Charts
Trait Correlation Between Overall Mood and Time Spent on Consuming News Activities
Overall Mood Charts
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Time Spent On Consuming News Activities Charts
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Relationship Charts
Time Spent on Consuming News Activities Following Overall Mood
Correlation Between Overall Mood and Time Spent on Consuming News Activities by Duration of Action
Correlation Between Overall Mood and Time Spent on Consuming News Activities by Onset Delay
Average Overall Mood Preceding Time Spent on Consuming News Activities
Average Time Spent on Consuming News Activities by Previous Overall Mood
Abstract
Time Spent On Consuming News Activities was generally 68% higher than average after an average of 3.16 out of 5 of Overall Mood over the previous 24 hours.
Aggregated data from 33 study participants suggests with a HIGH degree of confidence (p=0.255, 95% CI -0.275 to 0.162) that Overall Mood has a very weakly negative predictive relationship (R=-0.0567) with Time Spent On Consuming News Activities.
The highest quartile of Time Spent On Consuming News Activities measurements were observed following an average 3.08 out of 5 Overall Mood.
The lowest quartile of Time Spent On Consuming News Activities measurements were observed following an average 3.21 out of 5 of Overall Mood.
After an onset delay of 0 seconds, Time Spent On Consuming News Activities is typically 29% lower than average over the 24 hours following around 3.21 out of 5 Overall Mood.
Objective
Participant Instructions
Manual Recording Option
Create a reminder for Overall Mood here
and record it daily by enabling notifications or using
the reminder inbox here
.
Automatic Import of Time Spent on Consuming News Activities via RescueTime
Get RescueTime here
and use it to record your Time Spent on Consuming News Activities. Then,
import your data here
.
Manual Recording Option
Create a reminder for Time Spent on Consuming News Activities here
and record it daily by enabling notifications or using
the reminder inbox here
.
Design
This study is based on data donated by 33 participants. Thus, the study design is equivalent to the aggregation of 33 separate n=1 observational natural experiments.
Data Analysis
Overall Mood Pre-Processing
Overall Mood measurement values below 1 out of 5 were assumed erroneous and removed. Overall Mood measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Overall Mood so any gaps in data were just not analyzed instead of assuming zero values for those times.
Time Spent on Consuming News Activities Pre-Processing
Time Spent on Consuming News Activities measurement values below 0 seconds were assumed erroneous and removed. Time Spent on Consuming News Activities measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Time Spent on Consuming News Activities data were unrecorded 0 seconds measurement values.
Predictive Analytics
It was assumed that 0 seconds would pass before a change in Overall Mood would produce an observable change in Time Spent On Consuming News Activities.
It was assumed that Overall Mood could produce an observable change in Time Spent On Consuming News Activities for as much as 24 hours after the stimulus event.
Statistical Significance
Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Time Spent On Consuming News Activities is not statistically significant at a 95% confidence interval. This suggests that the Overall Mood value does not have a significant influence on the Time Spent On Consuming News Activities value.
After treatment, a 10.6% decrease (negative 99 seconds) from the mean baseline 15 minutes was observed. The relative standard deviation at baseline was 176.17%. The observed change was 0.33304 times the standard deviation.
A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).
Data Sources
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.
Time Spent On Consuming News Activities 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.
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.
Criteria For Causal Inference
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 (R = -0.0567) relationship between Overall Mood and Time Spent On Consuming News Activities.
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. 4882 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Overall Mood 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,
0 humans feel that there is a plausible mechanism of action for a relationship between Overall Mood and Time Spent On Consuming News Activities.
0 humans feel that any relationship observed between Overall Mood and Time Spent On Consuming News Activities 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.
Plausibility
A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 0 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Overall Mood and Time Spent On Consuming News Activities is coincidental.
Relationship Statistics
| Property | Value |
|---|---|
| Cause Variable Name | Overall Mood |
| Effect Variable Name | Time Spent On Consuming News Activities |
| Sinn Predictive Coefficient | 0.0027304361566966 |
| Confidence Level | HIGH |
| Confidence Interval | 0.21859046960141 |
| Forward Pearson Predictive Coefficient | -0.0567 |
| Critical T Value | 1.7301818181818 |
| Average Overall Mood Over Previous 24 hours Before ABOVE Average Time Spent On Consuming News Activities | 3.08 out of 5 |
| Average Overall Mood Over Previous 24 hours Before BELOW Average Time Spent On Consuming News Activities | 3.21 out of 5 |
| Duration of Action | 24 hours |
| Effect Size | very weakly negative |
| Number of Paired Measurements | 4882 |
| Optimal Pearson Product | 0.054247874858577 |
| P Value | 0.25478218317472 |
| Statistical Significance | 0.2869 |
| Strength of Relationship | 0.21859046960141 |
| Study Type | population |
| Analysis Performed At | 2021-08-30 |
| Number of Participants | 33 |
Overall Mood Info
| Property | Value |
|---|---|
| Variable Name | Overall Mood |
| Aggregation Method | MEAN |
| Analysis Performed At | 2020-09-12 |
| Duration of Action | 24 hours |
| Kurtosis | 3.3832907631011 |
| Maximum Allowed Value | 5 out of 5 |
| Mean | 3.1202433341482 out of 5 |
| Median | 3.1415600073553 out of 5 |
| Minimum Allowed Value | 1 out of 5 |
| Number of Aggregate Predictors | 6425 |
| Number of Aggregate Outcomes | 712 |
| Number of Measurements | 617070 |
| Number of Measurements (including those generated by tagged, joined, or child variables) | 561596 |
| Public | true |
| Onset Delay | 0 seconds |
| Standard Deviation | 0.38176118810538 |
| Unit | 1 to 5 Rating |
| User Variables | 9142 |
| UPC | 767674073845 |
| Variable Category | Emotions |
| Variable ID | 1398 |
| Variance | 0.30220747449488 |
Time Spent on Consuming News Activities Info
| Property | Value |
|---|---|
| Variable Name | Time Spent On Consuming News Activities |
| Aggregation Method | SUM |
| Analysis Performed At | 2020-09-23 |
| Duration of Action | 7 days |
| Filling Value | 0 |
| Kurtosis | 113.55700819215 |
| Maximum Allowed Value | 7 days |
| Mean | 9 minutes |
| Median | 4 minutes |
| Minimum Allowed Value | 0 seconds |
| Number of Aggregate Predictors | 626 |
| Number of Aggregate Outcomes | 164 |
| Number of Measurements | 29847 |
| Number of Measurements (including those generated by tagged, joined, or child variables) | 1836 |
| Public | true |
| Onset Delay | 0 seconds |
| Standard Deviation | 0.24480487213726 |
| Unit | Hours |
| User Variables | 84 |
| Variable Category | Activities |
| Variable ID | 111622 |
| Variance | 0.13567951404897 |