For most, Facebook Likes is generally highest after an average of 63 percent of Productivity Pulse over the previous 7 days.
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People with higher Productivity Pulse usually have higher Facebook Likes
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Productivity Pulse on each day of the week.
This chart shows the typical value recorded for Productivity Pulse for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Facebook Likes on each day of the week.
This chart shows the typical value recorded for Facebook Likes for each month of the year.

Abstract

Aggregated data from 18 study participants suggests with a medium degree of confidence (p=0.20253787417911, 95% CI -0.616 to 0.531) that Productivity Pulse has a very weakly negative predictive relationship (R=-0.04) with Facebook Likes. The highest quartile of Facebook Likes measurements were observed following an average 34.38 percent Productivity Pulse. The lowest quartile of Facebook Likes measurements were observed following an average 40.715879696956 % Productivity Pulse.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Productivity Pulse and Facebook Likes. Additionally, we attempt to determine the Productivity Pulse values most likely to produce optimal Facebook Likes values.

Participant Instructions

Get RescueTime here and use it to record your Productivity Pulse. Once you have a RescueTime account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Record your Facebook Likes daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 18 participants. Thus, the study design is equivalent to the aggregation of 18 separate n=1 observational natural experiments.

Data Analysis

Productivity Pulse Pre-Processing
No minimum allowed measurement value was defined for Productivity Pulse. No maximum allowed measurement value was defined for Productivity Pulse. No missing data filling value was defined for Productivity Pulse so any gaps in data were just not analyzed instead of assuming zero values for those times.
Productivity Pulse Analysis Settings

Facebook Likes Pre-Processing
Facebook Likes measurement values below 0 event were assumed erroneous and removed. No maximum allowed measurement value was defined for Facebook Likes. It was assumed that any gaps in Facebook Likes data were unrecorded 0 event measurement values.
Facebook Likes Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Productivity Pulse would produce an observable change in Facebook Likes. It was assumed that Productivity Pulse could produce an observable change in Facebook Likes for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Productivity Pulse data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.

Facebook Likes data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply correlation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out non-existent relationships. For instance, if we discover no relationship between mood and an antidepressant this information is just as or even more valuable than the discovery that there is a relationship.
We can also take advantage of several characteristics of time series data from many subjects to infer the likelihood of a causal relationship if we do find a correlational relationship. The criteria for causation are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence.

The list of the criteria is as follows:
Strength (A.K.A. Effect Size)
A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. There is a very weakly negative relationship between Productivity Pulse and Facebook Likes

Consistency (A.K.A. Reproducibility)
Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 237 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Productivity Pulse values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate.

Specificity
Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

Temporality
The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

Biological Gradient
Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.

Plausibility
A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Productivity Pulse and Facebook Likes is coincidental.

Coherence
Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.

Experiment
All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

Analogy
The effect of similar factors may be considered.

Relationship Statistics

Property Value
Cause Variable Name Productivity Pulse
Effect Variable Name Facebook Likes
Sinn Predictive Coefficient 0.001609528732044
Confidence Level medium
Confidence Interval 0.57385630764177
Forward Pearson Correlation Coefficient -0.0425
Critical T Value 1.657
Average Productivity Pulse Over Previous 7 days Before ABOVE Average Facebook Likes 34.38 percent
Average Productivity Pulse Over Previous 7 days Before BELOW Average Facebook Likes 40.715879696956 percent
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 237
Optimal Pearson Product 0.065956777811312
P Value 0.20253787417911
Statistical Significance 0.83105555797617
Strength of Relationship 0.57385630764177
Study Type population
Analysis Performed At 2019-01-29
Number of Participants 18

Productivity Pulse Statistics

Property Value
Variable Name Productivity Pulse
Aggregation Method MEAN
Analysis Performed At 2018-12-22
Duration of Action 7 days
Kurtosis 3.1296042083271
Mean 39.953302739726 percent
Median 37.842465753425 percent
Number of Correlations 640
Number of Measurements 20510
Onset Delay 0 seconds
Standard Deviation 20.629621696992
Unit Percent
Variable ID 111162
Variance 498.59690902432

Facebook Likes Statistics

Property Value
Variable Name Facebook Likes
Aggregation Method SUM
Analysis Performed At 2019-01-27
Duration of Action 7 days
Kurtosis 395.65850570769
Mean 0.24544465928854 event
Median 0.011857707509881 event
Minimum Allowed Value 0 event
Number of Correlations 573
Number of Measurements 141294
Onset Delay 0 seconds
Standard Deviation 1.1730255645332
Unit Event
Variable ID 1883
Variance 3.8345376526322

https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn