Time spent online: Latent profile analyses of emerging adults’ social media use

Abstract

Studies of youth social media use (SMU) often focus on its frequency, measuring how much time they spend online. While informative, this perspective is only one way of viewing SMU. Consistent with uses and gratification theory, another is to consider how youth spend their time online (i.e., degree of engagement). We conducted latent profile analyses of survey data from 249 U.S. emerging adults (ages 18–26) to explore their SMU in terms of frequency and engagement. We derived separate 3-profile solutions for both frequency and engagement. High frequency social media users tended to be women and to have more Facebook friends. Highly engaged users (i.e., those most interactive online) tended to be White and more highly educated. Findings from this exploratory study indicate that youth SMU frequency and SMU engagement warrant separate consideration. As SMU becomes more ingrained into the fabric of daily life, it is conceivable that engagement may be a more meaningful way to assess youth SMU, especially in relation to the digital divide, since it can be used to meet important needs, including social interaction, information exchange, and self-expression.

1. Introduction

Social media use (SMU) has become the most popular daily activity for the majority of emerging adults (18–29 years old) in the U.S.: 90% of U.S. emerging adults use social media every day (Perrin, 2015) and 24% of adolescents (13–17 years old) use it “almost constantly” (Lenhart, 2015, p. 2). Given the daily prominence, the current study sought to examine emerging adults’ SMU more thoroughly. To this end, we used a person-centered analysis, latent profile analysis, to explore the dimensions of their SMU in terms of frequency of use and degree of engagement. Social media is defined as “a group of internet-based applications that build on the ideological and technological foundations of Web 2.0 and permit the continuous creation and exchange of User Generated Content (UGC)” (Kaplan & Haenlein, 2010, p. 61). Social media includes blogs (e.g., Tumblr), virtual game and social worlds (e.g., World of War Craft and Second Life, respectively), collaborative projects (e.g., Wikipedia), content communities (e.g., YouTube), and social networking sites (SNS) (e.g., Facebook) (Kaplan & Haenlein, 2010). Of the various types, SNS are the most popular, especially among youth (Kaplan & Haenlein, 2010). Facebook has been the dominant SNS in the U.S. for almost a decade (Greenwood, Perrin, & Duggan, 2016; Lenhart, Purcell, Smith, & Zickurh, 2010). In fact, it dominates by a substantial margin: 88% of online American emerging adults use Facebook, which is more than double the share of use reported for other popular sites (e.g., 36% use Twitter, 36% use Pinterest, 59% use Instagram, and 29% use LinkedIn; Greenwood et al., 2016). Thus, although other SNS may become trendy, they tend to complement Facebook, not displace it (Greenwood et al., 2016; Lenhart et al., 2010). Youth are especially likely to be multi-site users: in 2015, 71% reported using two or more SNS each day (Lenhart, 2015).

Social media is a central conduit to information, advice, and relationships for youth (Coyne, Padilla-Walker, & Howard, 2013; Davis, 2012). It can expand their reach, enrich the quality of their social networks, and facilitate social engagement (Ellison, Steinfield, & Lampe, 2007) during a period when peers are a powerful source of influence (Borsari & Carey, 2001, 2003). Social media use is also associated with important psychological dividends for youth, such as diminished loneliness (Lee, Noh, & Koo, 2013), higher self-esteem (Steinfield, Ellison, & Lampe, 2008), and perceived social support (Best, Manktelow, & Taylor, 2014). Finally, SMU may enable identity development. Spies Shapiro and Margolin (2014) found that SNS provided ethnic and sexual/gender minority youth with a safe and supportive environment to explore their identities and forge communities, which importantly, often crossed over into offline life.

Social media use may be widespread in the U.S., but it is not uniform across demographic groups. For example, emerging adults are the most likely age group to report using social media (Perrin, 2015). Perrin also found that the previously observed gender difference that women use social media more than men do, is no longer statistically significant. Nevertheless, gender differences in types of use remain, with women preferring SNS and men favoring virtual games (Lenhart, 2015). The reliance on undergraduate samples means that much of our understanding of SMU among emerging adults neglects the experiences of those not attending college (Coyne et al., 2013). Similar to gender, studies have not revealed significant racial or ethnic differences in SMU in general, but have documented differences in preferences for specific sites (Krogstad, 2015). For example, Latino/a and African American users report a preference for Instagram while Pinterest is more popular among White users (Krogstad, 2015). Socioeconomic status (SES) is also correlated with SMU: 78% of users with a household income of $75,000 or more use social media compared to 56% of users with income below $30,000 a year; and individuals with at least some college experience use social media more than those with only a high school education or less (Perrin, 2015). Finally, due to the extensive reach of social media, particularly Facebook, the scope and depth of peer networks is growing (Davis, 2012). Consequently, emerging adults report having more friends than ever before (Davis, 2012). Previous studies have found that the number of Facebook friends varies greatly across users (range: 46–2045; Ellison, Steinfield, & Lampe, 2011; Moreno et al., 2014; Steinfield et al., 2008).

1.1. Social media use: distinguishing engagement from frequency

A significant body of research indicates the prominence of SMU in youths’ daily lives, but SMU warrants more discerning examination. The vast majority of studies document youth SMU in quantitative terms of its prevalence and incidence, such as minutes spent online each day and frequency of checking SNS (Marshall, Gorely, & Biddle, 2006; Pempek, Yermolayeva, & Calvert, 2009). While informative, these metrics may not reflect the subjective meaning or centrality of SMU in an individual’s life.

Engagement is another possible dimension of SMU. The “participative” (Korda & Itani, 2013, p. 15) design of social media, specifically SNS, allow for various modes of engagement, including but not limited to: reposting other users’ content; responding to others’ content; filtering content by blocking or untagging; and posting original text, images, and/or videos (Hampton, Sessions Goulet, Rainie, & Purcell, 2011). This flexibility indicates that two individuals with equivalent, near-constant social media access might both log significant time on SNS, but while one might habitually engage, another might lurk.

Uses and gratification theory (U&G) was developed and used to explain diverse media use practices (Katz & Foulkes, 1962; Katz, Gurevitch, & Haas, 1973; McQuail, 1994). The basic premise of U&G is that people are active and intentional in their media use (Katz & Foulkes, 1962; Katz et al., 1973), engaging those forms that are most likely to meet specific needs (e.g., social interaction, information seeking and sharing, entertainment, self-expression, and surveillance of others; Arnett, 2007; Katz & Foulkes, 1962; Katz et al., 1973; McQuail, 1994; Whiting & Williams, 2013). Today, U&G is increasingly used by researchers to understand SMU (Coyne et al., 2013; Wang, Tchernev, & Solloway, 2012) and related behaviors, such as photo sharing on SNS (Malik, Dhir, & Nieminen, 2016; Pittman & Reich, 2016).

As SMU proliferates daily life, it becomes especially important to understand the time youth spend online. Notably, this means studying not only how much time they spend online (i.e., frequency of SMU), but also how that time is spent (i.e., degree of SMU engagement). Studies that accommodate the breadth, diversity, and interrelation among modes of SMU (e.g., posting captioned photos might spark multiple and ongoing online exchanges with others across sites) while examining not just SMU frequency, but also SMU engagement, are timely and relevant.

1.2. Current study: person-centered analysis of SMU

Empirical literature regarding youth SMU is dominated by descriptive overviews and variable-centered findings (e.g., see Nadkarni & Hofmann, 2012; Tosun, 2012; Wang et al., 2012). These correlational approaches preclude more detailed investigations, especially pertaining to interindividual variation (Jung & Wickrama, 2008). Person-centered approaches, such as latent profile analysis (LPA), are timely and an important complement to existing literature. As a cross-sectional approach, LPA examines the relations among individuals rather than variables (Muthén & Muthén, 2000). The goal is to classify individuals into distinct meaningful groups (i.e., latent profiles) based on self-reported response patterns to continuous variables (Jung & Wickrama, 2008). Therefore, LPA can show how SMU varies across groups of individuals where group membership is not observed (Khang, Han, & Eyun-Jung, 2014). Finally, LPA permits simultaneous tests for differences among profiles and individual characteristics (Clark & Muthén, 2009).

We used LPA as a method of exploring the number and types of latent profiles among a sample of emerging adult social media users. We were also interested in examining if derived SMU profiles (frequency and engagement) differed by demographic and individual characteristics (e.g., SES, gender, and number of Facebook friends). In keeping with the exploratory nature of this study, we initiated analyses with only two hypotheses: (1) based on preliminary analyses, we hypothesized that we would find distinct profiles for SMU frequency and SMU engagement; and (2) in accordance with previous research (e.g., see, Lenhart, 2015; Perrin, 2015), we hypothesized that women and those with higher SES would be report more frequent SMU.

2. Method

2.1. Participants

Participants were 249 emerging adults aged 18–26, residing in the U.S., and with experience using at least one SNS. Table 1 presents the characteristics of the sample. The average age of participants was 23.06 years (SD = 1.91; range 18–26). Slightly more than half were men (56.2%) and not students (52.6%). Participants were predominantly White (67.9%) with a median annual household income of $30,000 – $39,000 (SD = 2.29), and 90% reported having at least some college education. Finally, 88.4% of sample used Facebook and on average had 101–250 Facebook friends (M = 2.26; SD = 1.21).

Table 1.

Characteristics of Participants.

Participant Characteristics	N (%)
Age (n = 249; range 18–26)	M = 23.06 (SD = 1.91)
Gender (n = 249)
Men	140 (56.2)
Women	109 (43.8)
Student status (n = 249)
No	131 (52.6)
Yes	118 (47.4)
Race (n = 248)
White	169 (67.9)
Person of color	79 (31.7)
Asian/South Asian/Pacific Islander	38 (15.3)
Black/African American	15 (6.0)
Latina/Chicana/Hispanic	17 (6.8)
Other	10 (4.0)
Income (n = 245)
Less than $20,000	63 (25.3)
$20,000 – $29,999	37 (14.9)
$30,000 – $39,999	35 (14.1)
$40,000 – $49,999	33 (13.3)
$50,000 – $59,999	19 (7.6)
$60,000 – $74,999	17 (6.8)
$75,000 – $99,999	24 (9.6)
$100,00 or more	17 (6.8)
Education (n = 248)
High school/GED or less	25 (10.0)
Some college	100 (40.0)
Associate′s degree	25 (10.0)
Bachelor′s degree	87 (35.0)
Master′s degree/Doctorate	11 (5.0)
Facebook friends (n = 249; range: 0–4)	M = 2.26 (SD = 1.21)

Note. Gender was coded: 0 = man; 1 = woman. Student status was coded: 0 = not a student; 1 = currently a student. Race was coded: 0 = person of color, 1 = White.

The mean of 2.26 corresponds to 101–250 Facebook friends.

2.2. Measures

2.2.1. Participant characteristics

The survey included questions about age, gender, student status, race, current annual household income (less than $20,000 – $100,000 or more) and highest level of education (high school/GED or less – masters/doctorate). We also asked how many Facebook friends participants had at the time of data collection (0 = none, I don’t have Facebook, 1 = 1–100, 2 = 101–250, 3 = 251–500, and 4 = more than 500). For analyses, we dummy coded each educational attainment (0 = no, 1 = yes), and race (0 = person of color, 1 = White).

2.2.2. SMU frequency

We assessed SMU frequency using five items adapted from the Pew Research Center. We asked participants to report: (a) how many minutes a day they spent on social media (in 10 min increments, starting at 1 = 0–10 through 13 = more than 120 min/day); (b) how often they visited any SNS (1 = less often, 2 = every few days, 3 = 3 to 5 times a week, 4 = about once a day, and 5 = several times a day); (c) if SMU was part of their daily routine (0 = no, not at all, 1 = yes, some days, 2 = yes, almost every day, and 3 = yes, every day); (d) which days of the week they typically used social media; and (e) for which SNS, from a list of the 15 most used sites at the time of data collection (Perrin, 2015; Pew Research Center, 2015), they maintained an account. For analysis, count variables were created for both the number of SNS used and the number of days per week SMU was reported. Although these were counts, they were approximately normally distributed and treated as continuous.

2.2.3. SMU engagement

We assessed participants’ degree of engagement using ten items adapted from the Pew Research Center. Unless specified, responses were measured on a 4-point scale (0 = never, 1 = rarely, 2 = sometimes, and 3 = often). These ordinal variables were approximately normally distributed and treated as continuous. Participants were asked to report how often they: (a) commented on their friends’ posts (0 = never to 4 = daily); (b) shared videos; (c) shared pictures; (d) updated their status (0 = never to 4 = daily); and (e) sent other users private messages (through social media) (0 = never to 5 = several times a day); (f) removed their name from pictures they were tagged in; (g) deleted comments friends made; (h) posted content they later regretted sharing; (i) blocked people; and (j) deleted people from their friend list.

2.3. Procedure

We collected data for the current study using Amazon Mechanical Turk (MTurk). MTurk is an online labor market where requesters (e.g., research investigators and/or businesses) post web-based, time-limited jobs (human intelligence tasks [HITs]) for the anonymous on-demand human workforce (Turkers) to complete (Paolacci, Chandler, & Ipeirotis, 2010). Turkers select from the list of available HITs they want and are qualified to complete in return for a wage (Mason & Suri, 2012). For further information regarding MTurk’s design and purpose, (see: Berinsky, Huber, Lenz, & Alvarez, 2012; Paolacci et al., 2010; Paolacci & Chandler, 2014).

MTurk shares many of the same benefits as any online convenient sample (e.g., minimal cost, rapid data collection, and easy access to an anonymous pool of participants) and it has been found to yield psychometrically sound, high quality data, thereby increasing its popularity within social science research as a way to recruit study participants (Berinsky et al., 2012; Buhrmester, Kwang, & Gosling, 2011; Crump, McDonnell, & Gureckis, 2013; Goodman, Cryder, & Cheema, 2013; Hauser & Schwarz, 2015). Of particular relevance to the current study, U.S. Turkers primarily consist of emerging adults (25% are 25 or younger; Ipeirotis, 2010) who are more representative of internet users (Crump et al., 2013; Paolacci et al., 2010) and are more diverse in terms of race, ethnicity, geographic distribution, and SES than conventional samples, including undergraduate students (Berinsky et al., 2012; Buhrmester et al., 2011; Crump et al., 2013; Goodman et al., 2013; Mason & Suri, 2012; Paolacci et al., 2010), community samples (Goodman et al., 2013), and visitors to online discussion boards (Bartneck, Duenser, Moltchanova, & Zawieska, 2015; Paolacci et al., 2010). Given the demographic profile of the Turker pool and our explicit interest in emerging adult social media users, we judged MTurk to be a rational and efficient means of collecting data for the current study.

Data for this analysis were drawn from an IRB-approved survey. We first administered a screening question to identify prospective participants between the ages of 18 and 26. The question was available only to Turkers residing in the United States. Of the 375 participants who met the age criterion, 255 (68%) completed the survey. Six of those were excluded from the current study, as they reported having no SMU experience. This resulted in a final sample of 249 users. The HIT for the current study ran on MTurk between April and May 2015. The study survey was hosted by SurveyMonkey.com and the link for it was provided within the study HIT. The survey took 30 min to complete and Turkers were paid $4.00, issued through the MTurk system.

2.4. Analysis plan

The first goal of the analyses was to distinguish distinct profiles of social media users. SMU profiles were derived through LPA of participants’ responses to five frequency items and ten engagement items. We ran one-through five-class models for both sets of SMU indicators (i.e., frequency and engagement, resulting in 10 models in total).

We conducted analyses using MPlus version 7.0 (Muthén & Muthén, 1998–2012). LPA can accommodate deviations from normality and maximum likelihood estimation accounts for minor variations in the variances of the latent classes (McLachlan & Peel, 2000). Maximum likelihood estimation was used to handle missing data (Schafer & Graham, 2002). Our models were compared using four noted fit indices recommended by Nylund, Asparouhov, and Muthén (2007), and Muthén and Muthén (2000). First, the Lo-Mendell-Rubin likelihood ratio test of model fit (LMR; Lo, Mendell, & Rubin, 2001) statistically tests a model with k profiles to a model with k-1 profile. A significant p-value indicates that the current model provides better fit than a model with one less profile (Jung & Wickrama, 2008). Second, the saBIC (Sclove, 1987) helps to reduce the typical BIC sample size penalty (Sclove, 1987). When compared across plausible models, the model with the saBIC closest to zero indicates better fit (Muthén & Muthén, 2000). Entropy is measured 0 to 1, with higher values (0.80 or greater) indicating better classification quality (Celeux & Soromenho, 1996). Lastly, the average latent profile probabilities that are close to 1 on the primary diagonal and 0 in the off-diagonal represent good fit (Jung & Wickrama, 2008). Final model selection for both SMU frequency and SMU engagement were based on comparative fit indices, parsimony, and substantive interpretability.

Wald tests of mean equality were used to determine whether profiles had statistically significant differences on each individual characteristic (Muthén & Muthén, 2010). The benefit of using Wald tests is that these analyses are conducted simultaneously with LPA (Clark & Muthén, 2009). Clark and Muthén (2009) refer to this technique of as the pseudo-class draws method (for more further information, see Asparouhov & Muthén, 2012). This method was chosen over other multistep approaches because results from these tests are easily interpreted and simulation studies have shown that it works well when entropy is high (Clark & Muthén, 2009). To evaluate the effect size for the Wald tests, Cramer’s V was calculated. Cramer’s V is a well-established test for gauging strength of relationships. Cohen (1992) recommends the following ranges when the chi-square test has two degrees of freedom: small (0.07–0.20); medium (0.21–0.34); and large (>0.35).

3. Results

We present correlations of all indicator variables in Table 2. All but one of the SMU frequency indices correlated at 0.21 or above. Only number of days in a week reported SMU (days used SM) and the number of SNS reported (quantity of SNS) were uncorrelated. Similarly, all of the SMU engagement indices correlated at 0.13 or above. The five SMU frequency indices were correlated at.18 or below to four SMU engagement indices.

Table 2.

Correlations among Frequency and Engagement Items.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Time in mins	–
Daily routine	0.45**	–
SNS often	0.47**	0.57**	–
Quantity of SNS	0.21**	0.23**	0.22**	–
Days used SM	0.29**	0.40**	0.33**	0.08	–
Comment on posts	0.03	0.00	0.04	−0.01	−0.05	–
Share videos	0.09	0.05	0.09	0.13*	−0.11	0.39**	–
Share pictures	0.18**	0.10	0.11	0.02	−0.05	0.53**	0.51**	–
Status updates	0.08	0.04	0.05	−0.04	−0.07	0.56**	0.38**	0.52**	–
Send private msgs	0.03	0.04	−0.13*	−0.03	−0.03	0.36**	0.14**	0.30**	0.28**	–
Untag pictures	0.01	0.11	0.02	−0.02	−0.06	0.21**	0.13*	0.13*	0.13*	0.19**	–
Delete comments	0.01	0.01	−0.04	−0.02	−0.09	0.25**	0.23**	0.29**	0.23**	0.23**	0.59**	–
Regret sharing	−0.05	−0.06	−0.01	0.02	0.15*	0.21**	0.31**	0.21**	0.32**	0.16*	0.29**	0.38**	–
Block people	−0.03	−0.06	−0.03	0.07	−0.09	0.31**	0.32**	0.37**	0.27**	0.17**	0.39**	0.58**	0.35**	–
Delete people	−0.08	−0.03	−0.03	0.01	−0.17**	0.32**	0.21**	0.29**	0.23**	0.24**	0.41**	0.52**	0.29**	0.53**	–

Note. Mins = minutes. SNS = social networking sites. SM = social media. Msgs = messages. The correlation results are summarized on p. 11.

^*p < 0.05,

^**p < 0.01.

3.1. Comparative model fit

We report model fit indices in Tables 3 and 4. For both SMU frequency (see Table 3) and SMU engagement (see Table 4), the three-profile solutions provided the best overall model fit to the data. For SMU frequency (Table 3), the three-profile solution had the highest entropy value (0.98) and the highest average latent class probabilities (0.99–1.00). Although the saBIC for the three-profile solution was slightly higher (4696.15) than that of the four- (4634.51) and five-profile solutions (4334.96), the three-profile solution was chosen upon examination of the LMR. The LMR tests indicated that the two-profile solution was an improvement over the one-profile solution (p = 0.00), and the three-profile solution was an improvement over the two-profile solution (p = 0.02). Further, the four-profile model was not an improvement over the three-profile solution (p = 0.43) nor was the five-profile solution over the four-profile model (p = 0.45). Finally, the class breakdown for the three-profile model was acceptable, with 55% of the sample in the high frequency profile, 35% in the medium frequency profile, and 10% in the low frequency profile.

Table 3.

Comparative Latent Profile Analysis Overall Model Fit Statistics for SMU Frequency.

	Overall model fit
	1-profile	2-profile	3-profile	4-profile	5-profile
saBIC	5074.64	4843.93	4696.15	4634.51	4334.96
Entropy	1.00	0.96	0.98	0.94	0.97
ALC-Prob	–	0.98–0.99	0.99–1.00	0.96–0.98	0.95–1.00
% sample/class
1	249 (100%)	42 (17%)	23 (10%)	69 (28%)	22 (9%)
2		207 (83%)	88 (35%)	41 (16%)	96 (38%)
3			138 (55%)	96 (39%)	20 (8%)
4				43 (17%)	42 (17%)
5					69 (28%)
LMR (p value)	–	237.61 (0.00)	157.12 (0.02)	93.14 (0.43)	25.30 (0.45)

Note. saBIC = sample size adjusted Bayesian information criterion. ALC-Prob = average latent profile probability for most likely latent profile membership (row) by latent profile (column). % sample/class = final profile counts and proportions for the latent profiles based on their most likely latent profile membership. LRM = Lo-Mendell-Rubin adjusted likelihood ratio test. N = 249 for all one- through five-profile SMU frequency models.

Table 4.

Comparative Latent Profile Analysis Overall Model Fit Statistics for SMU Engagement.

	Overall model fit
	1-profile	2-profile	3-profile	4-profile	5-profile
saBIC	6540.85	6189.62	6020.25	5908.05	5848.57
Entropy	1.00	0.79	0.89	0.82	0.82
ALC-Prob	–	0.92–0.94	0.93–0.96	0.87–0.99	0.87–0.99
% sample/class
1	248 (100%)	91 (37%)	69 (28%)	72 (29%)	42 (17%)
2		157 (63%)	167 (67%)	87 (35%)	58 (24%)
3			12 (5%)	78 (32%)	11 (4%)
4				11 (4%)	68 (27%)
5					69 (28%)
LMR (p value)	–	370.89 (0.059)	191.98 (0.00)	135.74 (0.31)	83.88 (0.62)

Note. saBIC = sample size adjusted Bayesian information criterion. ALC-Prob = average latent profile probability for most likely latent profile membership (row) by latent profile (column). % sample/class = final profile counts and proportions for the latent profiles based on their most likely latent profile membership. LRM = Lo-Mendell-Rubin adjusted likelihood ratio test. There was one case was missing data on all variables except x-variables, thus not included in analysis, resulting in N = 248 for SMU engagement one- through five-profile models.

For the SMU engagement models (Table 4), similar to the frequency models, the three-profile solution had the highest entropy (0.89) as well as the highest average latent class probabilities (0.93–0.96). Although the three-profile model had a slightly higher saBIC (6020.25) than that of the four- and five-profile models (5908.05 and 5848.57, respectively), the three-profile solution was chosen upon examination of the LMR. The LMR test affirmed that the three-profile was an improvement over the two-profile model (p = 0.00), which was not an improvement over the one-profile (p = 0.06). Further, the four-profile solution was not an improvement over the three-profile model (p = 0.31) nor was the five-profile solution over the four-profile model (p = 0.62). Finally, the three-profile model for engagement had acceptable profile breakdown, with 67% of the sample in the largest profile (moderately engaged), 28% in the mid-sized profile (minimally engaged), and 5% in the smallest profile (highly engaged). Despite its small size, we judged the highly engaged profile to merit inclusion based on previous research that found relatively few users post a large amount of content (e.g., Beullens & Schepers, 2013; Moreno et al., 2010).

3.2. Description of best fitting models

The means and standard errors estimated for each of the variables used in deriving the profiles are provided in Table 5 (SMU frequency) and Table 6 (SMU engagement). These tables also include the means and standard errors for the Wald tests of equality.

Table 5.

Descriptive Information for 3-Profile Model of SMU Frequency.

	Low Frequency n = 23 (10%) M (SE)	Medium Frequency n = 88 (35%) M (SE)	High Frequency n = 138 (55%) M (SE)
Variables defining latent profiles
Time in minutes (range: 1–13)	3.03 (0.52)	3.29 (0.22)	7.18 (0.31)
Daily routine (range: 0–3)	0.93 (0.27)	1.58 (0.10)	2.52 (0.06)
SNS often (range: 1–5)	1.73 (0.10)	3.78 (0.05)	5.00 (0.00)
Quantity of SNS (range: 1–15)	5.32 (0.57)	5.30 (0.26)	6.77 (0.22)
Days used SM (range: 0–7)	3.29 (0.51)	4.28 (0.23)	5.37 (0.18)
Auxiliary variables
Age (range: 18–26)	23.12 (0.42)	23.08 (0.20)	23.04 (0.16)
Gender*	0.34 (0.10)	0.33 (0.05)	0.52 (0.04)
Student Status	0.53 (0.11)	0.45 (0.05)	0.48 (0.04)
Race	0.57 (0.10)	0.70 (0.05)	0.69 (0.04)
Income (range: 1–8)	3.73 (0.39)	3.66 (0.27)	3.48 (0.19)
Education	2.70 (0.21)	2.80 (0.13)	2.88 (0.10)
High school/GED or less	0.14 (0.07)	0.08 (0.04)	0.11 (0.03)
Some college	0.44 (0.10)	0.38 (0.05)	0.41 (0.04)
Associate′s degree	0.13 (0.07)	0.09 (0.03)	0.10 (0.03)
Bachelor′s degree	0.25 (0.09)	0.41 (0.05)	0.32 (0.04)
Master′s degree/Doctorate	0.04 (0.04)	0.02 (0.02)	0.06 (0.02)
Facebook friends** (range: 0–4)	1.62 (0.20)	2.08 (0.13)	2.50 (0.10)

Note. SNS = social networking sites. SM = social media. Gender was coded: 0 = man; 1 = woman. Race was coded: 0 = person of color; 1 = White. Income ranged from 1 = less than $20,000 to 8 = $100,000 or more. Originally, education was coded 1 = high school/GED, or less to 5 = master’s degree/doctorate. After, education variables were dummy coded 0 = no, 1 = yes. Omnibus Wald test mean and standard errors are reported in the table.

^*p < 0.05,

^**p < 0.01 for significant omnibus tests.

Table 6.

Descriptive Information for 3-Profile Model of SMU Engagement.

	Minimally Engaged n = 69 (28%) M (SE)	Moderately Engaged n = 167 (67%) M (SE)	Highly Engaged n = 12 (5%) M (SE)
Variables defining latent profiles
Comment on posts (range: 0–4)	0.93 (0.21)	2.25 (0.09)	3.13 (0.20)
Share videos (range: 0–3)	0.14 (0.06)	0.68 (0.07)	2.55 (0.30)
Share photos (range: 0–3)	0.62 (0.12)	1.81 (0.09)	2.53 (0.17)
Status updates (range: 0–4)	0.89 (0.18)	2.03 (0.09)	3.00 (0.28)
Send private msgs (range: 0–5)	1.24 (0.20)	2.42 (0.10)	2.71 (0.43)
Untag photos (range: 0–3)	0.53 (0.16)	1.16 (0.08)	2.39 (0.20)
Delete comments (range: 0–3)	0.14 (0.06)	0.84 (0.07)	2.28 (0.26)
Regret sharing (range: 0–3)	0.27 (0.09)	0.81 (0.07)	2.00 (0.28)
Block people (range: 0–3)	0.34 (0.13)	0.96 (0.05)	2.28 (0.20)
Delete friends (range: 0–3)	0.58 (0.15)	1.25 (0.06)	2.36 (0.22)
Auxiliary variables
Age (range: 18–26)	23.05 (0.23)	23.08 (0.15)	22.73 (0.64)
Gender	0.40 (0.06)	0.46 (0.04)	0.35 (0.14)
Student Status	0.45 (0.06)	0.48 (0.40)	0.40 (0.15)
Race*	0.66 (0.06)	0.68 (0.04)	0.91 (0.09)
Income (range: 1–8)	3.63 (0.31)	3.49 (0.18)	4.30 (0.69)
Education**	2.05 (0.12)	3.07 (0.08)	4.51 (0.01)
High school/GED or less**	0.08 (0.04)	0.12 (0.03)	0.00 (0.00)
Some college	0.38 (0.06)	0.40 (0.04)	0.58 (0.15)
Associate′s degree**	0.11 (0.04)	0.10 (0.02)	0.00 (0.01)
Bachelor′s degree	0.39 (0.06)	0.33 (0.04)	0.42 (0.15)
Master′s degree/Doctorate	0.03 (0.02)	0.04 (0.02)	0.00 (0.01)
Facebook friends (range: 0–4)	2.34 (0.16)	2.22 (0.10)	2.16 (0.35)

Note. Msgs = messages. Gender was coded: 0 = man; 1 = woman. Race was coded: 0 = person of color; 1 = White. Income ranged from 1 = less than $20,000 to 8 = $100,000 or more. Originally, education was coded 1 = high school/GED, or less to 5 = master/doctorate. After, education variables were dummy coded 0 = no, 1 = yes. Omnibus Wald test mean and standard errors are reported in the table.

^*p < 0.05,

^**p < = 0.01 for significant omnibus tests.

3.2.1. SMU frequency

As depicted in Table 5, the low frequency profile (n = 23, 10%) was associated with the lowest mean values on all indices. Judging from the mean scores, the low frequency profile was the least likely profile to report that SMU was part of one’s daily routine (0.93 = yes, some days), the lowest mean value of how often SNS were visited (1.73 = every few days), and the smallest number of reported days in a week social media was used (3 out of 7 days) compared to both the moderate and high frequency profiles. When comparing the amount of time in minutes spent using social media and number of sites used, the low frequency profile and the medium frequency profile had very similar means values (approximately 21–30 min/day and 5 out of 15 SNS, respectively).

The medium frequency profile (n = 88, 35%) was associated with lower mean values on all indicators compared to the high frequency profile. Specifically, the medium frequency profile was associated with less frequent SNS use (3.78 = about once a day), lower reported daily use (1.58 = yes, almost every day), lower number of SNS used (5 out 15 SNS), and fewer days in a week (4 out 7 days a week).

The high frequency profile (n = 138, 55%) was associated with highest mean values on all indicators compared to the low and medium frequency profiles. The high frequency profile was associated with the highest average minutes of use a day (approximately 61–70 min/day), highest average report of daily routine (2.52 = yes, every day), and the highest mean rate of SNS usage (5 = several times a day).

3.2.2. SMU engagement

As outlined in Table 6, the minimally engaged profile (n = 69, 28%) was associated with the lowest mean values for all indices compared to both the moderately and highly engaged profiles. For example, the minimally engaged profile was associated with the lowest mean value for commenting on friends’ posts (0.93 = rarely), sharing status updates (0.89 = rarely), and sharing videos and photos (0.14 and 0.62 = rarely).

The moderately engaged profile (n = 167, 67%) was associated with lower mean ratings on all indices compared to the highly engaged profile. Most notably, the moderately engaged profile had substantially lower mean values for sharing videos compared to the highly engaged profile (0.68 = rarely vs. 2.55 = often, respectively).

The highly engaged profile (n = 12, 5%) was associated with the highest mean ratings on all indicators. Aside from the previously noted pronounced difference in video sharing, the highly engaged profile was associated with the highest mean rate of commenting on friends’ posts (3.13 = often), sharing videos, pictures, and status updates (2.55, 2.53, and 3.00 all = often, respectively), and sending private messages (2.71 = often) compared to the moderately and minimally engaged profiles.

3.3. Wald tests of association: latent profiles and background characteristics

3.3.1. SMU frequency

We present the results of the Wald tests of equality for the association between SMU frequency profiles and background characteristics in Table 5. Only the omnibus test for gender (χ² = 8.56, p = 0.01, Cramer’s V = 0.13) and number of Facebook friends (χ² = 16.50, p = 0.00, Cramer’s V = 0.18) were significant, with small to medium effect sizes. Pairwise comparisons for gender revealed one significant finding: the high frequency profile was associated with significantly more women compared to the low frequency profile, with a small to medium effect size, χ² = 7.94, p = 0.01, Cramer’s V = 0.13. The pairwise comparisons for the number of Facebook friends revealed that the high frequency profile had significantly higher number of friends compared to the both the low (χ² = 6.25, p = 0.01, Cramer’s V = 0.17) and medium frequency profiles (χ² = 14.37, p = 0.00, Cramer’s V = 0.11), with small to medium effect sizes. The pairwise comparison between the low and medium frequency profiles was approaching significance, with a small effect size, such that, the low frequency profile had fewer friends compared to the medium frequency profile (χ² = 3.41, p = 0.07, Cramer’s V = 0.08).

3.3.2. SMU engagement

Table 6 contains the results of the Wald tests of equality for the associations between SMU engagement profiles and background characteristics. The omnibus test was significant for race, in the small to medium effect size range, χ² = 6.60, p = 0.04, Cramer’s V = 0.12. The pairwise comparisons revealed that the highly engaged profile was significantly more likely to be White compared to both the minimally (χ² = 5.88, p = 0.02, Cramer’s V = 0.11) and the moderately engaged profiles (χ² = 6.42, p = 0.01, Cramer’s V = 0.11), in small to medium effect size ranges. The pairwise test comparing the minimally and moderately engaged profiles was not significant, χ² = 0.04, p = 0.84.

The omnibus test for education was also significant, with a large effect size, χ² = 75.11, p = 0.00, Cramer’s V = 0.39. Significant differences were found for each pairwise comparison. Specifically, the highly engaged profile was significantly more likely to have higher education compared to both the minimally and moderately engaged profiles, in the medium to large effect size ranges (χ² = 71.93, p = 0.00, Cramer’s V = 0.38, and χ² = 27.18, p = 0.00, Cramer’s V = 0.23, respectively). Additionally, the moderately engaged profile was associated with significantly higher education compared to the minimally engaged profile, with a large effect size, χ² = 46.03, p = 0.00, Cramer’s V = 0.30. In an effort to understand the differences further, each level of education was dummy coded and examined separately, revealing two notable significant differences. First, the omnibus test for high school/GED or less was significant in the low to medium effect size range, χ² = 16.91, p = 0.00, Cramer’s V = 0.18. Pairwise comparisons revealed that the highly engaged profile was significantly less likely to associated with high school/GED or less education compared to both the minimally engaged profile (χ² = 5.47, p = 0.02, Cramer’s V = 0.10) and the moderately engaged profile (χ² = 20.43, p = 0.00, Cramer’s V = 0.20), with small to medium effect sizes. The pairwise comparison between the minimally and moderately engaged profiles was not significant (χ² = 0.52, p = 0.47). Secondly, the omnibus test for associate degree was also significant, in the small to medium effect size range, χ² = 13.36, p = 0.00, Cramer’s V = 0.16. The pairwise comparisons revealed similar patterns as high school/GED or less. The highly engaged profile was significantly less likely to be associated with having an associate degree compared to minimally and moderately engaged profiles, in the small to medium effect size ranges (χ² = 7.36, p = 0.01, Cramer’s V = 0.12 and χ² = 13.77, p = 0.00, Cramer’s V = 0.17, respectively). The pairwise comparison between the minimally and moderately engaged profiles was not significant for associate degree (χ² = 0.04, p = 0.85).

4. Discussion

Our goal in this study was to explore emerging adults’ social media use (SMU) in terms of not only how much time they spend online, but also how they spend that time. We used latent profile analysis (LPA) to determine whether SMU frequency and SMU engagement were meaningful and distinct ways of grouping participants. We also used Wald tests of mean equality to examine differences among participant characteristics for each perspective on SMU.

The high frequency profile (the largest profile and included over half of the participants) was associated with spending 61–70 min a day on SNS and checking social media several times a day. By comparison, the low-frequency users (10% of the sample) reported checking social media only every few days and when they did, spent half as many minutes doing so (21–30 min). While these results are consistent with previous research indicating a wide range in minutes logged on social media (0–165 min per day; Pempek et al., 2009), they also expand this finding by indicating where interindividual differences occur. Our finding that 90% of our participants (i.e., those comprising the high and medium frequency profiles) reported at least daily SMU conforms to Lenhart (2015) estimate that 90% of emerging adults use social media daily. Lenhart also reported that 24% of 13–17 year olds say they use social media “almost constantly.” This may be analogous to our response category of “several times a day.” It is notable that twice as many of our emerging adult participants (55%) fell into this high frequency group compared to Lenhart’s adolescent sample (24%). This suggests that high frequency SMU may become more prevalent with age. A possible reason for the age difference could be that compared to adolescents, emerging adults, especially those from higher SES, are more likely have smartphones and other, more sophisticated electronic devices that enable frequent use. This reasoning aligns with evidence that smartphone ownership increases with age and SES (Greenwood et al., 2016; Smith, 2011). Emerging adults may also have more autonomy and opportunity over the course of the day than adolescents. Furthermore, even participants in the low frequency profile had active accounts with an average of five platforms, confirming Lenhart’s finding that youth have diversified social media portfolios. Our examination of participants’ SMU in quantitative terms, together with existing literature, indicates that for many emerging adults, being online is so pervasive that it is less a discrete daily activity than a constant condition of waking life.

Our analysis of SMU engagement also yielded three profiles. Consistent with uses and gratification theory (U&G), highly engaged youth participated on social media platforms often and in diverse ways: messaging friends, reacting to and circulating others’ posted content, and generating their own. Moderately and minimally engaged participants were less active, in proportion to their profile names. We noted a particularly large difference among profiles in video sharing (almost never in the minimally engaged profile, often in the highly engaged profile). This finding may indicate something unique about the highly engaged user. It may be interesting to explore in future research whether high rates of video sharing is a function of technological advances that facilitate video user generate content (UGC) (and differential access to such technology) or a reflection of individual differences among users. It may also be beneficial to understand how video sharing is related to individuals’ risk and protective factors, such as their peer relationships and social capital. Future studies could also focus on the content of shared videos and the relation to behaviors (e.g., whether alcohol-related videos are linked to drinking behavior).

We also examined the participant characteristics of the profiles derived for frequency and engagement, respectively, to see if these had distinct demographic patterns. Among the frequency profiles, we observed only two differences: participants in the high frequency profile reported more Facebook friends than those in the other two profiles; and more participants in the high frequency profile were women compared to the low frequency profile. That those in the high frequency profile have more friends makes intuitive sense, but it is unclear whether this is a liability or an asset. Some studies have detected associations between having a large number of Facebook friends and increased risk behavior (e.g., drinking; Moreno et al., 2014). Alternatively, frequent interactions with an extensive social network might be cast as a protective factor insofar as it comes with perceived social support (Best et al., 2014) and greater social capital (Ellison et al., 2007). The ramifications of having a large number of friends is one aspect of SMU that might become clearer through future analyses. The finding that more high frequency users were women compared to the low frequency users aligns with research regarding gender differences in SMU (Anderson, 2015) and evidence that women spend considerable time tending to their relationships, both on- and offline (Subrahmanyam, Reich, Waechter, & Espinoza, 2008).

Our analysis of the engagement profiles revealed different demographic patterns than those for frequency. Compared to the low and the moderate SMU engagement profiles, the highly engaged profile tended to be White and more highly educated. As detailed in subsection 3.3.2 and Table 6, our analyses of differences at particular levels of education echoed the overall finding that more education is associated with greater social media engagement. These findings are notable insofar as they contribute to ongoing efforts to understand if and how a “digital divide” manifests between marginalized and privileged groups in the U.S. Increasingly, researchers cast digital inequalities not in terms of if someone has access, but the quality of their access (e.g., device type, reliability, privacy, functionality, speed). For evidence and analyses of the digital divide, see: Brown, Campbell, & Ling, 2011; Jackson, Zhao, Kolenic Fitzgerald, Harold, & Von Eye, 2008; Martin & Ito, 2015.

4.1. Limitations, implications, and future research

Our findings must be considered in light of several methodological limitations. First, our cross-sectional data prohibit any speculations regarding causality. Secondly, although Turkers’ SMU resembles that of other emerging adults (e.g., in terms of minutes logged each day and daily routine use; Lenhart, 2015; Pempek et al., 2009) our sampling from Amazon Mechanical Turk (MTurk) curtails the generalizability of our findings. Finally, our relatively small and racially homogenous sample prohibited us from examining variations among demographic groups. Understanding differences that may exist among minority groups (e.g., racial and/or sexual/gender minorities) is an important next step.

Nevertheless, the study’s findings have important theoretical and practical implications for researchers and practitioners interested in youth SMU. Drawing on U&G, we can understand youth SMU as an indication of their interests and needs and how social media satisfies these. Indeed, it is important to appreciate that SMU is not mindless, but motivated. For instance, frequent posting about one’s own experiences or commenting on friends’ posts, as in the case of the highly engaged users in our study, are meaningful and gratifying forms of social interaction, information exchange, and self-expression. The presence of distinct user profiles shows that youth should not be assumed to be homogeneous in their SMU. Understanding variations in SMU among youth could be useful in effectively leveraging social media for service and support provision.

Additional, more sophisticated studies are needed in order to pursue these possibilities. Larger and more diverse samples could enable comparative group analyses. The inclusion of measures tapping other behaviors (e.g., “in real life” friendships, sexual and romantic relationships, and alcohol use) would allow researchers to study the differential ramifications of SMU frequency and engagement for youths’ well-being and experiences. Longitudinal or prospective designs, including event-sampling techniques, would be important improvements over our cross-sectional study and permit more sophisticated and reliable analyses. Finally, we look forward to examining SMU engagement more closely, including careful differentiation among active, passive, and reactive modes of engagement.

4.2. Conclusion

Evidence from the current study indicates that we have much to gain from viewing emerging adults’ SMU in light of frequency and engagement. Distinguishing how much time youth spend online from how they spend their time deepens our understanding of SMU and the purpose it may serve for youth who are striving for interaction, exchange, and self-expression. As SMU becomes more accessible and further embedded in the fabric of daily life, it is conceivable that engagement may be a more meaningful way to assess the purpose and ramifications of SMU in youths’ lives. Indeed, it may be that we are trending toward a state in which all or most youth are online – via phones, watches, tablets, laptops – all the time, rendering “frequency” an irrelevant or anachronistic construct. Additionally, our findings of differences across profiles, particularly SES indicators and SMU engagement, are novel and notable. They contribute to ongoing efforts to understand how SMU affects the lives of emerging adult users, including if and how a digital divide manifests. As in the case of the current study, in which race and education were associated with engagement and not frequency, it is only when the degree of SMU engagement is considered that some disparities, benefits, and/or risk may become apparent. Thus, the distinction between frequency and engagement may allow researchers and practitioners to identify and respond to patterns in SMU that impact youth – for better and for worse.