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Plenty of Fish Relationship Chemistry Predictor

For your satisfaction and ease of finding an ideal romantic partner, PlentyofFish has enlisted the help of the world’s foremost team of academic psychologists who specialize in Chemistry Testing to develop the Plenty of Fish Relationship Chemistry Predictor (POFCP) – the only tool of its kind to match you to people worldwide. What has resulted is a short, yet empirically valid, relationship test capable of distinguishing individual differences on some of the key factors that define satisfying and stable relationships. Validation of the POFCP was done using a national sample of individuals with the same demographic characteristics as members of the Plenty of Fish community. Analyses of the data have identified a number of robust personality factors that are known to be predictors of relationship satisfaction and longevity. For example, regardless of age or sex, there are certain personality dimensions that are strongly associated with relationship satisfaction. In addition, similarities and differences in these dimensions can predict how committed and satisfied two people are in their relationship. That’s not to say that every couple who is similar on a certain personality dimension is destined to live happily ever after. Instead, it means that, on average, two people that are similar on a certain personality dimension have a high likelihood of being satisfied in their relationship. We want to emphasize that we are talking about generalizations here, and these generalizations don’t apply to all people. To illustrate, consider the generalization that men are generally taller than women. This does not mean that every man is taller than every woman. Instead, it means that, on average, men are taller than women. This same logic applies to the feedback that is given on this site. Even though, on average, people who are Family Oriented, for example, tend to want children, not necessarily everyone who is Family Oriented wants children.

Frequently Asked Questions about the Plentyoffish CP:

1. Where did the personality questions come from?

Development of the CP items involved multiple stages. First, a list of more than 150 items was generated that past research showed to assess key elements of personality. Next, a culturally diverse focus-group was assembled to identify items and words in items that might be confusing or difficult for non-native English speakers to understand. Based on this exercise, several items were either removed or revised from the list of personality items. This list of personality items were then administered to hundred of individuals around the country. The final selection of items was based on analyses of the responses from this sample of respondents. Specifically, Item Response Theory and Rasch Scaling analyses were used to first, identify which items provided “good” indicators of certain personality characteristics and second, which items were biased for men or women. This multi-step process resulted in a 46-item relationship test that assesses individuals’ standing on five broad dimensions of personality. This is the only known relationship test where the test scoring and matching algorithm are based in the statistical gold standard of modern test theory – in other words it is psychometrically the most sophisticated Chemistry Test available today.

2. How were the labels for the personality dimensions derived?

First we identified the basic personality dimensions based on analyses of hundreds of respondents. Next, a group of experts independently generated labels for each personality dimension. We then compared the labels generated by each expert and selected those that more than one expert suggested and which was understandable to everyday people.

3. What is the feedback based on?

All the feedback you see here is based on statistical analyses of personality data collected from several hundred people. None of the feedback is based exclusively on our intuitions or theories about personality. Thus, the feedback is entirely driven by the data we have collected.

4. Why is the feedback sometimes very different from how I see myself?

The feedback is not meant to suggest that that everyone who is high or low on a personality dimension is exactly the way described. Indeed, the feedback was based on AVERAGE scores for people high, moderate, and low on each personality dimension. This means that the feedback could be less accurate for individuals whose scores are in between the moderate and high, or moderate and low scores. If you feel the feedback did not match you very well, this could be one reason why.

5. Why does the feedback sometimes give contradictory information?

Sometimes the feedback will give respondents apparently contradictory feedback. This is an unfortunate consequence of making generalizations. Quite often, individuals cannot be captured by the general trends derived from analyses of large numbers of people. Of course, while our generalizations do get it wrong sometimes they tend to get it right more often than not.

6. How does the CP matching algorithm work?

The mechanics of the CP are proprietary, and its success in creating online dating partners will soon be published in an academic journal together with outcome data from actual daters. This will be a first in the industry, and PlentyofFish is proud to lead the way in setting quality standards for Chemistry Testing in the industry. That research article will provide more detail, but until then let’s describe the basics.

The CP was designed to pair people who come from very diverse backgrounds – such as are found on dating sites like POF that have a huge, global membership base. In a group like this it is always possible to find people with significant and healthy differences (or complementarities) in their personal characteristics. Accordingly, the CP firstly assesses the degree that two people have the right recipe of similarities. In other words, good partner candidates should have similar test results of personal characteristics.

test result similarity alone is not sufficient. Decades of empirical research on the ingredients that make a healthy and satisfying relationship suggest that intimate partners who have strong similarities across key personal characteristics are more satisfied in their relationships and are less likely to break up than are partners that differ on those key characteristics. Thus, secondly, regardless of test result, simply being similar matters as well.

Thus, to maximize potential satisfaction the matching algorithm takes into account both the rank ordering between two persons’ test results and the overall level of their scores on the relationship factors. This approach works extremely well, while simultaneously avoiding some of the problems in other systems that focus on one of these approaches in isolation.

For example look at the graph below that depicts two hypothetical couples with each factor shown horizontally (X-axis) and the scores vertically (Y-axis). These two couples can be seen similar or different, depending on which criterion is being used. The data of the couple shown in the top section are very similar scores (i.e., the values are roughly the same), but their test results (i.e., the overall shape of the lines) bear no relation to each other. By contrast, the data of the couple in the bottom section are very different (i.e., one person is “high” overall, whereas the other is “low”), but their test results are very similar.

The preceding thus illustrates that depending on which type of “similarity” is being used, quite different results are obtained. In the top portion of the graph the two people are similar because the two people are both “average” in Confidence, Openness, Easy Goingness, Self-Control and Family Orientation. The pair in the bottom section is “similar” because both people score highest on Family Orientation and lowest on Openness – i.e. the two sets of scores are ranked about the same.

These examples show that relying on only difference scores or just test result correlations can generate confusing and potentially misleading interpretations. Also, we found that using both types of similarity simultaneously better captures the people’s individual characteristics and the dynamics of a couple. Therefore, our similarity algorithm combines the two types of indices in a proprietary fashion.

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