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Tracking the Status of Congressional Redistricting

It’s a midterm election year, and tensions are running high. A lot is riding on this election. The majority party in the U.S. House of Representatives could change hands, and there are many close races in swing districts. 

To double down on the drama, it’s a redistricting year. Congressional districts are being re-drawn using new 2020 census data. With some districts contesting in the courts we lack total clarity on what the political map will look like for the November elections. It’s a dynamic situation, and MSG is tracking it carefully.  

  • Each week we issue an updated table showing state by state status (you can download it as a spreadsheet, too)
  • Each week we update the US map to show which states have approved redistricting, which are pending, and which have been proposed

Congressional Math

Every 10 years the United States Census captures significant geo-demographic trends—which populations are up, which are down. The federal government uses decennial Census population numbers to reapportion Congressional Districts for each state. States with more population get proportionally more seats in The House of Representatives.

After reapportionment, U.S. Congressional districts must be re-drawn. Reapportionment and redistricting is a numbers game:

States That Gained House Seats 

States That Lost House Seats

You can see a pattern here. The Northeast and Midwest states are losing population, the South and West are gaining. 

It is left up to the states to draw new boundaries for each district. Some districts will become more competitive, some “safer” for the party currently holding the seat. 

Analysts at Fivethirtyeight.com reported that Republicans have power over the redrawing of 43% of congressional districts at the state level. Democrats control 17% of the districts. Independent commissions or party splits are in control of 38%. 1% of the districts won’t need to redistrict at all, and one “at large” district will cover the whole state.

With both parties potentially trying to shape Congressional districts to put themselves at an advantage, state supreme courts typically have the final say. A lot depends on population patterns—what regions are seeing the biggest population shifts. The results will no doubt have an impact on national politics for the next decade.

Stay on top of the latest developments by visiting our Genesys Redistricting Tracker.

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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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Information Security: Your Peace of Mind is Our Responsibility

Protecting the integrity of customer data and ensuring its reliability has always been in our DNA. In the constantly changing landscape of cyber threats, organizations need a robust set of processes and specialized individuals to ensure that new risks are monitored, and systems are adapted accordingly.

To that end, we are proud to announce that Marketing Systems Group recently achieved ISO 27001 certifications. ISO 27001 is an international standard that details requirements for establishing, maintaining, and updating an information security management system (ISMS).

This standard requires systematic examination of information security risks, design and implementation of controls and risk treatments, as well as adoption of a management process to continuously meet ongoing security needs.

How Do We Implement This?

As we see it, information security has two parts that must be executed in tandem:

  1. Implementing information protection
  2. Monitor the implementation and improve as new threats surface  

Implementing security controls around information is a lot like measures we take to physically secure our home and family.

You Would Do the Following:

  • You would look for a home in a nice neighborhood.
  • Keep an eye out and control your visitors and what they do in your home. In essence, who plays in the sandbox with our kids and what do they play?
  • You would install a home monitoring system so that you are made aware of any threats.
  • You would educate yourself and your kids on staying safe, communicating their activities, and set rules on what is allowed and what is not.
  •  You would create a “Plan B” that will allow you to find a safe way out in case of an emergency.

We follow a similar model when it comes to protecting information:

  • A nice neighborhood – We ensure that our data resides in data centers that have proper security controls in place.
  • Who plays in the sandbox – We ensure that all vendors and partners, in specific the ones who deal with our data have similar controls in place by conducting risk analysis with them on regular intervals. We also ensure that proper access controls are in place.
  • Monitoring – All our environments are monitored 24/7 and we have dedicated and trained staff in charge of security and threat monitoring.
  • Continuous education – We provide continuous training to all our staff members on information security and risks. We also conduct simulated threat assessments to understand preparedness by our staff members.
  • Plan B – We develop disaster and business continuity plans that account for how we would recover and communicate with stakeholders to get back on our feet to continue providing services to our customers.

4 Steps for Continuous Improvement (PDCA):

  1. Plan – As part of our operating procedure, we retrospect problems and collect useful information to evaluate security risk and root cause. We then define policies and procedures that can be used to address root causes of problems. Next, we develop methods to establish continuous improvements to information security management capabilities.
  2. Do – We implement the developed security policies and procedures based on best practices.
  3. Check – We monitor effectiveness of ISMS policies and controls and evaluate tangible outcomes as well as behavioral aspects associated with the ISM processes.
  4. Act – We continuously improve by means of documenting results, sharing knowledge, and using feedback loops to address future iterations of the PCDA model implementation of policies and controls.

Certified, Authorized, and Compliant

SOC 2 Type II Certification – Our cloud data centers are SOC 2 Type II certified for the trust principles of Security, Availability, and Confidentiality.

ISO 27001Certifications – Marketing Systems Group achieved ISO 27001 certifications. For more information about ISO 27001, check out the ISO website.

All certificates and reports can be provided upon request.

Featured

Defining Small Area Geographies with Radius Sampling

In this blog we look at different ways to define small area geographies. How can we define a trade area or location using radius sampling (defining a geography around a particular point at different size ranges)? It sounds simple enough. You pick a point and draw a circle around the location. Done, right? Not exactly. The issue is how you identify geography within the circle. How precisely can we get at those addresses?

You can look at census blocks or block groups, or you can look at zip codes. It all depends on how large the radius is. Typically, we use census blocks for 5, 8, and 10 mile diameters, and because the blocks tend to fit well around the edges, we only need to do some minor carving.

With smaller radii, it becomes more of a challenge, though. Census blocks or block groups tend to fall way out of that range, or we get under coverage because not enough of the blocks or block groups fit inside the circle.

In episode 7 of our Coffee Quip video series, subject matter experts David Malarek (Senior Vice President, Sampling & Database Services) and Dennis Dalbey (Manager, Geodemographic Services) demonstrate the two basic ways to define radius geography using census blocks within a 10-mile radius—area inclusion using polygons and block centroids.

For example, we can start with a 10-mile radius around a point. Then we overlay census blocks (or block groups). On the overlay all block groups that intersect or have some relationship to the 10-mile radius can be seen. Where the color overlay falls outside the radius, we can decide which block groups to keep in or out of the sample frame.

The Polygonal Approach

By using polygon geometry, we can apply area inclusion. We can determine or make a cut on the percentage of the polygon area within the radii. For instance, any block group where the geographic area is at least 50% or more within the radius can be selected. When this is mapped, you will find holes along the edges of the circle where the geography once existed but was cut out because the inclusion was under 50%. Note that no matter how you carve the fringe, there will always be some overstatement of the sample frame or some under coverage depending on how well the block group geography fits the radius.

The Block Centroid Approach

Another way to do this is to use the center point of a polygon to assign geography to a radius. It’s a different type of geometry being put to use. Instead of using polygons where we’ll make an inclusion, we are actually using what’s considered the center of the polygon—the census block centroid. This method will allow us to select blocks that are theoretically 50 percent or more within the radii without having to apply an inclusion like we did with the polygon earlier. It is a more efficient way of doing it, but keep in mind that we are making a 50 percent cut across the board. You will encounter tradeoffs such as introducing under coverage or over coverage here, too.

One of the benefits of using the block centroid is that we can vary the distance. Let’s say we are not really sure whether the 10-mile radius is going to meet your quota. We may want to overstate the geography at 20 miles. With block centroid we can apply the distance and go from 10 to 15 to 20 miles until we meet the population or household quota. Note that this can be applied to polygons as well, but it is much easier to do with the block centroid.

The Address Level Approach

Yet another way to do this is to plot all the known addresses that fall within the blocks or block groups touching the circle and exclude any address that falls outside the circle. This is the most accurate way of getting accurate household counts and eliminates under and over coverage. It’s a two-step process and a more involved methodology but it is also the most accurate. One downside to this approach is demographic data is only available at some larger aggregation of geography such as block group and not by individual addresses.

Dave and Dennis compare the polygon and block centroid methods, and why they sometimes yield different results. Sometimes we actually need to plot ABS address locations in the blocks within the radius and remove the ones outside the radius, so we get a better fit for the overall target population. This is really the best methodology in terms of making sure that households are completely within a radius without having to worry so much about the regular geography, where part of it is in and part out.

Find more details and visualizations of these methods, in our Coffee Quips #7 video. It is the first in a series of geodemographic coffee quips. For additional information on our geodemographic services click here.

Featured

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.

Featured

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!

Hybrid Sampling: Why a Blended Sampling Approach Is a Sensible Option

In an ideal survey research world, it is preferable to work with a single probability-based sample as it provides the best representation of the target population. In the real world, however, cost and feasibility often prohibit the luxury of using purely probability-based samples. This is where different sampling methods come into play to reduce cost and improve feasibility, especially those that rely on online panels. All in all, online sampling isn’t ideal, since such samples are void of “organic” representation. If you can’t get generalizable results from your surveys, then what’s the point?

A blended (hybrid) sampling approach can offer an effective and practical alternative, through which multiple frames are used for sample selection—oftentimes a combination of probability-based and convenience samples from online (opt-in) panels. Further, we might start with a fully probability-based sample from a telephone or address frame, but then tap into online panels to supplement what we get from the main probability sample.

Taking a hybrid sampling approach sounds well and good, but just because you’ve gone hybrid doesn’t necessarily equate to unbiased survey results. Sampling from online panels is always a little tricky because if you don’t know what you’re doing, you can end up taking a seemingly inexpensive sample component, mix it with your precious probability-based sample and end up with a poor combination.

Sure, theoretically it’s preferable to have all or most of the samples be probability-based, but they are expensive. At the same time, you don’t want samples from opt-in panels dwarfing your precious probability-based sample. As a general rule of thumb, something on the order of no more than 50% of your sample should be coming from opt-in panels. Keep in mind that budget and other factors may dictate a higher or lower contribution.

The selection of samples from opt-in panels needs to be carried out sensibly. Equally important is the way you blend the probability and nonprobability-based sample components to produce a single database capable of producing reliable conclusions. It’s a little bit like chemistry when different materials are tossed into the mix to produce an alloy with higher-level properties; you have to be measured about it and get the ratios down just right using correct weighting and calibration adjustments.

As response rates continue to decline into single digit territory, even with fully probability-based samples, geodemographic weighting of survey data becomes essential. This is proven true since nonresponses are always different in nature. However, this issue will magnify with hybrid sampling when part of the sample may come from opt-in panels. Hence, in addition to basic weighting, additional calibration adjustments become necessary as well. This means going beyond geodemographics and applying corrections based on attitudinal and behavioral characteristics to ensure respondent representation for their population.

If you are looking to enhance your phone or address-based surveys and supplement them with samples from online panels, survey research scientists at MSG have decades of knowhow and hands-on experience to support your hybrid sampling methods. Our experts can assist you with sample selection, survey administration and questionnaire design, as well as state-of-the-art weighting and calibration procedures. Additionally, we can support you with reporting and analysis of data from complex surveys.

To learn more about our hybrid sampling products and services, click here, or contact one of our specialists.

For a deeper dive, watch Episode 06 of our Coffee Quip YouTube series, wherein the panelists discuss the intricacies and benefits of Hybrid Sampling!

MSG data fusion techniques seed new growth opportunities for fresh-foods company

What does the word Fusion mean to you? You might identify that term with nuclear power, the holy grail of carbon-free energy creation. Or you might think of Jazz fusion, the blend of traditional jazz instrumentation with electronic rock instruments. In the market research industry, fusion refers to the powerful melding of data from various sources with analytical and segmentation intelligence to account for challenges and deficiencies in survey research. 

While there is no doubt that customer surveys will always play a key role in forming business strategies, the fact of the matter is, respondents are less likely to fill out long and complex questionnaires than they used to be. We live in a world of diminishing returns.

But this is only half the picture. When it comes to ancillary data from commercial sources, the harvest of quality data about consumers is a rich and bountiful yield. Companies now have the power to augment their internal customer data with supplemental external data. 

What kinds of data do we have in mind? Think about how data such as granular geodemographics, socioeconomic characteristics, attitudinal and behavioral indicators could augment existing surveys and records. This is where the fusion concept applies. By fusing some or all of these aspects, you are likely to get a fuller, more accurate picture of your customer base. 

MSG’s data scientists work with clients to pull data from various sources, apply nuanced analytical and segmentation techniques to it, then output a robust, empirical basis for business decision making. Fusing data, then applying advanced analytics, means businesses are less in the dark than before. By reaching beyond simple statistical analysis, better inferences and more nuanced decisions can be made. 

Client Case Study

To see how this works in the real world, read this case study about one of our clients is in the fresh-fruits and vegetables delivery business. They take fresh produce that isn’t pretty enough to go on the supermarket shelves, which might otherwise go to waste, and get it into the hands of customers. The problem was the company didn’t know enough about their current customer base to be able to formulate a plausible growth plan. They knew that new market opportunities were out there, but they didn’t know where to look for them, because data items for each customer were scant. They needed better answers to questions like 

  • What are the key characteristics of profitable customers across multiple markets?
  • What characteristics differentiate loyal customers from the rest?
  • Which geo-demographic segments include higher concentrations of loyal customers?
  • How can loyal customers be located in new markets?

To tackle these questions, MSG developed a fusion plan involving an array of techniques:

  • Map creation and plotting of all current customer locations
  • Individual and household demographic variables appended to each customer.
  • Classification and regression analytics used to zero-in on key predictors. 
  • A series of spatial analyses to identify geographic clusters in new markets similar to existing markets.

Check out the full case study to see how the company achieved a much better grasp of their good recurring customers, where they were clustered geographically, what they looked like demographically, and what areas in markets of interest had the highest likelihood of potential new customers (people with similar demographics as the known customers).

Fusion of internal and external data can help your company to fill in the knowledge gaps, make more accurate inferences, and seed growth opportunities for new products, services, and markets. 

The Advantages of the Advanced Cellular Frame

One of the strengths of MSG’s new Advanced Cellular Frame is its ability to address inward and outward migration of cellular telephone users.  This feature allows for the employment of Disproportionate Stratified Sampling (DSS) designs – or in other vernacular – split-frame designs. We can illustrate this using a typical scenario.  

Say you are targeting a metro area like Multnomah County, OR – home to the city of Portland.  There are, in essence, three ways you can define a cellular telephone sample frame targeting Multnomah County. 

One method is using a traditional EPSEM (equal probability of selection method) RDD design.  This is the method that has been around for well over 10 years.   We first identify the rate centers that best fit Multnomah County.  Every possible telephone number in the cellular thousand blocks (first 7 digits of a telephone number) that originate from the selected rate centers make up the sample frame.  This RDD frame design contains a mixture of working and non-working/unassigned telephone numbers. 

Multnomah County, OR 
RDD Frame (Traditional EPSEM) 2,057,000 100.0% 
Listed In-Area 348,497 16.9% 
Listed Out of Area 804,686 39.1% 
Unlisted/Unassigned 903,817 43.9% 

This method, however, has some serious drawbacks, but more importantly, it does not address migration – people who have moved in and out of Multnomah County and kept their cellular telephone number.   Look at the highlighted line in the table above.  39% of the RDD EPSEM frame is known to be outside of Multnomah County.  Additionally, the RDD EPSEM frame excludes anyone who has since moved into the county from other parts of the country.  The result of this behavior is high under coverage and high out-of-area.  It’s simply an inefficient and costly design. 

The Advanced Cellular Frame dramatically improves upon the shortcomings of the traditional RDD design.  We can still employ an EPSEM design by including both known and unknown cellular telephones.  But, this design addresses the migration issue.   Look at the highlighted row in the table below.  This design now includes cellular numbers for individuals who have since moved into Multnomah County from other parts of the state or country (inward migration).  It also excludes cellular numbers known to be outside Multnomah County (outward migration).  This all but erases the inefficiencies of dialing known out of area out of area numbers while at the same time improving in-area coverage.  This design is still an EPSEM methodology because it includes the unlisted/unassigned portion in order to provide full coverage of Multnomah County.  When sampling, every telephone number has an equal probability of selection.  

Multnomah County, OR 
ACF Frame (EPSEM) 1,449,089 100.0% 
Listed 545,272 37.6% 
In Area 348,497 24.0% 
Inward migration 196,775 13.6% 
Outward migration 0.0% 
Unlisted/Unassigned 903,817 62.4% 

Building on the improved EPSEM design noted above is the fact that we can now employ Disproportionate Stratified Sample designs (DSS) using the Advanced Cellular Frame.  We can separate the Listed and Unlisted/Unassigned components of a sample frame into individual strata.  This enables you to sample the more efficient listed stratum at a higher rate and under sample the less efficient unlisted/unassigned stratum at a lower rate.  Every number in the cellular sample frame still has a probability of selection but DSS will yield a more productive sample over a simple EPSEM design. 

Click Here to learn more about the Advanced Cellular Frame today!

Coffee Quip: an insightful new video series about survey research topics that matter

Have you ever noticed how many of the best workplace conversations tend to happen around the water cooler or the coffee lounge? When coworkers converse in the midst of informal “stop and chat” spaces, they are more candid than they would be in an official meeting. Often, they tell better stories, too. You can pick up a lot of useful knowledge from experts by hanging around such spaces.  

To emulate those kinds of conversations, we would like to introduce you to Coffee Quip, a new MSG series of short video chats between a panel of product and subject matter experts. 

Click Here to Watch Now

The field of survey research is an ever more complex and dynamic one, and it can be hard to get at the inside knowledge needed to accomplish what you want to do. At MSG, we want our customers to benefit from the experience and wisdom of industry-leading Subject Matter Experts. They are the Yoda, the Obi-Wan, the architects of our products and services. They know how survey research gets done, what works and what doesn’t, and why.   

Coffee Quip: Need to Know Info That’s Easy to Watch 

Sometimes the best things come in small packages, and too much of a good thing can get to be too much. Sure, you can gain quality industry knowledge is by attending a formal webinar, but that can be time consuming. In light of this reality, we have produced each Coffee Quip episode to run a mere 8 to 12 minutes, less time than it takes to down your first cup of morning java.  

The informal, light, unscripted discussions are hosted by Marketing and Communications team member Greg Pizzola. You will learn about critical and timely survey research topics and products, such as Advanced Cellular Frame, Hybrid Sampling, and our ARCS platform. You also get to see members of our Sales Development and Customer Success teams, who are on the front lines working with clients every day. They pose the kinds of questions to our experts that customers are asking them.   

Coffee Quip is about your needs and interests, and we encourage you to submit topics for future discussion. Send your questions and topic ideas to SusanEmery@m-s-g.com

Make sure to subscribe to the MSG YouTube channel so you don’t miss any of the upcoming episodes.