Motivational, emotional, and behavioral correlates of fear of missing out- 1
Motivational, emotional, and behavioral correlates of fear of missing out – “Fear of Missing Out” (FOMO) is a pervasive sensation linked to mental and emotional stress.
Motivational, emotional, and behavioral correlates of fear of missing out
Our objective in the first study was to create a robust individual differences measure of fear of missing out.
More specifically, we wanted to create a brief,
self-report assessment that minimized participant burden and provided maximal information about an individual’s level of FOMO.
To achieve this goal we paired a the- or guided method with latent trait theory analysis,
to craft a robust assessment of fear of missing out.
To take full advantage of this approach we needed to start with a large pool of potential FOMO items.
Based on a review of popular and industry writing on FOMO (e.g., JWT, 2011; Morford, 2010; Wortham, 2011)
we drafted 32 items meant to reflect the fears, worries, and anxieties
people may have in relation to being in (or out of) touch with the events, experiences,
and conversations happening across their extended social circles.
We framed participants’ reading of and responses to scale items in terms of what really
reflected their general experiences instead of what they thought their experiences should be
Method
Participants were 672 men and 341 women (n = 1013), ranging in age from 18 to 62 years (M = 28.5, SD = 8.55).
All participants were fluent in English;
41.1% lived in the United States, 35.9% India, 5.6% Australia, 3.9% Canada, 3.2% United Kingdom,
and 10.3% re- sided in other nations (each not exceeding 2%).
Participants were recruited online through Amazon’s Mechanical Turk worker system;
each participant was compensated $0.30 each for completing the questionnaire.
Fear of Missing Out scale (FOMOs)
Participants completed basic demographic questions followed by the 32 candidate items drafted for the FOMOs by way of an HTML questionnaire.
Instructions stated: ‘‘Below is a collection of statements about your everyday experience.
Using the scale pro- vided please indicate how true each statement is of your general experiences.
Please answer according to what really reflects your experiences rather than what you think your experiences should be.
Please treat each item separately from every other item’’,
The presentation order of items was randomized for each participant and items were paired with a five-point.
Likert-type scale: 1 = ‘‘Not at all true of me’’, 2 = ‘‘Slightly true of me’’, 3 = ‘‘Moderately true of me’’,
4 = ‘‘Very true of me’’, and 5 = ‘‘Extremely true of me’’.
Factor and IRT analyses
The purpose of this study is to select a small set of unidimensional items
that reliably assess all levels of fear of missing out.
In line with this, the analytic approach we adopted to achieve this end was comprised of two steps.
First, we conducted a principle components analysis using a maximum likelihood estimation method including all the 32 candidate items.
Preliminary investigation of the data suggested a strong single factor solution,
but there were some items that had small suboptimal factor loadings,
and others that lowered the overall model fit considerably.
Following an iterative process of confirmatory factor analysis we eliminated suboptimal items
and retained 25 of the original 32 items.
These items produced a good fit to the data, v2 (275) = 1778.1, p < .01, RMSEA = .073, SRMR = .056.
Second, to further reduce the number of items while maximizing the sensitivity of the scale to all levels of the fear of missing out,
we estimated item parameters using an Item Response Theory (IRT; De Ayala, 2009) approach with PARSCALE (Muraki & Bock, 1998).
Specifically, we applied a graded response model to the data and estimated individual item information curves,
which describes the amount of information the individual items provides at various points along
the latent trait (i.e., fear of missing out) spectrum (Samejima, 1969).
From this we were able to identify 10 items that jointly showed high amount of information across a broad range of the FOMO continuum.
Fig. 1 provides a graphic depiction of the test information curve – the sum of the individual item information curve – of this final 10-item scale.
The latent trait was scaled with mean of 0 and SD = 1.0 and the maximum information
were observed at a slightly positive level of the latent trait (h = .51).
This indicates that the final scale is most sensitive to assessing participants with moderate to high fear of missing out.
However, overall the curve was quite well distributed,
suggesting that this scale can reliably assess participants with a broad range of FOMO (i.e., low, medium, and high).
We also computed latent trait scores for participants using the graded response model and correlated
them with scale scores computed by averaging the row rating scores of the final 10-item scale.
The resulting correlation (r = .95) indicated that overall FOMO scores for individuals
could be computed simply by averaging across the raw rating scores (M = 2.56, SD = 0.82).
The final scale items, presented in Appendix A, showed good consistency
(a = .87), as well as an acceptable distribution in terms of both
skewness (0.27) and kurtosis (—0.48).
FOMO in society
In our second study we recruited a representative adult sample to explore
how fear of missing out related to demographics, individual differences,
and social media engagement across the general population.
Our aims in this study were twofold. First, we wanted to examine how demographic factors,
such as age and gender related to FOMO on the population level.
Our second goal was to ap- ply the motivational framework of SDT to understand how individual
differences in need satisfaction and well-being related social media engagement.
This took the form of three research questions.
First we hypothesized that individuals who have had their basic needs for competence, autonomy, and relatedness satisfied,
on a day-to-day basis would be lower in fear of missing out.
Second, we hypothesized that FOMO would be negatively associated with indicators of psychological well-being.
That is, we expected that experiencing lower levels of general mood and lower overall life satisfaction would report higher levels of FOMO.
Finally, we hypothesized that FOMO is robustly related to social media use.
Specifically, we predicted that FOMO would mediate the relations (if any) linking individual variation,
in basic need satisfaction, general mood, and life satisfaction to behavioral engagement with social media.
Brief conclusion
In this study we recruited a large and diverse sample of participants
who rated a pool of items drafted to reflect individual differences in fear of missing out.
We pursued a data-driven approach guided by existing views of the phenomenon to create a self-report instrument of FOMO.
As a result, we were able to identify ten items that accurately tapped into between-persons variability in FOMO.
This assessment, labeled the Fear of Missing Out scale (or FOMOs),
Is brief and is sensitive to those who evince low, moderate, and high levels of fear of missing out construct as an individual difference.
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