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Drawing the Boundaries of Suburban Geography

Suburbs can be amorphous and hard to grasp. Where does a city end and a suburb begin? Where does a suburb end and a rural area start? Reportedly, 52% of Americans describe their home neighborhood as suburban. Yet, the US Census does not have a definition for a suburban area. The term suburban is considered more of a colloquialism in today’s world. 

In this piece we will look at how MSG defines suburban geography and how the geo-demographic team compensates for the US Census’ shortcomings and discrepancies.

To define a suburban area properly, we must look at all underlying geographic components – in particular urban and rural. To do this, we start by looking at Metropolitan Statistical Areas (MSA) as defined by the Office of Management and Budget (OMB). 

MSA’s are defined as urban areas of at least 50,000 people with one or more adjacent counties that are socioeconomically tied to that urban center. There are over 360 MSA’s currently defined in the US and each MSA has one or more principal cities. 

Traditionally, we consider principal cities to be the core socioeconomic center with the counties surrounding it dependent on it.  

Now, let’s think about how the US Census delineates cities and urbanity. The Census defines urban as being within the principal city of an MSA and any census block outside of the principal city that meets a specific population threshold. 

The problem is that the US Census does not distinguish between suburban or rural areas. It just assumes that anything not urban is rural. In fact, no government entity defines what a suburban area is.

Here we encounter a massive disconnect. According to Pew Research, a growing share of the population in the United States is living in suburban counties of large metropolitan areas. And as we already noted, a majority of Americans say they live in the suburbs. Suburban communities, moreover, have a distinct geo-demographic identity compared to urban and rural areas, yet the government doesn’t really account for it! Compounding the problem is the slippery nature of the term “suburban.” It can mean different things in different parts of the country. 

All of this means that we have to define what suburban is ourselves. Marketing Systems Group’s geo-demographic team has developed their own definitions of suburban areas to compensate. 

Taking a simplistic approach, we take the census blocks within the principal cities of the MSA’s and keep them coded as “urban.” Any census block outside these principal cities that was classified as urban by the Census we now classify as “suburban.” On the other hand, any census block classified as rural by the Census remains “rural”.

Additionally, we can apply an urban/suburban/rural rule to any kind of geography or polygon. For example, we could take a ZIP code and classify it as urban, suburban, or rural. This is done by rolling up the underlining geography (census blocks) and determining whether the population (or land area) within a ZIP code is predominantly urban, suburban or rural. However, we could end up with a checkerboard affect with how the census blocks distributed across urban, rural, and suburban, so some care is needed.

We can also leverage our Addressed Based Sampling (ABS) frame to our advantage. Using ABS, we can identify population growth in areas in a shorter time frame instead of relying upon decennial Census data, which is only updated once every 10 years. If you are trying to define suburban areas, which can grow suddenly and dramatically, you will see how stale the data can become if you merely rely on decennial Census data. Using ABS gives you a more current, more accurate picture. 

For more insight, click here and check out Coffee Quip episode 8, Geo-demographic Methods: Suburban Geography, featuring Dennis Dalbey, Manager, Geo-demographic Services, and David Malarek, Senior Vice President, Sampling & Database Services. 

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Key Advantages of Advanced Cellular Frame (ACF) Over Traditional RDD

In a previous blog we introduced you to one of Marketing Systems Group’s newer products, Advanced Cellular Frame (ACF). This time, we will discuss how the frame is compiled and the key advantages it brings you compared to traditional RDD by itself.

Let’s start with a few key facts. When we do a traditional RDD sample of a county or group of counties, the telephone numbers within that frame are defined using rate center geography. The RDD frame has zero bearing on your ability to place numbers geographically outside of a rate center geography. It’s just not built for that capability.

With ACF, we have a better ability to actually place numbers within the correct geography, because we have more known information about more of the numbers (approximately 45% of them).  

The Power of Split Frames

ACF can split an RDD frame into two pieces:

  • The listed / matched phone numbers we have information about for a particular geography
  • Everybody else, including unlisted / unmatched

The listed component we can put precisely within whatever target geography you’re doing, if it’s a ZIP Code, county or a CBSA.

The unlisted or unmatched numbers get pulled in using the old traditional rate center methodology, but we pull only the unlisted ones. All listed and matched numbers are excluded from this component.  

A County-level Example

To illustrate how this all works, check out our Coffee Quip videos, specifically episode #3 featuring Subject Matter Expert David Malarek. There you will see him explain a case involving Multnomah County Oregon, home to the city of Portland.

The ACF frame does a better job targeting an RDD sample within Multnomah county. Dave shows how you can take the listed and unlisted portions and create a split frame, in which you can sample the listed’s independent of the unlisted’s or unmatched.

The ACF RDD universe is about 1.5 million numbers. About one-third of them are listed. We know they are in Multnomah county. The balance of the records (993,000) are coming from the rate centers that best fit Multnomah county geography.

But remember, rate center geography does NOT conform to any census geography we are accustomed to. It doesn’t conform to counties or city boundaries. Rate center geography is really just based on where the telephone companies run their wire lines.

This means you will have some over coverage and some under coverage, because you could miss a spot within the county or flow into adjacent counties outside the county.

With a split frame, you get the two components of ACF RDD that best fit Multnomah county.

How We Address the Migration Problem with ACF

Now we hit the biggest advantage of ACF: how it addresses migration — people who have a cell phone in one part of the country then move to another part of the country but keep the old cell number tied to the old rate center geography.

For the “known” listed cellular telephone numbers, ACF allows us to identify people who have migrated from other parts of the country into Multnomah County, Oregon. Conversely, we can exclude from the frame everybody who moved out of Multnomah County.

If you had defined the frame by rate center and just did a traditional cellular RDD sample, a huge chunk of those listed numbers would actually be outside the county. And you would be excluding 27% of the numbers inside Multnomah county from your sample frame. Not good.

Simply put, ACF does a better job in terms of getting the people who really live in your target geography into the sample frame and leaving out the people who’ve left.  

What About Lower-level Geographies?

ACF is great for smaller geographies, especially city limits not easily defined by rate center, which tend to be fairly large. Rate centers might serve four or five dozen different communities; whereas with ACF (at least for the listed portion) you can pinpoint a town or city you want to sample and exclude the other towns around it.

You can see why we are so excited about Advanced Cellular Frame; it enriches the sample and empowers you to target smaller geographies with more precision.

For more details, check out Coffee Quip episode #3, with Subject Matter Expert David Malarek or click here.

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Building on Tradition: How Advanced Cellular Frame Adds Diversity to RDD

Is it possible to take traditional RDD cellular telephone sampling and make it better? The answer is yes, thanks to one of our newer products, Advanced Cellular Frame (ACF). 

Advanced Cellular Frame is built upon the traditional RDD frame. It takes all possible telephone numbers in the RDD frame and adds something more. This makes for a much more versatile sampling tool, both for doing RDD and a targeted sample. 

How is ACF Compiled? 

Think of it as one database with two components inside. First, there’s the matched component. In the old days, this was known as “Listed.”  Second, there’s “everything else”: the unmatched, unlisted, unassigned telephone numbers.

We take the original RDD frame, which includes every single thousand block that was dedicated to cellular servers. Next, we identify all the telephone numbers from a set of half a billion or so. We advance the frame by attaching as much ancillary data to the numbers as we can: names, addresses, individual demographics, household demographics, and geography. 

Let’s say you’d like to do a targeted sample. For that, we would go into the database and fish out those telephone numbers matching the specific geographic and demographic criteria that you are targeting. 

If you want to do an RDD, we go in and include everything within the geography you are sampling. All numbers have an equal probability of selection both from the listed (matched) component and the unlisted component. 

KEEPING DATA FRESH

The database is updated quarterly.  It’s true that with any database on the marketplace, there’s always going to be aging. There will be lag time between the vendor compiled data and loading it into a production environment.  We compensate for the lag by sending selected telephone numbers out for a real-time name and an address append. 

Because people tend to move over time for one reason or another, this method ensures that we are appending the most current information available in terms of names and addresses, for the sample we provide. 

ADDRESSING THE MIGRATION PROBLEM

So, what happens when for example, a person in the listed portion (name and address) was geocoded, but that person actually moved? Many of these people will have carried their existing telephone number from one geography to the new one. Will Advanced Cellular Frame RDD move that number to the new frame? 

Yes, the person will be identified based off the new address. That’s the beauty of the ACF frame. It does an excellent job at addressing migration. You can include for your target geography (like a state) all phone numbers from all area codes across the country of people who it so turns out have actually moved into that geography. 

The converse is true as well. Anyone who has moved out of state will be excluded from the frame. 

This improves your coverage and the quality of data collection and cuts down on collection costs. 

HOW ACCURATE IS ACF?

Advanced Cellular Frame pulls on new technology to try to accurately link a telephone number to a name and address. It utilizes ID authentication, the technology used to validate transactions online. That information is used to help clean up and tighten up the ACF frame, which significantly boosts the matching accuracy.

WHAT ABOUT WORKING RATE?

You would expect a 75% to 80% overall working rate in ACF. That working rate jumps up to about 95% within the listed portion because there’s so much information known about those telephone numbers that they’re actually known to be working. The result is a much higher working rate than a traditional RDD frame.  

LEARN MORE

To learn more about ACF, click here and check out the first video episode of our YouTube series “Coffee Quip” an informal series of information talks with a panel of MSG experts. In this episode, Hillary McDonough, Raj Bhai, and Greg Pizzola chat about Advanced Cellular Frame with subject matter expert David Malarek, Senior Vice President of sampling and database services. 

Follow us on YouTube here for more Coffee Quip Episodes!

Helping People Through the Power of Data

In these trying times we are poised to deliver the data you need for the critical task at hand. Marketing Systems Group is always on call.

Never was timing more important than last weekend. As the COVID-19 virus was spreading through California, we received a call from the Stockton Economic Empowerment Demonstration. They had an urgent need to reach those most impacted by the virus: high risk people in need of critical services and food delivery.

Could we help? You bet.

We first mapped the city of Stockton by census block and using our listed landline and consumer cellular database, targeted records that had the presence of ages 65 and over. Each record had full address and phone number which allowed for phone and text messaging. We delivered the files to the client on the same day, enabling the mayor’s office and the Reinvent Stockton Foundation to begin their outreach over the weekend.

Time was of the essence to deliver these essential services. Kudos to the California mayor’s office and the Reinvent Stockton Foundation for their kind efforts during this pandemic.

Marketing Systems Group has the technology, know-how, and data to deliver lightning fast turnaround.

There is no time to waste. We’re all in this together. We are here to help.

The ABCs of ABS: Why the Address Based Sampling frame works so well

The past 10 to 15 years have been very good to Address Based Sampling (ABS). ABS has grown so much that it is now perceived as a substitute to random-digit-dial dual frame sample designs, and arguably, it has become the dominant sample survey design in the USA.

ABS is a special type of sampling frame, distinguishable from telephone surveys in its flexibility. The frame can support many methods and modalities: web, phone, and mail. In this article we will briefly explore the popularity of ABS and the problems it attempts to solve.

First, a quick historical lesson. Let’s look at what has happened to traditional telephone surveys. Response rates have tanked and many households have scrapped their land lines, forcing survey designers to sample both land lines and cell phone frames. To be fair, as recently as the late 2000’s telephone surveys were still doing rather well. They were still cost efficient and dual-frame survey designs (landline and cell phone) were gaining traction. While it is true that response rates were already in decline, data quality was not suffering.

The picture has changed in ten years. Telephone response rates have continued their precipitous decline (now down into the single digits) and associated risks of systematic bias have risen. Researchers have been forced to adapt by choosing alternative methods without sacrificing sampling integrity. ABS is a countermeasure for the trends we have witnessed with telephone response rates. The costs of telephone surveys have risen as well, compared to ABS. Not only has ABS solved some of those problems, it has opened the door to mixed modes of contact and data collection.

ABS from the ground up

The foundation of Address Based Sampling (ABS) is the United States Postal Service USPS Delivery Sequence File. Marketing Systems Group was one of the first companies to get approval for providing the Computerized Delivery Sequence (CDS) File, which contains just about every deliverable postal address. That’s more than 135 million residential addresses to date.

ABS merges the CDS with other data sources that contain geographic and demographic data. This is like cranking up the volume on your guitar amplifier to “11”. Data sources consist of both publicly available sources such as the Government’s American Community Survey, the Current Population Survey and decennial Census data. Beyond that, ABS can mine commercial databases for additional data.  You can append demographics such as age, gender, income, education, and more. By meshing data sources together, the odds for positive matches are increased and the negative impact of coverage lapses are decreased.  By targeting the household instead of the telephone number, ABS avoids the under and over coverage downside risks of telephone samples.

The difference maker: Geocoding.

Geocoding is the key ingredient which effectively launched ABS as a valid alternative. Geocoding is the application of geographical coordinates to a corresponding postal address location. Why does this matter so much? It means researchers can reach the majority of U.S. households more inexpensively and faster than ever before.

The basic geocoding method works like this:  addresses are coded using linear interpolation, constructing geographical data points within each street segment based on the numeric addresses as end points.  The interpolation is accurate to the street level but not necessarily to the actual rooftop level due to factors such as property size and park spaces. Still, you can get very accurate correspondences with geocoding.

There is no better approach for standard mail surveys as ABS has also solved problems with respect to in-person household surveys. Because CDS does not include census geography, it was a problem to design samples for in-person households. In the old days this was solved through costly methods: multi-stage sampling of primary and secondary sampling units based on census blocks and field-testing every address in a segment. ABS removes those obstacles. Every address is geocoded to a census block, with some exceptions such as P.O. boxes, rural routes, and simplified addresses (rural routes, P.O. boxes with no physical address). While it is true that simplified addresses are a nagging problem –the good news is that the scope of the problem has diminished: the number of simplified addresses, once upwards of 10 million addresses, has been reduced to the hundreds of thousands. Not insignificant, but a vast improvement.

In the past, ABS was hampered by some systematic nonresponse factors. For instance, ABS respondents were more likely to be college grads and less likely to be non-White, as compared to RDD samples. Lately however, mitigation efforts have made real progress due to Census data appends that can be used to predict areas of high nonresponse. You can oversample areas that tend to respond less frequently to ABS surveys. Consumer data also can be appended based on trackable behaviors and predictive models. This too can be used for oversampling nonresponsive areas.  In short, there are fewer reservations attached to the use of ABS, hence its increasing popularity.

ABS isn’t just a “one-trick pony”

To appreciate the raw power of ABS, you need to think of it as much more than its source USPS CDS file. It is an enhancement of it: CDS plus demographics plus geocoding. The effect is empowering. Researchers can increase the range of analysis options for testing hypotheses and models. And the ease of use has fueled the use of multimode surveys to combat the telephone survey problems mentioned above. ABS is also useful for probability-based panel recruitment, non-response follow ups, and for reaching more inaccessible populations with stratified samples. ABS gives you that flexibility. Samples can be drawn to custom specifications without sacrificing representation.

Key Advantages of ABS

  • Single frame. Does away with dual-frame uncertainty.
  • Expansive coverage.
  • Straightforward weighting protocols.
  • Higher response rates, especially when multimodes are used.
  • More precise.

For all the reasons mentioned above, ABS is proving to be the best balance between coverage and cost for many researchers, but we can only outline the many factors involved in a short blog article.

Call MSG today to discuss how ABS can be a difference maker in your survey research.

 

The Power of Conversation

Observers of the market research industry have been noticing a trend of late: researchers are acknowledging the limitations of large-scale surveys and are rediscovering the value of qualitative research, namely, real conversations with real people. Why?

That’s precisely the question, and also, the answer. “Why.” Quantitative research often has difficulty answering the “why?” questions. While it is true that much insight can be gained by analyzing big data, why not go directly to the source and talk to them? By interviewing and hearing people’s stories and insights, you can understand data better. Why do products sell? Why is growth not taking off? Why do preferences emerge for one brand and not another? Some answers are more readily gained by simply talking to people, then interpreting the results.

Continue reading “The Power of Conversation”

Why Location Continues to be a Difference Maker

Last Christmas I wanted to buy a turntable for my daughter. Thanks to an online message forum, I discovered that Target was selling a new brand of turntable at an affordable price point with features typically seen on higher-end models. It was early in the Christmas buying season, and I had a hunch that a product like this might sell out quickly. So I researched the Target.com website, checked their inventory and used the store locator to find the nearest Target with the turntable in stock. At this point many would click the “buy now” button and have the product shipped. Instead, I hopped in the car and drove to the store. Why, you ask? I wanted to see the product for myself before buying it. Once inside the store, my smart phone told me which aisle to go to. With a little help from my friend the store clerk, I located the turntable, looked it over, bought it, and wrapped it up for Christmas. What this very short story teaches us is that while technology has become a key component in the way we consume, we aren’t quite willing to let go of location-based purchasing decisions. Continue reading “Why Location Continues to be a Difference Maker”

County Level Cell Phone Only Estimates

Probability based telephone surveys must utilize a dual frame approach in order to capture the ever increasing cell phone only population.   Until the day comes where it’ll be a single frame approach of only cellular numbers, researchers need to ensure they get the appropriate blend of cell only vs. dual phone users in their sampling allocations. Continue reading “County Level Cell Phone Only Estimates”

Quality Starts with Survey Design: Tips for Better Surveys

Marketing researchers are all facing two important challenges to data quality. First is the question of representativeness: with response rates plummeting, we need to make surveys shorter, more engaging, and easier for respondents to complete. Second is the issue of data accuracy: we must make sure that survey questions measure what we think they measure. Continue reading “Quality Starts with Survey Design: Tips for Better Surveys”