Mike P. Sinn
PRINCIPAL INVESTIGATOR
Mike P. Sinn

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For most, Overall Mood is generally highest after a daily total of 2.3 event of Facebook Likes over the previous 7 days.
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People with higher Facebook Likes usually have lower Overall Mood
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.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Mood_(psychology) on each day of the week.
This chart shows the typical value recorded for Mood_(psychology) for each month of the year.

Abstract

Aggregated data from 65 study participants suggests with a medium degree of confidence (p=0.22311365177171, 95% CI -0.235 to 0.374) that Facebook Likes has a very weakly positive predictive relationship (R=0.07) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 1.14 event Facebook Likes per day. The lowest quartile of Overall Mood measurements were observed following an average 0.64325434593384 event Facebook Likes per day.

Objective

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

Participant Instructions

Record your Facebook Likes daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Mood_(psychology) daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 65 participants. Thus, the study design is equivalent to the aggregation of 65 separate n=1 observational natural experiments.

Data Analysis

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

Mood_(psychology) Pre-Processing
Mood_(psychology) measurement values below 1 out of 5 were assumed erroneous and removed. Mood_(psychology) measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Mood_(psychology) so any gaps in data were just not analyzed instead of assuming zero values for those times.
Mood_(psychology) Analysis Settings

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

Data Sources

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.

Overall Mood 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 causation. 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 positive relationship between Facebook Likes and Overall Mood

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. 116 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Facebook Likes 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 3 feel that any relationship observed between Facebook Likes and Overall Mood 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 Facebook Likes
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.038706899120539
Confidence Level medium
Confidence Interval 0.30462369893421
Forward Pearson Correlation Coefficient 0.0692
Critical T Value 1.6910285725457
Total Facebook Likes Over Previous 7 days Before ABOVE Average Overall Mood 1.14 event
Total Facebook Likes Over Previous 7 days Before BELOW Average Overall Mood 0.64325434593384 event
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 116
Optimal Pearson Product 0.048345729171211
P Value 0.22311365177171
Statistical Significance 0.22190153911137
Strength of Relationship 0.30462369893421
Study Type population
Analysis Performed At 2019-01-30
Number of Participants 65

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

Mood_(psychology) Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-01-27
Duration of Action 24 hours
Kurtosis 3.7383708126619
Maximum Allowed Value 5 out of 5
Mean 3.1156748504321 out of 5
Median 3.1369047348216 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1149
Number of Measurements 605816
Onset Delay 0 seconds
Standard Deviation 0.56833853113207
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.43884384603152

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