1 - Social Media Use Integration Scale.pdf

Psychology of Popular Media Culture © 2012 American Psychological Association2013, Vol. 2, No. 1, 38 –50 2160-4134/13/$12.00 DOI: 10.1037/a0030277 Development and Validation of a Social Media Use Integration Scale Michael A. Jenkins-Guarnieri, Stephen L. Wright, and Brian Johnson University of Northern Colorado The present study developed a scale of online social media use that measures the integration of the social behavior and daily routines of users, along with the importance of and emotional connection to this use. Using a sample of 616 emerging adults in college, exploratory factor analysis was conducted with a calibration sample of 308 participants and confirmatory factor analysis was conducted using an equal hold-out sample to yield a final 10-item two-factor Social Media Use Integration Scale (SMUIS). Strong reliability evidence was found for data collected with the total scale (␣ ⫽ .914), the first 6-item subscale called Social Integration and Emotional Connection (SIEC) (␣ ⫽ .893), and the second 4-item subscale entitled Integration into Social Routines (ISR) (␣ ⫽ .828). Test–retest over a 3-week period suggested that SMUIS responses remained stable, with reliability correlations of r ⫽ .803 for total scale, r ⫽ .804 for subscale 1, and r ⫽ .676 for subscale 2. In addition, high correlations with previously published social media use measures provided convergent validity evidence, whereas nonsignificant correlations between the SMUIS subscales and other measures unrelated to online social media use offered discriminant validity evidence. The SMUIS was first developed to measure Facebook use; however, it was purposefully designed to be adapted to measure other forms of online social media use. Implications for future research and practice are discussed. Keywords: Facebook, online social media, college students, measurement scale Internet use for communication and social quickly become the most popular SNS for behavior is becoming increasingly integrated young adults, especially those in college into the lives of North Americans (Correa, Hin- (Cheung, Chiu, & Lee, 2011). As this online sley, & de Zúñiga, 2010; Ross et al., 2009), with social medium is increasingly integrated into 95% of young adults aged 18 to 33 years re- the daily lives and social behavior of young porting activity online in recent years (Zickurh, adults (Correa et al., 2010; Steinfield, Ellison, & 2010). One specific area that has seen a signif- Lampe, 2008), new research must seek to assess icant and rapid rise to prominence (Pempek, and understand the nature of using this medium Yermolayeva, & Calvert, 2009) has been social and its potential implications, especially for so- networking sites (SNS) such as Facebook.com, cial behavior and development (Brown, 2006). online social media through which to share ex- It is imperative to use well-developed measures periences and communicate within social rela- to effectively investigate online social behavior; tionships (Ross et al., 2009). Facebook has however, recent research on online social media has often used inadequate measures for opera- tionalizing social media use and its integration into social behavior. Furthermore, based on a This article was published Online First October 22, 2012. thorough literature review, to date, no published Michael A. Jenkins-Guarnieri, Stephen L. Wright, and scale has been developed following more for- Brian Johnson, Department of Counseling Psychology, Uni- versity of Northern Colorado. mal and rigorous methods of scale development Correspondence concerning this article should be ad- and validation for measuring how social media dressed to Michael A. Jenkins-Guarnieri, Department of is used and integrated into the daily lives of Counseling Psychology, University of Northern Colorado, 501 20th Street, McKee Hall 248, Campus Box 131, Gree- users. Thus, the present study used methodolog- ley, CO 80639. E-mail: Michael.JenkinsGuarnieri@gmail ically rigorous techniques for scale develop- .com ment (DeVellis, 2003) to create a novel measure 38 for example. & White. & Farrington-Flint. & ships through Facebook. other sites like Twitter. Steinfield. cebook use. Kwon & Wen. 2011. Social behavior and interper. 2011. they complex constructs. 2007) and maintaining social connec. Green. Graham. LaFlam. 2010. and the dia to such an extent that developmental tasks importance of and emotional connection to this associated with this stage (Arnett. Ong et al. For- versity and significant numbers of online social nasier. 2009). maintaining this which social media is integrated into the social high level of social activity through social me.. social iden. nicating with friends (Ellison. Key developmental ac. & Zhao. 2010. commu.g. In reviewing the development of SNS. Pempek et al. suffers from a lack of methodological rigor. Therefore. and most likely involves a user’s emotional connec- LinkedIn emerged and began attracting users to tion to and integration of use in their daily social consume and share media. Carpenter. 2009. even the most recent research published tiple manifestations of online social media. far has used psychometrically weak measures. Kujath. Martin. 2011) and did . tation of how they progressed through formal pears to play an increasing role in these areas procedures for scale development and valida- (Reich. & Salim. emerging adulthood populations from their new measures to answer subsequent often initiate and maintain meaningful relation. Other authors provided only vaguely of its most popular manifestations is even defined measures (e. 2010) appear to be carried out through cial media. In conjunction around this yet the nature of these social media services time. 2008). this in peer-reviewed journals used somewhat lack- study focused on Facebook. This focus represents a unique contribution enacted in part online. such as estimates of the average daily Emerging Adults and Social Media Use activity of use in minutes or account logins per week (e. and sense of self (Peluchette & surement scales for their studies involving so- Karl. we de- reached college age and developed into an fined online social media use as the degree to emerging adult population. SOCIAL MEDIA USE INTEGRATION SCALE 39 for social media use integration.. of our study to the literature in this area. Some researchers have developed novel mea- khan. researchers often use lege-aged adults. tions (Subrahmanyam. Litt & Boyd and Ellison (2007) highlighted the di. flexible enough to be adapted for use with mul. especially with col. Reich. research questions (e.. & This method for operationalizing Facebook use Espinoza. the for- media introduced since their emerging forms as mat and response scales for this type of question early as 1997. In chometric analyses before using data collected conjunction. with poor reliability esti- highlight MySpace as the first major main. ing assessment measures to operationalize Fa- nence in North America. & 2011.g. 2000) may be use. as these online communication varied considerably across studies. Stock. 2008).. Lampe. tity (Manago. ing adult populations. 2011. 2007). mates and high measurement error. Kerlin. 2010). stream service. Additionally. most research on social media use thus initial validity evidence for its use with emerg. Although intended to be Based on scale development theory (DeVellis... and social media ap.. Waechter.g. concept of engaged use. 2011). with teenagers joining in record much previous research in this area focused on numbers and later migrating to the incredibly the behavioral frequency of social media use. Ong et al. Wilson. 2003). these authors did not provide detailed documen- ment of emerging adults. For example.. 2009. and established newer. but did not conduct rigorous psy- social media and SNS such as Facebook. as sonal relationships are essential to the develop. as our tivities such as exploring individual identity measure was developed to capture this broader (Grasmuck. popular Facebook. 2010). Ross et al. behavior and daily routines of users. Few authors developing their own scale used exploratory factor analysis (EFA). tion. Underwood. and Measuring Social Media Use none completed a confirmatory factor analysis (CFA) or provided detailed psychometric statis- Perhaps because the concept of social media tics such as test–retest reliability coefficient es- use is relatively new and the rise to prominence timates. Baker & Oswald. YouTube. Greenfield. Single items tools began to evolve with different features and such as these often perform poorly in measuring foci. These users soon lives (Ellison et al. In addition. a single item assessing factual information about use. given its promi. 1 18. 2012 produced two distinct factors that they called Attitudes (␣ ⫽ . they did not report any addi- tional analyses in developing this scale. CFA ⫽ confirmatory factor analysis. AND JOHNSON Note. Although developed for the purposes of their study. strong psychometric evidence. 5-point Likert scale. (2007). Thus. Ryan & Xenos. On the other hand. Another scale that has been used by others Undergraduate college Undergraduate college Undergraduate college (e.. its usage is far from uniform across the literature. This 28-item survey included the six attitudinal items adapted from Ellison et al. This Authors and year Jenkins-Guarnieri.74 ISRb ␣ ⫽ .89 Reliability OSFb ␣ ⫽ .. OSF ⫽ Online Sociability Functions subscale. & Johnson (2012) refers to the present study’s data. Valenzuela. 2011) is the Facebook Questionnaire developed by Ross et al. and no additional psychometric evidence has been pub- a lished on data collected using this scale. 2010). 2009 Johnson.85 Test–retest ⫽ .g.g. Addi- 5-point Likert scale 6-point Likert scale Response format tionally. some researchers have EFA EFA used measures of Facebook use based on pre- viously published scales (see Table 1 for over- Number of items Attitudesb ⫽ 7 view). making evaluations of their instruments difficult. Ellison and colleagues Attitudesb ␣ ⫽ . EFA ⫽ exploratory factor analysis. SIEC ⫽ Social Integration and Emotional Connection subscale.g. 2010.4 21. ␣ ⫽ Cronbach’s alpha coefficient.. Peluchette & Karl. suggesting OSFb ISRb multiple-choice formats that ranged from a 4-item to a 9-item multiple choice. ISR ⫽ Integration into Social Routines subscale.g. and Morris (2011) treated the first two items assess- Variablec ing behavioral frequency of usage as two dis- tinct variables and the mean of the six remain- over a 3-week interval. (2009) Sample during the course of their study on Facebook students students students use and personality factors (see Table 1). Kwon & EFA and CFA Methods Wen. WRIGHT.. SMUIS total score test–retest Two factual items and six items using the 5-point Likert scale. Items were Female ⫽ 188 Female ⫽ 389 rated by participants on a mixture of Likert-type Female ⫽ 82 Participants Male ⫽ 162 Male ⫽ 98 Male ⫽ 15 rating scales and dichotomous responses (e. One commonly used measure created by SMUIS ⫽ 10 SIECb ⫽ 6 OSFb ⫽ 5 Ellison et al. Ross and colleagues (2009) performed principal components factor analysis with a va- rimax rotation to yield latent variables instead Ellison et al.83 participants. Park. Jenkins-Guarnieri. 2009. (2007) and an addi- tional item created by the authors. SMUIS SIECb and administration standardization..91 SIECb ␣ ⫽ . b Indicates subscale. and reported item means and inter- nal consistency reliability estimates from these ␣ ⫽ . However. ing items assessing emotional connection to Facebook Intensity active Facebook use as another variable.83 data. along with a number of items assessing behavioral usage of Mean 20. and not offer detailed psychometrics (e. n ⫽ 286 n ⫽ 552 Measures of Facebook Use n ⫽ 97 yes/no). 2007 of using single indicators in their analyses. & items adapted from Ellison et al.74). the “Tag” function and “Wall” posts). which comprised the six Wright. it may be a weak measure of Facebook use.80 (2007) created their scale using college student SMUIS ␣ ⫽ .7 age Facebook and its various features (e.. Ross et al. and Table 1 Online Sociability Functions (␣ ⫽ .40 JENKINS-GUARNIERI. 2009).85). Wright. which has been used by a number of researchers (e. which comprised items assessing behavioral frequency a . subscale names Questionnaire Measure and even the popular Facebook Use Intensity scale Scale (FIS) suffers from a lack of methodological rigor in Attitudesb Facebook development. Orr et al. c Items used variable response formats that included: yes/no. Kalpidou. & Kee.. for example. Costin..g. (2007) is the Facebook Use Inten- ISRb ⫽ 4 FIS ⫽ 8 sity scale. SMUIS ⫽ Social Media Use Integration Scale. 2010). However. Ross et al. with 72. a measure of social media rejection of items (Worthington & Whittaker. and emerging adults often use Facebook to initiate and maintain relationships After receiving institutional review board ap- (Ellison et al. In this way. and did not directed them through a hyperlink to the study’s present a measure of the degree to which social online survey. ods of assessing Facebook use appear to suffer ship between factors.com.1% reported being female. Thus. tegration but was intended to be flexible enough In addition.e.. For the cur.. This instru- results are an artifact of the specific methods ment was designed to assess Facebook use in- used. because college students between the ages of 18 and 25 years are described as emerging adults Procedures (Arnett. count were included in this study. researchers have focused on the total sample (N ⫽ 616). they do not present strong psycho. the novel part of this scale first-year students to participate in this research involved behavioral frequency items only spe. (2007) Mountain region university. 1. A total of 627 participants re- media use is integrated into overall social be.022 students. African American.. to be adapted to other SNS and online social sentially used Ellison and colleagues’ (2007) media. and the demographics of this 2008). 2006. The mean age of participants was 18. 482 participants chose integration of social media into the daily lives to enter their e-mail address after completing and social behavior of users (Steinfield et al. sponded to the invitation e-mail and began the havior. especially Facebook. Given the call for further research on the 11% multiracial. use integration with detailed psychometric evi- 2006). and 0. from a medium-sized (N ⫽ 12.000) Rocky metric evidence for the Ellison et al. 9. As Internet use increases in emerging later with an invitation to retake the same 22- adults (Zickurh. we invited all 3. Participants resented a different latent construct. between the ages of 17 and 25 years recruited book. the present with correlated factors may yield overestimated study developed the Social Media Use Integra- loadings as well as inappropriate retention and tion Scale (SMUIS). 2000). Thus. through their university e-mail accounts and cific to one SNS (i. 4. SOCIAL MEDIA USE INTEGRATION SCALE 41 of Facebook use.996) years.74% response rate). and using this rotation from a lack of methodological rigor.5% Native sonal relationships and social skills (Thayer & American. the Ross et al.42 (standard deviation Study Rationale [SD] ⫽ 0. which risks a solution that is an artifact dence for an emerging adult population.. and the strong psychometric evidence for its use with 616 (98%) who reported using a Facebook ac- emerging adults. From this Ray. and 71.com. Although these results present a possible measurement All participants were undergraduate students scale of behavioral frequency specific to Face. In ad- of the methods used. data were collected through a secure . this measure did not dition.6% identifying as Caucasian. 2009). 2008). as well as the sponded within 5 days to complete all the items role of this use in social behavior (Raacke. the survey and were e-mailed again 3 weeks 2008). and developed behavioral frequency items that rep. Facebook Use Intensity scale items (which cap- tured integration of usage into social behavior Methods and the emotional connection to this use). Using a conve- scale’s data collected from a sample of college nience sampling method.6% Hispanic/Latino.. and the internal structure as well as convergent and future research must determine whether similar discriminant validity of the SMUIS. As recent meth- assumes an orthogonal/uncorrelated relation. Ninety-five of these participants re- (Brown. 0. we presented preliminary evidence for benefit from strong psychometric analyses. Facebook). we chose to use a college sample larger sample. 2007) and social connections proval.3% Asian. sample did not differ significantly from the rent study.5% potential for Internet use to influence interper.3% Pacific Islander. the varimax rotation (Subrahmanyam et al. (2009) scale es. this measure also fails to present online survey (20. a second time. 2006). researchers have urged item scale for use with calculating test–retest further investigations into social media use reliability. no study to provide convergent validity evidence new original items were created from this for the SMUIS.6% participants stated “Please indicate how much Asian. The instructions for the 8. The test–retest with numbers two through five listed sequen- sample of 95 reported being 67.795) years.’s scale. agreement or disagreement with each item’s 73. and 0.’s (2007) and Ross et the average number of minutes spent actively al. group. and The calibration sample had similar demo.6% Pacific Islander. respectively.938] years.1% female. on which participants could lapped with two items on Ellison et al. analyses (i. familiar with current research on social media Facebook use intensity. using Facebook is part of my every. 1% Asian.83 for proposed items (DeVellis. Finally.1% Asian. . mean age of 18.’s scale elect one of the nine custom answers (e. Ellison et al. 72. 2007. 2003) by comparing data obtained from a sample of emerging adults the new items with previously published items in college. use and emerging adult populations. The item pool that then underwent a process of Conscientiousness and Agreeableness subscales revisions by the workgroup of collaborators to of the Big Five Inventory (BFI.7% multiple races. and 4. agreement or disagreement.g. “0 ⫽ that included: (1) “Facebook has become part of 10 or less. The first two items asked about scales such as Ellison et al. For our data. 10.e. two doctoral students.5% multiple heritages. of people connected to a user’s account (i.com gift card ($2) as an incen.. and three (“Strongly Agree”) to indicate their level of undergraduate students was conducted to deter. EFAs re- a mean age of 18..828. and 9 were removed because the participants who completed the survey earned a content of these statements was redundant with small Amazon. mean age of 18..5% Hispanic.4% Caucasian. (“Strongly disagree”) to 6 (“Strongly agree”). Table 2). 4. Ross et al.9% African American. and sulted in a 13-item two-factor solution (see identified as 69% Caucasian. 4. The first three items focused on fre- EFA and a hold-out sample (n ⫽ 308) for a quency of social media use. 11. minutes spent using these services per day. 9.4 [SD ⫽ 1. 1.4% Hispanic. This scale was administered in our (e. Naumann.” and this last item was tion to the site and its role in their social rela- also included on Ross et al.914. Measures with higher scores reflecting more engaged use and integration of social media.37 [SD ⫽ 0.e. the remaining items were created to capture a graphic characteristics as the full sample user’s integration of the site into social behav- (70.com (SurveyMonkey. as well as emotional invest- years.4% created for participants to indicate their level of female... such as average CFA to test the proposed scale’s fit to the data. John.42 JENKINS-GUARNIERI. and 1. 2011).1% Caucasian.2% African American. day routine) related to social routines that over.1% statement using anchors ranging from 1 Hispanic. In addition.6% Native you agree or disagree with the following state- American). respectively. and ␣ for scores on the sub- through collaboration with two psychologists scale 1 and 2 were. and 0. and the items tive. We randomly split the full sample in half did not add to the psychometric strength of the to form a calibration sample (n ⫽ 308) for an measure. These procedures yielded an initial 34.3% Native American).. Ellison et al.4 (SD ⫽ 0. Ellison et al.’s (2009) measures. 10.5% multiple races.. AND JOHNSON Web-based survey site hosted by SurveyMon. A pool of the Cronbach’s alpha coefficient for total scale potential scale items for the SMUIS was created scores was . Our final scale had one using Facebook per week and about the number item (i.” and a Likert-type response scale was similar demographics to the full sample (72. (2007) mine the appropriateness and usefulness of the reported a Cronbach’s alpha estimate of . Social media use integration. 22. 1 ⫽ 11–50”). items 13. yield a scale composed of 22 potential items. Five-factor model personality traits.893 and . The hold-out CFA sample also had ments. .4% female.05] iors and routines. WRIGHT. ment in the site’s use. an tionships. 2009). key. 0. the first 25 Items 15. CFAs) resulted in a final 10-item two-factor scale (see Table 3 for scale items). Facebook friends).g. The remaining six my daily routine” and (2) “Facebook is part of items asked users about their emotional connec- my everyday activity. (2007) created an 8-item scale called Facebook we adapted items from previously published Use Intensity. and participants used a Likert-type informal focus group that consisted of three scale ranging from 1 (“Strongly Disagree”) to 5 psychologists. and subsequent scale development 9. 10. had tially and spaced evenly in between.e. 163 0. adults. and also presented evidence for conver.52 0. Shaw..758 7a I would be disappointed if I could not use Facebook at all 3.30 1. how many days per week do you use Facebook? ⫺0. recommended a minimum sample size of 200 Table 3 The Final 10-Item Scale and Descriptive Statistics Using the Total Sample Item Item text Mean SD ITC a 5 I feel disconnected from friends when I have not logged into Facebook 3.886 ⫺0. (r) ⫽ item 11 reverse coded.61 1.108 0.084 7 I would be disappointed if I could not use Facebook at all 0. ITC ⫽ corrected item-total correlation. n ⫽ 552.501 0.39 0. mum likelihood extraction method and a pro- gent. with establishing discriminant validity evidence These subscales each consist of nine items on for our new scale.533 14b Using Facebook is part of my everyday routine 4.84 1. ranging from 0. b Integration into Social Routines subscale.42 0. & Soto. Bold items were retained for respective factors.750 8a I get upset when I can’t log on to Facebook 2. a Social Integration and Emotional Connection subscale. SOCIAL MEDIA USE INTEGRATION SCALE 43 Table 2 Scale Items and Pattern Coefficients for 13-Item Two-Factor Model Two-factor solution Item Item text Factor 1 Factor 2 8 I get upset when I can’t log on to Facebook 0. Pre.670 Note. factor 2 ⫽ integration into social routines.491 Note.092 6 I would like it if everyone used Facebook to communicate 0.24 2. and Ke (2005) scientiousness (Ross et al.683 17b I respond to content that others share using Facebook 3. (r) ⫽ item 11 reverse coded. which participants indicated their disagreement or agreement with item statements on a Likert.50 0.07 1. .55 0.642 13a Facebook plays an important role in my social relationships 2. and were chosen for use two constructs from the Five-Factor Model.70 1.319 0. all 19 proposed timates with U.153 2 On average. 2008) were developed to measure those lated to Facebook use.709 4b I enjoy checking my Facebook account 4.873 ⫺0.067 0. Scale range for items: 1 ⫽ strongly disagree to 6 ⫽ strongly agree.69 0.90.56 0.630 0.691 6a I would like it if everyone used Facebook to communicate 3. 2009) were unre.58 1.75 to items were submitted to an EFA using maxi- 0.172 13 Facebook plays an important role in my social relationships 0. John et al.190 18 I share many of my day to day activities through Facebook 0. Data Analyses type rating scale ranging from 1 (“Disagree strongly”) to 5 (“Agree strongly”).786 4 I enjoy checking my Facebook account 0.99 1. lease 19).703 11 I don’t like to use Facebook (r) ⫺0. Mundfrom.96 1.597 17 I respond to content that others share using Facebook 0. Fornasier. max (oblique) rotation (k ⫽ 4) with Kaiser vious research has found that Agreeableness normalization using IBM SPSS Statistics (re- (Wilson.697 0.22 0.839 14 Using Facebook is part of my everyday routine 0. 2010) and Con.147 10 I prefer to communicate with others mainly through Facebook 0. and concurrent validity. pants in the calibration sample.S.020 0.24 0. Using the data collected from 308 partici- (2008) previously found adequate reliability es. Factor 1 ⫽ social integration and emotional connection.42 0.075 12 I check Facebook immediately when I am alerted of new activity on my account 0. discriminant.683 0.692 11b I don’t like to use Facebook (r) 4.594 0.701 10a I prefer to communicate with others mainly through Facebook 2.685 0.70 1. Item 2 was standardized owing to a different scale being used for the item that consisted of the numbers 1 through 7. & White.137 5 I feel disconnected from friends when I have not logged into Facebook 0. SD ⫽ standard deviation. n ⫽ 279. . and were removed hold-out sample. the 19 items were subjected to an EFA Generating (MG) approach (Jöreskog.843 for factor 2. 1993) to with data from 268 participants (40 participants Structural Equation Modeling (SEM) in con.32 (Tabachnick & Fidell. (factor 2). and all items displayed skew and kurtosis sis was conducted over a number of iterations. ticity. AND JOHNSON for EFA with a variables-to-factors ratio of six. 3.845) statistics fell . There was no evidence of collinearity in the pretability of the factor solutions. and kurtosis statistics and plots of standardized ence K ⬎ .281 and were identified using a unit loading constraint accounted for 9. data. and inter. With data from 279 scale from novel items that adequately measures participants available. subjected to a second iteration of EFA. out sample after using listwise deletion to ad- Owing to the imbalance in the gender and dress omitted responses. Using the calibration Based on these results. Weston & Gore. WRIGHT. Whittaker.9 (Tabachnick & Fidell. Items 20 and 21 data on the same scale items from the separate loaded on their own factor. were r ⫽ .44 JENKINS-GUARNIERI. Both Bartlett’s and items were removed if they displayed a Test of Sphericity (p ⬍ . the interfactor correlation was ⬎2 and ⬎7 (Hoyle. values within acceptable ranges. Multi- ple criteria were used to determine retention of Results factors. including eigenvalues ⬎1. 2011.45 or if they loaded on Meyer–Olkin measure (KMO ⫽ . were excluded using listwise deletion owing to ducting a CFA using EQS software (release 6. and . Following this method.833% of the variance.738 mended. Factor analy. 16. 2001). we followed a Model sample. 2001). The 13-item two-factor ethnicity of study participants.05/2 ⫽ .920) sup- more than one factor with a pattern coefficient ported the factorability of these data (Tabach- of ⱖ.901 for factor 1. 2001). (ranging from ⫺1. Cronbach’s alpha reliability examining the squared multiple correlations for estimates calculated from this scale’s data were values ⬎. which The hold-out sample meets the minimum size produced a clear and interpretable rotated factor requirements of 200 for most applications of structure: a 13-item solution with two factors SEM (Kline. Factor 1 had an eigenvalue of 6. this approach matches and 19 were removed because of low pattern the purpose of the current study in developing a coefficient loadings (⬍.713. al. and . hypothesized models whereas factor 2 had an eigenvalue of 1. independence of observations. that we named “SIEC” (factor 1) and “ISR” though greater numbers are always recom. In addition. SMUIS subscale model of the SMUIS suggested by earlier EFA mean scores were examined simultaneously for analyses was titled model 1 and subjected to a potential differences by gender and ethnicity CFA using the MG approach to SEM. ful model (Byrne. examined for indications of deviation from the There were 273 participants from the hold- assumption of normality underlying SEM. skew (Caucasian and non-Caucasian) using a multi.444 to 0. 1995). and three to evaluate the fit of the observed indicators factors were retained using the aforementioned selected by the EFA (the a priori model) to the criteria after the first iteration. the results from these analyses daily functioning. and because they grouped were used to respecify the model to yield a into a factor of only two items (Worthington & statistically plausible and practically meaning. our sample residuals were examined to evaluate whether size met these minimum requirements.188 to 1. respectively. conducting SEM analyses. 2001). 2006). Items 1 these data met the assumptions of homoscedas- through 3 were standardized before being in. the pat- for the first indicator of each factor. and then. 2006). the because of conceptually different item content hypothesized model’s fit to the data was first concerning social media use’s interference in tested. Following Kline’s (2011) guidelines in and accounted for 51.025).747) and kurtosis variate analysis of variance (alpha level with (ranging from ⫺1. the remaining items were the construct of social media use integration. and mul- cluded in analyses. Skew wide communality.1) missing data from omitted responses). nick & Fidell. a visual analysis of the produced Scree plot. Bonferroni adjustment: .45).001) and the Kaiser– pattern coefficient of ⬍.921 for skew and kurtosis statistics with absolute values the total scale.98 (“excellent” criteria). content and large ranges and variances. given the nature of their tivariate normality underlying this analysis. and a coefficient of congru.854% of the variance. data were tern coefficients for the included items are dis- screened for any evidence of collinearity by played in Table 2. items 1. SMUIS total 0.39 1. model size. Following the MG approach to SEM.96.54 Note. Although the ␹2SB was statis- model fit can be overly sensitive and biased by tically significant. and thus.94.06 1.001]).001) moderately large and positive correlations modifications were made to model 1 based on with the Facebook Use Intensity Scale’s total low R2 values and large univariate Lagrange mean score (items standardized).303 and r ⫽ . Maxi. The ␹2 statistic for exact and NNFI ⫽ 0. fit statistics for subscale SMUIS and the full data set (N ⫽ 552 model 1 are shown in Table 5.951 [p ⬍ . SMUIS ⫽ Social Media Use Integration Scale.697ⴱ 0.031 — .06 for the root alternate one-factor 13-item model produced in- mean square error of approximation (RMSEA) adequate fit. mean scores used to calculate correlations. 2005).09 4.71 5. 12.051.893 3.038 0.95 for the non. RMSEA ⫽ 0.959ⴱ 0.102. ␹2SB/df ratio of 2. erations of sample size.106 0.284. a Items standardized before any calculations owing to differing item ranges. and suggested after listwise deletion).705ⴱ — . based on the . and ⬎.041. and are dis- multiplier test statistics that indicated high error played in Table 4 along with Cronbach’s reli- covariances and cross-loadings for some items.01. resulting in model 2 that con. which fell within acceptable from model 1.59 1. and the Convergent validity evidence was established potential for non-normality in the data between the SMUIS and the Facebook Use In- (Hutchinson & Olmos.009 0.759 4.017 ⫺0. RMSEA ⫽ 0. 1998). A CFA conducted on the square residual (SRMR). suggesting ing approximate fit statistics and cutoff criteria strong validity evidence for the internal struc- were used: ⬍. mates were all statistically significant and prac- lished model evaluation guidelines.001) and positively correlated with the SMUIS aforementioned procedures for model 1. the two original items as- sisted of 11 items. the revised criterion of ␹2/df ⬍ dardized and unstandardized parameter esti- 3 (Iacobucci. close to . 102.828 4.53 was ⬍3. cations. Analyses of fit statistics for sessing behavioral frequency of social media model 2 also did not demonstrate a good fit to use (one and three) were significantly (p ⬍ the data and item 18 was removed.914 3. which retained only nine items (SRMR ⫽ normed fit index (NNFI) and comparative fit 0.750ⴱ 0. 1987) 3’s final 10-item two-factor structure demon- estimation methods were used with the robust strated good fit to the data. Model mum likelihood (Bentler & Chih-Ping. suggesting inexact fit.075 (90% confi- with non-normally distributed data (Hutchinson dence interval of 0. and largest standardized residual was .10 3. ␹2 ⫽ 127.495. SIEC subscale (r ⫽ . Conscientiousness ⫺0. . In addition. stan- 2011).55 6. and ␹2SB ⫽ These statistics were selected owing to consid.095). the follow. both subscales and total that model 1 did not demonstrate good fit to the mean scores demonstrated significant (p ⬍ data. ability estimates calculated from the final which suggested that items 2 and 12 be removed scale’s data. 2010) was used. 1998).878ⴱ — .737 3.022 0. CFI ⫽ 0. and index (CFI) (Beauducel & Wittmann. . Agreeableness ⫺0.198) and the Table 4 Descriptive Statistics and Bivariate Correlations for the Final 10-Item SMUIS and Other Measures Measure 1 2 3 4 5 6 ␣ Mean SD 1. Based on pub.70 0. ranges. & Olmos. tically meaningful (see Table 6).056 – 0.387ⴱ — . Using the final 10-item two- Based on the CFA analysis. tensity Scale. the a number of model characteristics (Kline. SMUIS factor 1 — .852 0. even after multiple model modifi- (Hu & Bentler (1999). In addition.23 2.08 for the standardized root mean ture of the SMUIS. ⴱ p ⬍ .772ⴱ — . CFI ⫽ 0.95. statistics falling within acceptable ranges: 1988) to avoid potential bias in the ␹2 statistic SRMR ⫽ 0.01 0. factor 2 ⫽ integration into social routines.038 0. SOCIAL MEDIA USE INTEGRATION SCALE 45 within acceptable ranges for all items. NNFI ⫽ 0. Spe. SMUIS factor 2 0. with approximate fit Satorra–Bentler ␹2 statistic (Satorra & Bentler. Facebook use intensitya 0.006 0. 18 are listed in the footnote of Table 5. n ⫽ 552 using listwise deletion. and the cific item content for removed items 2.92.079 0. factor 1 ⫽ social integration and emotional connection. 065 .784 0.” latent construct/subscale 2 ⫽ “Integration into Social Routines.335ⴱ 34 43.858ⴱ 43 98. evidence of discriminant Using data from the 95 participants who re- validity was demonstrated using the BFI Con.001 .670 7 1 1.770 . timate was r ⫽ .96 are significant at p ⫽ .084 0.93 3 97. SRMR ⫽ standardized root mean square residual.91 0.01 (Thompson.719 17 2 0.617 0. respectively). ␹SB2 ⫽ Satorra–Bentler scaled chi-square statistic. sample size.065 .712 13 1 0.96 0. the Facebook use (e.615 0. 544) ⫽ 0. significance to help provide “very strong ev- From a conceptual point of view. SMUIS ISR subscale (r ⫽ . model 3 excluded items 2.05 and .787 0.. 2000). respectively) and SMUIS-ISR sub.999.) can be calculated by dividing unstandardized estimates by standard errors.658 0. C.055 0.01.623 0.676 for subscale 2’s mean score. F(2.741 0. CFI ⫽ comparative fit index.493 0. Robust statistics used with robust ML estimation method. Ross et al.060 .619 10 1 0.789 . 12. 313) that individuals’ levels of conscientiousness and to reduce type I errors with our larger or agreeableness would determine their Face.432ⴱ 0.101 0.147 .552 0. the test–retest reliability es- mean scores.066 . Latent construct/subscale 1 ⫽ “Social Integration and Emotional Connection.062 and p ⫽ .368 and r ⫽ .889 .756 0. we decided effects (based on Wilk’s criterion) due to gender to use a critical value of .919 .702 0. n ⫽ 273.483 0.883.94 0. 2009). ing potential differences in the two subscale scale (p ⫽ .05.620 14 2 1 . score was r ⫽ .013. book use. All unstandardized parameter estimates and error variances statistically significant at p ⬍ .672 F1–F2 1.90 2 141. p ⫽ .804 for subscale 1’s mean score ing that they were not significantly related to and r ⫽ .693 .695 0. Model 1 included all 13 items. Results from a multivariate analysis with the SMUIS-SIEC subscale (p ⫽ .95 Note.119 Note. gated into white and nonwhite owing to the low ing the frequently published critical p values number of people of color) indicated no main of . and 18 (I share many of my day to day activities through Facebook). ⬎ ⫾1. n ⫽ 273.803 (p ⬍ . ⴱ p ⬍ .811 0.R.585 5 1 0.067 . RMSEA ⫽ root mean square error of approximation.067 .46 JENKINS-GUARNIERI. given previous research suggest.g.001 for all correlation scales displayed nonsignificant correlations statistics). These test–retest correlation for the 10-item total mean Conscientiousness and Agreeableness sub.062 . AND JOHNSON Table 5 Overall Model Fit Statistics for Two-Factor Baseline and Nested Models Model ␹2 ␹SB2 df ␹2 difference RMSEA SRMR CFISB NNFISB 1 240.064 .01 to determine (␭ ⫽ .R.892ⴱ 86.041 0.047 0. WRIGHT. .571 0.620 0.549 0.614 4 2 0.301ⴱ 124. Therefore.785 .849 .879.611 and of variance using all 616 participants investigat- p ⫽ .655 6 1 1.05.129.617 11 2 0. it is unlikely idence against the null” (Royall.290ⴱ 64 — 0.935 ..196). ␩2p ⬍ Table 6 Unstandardized and Standardized Coefficients for the Final 10-Item Model Latent construct/ Standardized Item subscale B ␤ SE R2 error variances 8 1 1 . 1986. model 2 excluded items 2 (on average.743 0.067 . p. In consider.” Critical ratios (C. how many days per week do you use Facebook) and 12 (I check Facebook immediately when I am alerted of new activity on my account). NNFI ⫽ non-normed fit index.075 0. mean scores for gender and ethnicity (aggre- results are presented in Table 4.409ⴱ 0.831 0.110ⴱ 213. took the final 10-item SMUIS 3 weeks after the scientiousness and Agreeableness subscale first administration. 001) or ethnicity (␭ ⫽ . SOCIAL MEDIA USE INTEGRATION SCALE 47 .. cused on more factual information about Face- 2011. ␩2p ⫽ . Ellison and col. the original scale development item pool fo- idence for data collected (e. relations were found between the SMUIS and Facebook Use Intensity Scale. the SMUIS was designed to be over time..419. 2003). separate construct from behavioral frequency of use. and subscale 2 called ISR (see Tables 2 and 3). potential differences between the mean scores ments with the name of other social media ser. ability was established over a 3-week interval.995. 544) ⫽ 1. Authors developing measuring minutes spent using the site and scales of social media use have focused mainly number of Facebook friends. the benefits from detailed information about the present study developed a novel measurement scale’s psychometric properties. our results suggested that measuring subscale 2 scores). items (e. In addition. Ross et al. levels than correlations with the Facebook Use 2009). It is likely that although the quantity of social media use (frequency/ The SMUIS was designed to assess engaged intensity) will vary depending on various fac- use of a variety of social media in emerging tors in one’s life.. Baker & Oswald. nection one forms with it will be more stable however.g. To address these Facebook use into one’s daily routines or emo- limitations. an analysis of placing the word “Facebook” in the item state. an EFA followed by a CFA with a different with detailed psychometric data from a college sample to establish validity evidence for the student sample. importance in one’s life and the emotional con- cused on the social media site Facebook.828 for (2007). social media use should focus on the integration . perceptions of social media’s adult populations. used inadequate measures such as single Intensity scale scores. and nonsignifi- Discussion cant correlations between the SMUIS and two scales from the BFI provided evidence for con- Previous measurement instruments for social vergent and discriminant validity.. 0.com. 2007). EFA analyses suggested that of its use into the daily lives and social behavior these items were not strong measures of Face- of users.243.g. media use in emerging adults have suffered both subscales’ scores exhibited significant pos- from a lack of detailed psychometric support itive correlations with the two factual items and validity evidence. scores. F(2. p ⫽ .893 for subscale 1 scores. Carpenter et al. and differences were evident in the data. Significant positive cor- scale mean scores.914 for total scale Similar to the original efforts of Ellison et al. we fo. although at lower on behavioral frequency of use (e. or that behavioral measures may be weaker Reliability and Validity Evidence for the means of operationalizing social media use and SMUIS its integration. such as number of average daily leagues have also suggested that measures of minutes spent using the site and number of social media use should capture the integration Facebook friends. as well as the emotional connection a book use integration. Ellison et al. 2010). Finally. This may be an important area of adapted to other forms of social media by re. We conducted scale for assessing social media use integration.g. 0. there were no Validity evidence was demonstrated in a num- gender or ethnic differences in the SMUIS sub. we developed a brief 10-item scale tional attachment to this use. or failed to It is important to note that three items from present adequate psychometric and validity ev.. Thus.. therefore. It was developed using rigorous scale ethnicities suggested that no significant group development methods (DeVellis. book use. and produced a dence for its use with this population and the two-factor model with subscale 1 called SIEC social media service Facebook. These results sug- of social media use integration called the gested either that social media integration is a SMUIS. In its development.com. and strong test–retest reli. for both subscales for different genders and vices.005). Scores from the 10-item two-subscale SMUIS Practical Implications demonstrated adequate internal consistency es- timates in this sample (0. ber of different ways. study for future research. and that they were weaker user develops to the media rather than simply than other items assessing the integration of frequency-of-use estimates. and preliminary validity evi- instrument’s internal structure. More and more individ- Limitations and Directions for Future uals. given the prevalence of Face- were removed because they did not fit well in book and other social media in the United the model. these results may have limited generalizabil- measurement issues and poor scale develop- ity. Additional work is needed to reflecting this type of content were retained in amass validity evidence. or Similarly. Although our creasingly incorporating social media into their scale was developed focusing exclusively on daily lives and social behavior. whereas items assessing the convergent. researchers and clini- To establish evidence for the validity and cians can better measure and operationalize so- utility of any new scale. It is also possible that traits associated dia. Social media is becoming increasingly more prominent in our lives. media and characteristics of its early users. integration. However. As this level of Facebook. such as (e. Our results support the researchers may want to examine the various conclusions of Ellison et al. laptops. benefited from rigorous methods in their devel- opment. it is intended to be flexible enough to incorporation increases. including support for the final model. Thus. future strong validity evidence. it is not surprising that be adapted for use with other types of social items assessing integration and emotional con. and fined and measured by looking at “quantity” composed of voluntary participants. Flickr. WRIGHT. measure’s appropriateness with noncollege stu- rience with their use. efit from a focus on the integration of media into phones. In the present study. desktops. nection thereto. Although previous establish validity evidence when adapting the scales of Facebook use have been published SMUIS to other types of social media. further research must seek to determine research by Reich (2010) and Raacke (2008).48 JENKINS-GUARNIERI. Fur. Although previous research Given the underrepresentation of racially has attempted to understand the impact of social and ethnically diverse students in this study. currently no scales have YouTube. The sample used was nonrandom.. suggesting media platforms that are frequently used to ac- that assessment of social media use would ben.. However. AND JOHNSON of a site into one’s social behavior and routines. populations of social media users. items dent samples. further research is cial media use integration in research focusing needed to confirm the strong psychometric on emerging adults. Vimeo. in development. when using this new scale. social routines as well as one’s emotional con- thermore. In addition. properties we have reported and to identify the as well as the emotional connection users expe. media. the SMUIS can be used Conclusions in research that calls for measuring social media use integration in emerging adult populations. the data collected with the SMUIS did not exhibit gender or ethnicity ment have hindered progress in this emerging differences for the subscale or full-scale mean area of research. which indicators focusing on behavioral frequency of may have produced significant selection bi. this scale’s appropriateness for use with other which suggested that emerging adults are in.. discriminant. With the SMUIS. our results suggest that the quan- with study participation may have influenced tity of social media use is not as useful as the manner in which they responded to the measuring how one integrates this use into scale items and thus biased the results. Ross et al. cess social media (e. use and amount of interaction with social me- ases. more research is needed to havioral frequency of use. data were collected only from par. and so forth. Twitter. Social media use is often de- scores. detailed psychometric support. rather than behavioral frequency or intensity of use. and tablets) to test for differences one’s life and the emotional connection to this among platforms. 2009).g. media. and instrument with psychometric and validity evi- research on the use of our SMUIS scale with dence that is adaptable to a variety of social other social media services is needed. groups. and organizations are joining and Research using social media networks as a primary means for communication. . The SMUIS provides a brief ticipants who had a Facebook account. given our focus on Facebook nection would be stronger indicators than be. (2007). and concurrent valid- more quantitative aspects of social media usage ity.g. 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