Who and Where Is SIOP? An Inside Look Into Our Current Member Demographic Data and Potential Uses for the Future

Victoria Lykins

In this digital age, it seems that every organization and subscription service is bombarding us with requests for information and surveys about our experiences, and for what? Does the information really get utilized, or does it simply slip into the ether? I thought filling out my SIOP demographics profile would be the same experience; I would fill out the information, update periodically, and it would sit on a server somewhere expiring. However, after a conversation with Caitie Jacobson and Amy DuVernet, co-chairs of the Membership Analytics Subcommittee, I learned just how wrong my preconceived notion was.

SIOP is an organization that values and investigates the demographic data of its members. Filling out the demographic sections of your membership profile helps the Membership Analytics Subcommittee put together reports to inform and allocate resources for attracting, selecting, and retaining SIOP members.

What Is the Membership Analytics Subcommittee and What Do They Do?

The Membership Analytics Subcommittee is responsible for analyzing and sharing membership composition trends to support SIOP’s mission of meeting the needs of all members. This relatively new subcommittee is part of the overall Membership Committee, led by Tiffany Poeppelman, and is staffed with over 35 volunteers who are passionate about SIOP, its mission, and gathering specific data to support current members and engage with future members of the SIOP community. 

Most recently, the subcommittee completed a deep dive into our membership demographics and found many interesting trends that highlight the top locations of our members, membership data type, and the types of decisions that can be made leveraging this critical data.

Key Findings for U.S. Data

Overall Data
Data collected from 2019, the most recent complete year of data, can be interpreted in a few ways. It is important to note that the data analyzed are based on United States membership trends, which make up approximately 90% of membership statistics. The first is a look at overall data for U.S. membership, which shows the states with the largest concentration of members. Figure 1 illustrates that the six states with the most members in descending order are California (8%), New York (8%), Virginia (7%) Texas (7%), Illinois (7%), and Florida (6%).

These states have been the most densely populated across the last few years and each consistently encompass above 5% of members each year. Although California remains the most frequently reported state, there are recent trends that show that members are becoming more common across more central and eastern regions of the United States.

Metropolitan Area Data¹

The data were also examined by metropolitan area, as can be seen in Figure 1² with the gray bubbles. As shown in Figure 2, the top five metropolitan areas represent over 31% of all members.

The top three metropolitan areas for membership since 2017 are the Chicago, New York, and Washington DC areas. Those three metropolitan areas combined consistently make up 21–26% of all SIOP U.S. memberships.
 

Another interesting finding about metropolitan data was that while 31% of members could be found in the top five metro areas, the majority of members were located in areas that each made up less than 2% of the overall SIOP membership, as seen in Figure 3. This reveals that SIOP members can be consistently found not only in densely populated metropolitan areas but also throughout the country.

Regional Data

Each region of the country was broken down to examine where the largest and smallest number of members reside, as seen in Figures 4–7. In 2019, the concentration of members in each state and region was largely driven by the major metropolitan areas. For the Western region, the top metro areas were Los Angeles, Seattle, San Francisco, San Diego, and Denver. For the Midwest, the top metro areas were Chicago, Minneapolis, Detroit, St. Louis, and Cleveland. For the Southern region, the top metro areas were Washington DC, Atlanta, Houston, Dallas, and Orlando. For the Northeast, the top metro areas were New York, Philadelphia, Boston, Pittsburgh, and Hartford.

Table 1 provides a summary of which states have the highest concentration by region. Overall, the Southern region has the most members (41%), likely due to the number of states included. This is followed by the Midwest (25%), Northeast (18%), and Western (16%) regions.

Table 1
Membership Concentration Within U.S. Region

The Top Metropolitan Areas 2017 Through 2019³

From 2017 to 2019, three metros consistently represented the most populated areas as far as membership totals: New York, Washington DC, and Chicago. The top six metropolitan areas have remained consistent from 2017 to 2019, but there have been shifts in other metro areas. The San Francisco Metro Area has been declining since 2017, and the Houston Metro Area has been increasing since 2017.

Membership Type Data

The location data for members in 2019 were also broken down by membership type. Table 2 shows the top five or six states for each membership type, broken down by percentage of the total. Additionally, the tables show the number of location fields that were left blank. As can be seen, students were least likely to leave their location blank. (Great job filling out your profiles, students!)

Table 2
Membership Type Breakdowns by State

What Data Matters and Why?

Your answers to SIOP membership profile questions are compiled into a database that the committees like the SIOP Conference Committee may be able to use to determine SIOP events, such as where the annual conference should be held. According to location data from 2016–2019 members, there are high concentrations in California, New York, Texas, Florida, Illinois, and Virginia. Going back to 2016, SIOP conferences were held in California (Anaheim), Florida (Orlando), Illinois (Chicago), Virginia (National Harbor), and the 2020 conference was set to be held in Texas (Austin). Thus, there is reason to believe that site location and membership concentrations are related—membership is likely to increase in places where conferences are held or are likely to be held.

Another possible use for knowing the geographical data of SIOP members is creating better opportunities for local networking. For instance, knowing where members reside may allow for better opportunities to match mentors and mentees in a given geographical area. Moreover, location information leads to awareness of areas that may be able to form or re-energize local groups for networking and socializing. In the future, member data such as applied or academic interest may also be leveraged to create location-specific networking groups focused on specializations of I-O. Additionally, the overall Membership Committee can use location data to create more targeted and relevant diversity and inclusion initiatives, in partnership with the D&I portfolio. Local connections and events would allow members to see each other more than once a year at the annual conference and to build meaningful connections. Moreover, given the impacts of COVID-19, valuable virtual connections can be fostered through meetings, presentations, and social gatherings among members using location and membership-type data.

The differences in member locations show how important demographic information can be for creating and implementing recruiting and retention initiatives for each member type. Events for each member type could be planned based on the needs of that group. For example, an area with a high concentration of students may benefit from a presentation on applying to doctoral programs or entering the workforce.

Let’s Fill Out Those Fields!

The membership data covered in this article are based solely on two fields: location and member type. With only those two categories, information can be turned into decisions, but imagine how much more can be done if we fill out the entire demographic information section.

As we fill out our individual membership fields, the Member Analytics Subcommittee plans on using data to compile reports that summarize other aspects of membership data, such as ethnicity, gender, interest areas, and if members are practitioners or are academically focused. These future reports will further help with networking opportunities and the goal of connecting SIOP members.

Now I am sure that you are as invigorated as I am to update your membership data, but how do you do that? To find and fill your empty fields follow the below steps:

There you go! Your newly updated information can now be used by the Member Analytics Subcommittee to make new reports and the Membership Committee to implement new initiatives for the members of SIOP.

I am off to update my member information and hope you will join me!

Notes

¹ Overall, metro areas were found by approximating a zip code for each city location entered by members then linking the zip code to metropolitan statistical area codes and names. Cities and zip codes that were not within a metropolitan statistical area were tagged as non-metropolitan areas. Individuals in a non-metropolitan section may not be counted with the metropolitan numbers.

² Washington–Arlington–Alexandria comprise the Washington DC Metro Area. New York–Northern New Jersey–Long Island comprise the New York Metro Area. Chicago–Naperville–Joliet comprise the Chicago Metro Area. Atlanta–Sandy Springs–Marietta comprise the Atlanta Metro Area. Los Angeles–Long Beach–Santa Ana comprise the Los Angeles Metro Area. Orlando–Kissimmee comprise the Orlando Metro Area.

3 Washington–Arlington–Alexandria comprise the Washington DC Metro Area. New York–Northern New Jersey–Long Island comprise the New York Metro Area. Chicago–Naperville–Joliet comprise the Chicago Metro Area. Atlanta–Sandy Springs–Marietta comprise the Atlanta Metro Area. Los Angeles–Long Beach–Santa Ana comprise the Los Angeles Metro Area. Minneapolis–St. Paul–Bloomington comprise the Minneapolis Metro Area. Dallas–Fort Worth–Arlington comprise the Dallas Metro Area. San Francisco–Oakland–Fremont comprise the San Francisco Metro Area. Houston–Sugar Land–Baytown comprise the Houston Metro Area. Seattle–Tacoma–Bellevue comprise the Seattle Metro Area.