If you're trying to learn what a genealogy FAN club (or the FAN principle) is, read this post, instead.
Today's post gets into a few more nitty-gritty details of how to start FAN club research for genealogy (also called cluster research or cluster genealogy).
IMPORTANT: This is the technique for traditional genealogy research, not "auto-clustering" which is used for DNA.
Before I dive in, why would you use cluster research for specific problems instead of just "normal" research? First, this is "normal" research, you're just researching beyond your direct ancestors and even beyond your ancestor's family (the ancestor's FAN club). You use FAN club research particularly for genealogy brick walls (i.e. a difficult problem) like finding parents' names, finding a maiden name, or problems professional genealogists often have to address like identity problems (example: "is this two men of the same name or one man?").
You can use cluster research for any problem you want: when you run out of direct evidence, to make sure you've done reasonably exhaustive research, and even for things like social history. It's really useful! But let's look at how to do cluster research.
Getting Started with FAN Club Research
In my previous post about what the FAN club principle is, I recommended capturing FANs before you knew you needed to use them in your family history research (before you had a problem where you were specifically going to use cluster research). So let's look at how to get started.
To do cluster research, you have a specific problem you want to solve. Whenever you have a tricky problem, you need to start by writing down the details of the EXACT problem you are working on. Writing it down makes sure all the details are clear in your mind. If you struggle to write a description of your problem, there is some additional research you need to do (or maybe some past research you need to review).
See this article about asking a good specific research question to get an idea of the types of details you want to include.
Think of this first step as a double-check that it's time to employ a more complex technique or strategy like cluster or collateral research.
[Jargon and Semantics: BE AWARE!
There is technically a difference between cluster research and collateral research---collateral research includes extended family, not just direct ancestors. The cluster is family and FANs. That means cluster research involves family and non-family whereas collaterals are only family.
"Cluster research" and "FAN club research" can be used interchangeably. Some people define the FAN club as being "friends, associates, and neighbors" which might not be any family---but you won't know this when you get started so it's easiest to think of the cluster and a FAN club as being equivalent terms.
People often use collateral and cluster interchangeably, too, although they are not technically equivalent. Rarely do the minor differences in the definitions make a difference as long as you understand what you need to do for your specific problem.]
You can collect FANs without fully doing FAN research. The "dividing line" so to speak is analysis. First, you need to identify FANs (the cluster). Then you do analysis that is appropriate for the question you are trying to answer.
Is that clear? FANs are defined by their relationship to a person, not a familial relationship but the fact they have some type of interaction with the person. These are people you want to "collect." Once you have a question you can't answer and want to use cluster research, you analyze what you know about the FANs and that is the actual "research" as opposed to just collection.
You will need to collect the FANs before you can do analysis and you must have a clear question before you do analysis. However, you can start building the cluster before or after defining your question. Without a research question, you are only "collecting," not doing "research." A collection of names (and other information) won't help you. You need to do something with those names.
You have already been doing analysis in genealogy (doing something beyond just collecting names and information) but it was so basic you didn't even think about it. You didn't just write down a name and birth information. You "analyzed" some piece of information that told you that name and birth information was for a person in your tree. You know, like something that said that person was your ancestor's father. If you could understand that your ancestor's brother shared the same parents without needing to be explicitly told the brother's parents' names, you were doing analysis.
To continue building your family tree, you'll need to learn to do more complex analysis and you'll need different information to analyze. That's the point of FAN club research. The analysis can be really complex (but doesn't have to be, it might turn out the neighbor's wife is the sister---all things you can easily understand, once you've gathered the right information). Because the analysis can be complex, but you have to start with collecting information, we'll look at how to do the easier work of "collecting," first.
We'll finish talking about collecting the names because if you can start building your cluster early (and keep track of your fan club research), you are more likely to use it successfully.
Collecting Key New Information on Family Members and FANs
When we collect FANs or people for our cluster we are gathering the person's information. To make this a little easier to discuss, as far as what information to collect, I'm going to refer to "data points." Data points are just pieces of information. It's fewer characters to type and to me, it is quicker to read.
Normally when I talk about data points in genealogy I mean first name, last name, estimated year of birth, birthplace, etc. But with cluster research, it is:
- first name,
- last name,
- location the name came in contact with your ancestor (or focus person),
- date the name came in contact with your ancestor, and
- the source where you found the information.
There may be additional data points but usually, those five points are all you hope for from most members of a cluster.
That’s also why I’ve said “name” instead of “person.” When you start, you may not be able to tell if the same names are the same person or ANYTHING about the person of that name other than the data points I’ve just mentioned.
As you begin building a cluster, it is insanely important to capture those five pieces of information, not just the name. You should ALWAYS have those five data points (ok, maybe you have either a first or last name but you should always have a name data point and the other three, even if place and date are estimated).
Tip: If you use a spreadsheet or database, make sure first and last name are treated as two separate data points (separate fields or columns). It took me years to realize Dennis Miller was Bud Miller. If I tried matching on "name" they wouldn't match up. But obviously their surnames match. Clusters can be massive so you might not "see" two people with different first names could be the same if you use a single "name" field.
If there are additional data points like the person's age, occupation, religion, role in the community (like Justice of the Peace), or anything that identifies the person, you should also record that. But don't get so excited when you have more information you miss out on the core data points. You don't want to waste time later trying to figure out when or where your person came in contact with the person in the cluster.
An Example of Using Basic Data Points
Let's say you've reached a point in your analysis where you've got two Dred Ledfords in the cluster. Not a really common name. You want to determine if you can combine this into one person. Combining FANs into a confirmed (or nearly confirmed) single person might be pivotal to deciding both the records are for your John Smith, not some other John Smith. One Dred Ledford is a J.P. The other lived on land adjoining a piece John Smith bought. Are they the same man?
I've told you nothing to help you say yes or no.
OK, let's add a data point we should have recorded. The J.P. was encountered in North Carolina, the neighbor in Oklahoma.
Still not a yes or no. If you found these records because you know your John Smith was in North Carolina and Oklahoma, Dred Ledford could just as easily have lived in both. So let's add more data points. The J.P. married a John Smith to Mary Jones (in North Carolina) in 1823. The neighbor was living in Oklahoma in 1880. Once again, if it was possible both John Smith's are your man, this could be one Dred Ledford.
Now you start doing cluster RESEARCH instead of just collecting names. You research Dred Ledford, treating them as two different men for now, and you find the J.P. died in North Carolina in 1841. Obviously not the same man. This doesn't mean it isn't one family with such an unusual name but you wouldn't base any analysis on this single individual because it's not one individual.
But what if your research revealed the neighbor died in 1885 at the age of 93, a native of North Carolina? Hmmm, that might be one man and an important cluster member. It's worth doing more research.
Without those basic data points, you can't even get started with cluster research. The name of a neighbor in a deed or land record is often just a name. You can determine the date and location from the source you're using.
You can also estimate the neighbor is an adult so you could also record a data point of "probably born before [date of record minus 20]" (if you remember from the previous post you should be taking notes so I literally record "This person should be an adult which would make him [or her] born before___. However, I haven't done any legal research or other research that could indicate he is under 21 so this is only a likely estimate." If you knew for certain the legal age is 21 (or older or younger), the person is a minor, or any other detail, make a note in your notes!
The J.P. is also an adult and likely not just 21 although it's possible. This is why notes are so important. You can record this "likely" information so you use the most likely information in your comparisons but have a record so you know it is possible your estimate is off (and how much it might be off).
[To learn more from real-life examples, look for a case study using cluster research. This post talks more about where to find case studies.]
Many members of the cluster will just be names. Those 5 basic data points are so important. A person's age doesn't help if you don't know WHEN they were that age. Locations are sometimes the best information we have to work with our cluster. In genealogy you don't get the absolute "best" information, you get the best available---you work with what you've got.
Make sure you capture what is available!
[Recap: We want the name and source for every FAN club member. From the source we can determine a location and date where our person and the FAN club member interacted. Those are our key 5 data points (given name is one data point, last name is one data point).
You may also be able to estimate the person is an adult. Make sure you write in your notes that you are estimating their year of birth as being before a certain date and how you are making your estimate. Clusters often have a lot of people with similar names so you will NOT remember "ideas" you have like one FAN club member was likely a young adult whereas another was likely older. Write it down in your notes. This applies to any ideas you have or estimates you make. You need estimates and ideas but you need to know they aren't "facts," too.]
Without analysis, you’ve just collected data points. When you start to analyze all your data points, though, you start to define people.
As a final note, developing a usable organization system for tracking your FANs can be really difficult. Each project can involve different data points and need different types of analysis. There isn't just one way to organize a cluster! A great way to get started is by taking good digital genealogy notes. These will be searchable. Check out this post for more about taking genealogy notes.
Do you have any tips for cluster research? Have you done it before? Let us know in the comments! Happy hunting!
For more related posts check-out more about research logs or creating a research plan template! Or check out our research services site.
