Social construct definitions are a handy way to categorize data sets.
The idea is that if we use a social construct as a classification metric, we can use that to predict what will happen to the data set.
If we have a social construction for a data set, we know what will be the most significant outcome in the data.
It’s the same with rumble.
When we want to classify data sets, we need to know which features are most relevant to the model we’re using.
The social construct is a way to get that information out of data sets we have to use.
It can be helpful for understanding how to build robust and reliable social models.
We have a number of rumble data sets that are fairly static.
We’ve had some time to play with them, and we’re confident that there are a lot of interesting things going on there.
We also have some data sets with significant correlations between the social construct and some other things.
We want to build a model that predicts the relationship between the rumble scores and a bunch of other things we care about.
One way to do this is to look at a social object, like a person or a company.
We can do this by looking at how they behave and how they react to events.
We’ll look at how much they like each other and how much dislike they have for each other.
For example, we’ll look for things like: • people are more likely to report being invited to rumble events • people have more positive experiences when rumble event invitations are given • people with higher scores are more inclined to seek out rumble-related social events We can also look at other variables like:• whether people prefer rumble to other forms of social interaction, like social events, meetings, or social media • whether people are motivated to go rumble when it’s an opportunity to meet and talk • whether they have greater emotional involvement in rumble than non-rumble people, like more of their friends or family • whether there are other characteristics that are important for rumbling that we haven’t identified yet.
In other words, we have lots of data points we can look at.
Here are some examples:• We have data that shows that rumble increases the likelihood of meeting people.
We know that people who are more active in rumbling tend to be more successful in getting people to participate in rumbles.
• We have similar data that show that people are attracted to rumblers by the fact that they’re often invited to attend events.• We know from past research that people like to socialize.
In the rumbly experience, they’re usually encouraged to socialise more.
We don’t know why, but rumble may be one way to encourage this.
• People who enjoy socialising are more often the ones who get invited to parties.
This is consistent with past research showing that people enjoy socializing more when they’re invited to a party.
• And we know that socializing can also help with rumbling.
A rumble participant who likes to social in general is more likely than someone who enjoys socializing to engage in social media (especially when the latter is used).
• The social component of rumbling has been linked to lower depression rates.
This has been established from several different research projects.
For this reason, we’d like to explore whether rumble might be associated with lower depression.
We’re not going to look specifically at depression rates, but we will look at factors like rumble frequency and social distance.
We already know that rumblers have lower rates of depression, and this has been shown to be true in both rumble participants and non-rumblers.
We would like to see whether there’s a relationship between rumble and depression rates in the rumbles sample.
For the purpose of this research, we’re looking at both rumbler and nonrumbler samples.
We need a way of determining if rumble makes people more likely or less likely to have a depressive episode.
This will help us to build reliable social network models.
We might start with a set of rumblers and nonrumblers, and then see if there’s any relationship between those characteristics.
If there is a relationship, then we can test that with other groups.
We could look at whether people with a high rumble score are more or less depressed than people with the lowest score.
For instance, we might test a group of people with high scores and people with low scores.
Or, we could look to see if rumbling increases the odds of being in a relationship.
We will be able to compare the relationship we get from the two groups with the relationship predicted by rumble predictors.
We start with our own data sets: we have rumble ratings for every rumble session.
We then look at the relationships we get for a couple of factors that may be associated in our own sample:• the number of participants• the age range of the participants• whether they’re in