity prevention that aligns with key 
recommendations by leading obesity prevention authorities.  
 
II. Background and Significance 
 
Overview of the Problem 
In recent years, the Institute of Medicine, Centers for Disease Control and other authorities have 
identified improving access to healthy foods as a strategy for local governments to help prevent 
obesity.(1-5) Many communities have limited access to healthy foods.(6, 7) Numerous studies indicate 
that supermarkets are more likely to be located in high-income and low-minority areas, and 
convenience stores are more likely to be located in low-income and high-minority areas.(6) 
Disparities in food access likely contribute to large-scale health disparities, in that better access to 
supermarkets is associated with healthier diets and reduced risk of obesity, and access to 
convenience stores is associated with increased risk.(6, 8-12) Supermarkets generally offer a wider 
variety of healthy, high-quality foods, and small convenience stores carry higher-calorie, processed 
foods at higher prices.(6, 13-16) Growing evidence suggests that increasing healthy food availability 
and decreasing high calorie, low nutrient products in convenience stores, or “corner stores,” may be 
a promising strategy for obesity prevention.(1, 2, 4, 17-19) However, much of the work in this area to date 
has been programmatic in nature (i.e., implemented by working one-on-one with stores via “healthy 
corner store” programs), which can be time- and resource intensive and have challenges for 
sustainability.(20) Policy initiatives may be able to expand these efforts by broadening programmatic 
reach, providing additional incentives for stores, and addressing compliance and enforcement 
issues. 
 
Potential Action for Research 
Although policy work in this area is limited, one potential action area is around the implementation 

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
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of health criteria, such as minimum stocking requirements for healthy foods, as a condition of food 
store licensing.(21) The Minneapolis City Council passed a Staple Foods Ordinance in 2008 requiring 
stores with grocery licenses to carry specific staple foods, including fresh fruits and vegetables.(22) 
This ordinance was originally passed as a crime prevention policy (targeting “food stores” that 
offered little food and served as venues for criminal activity) and contained numerous loopholes. 
For example, no minimum stocking quantities were specified in the ordinance, the minimum 
number of varieties mandated for each type of staple food were limited, and most specified foods 
were not subject to health criteria (i.e., requirements included only general categories like meat, 
bread/cereal and dairy). In an effort to increase healthy food access, the City of Minneapolis is 
revising the Staple Foods Ordinance, with implementation of the ordinance expected in April 2015. 
These revisions include numerous improvements to align the policy with the Dietary Guidelines for 
Americans and stocking requirements for WIC (Women, Infants and Children) program vendors.(23, 
24) Thus, stocking requirements for all eligible stores would increase dramatically across an array of 
nutrient-rich foods that are low in energy density, including fruits, vegetables, 100% juices, whole 
grains, and low-fat dairy. If successful, the new ordinance could serve as model legislation for other 
local governments, particularly those seeking to enhance these efforts by further expanding the 
scope of the requirements through more rigorous policies. 
 
Knowledge to Be Gained  
There is a great deal of knowledge to be gained from this study. The Minneapolis Staple Foods 
Ordinance is, to our knowledge, the first policy of its kind to be implemented in the United States, 
and as such, has the potential to substantially improve access to healthy foods in all neighborhoods 
and communities affected by the ordinance. It will be important to evaluate its impact as well as to 
identify potential challenges to the implementation, time to compliance, and factors that predict 
which stores may require support in implementation. Such an evaluation may provide valuable 
insights for other local governments looking to implement similar or more rigorous policies. 
Benefits to society greatly outweigh any possible minimal risks, and the impact of this proposed 
study has the potential to contribute positively to dietary behaviors and weight outcomes of 
neighborhood and community residents. 
 
Preliminary Studies 
- The role of corner stores in diet and weight outcomes. In 2009, Dr. Laska and colleagues 
conducted one of the first systematic reviews on disparities in access to healthy food in the United 
States.(7) Overwhelmingly, this evidence suggested that residents of low-income and minority 
neighborhoods are most often affected by poor access to healthful food. Furthermore, Dr. Laska 
also served as a co-investigator (and later, the Principal Investigator) of Project IDEA, a multilevel 
5-year longitudinal cohort study of young people to examine the etiology of adolescent obesity.(25) 
Our analyses of GIS data indicated that the only attribute of the food environment significantly 
associated with BMI Z-score and body fat was the presence of convenience stores within 1 mile of 
adolescents’ homes.(26) Sugar sweetened beverage intake was also significantly associated with 
proximity to convenience stores.(26, 27) In another study of 6th graders attending low-income schools 
in St. Paul (2008-09), we found that 67% of students bought food at a convenience store in the past 
week using their own money (Laska, unpublished data). Corner stores were the second most 
commonly reported location for food purchasing overall, and 71% of students reported spending 
money on food/ beverages for themselves in a typical day. Similar results have been observed 
elsewhere,(28) highlighting that corner stores are an important source of excess calories in children. 

  
 
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- Urban corner stores. In 2008, Dr. Laska and colleagues conducted store audits to assess food 
availability in >100 small and non-traditional stores in 4 urban areas (Baltimore, Minneapolis/St. 
Paul, Oakland, and Philadelphia).(29) Results indicated that availability of healthy food items was 
notably low, with only 50% having ≥1 type of fresh fruit or vegetable. There was significant cross-
site variability in the availability of healthy staple foods and snacks, emphasizing the influence of 
local contexts on store environments. In analyses of Minnesota-specific data from a larger sample of 
convenience stores,(30) we again found that a wide range of healthy snack options were typically not 
available, with many items stocked in <50% of stores, and even fewer stores carrying healthy 
snacks in single- serving packages. All stores had less healthful impulse items available at or near 
the cash register (e.g., candy). Only 46% carried healthier impulse items (e.g., fruit). Most stores 
had food/beverage advertising, and 94% of advertisements were for less healthful, energy-dense 
products (e.g., soda, beer, chips, prepared foods). These studies demonstrate feasibility in 
conducting brief, in-store audits over a short time period. In addition, this work was the first of its 
kind to document the limited availability of healthy foods in small, urban corner stores across 
multiple regions in the United States. Despite this limited availability, however, we showed that 
some stores were able to stock and sell healthy foods, thus indicating the capacity for success in 
improving healthy food availability in these settings. Finally, this work drew attention to less-
studied aspects of the retail food environment in small stores, including impulse purchasing 
displays, in-store advertising and serving size. 
 
- Challenges in making healthy changes in small and non-traditional food stores. Dr. Laska and 
colleagues have also examined barriers faced by small and non-traditional food store owners and 
the impact of other policy changes. In 2010, Dr. Laska helped lead an effort to assess the impact of 
the 2009 WIC policy changes across 8 cities in the United States on perceived sales, product 
selection and stocking habits of small stores.(31, 32) We found that store owners experienced 
numerous implementation challenges following the policy change, the most common of which 
were: (a) obtaining a steady supply of healthy, perishable foods, (b) having reliable relationships 
with healthy food suppliers and (c) working with customers to understand the new regulations. 
However, most store owners were able to overcome these challenges, and ultimately felt the WIC 
changes increased their number of customers, sales, and profits. Despite this, many small and non-
traditional food stores do not accept WIC, and thus may not currently be stocking a range of healthy 
foods. Some of the challenges overcome by store owners in our sample could be perceived as 
insurmountable by others. Dr. Laska and colleagues have also explored challenges among store 
owners through in-depth case study analyses of lessons learned from corner store programs in 4 
U.S. cities.(33) These analyses yielded important insights on establishing relationships with store 
owners, owner and customer relationships, stocking healthy foods, store evaluations, maintenance 
of changes in stores, and dissemination of findings. Most importantly, this work enhanced our 
understanding of the challenges faced by small and non-traditional food stores in providing healthy 
foods, as well as important factors in working with owners through research and evaluation 
initiatives. 
 
- Minneapolis Healthy Corner Store Program. This program is a city Health Department effort to 
increase fresh fruit and vegetable access in at-risk areas. It was implemented in 2 phases with a 
small number of stores (Phase 1: 2010, n=9 stores; Phase 2: 2012, n=30 stores). Stores received 
low-cost assistance from the Minneapolis Health Department or liaison neighborhood organizations, 

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
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including assistance in making produce displays visible and attractive and in hosting in-store events, 
like taste testings. Our team partnered with the Health Department to evaluate the program. In 
analyses of sales in 5 intervention and 2 control stores, we detected significant changes in store 
sales, with intervention stores experiencing a greater increase in fruit/vegetable transactions (146% 
vs. 11%, p=0.01) and a marginally greater increase fruit/vegetable sales (155% in program stores 
vs. 22% in control, p=0.057) between baseline (weeks 1-4) and follow-up (weeks 11-14).  
 
III. Specific Aims of the Study 
 
Aim 1: Assess changes in small and non-traditional, urban food stores from baseline to 4-, 12- and 
24-months post-policy implementation. 
Objective store assessments of healthy and unhealthy food availability, promotion, advertising, 
quality, price, and placement will allow us to assess the degree of policy implementation, as well as 
changes in other environmental outcomes not directly specified by the policy. To identify barriers 
and facilitators associated with implementation, we will also examine patterns of compliance over 
time (such as “early compliers” vs. “late compliers”) and store- and neighborhood-level predictors 
of these patterns. Our primary hypothesis is that the new policy will result in an increase in healthy 
food availability in Minneapolis stores at 4-, 12- and 24-months post-implementation, compared to 
stores in St. Paul, where no policy exists, and that differences in intervention vs. control stores will 
continue to increase over 24 months. 
 
Aim 2: Assess changes in the nutritional quality of consumer purchases, including assessment of 
calories and energy density, at small and non-traditional, urban food stores.  
We will conduct customer intercept surveys at small and non-traditional stores and assess 
purchasing via direct observation (i.e., “bag checks”). Our primary hypothesis is that, controlling 
for any baseline differences, energy density and calories purchased in Minneapolis stores at 4-, 12- 
and 24-months post-policy implementation will be less than in stores in St. Paul, our control 
community. 
 
Aim 3: Assess changes in home food availability among households that frequently shop at small 
and non-traditional, urban food stores. 
We will collect objective, longitudinal data among a sub-set of Aim 2 participants from baseline 
who shop at small and non-traditional stores ≥1 time per week. This will include a staff-
administered food inventory to assess the obesogenicity of the home food environment. Inventories 
will be conducted in participants’ homes using a tool that was developed and validated by our team. 
Our primary hypothesis is that home availability of healthy items, like fruits and vegetables, will be 
greater and obesogenicity scores will be lower among frequent shoppers in Minneapolis stores, 
compared to those in St. Paul, at 4-, 12- and 24-months. 
 
IV. Research Design and Methods  
 
Study Population 
- Store-Level (Store Assessment):  Store sample identification will begin by obtaining the total 
number of stores in Minneapolis and St. Paul with grocery licenses from the Minneapolis Health 
Department for Minneapolis stores and from the Minnesota Department of Agriculture for St. Paul 
stores. Stores will be deemed ineligible if they are supermarkets (which we define as places where 

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
7 
someone might regularly do a big shopping trip to feed their household, have grocery carts and not 
just shopping baskets and have at least three cash registers). Stores will also be ineligible if they 
accept WIC benefits, because these stores are presumed to already meet minimum stocking 
requirements, as well as if they have invalid licensing addresses, or if they are located in core 
downtown commercial districts (because these stores are not expected to stock a wide array of 
foods). Other stores not reasonably expected to stock a minimal variety of foods will also excluded, 
including those that have ≤9·29m2 (≤100 ft2) of retail space (such as auto-repair shops selling 
limited snacks), small vendors in market areas (e.g. produce stands), and liquor or specialty stores 
(e.g., spice shops).  
 
After applying these criteria and accounting for any errors, the recruitment process will begin with 
an invitation from our Minneapolis Health Department partners via a letter to eligible stores 
describing the study and the opportunity to participate. As St. Paul is serving as our comparison 
community, the invitation letter for these stores will be sent directly from our Principal Investigator, 
Dr. Melissa Laska.  University of Minnesota data collectors will then visit stores in person to 
request permission to collect data on the stores.  Data collectors will request permission at each data 
collection time point due to the cross-sectional design of the evaluation.  The anticipated enrollment 
is 208 stores. 
 
- Individual-Level (Customer Intercept Survey):  Customers exiting small and non-traditional food 
stores after appearing to have purchased a food or beverage item, and who also appear to be 18 
years of age or older, will be approached by the study data collection staff members and invited to 
participate in the study by taking a five-minute survey. Customers are eligible for participation in 
this survey if they: 1) are 18 years of age or older; 2) purchased a food or beverage item; and 3) 
speak and understand English.  Customers will be excluded only if they do not meet all inclusion 
criteria. Upon confirmation of eligibility and after the informed consent process is complete, staff 
will commence with the survey.  Health status will not be assessed nor considered for participation.  
The anticipated enrollment at each measurement time point (i.e., baseline, 4-, 12-, and 24-month 
post policy implementation) is 424 participants. 
 
- Individual-Level (Home Food Inventory):  Upon completion of the customer intercept survey 
(CIS) at baseline, participants who report shopping at small and non-traditional stores an average of 
at least once per week will be invited to participate in the Home Food Inventory (HFI) segment of 
this study. Eligible customers’ names and contact information will be recorded for future 
enrollment.  As with the CIS, health status will neither be assessed nor considered for recruitment 
and participation, and the age range for participation is 18 years of age or older.  Inclusion and 
exclusion criteria are identical to the CIS criteria as the HFI participant sample will be drawn from 
the CIS participant sample.  The anticipated enrollment is 68 participants. 
 
Data Collection Handling and Storage 
For all STORE study-related measurement data collection aspects, data collectors will be well 
trained to care for, protect and respect the data while in their possession. Data and signed gift card 
forms, which are used solely for accounting purposes, will be delivered to the study Project and 
Evaluation Coordinators on a weekly basis to ensure the safety and integrity of the hard copy data 
forms.  After collection and data entry, hard copy data forms will be stored in locked cabinets at the 
University of Minnesota’s Division of Epidemiology and Community Health offices, and electronic 

  
 
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files of the data will be stored on a secure server housed in the same division. The study’s computer 
programmer is then responsible for formatting the data files so they can be used in data analyses. 
Data will be provided to only those who require its use. Additional specifics for each study measure 
are described below. 
 
- Store-Level (Store Assessment):  Although data to be collected during the assessment of the store 
food environment is not private and will not require human subjection protections, our study team 
will take the same precautions to safeguard these data as we do with protected information.   
 
- Individual-Level (Customer Intercept Survey):  Data will be collected from customers exiting 
small and non-traditional, urban food stores after having purchased a food or beverage item (subject 
to study inclusion/exclusion criteria detailed above).  The role of the participant will not extend 
beyond the normal expectations of a consented study participant.  Once data are collected, the 
participant will not be responsible for anything further.  CIS participation will be confidential, and 
participant names will not be attached to or linked to the data or to a study-assigned ID.  
Participants will sign their names to a gift card form but this form will not be connected in any way 
to the subjects’ data, and is being collected solely for accounting purposes.  
 
- Individual-Level (Home Food Inventory):  Data will be collected from and at the homes of 
participants who participated in the CIS and report shopping at small and non-traditional, urban 
stores an average of at least once per week.  The role of the participant will not extend beyond the 
normal expectations of a consented study participant.  As this component of the study is 
longitudinal in design, the participants will be asked to participate at multiple time points (i.e., at 
baseline, 4-, 12- and 24-months post-policy).  HFI participants will provide protected information 
(such as name, address, phone number and email address).  The participants will also be issued a 
study ID to which their data will be linked.  To ensure the utmost of confidentiality and privacy, 
participant information will be stored in password-protected electronic files on password-protected 
computers.  All hard copy data forms will be stored in locked cabinets within in our offices at the 
University. 
 
Confidentiality Protections, Sharing of Data, and Disposition of Data 
Study participants will be assured during the informed consent process that all identifying 
information will be securely protected and stored (as detailed above). An ID number will be 
assigned to each participant (and store) at the start of the project and this number will be used for 
record keeping and data analysis. Participants will never see his/her name in any reports or 
published papers, and data will only be shared in aggregate and only after the grant has ended with 
appropriate parties, such as study staff and other NIH-funded researchers doing similar work. 
Participant information will never be shared or used beyond necessary means. Hard copies of data 
will be kept securely and privately stored for seven years after which time they will be securely 
destroyed.   
 
Study Timeline 
This study was officially funded with a start date of December 1, 2014. Baseline screening for 
eligibility, enrollment and evaluation measurements began in September 2014 through a pre-award 
mechanism through the University of Minnesota.  We will begin the baseline measures with the 
Customer Intercept Survey data collection. The original intent was to start with Store Assessment 

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
9 
(and Manager Interviews) followed by CIS and HFI, but to account for the colder temperatures in 
Minnesota in the late fall and winter, and the fact that CIS will require both staff and participants to 
stand outside, CIS data will be collected first at the baseline time point only. Both Store Assessment 
and Home Food Inventory data collection will then follow.  Baseline data collection is anticipated 
to end in early 2015. 
 
The first post-policy measurements will begin in August or September 2015 (i.e., 4-months after 
policy implementation in April 2015).  The second post-policy (12-month) measurement period will 
begin in April 2016, and the final (24-month) post-policy measurement period will begin in April 
2017.  At all post-policy time points, data collection will begin with Store Assessment (and 
Manager Interview) followed by the CIS and HFI data collection. 
 
Enrollment for the HFI data collection will occur at the baseline data collection time point.   
 
V. Data and Safety Monitoring Plan 
 
Study Recruitment and Retention Monitoring 
Due to the minimal risk nature of this study, we are following a Data and Safety Monitoring Plan 
rather than convening a Data and Safety Monitoring Board to oversee this project.  This plan/report 
focuses on close monitoring of study recruitment and retention data and the upholding of participant 
confidentiality by Dr. Melissa Laska, the study Principal Investigator, in coordination with Ms. 
Stacey Moe, the study Project Coordinator.  Ms. Moe will be responsible for assembling the data 
and producing this report and for ensuring that Dr. Laska, Dr. Darin Erickson (Study Statistician) 
and Dr. Susie Nanney (study Safety Officer) all obtain copies of this report after the end of each 
data collection phase, or four times total for the duration of the study. 
 
As proposed in the NIH-approved Data Safety Monitoring Plan, review of the rate of participant 
accrual and adherence to inclusion/exclusion criteria will occur quarterly during the active 
recruitment phases to ensure participants are recruited and enrolled at an acceptable rate. 
Additionally, recruitment and retention issues will be discussed at weekly staff meetings during the 
active recruitment phases of the study. The recruitment accrual to be reported in this Data Safety 
Monitoring Plan include the cross-sectional recruitment for the four time points of the customer 
intercept surveys and the longitudinal recruitment for the home food inventory sub-study at baseline 
(which will be monitored by retention reporting for the three follow-up time points). Furthermore, 
to ensure quality data is being collected, initial waves of basic data cleaning will implemented prior 
to data entry and additional edit checks will be conducted after data entry has been completed, 
resulting in a thorough and systematic examination of the data.  
 
Data Safety and Security 
Regarding safety and security of STORE study data, the research team will discuss, as the need 
emerges, any changes to study procedures necessary to ensure that data are kept secure. All study 
staff have completed trainings in the protection of human subjects and data security. In addition, the 
study Safety Officer will review data safety and security issues every six months. To help ensure the 
confidentiality of study participants, data will be linked to participants by their study ID number 
only. Data collection instruments will include only the ID number of the participant. Files including 

  
 
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the necessary information to link identifying information and study data will be available only to the 
research staff deemed necessary.  
 
Adverse Event Reporting 
In the very rare instance when a possible adverse event might occur, we will document the 
description of the event, the onset and ending date of the event, the severity and study-relatedness of 
the event and the outcome of the event.  If the event meets the definition of a serious adverse event, 
we will report the event to the IRB and NIDDK within 15 working days.  If the event is life 
threatening or fatal, we will report the event to the IRB and NIDDK within 5 working days. 
 
VI. Statistical Considerations  
 
Analyses That Will Be Performed 
The study design is a 2 (Condition: ordinance, no ordinance) X 4 (Time: Baseline, 4-, 12-, 24- 
month) quasi-experimental study. The primary hypothesis will be tested using traditional repeated 
measures modeling as well as growth curve modeling. The unit of analysis is the store, with Time a 
within-subjects factor and Condition a between-subjects factor. Condition, Time, and their 
interaction are modeled as fixed effects. The overall test of any difference by policy condition over 
time is the Time x Condition interaction, a 3 degree of freedom test. Planned contrasts will be used 
to examine specific hypotheses using single degree of freedom tests, assessing effects at 4-, 12- and 
24-month follow-up. Because stores are not randomized to Condition, the statistical models will 
include a number of potential store-level covariates as additional fixed effects that have been 
identified from previous work. Covariance pattern modeling implemented by mixed model 
regression will be used to estimate the repeated measures model; this model allows residuals to 
correlate to account for the same stores being measured over time. 
 
The repeated measures general linear mixed model includes time as a fixed effect and examines the 
effect of the policy on the outcome using the Time x Condition interaction. Using a series of 
planned contrasts, the effects of the intervention at each time point can be estimated (e.g., baseline 
to 4 months, baseline to 12 months, etc.). This model does not, however, explicitly model change 
over time. To examine change over time and how this relates to condition, we will augment these 
analyses with a series of conditional, multilevel models of change (i.e., growth curve models). 
These models examine inter-individual differences in intra-individual change by fitting a trajectory, 
with an intercept and slope, to each store. The intercept and slope can be regressed on predictors 
and covariates of interest. 
 
We will test Aim 1 (to assess changes in food availability pre- vs. post-policy implementation, as 
well as changes in food price, quality, promotion, and placement) by regressing each outcome 
variable separately on Time, Condition, and the Time x Condition interaction, as well as a number 
of potential covariates (store characteristics [e.g., type of store, size, customer base, store owner 
characteristics] and neighborhood characteristics [e.g., race/ethnicity, household income, poverty, 
education, employment]). The 3 df Time x Condition interaction is the overall test of the 
hypothesis; we hypothesize that this estimate will be significantly different from zero. We will 
conduct a set of single df planned contrasts to isolate effects. Controlling for any baseline 
differences, we will test differences between Condition (Policy vs. no Policy) at 4-, 12-, and 24-
months post policy change. We hypothesize that stores in Minneapolis will show significantly 

  
 
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11 
higher healthy food availability scores at all 3 time points. We will supplement these analyses using 
growth curve modeling. We will fit individual lines to each store’s 4 waves of healthy food 
availability scores, and regress these intercepts and slopes on Condition and the potential covariates. 
We hypothesize stores in Minneapolis will show no baseline differences and have significantly 
larger positive slopes for healthy food availability outcomes over time. We will use growth mixture 
modeling(34) to examine policy compliance. Growth mixture modeling relaxes the assumption of 
population homogeneity and tests the presence of heterogeneity in trajectories. These models will 
identify subgroups of stores that exhibit similar compliance trajectories over time. After 
determining the optimal number of mixtures (subgroups), we will regress mixture on Condition 
(Minneapolis vs. St. Paul) and covariates. It is hypothesized that there will be a number of 
subgroups (e.g., immediate compliers, delayed compliers, non-compliers), Minneapolis stores will 
be more likely to be in a compliant subgroup (vs. a noncompliant subgroup), and various store and 
neighborhood characteristics will be associated with subgroup.  
 
We will test Aims 2 (assess changes in nutritional quality of purchases) and 3 (assess changes in 
home food availability among households that shop at small and non traditional, urban food stores) 
using similar models. For Aim 2, the data are repeated cross-sections (rather than true panel or 
longitudinal data) of small store consumers. Although individual change cannot be examined, we 
will assess aggregate change attributable to the policy change. Each nutritional quality measure (i.e., 
energy density, calories) will be regressed on Time, Condition, and the Time x Condition 
interaction, as well as a number of individual-level covariates. We hypothesize that the interaction 
will be significant, with purchases made in Minneapolis stores showing significantly lower energy 
density and calories at 4-, 12-, and 24-months post policy. For Aim 3, the data are true longitudinal 
data of households that frequently shop at small and non-traditional, urban food stores. This uses an 
identical repeated measures model as Aim 1, now with household as the unit of analysis. We will 
regress each food availability outcome variable separately on Time, Condition, and the Time x 
Condition interaction, as well as a number of household-level covariates. We hypothesize that the 
interaction will be significant, with households in Minneapolis showing significantly higher 
availability of healthy food and significantly lower availability of unhealthy food at 4-, 12-, and 24-
months post policy. 
 
Analyses will be conducted with SAS or MPlus(35, 36) Covariance pattern and growth curve models 
will be estimated using the SAS v9.3 MIXED procedure, which can estimate a large number of 
models, including general linear mixed models designed to analyze repeated measures and 
multilevel change. This procedure has built-in statements for specific planned contrasts. Growth 
mixture models will be estimated using MPlus v7.(36)  
 
Sample Size Considerations 
Because this is a study of a natural experiment, the number of available stores is not controllable. 
Based on estimates using our inclusion criteria, we expect to have 208 stores at baseline. Using 
means and variances from a recent study evaluating effects of WIC revisions on HFS scores(37) and 
setting alpha=0.05 (two-tailed) and power=0.80, we will be able to detect a difference in HFS score 
of approximately 2.2 points at any one time point. This is approximately 0.4 SD units and is much 
less than the difference found by Andreyeva and colleagues of approximately 4 points on the HFS 
score.(37) Even with substantially smaller sample sizes (i.e., <120 stores), we would have sufficient 
power to detect effect sizes on this order. Factoring in 20% attrition, the detectable effect increases 

  
 
STORE Study Protocol and Statistical Analysis Plan 
 
 
 
12 
to approximately 2.4 units. Actual detectable effects will be much smaller given the additional 
power due to having multiple rounds of data and including covariates. Sample size calculations 
were also estimated for aims 2 and 3, also setting alpha=0.05 (two-tailed), and power=0.80. For aim 
2, using variance estimates from Borradaile et al,(28) 424 intercept surveys at four rounds will allow 
us to detect a 50-calorie difference (a very small effect size) between conditions over time. For aim 
3, 68 households will allow us to detect a 3-point difference (a small effect size) in fruit and 
vegetable availability and a 4-point difference in obesogenic home food score (also a small effect; 
variance estimates from Fulkerson et al(38), factoring in 20% attrition. 
 
Quality Control 
The first level of control involves monitoring adherence to data collection protocols. The Project 
Coordinator and Evaluation Coordinator are responsible for training and monitoring staff. All data 
collectors will receive standardized training, including both office training and subsequent in-field 
supervised training. For the first two weeks of all measurements, 2 data collectors will complete 
each set of measures; these will be checked for accuracy and inter-rater reliability. Re-training will 
be conducted for any problematic items. Frequent communication with data collectors will be used 
to address issues that arise in the field, to develop a standard procedure or decision rule for those 
issues, and to maintain consistency in handling those issues. Inter-rater reliability testing will be 
repeated annually to avoid observer drift. The second level includes monitoring completeness of the 
data. Once data have been recorded and returned to our research offices, staff will review records to 
identify empty fields; if incomplete forms are identified, the team will attempt to collect missing 
data. Next, we will build quality control into data file editing. Out-of-range or questionable values 
will be identified and corrections will be made using standard protocols. Only approved study team 
members will have access to files, which will be password-protected and secularly stored. We have 
very experienced team members who have been responsible for quality control for many large 
studies. 
 
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