© 2015 American Psychological Association 1040-3590/15/$ 12.00 http://dx.doi.org/10.1037/pas0000034

Psychological Assessment 2015, Vol. 27, No. 1, 302-313

Functions of Nonsuicidal Self-Injury: Exploratory and Confirmatory Factor Analyses in a Large Community Sample of Adolescents Orjan Dahlstrom and Maria Zetterqvist

Lars-Gunnar Lundh

Linkoping University

Lund University

Carl Goran Svedin Linkoping University Given that nonsuicidal self-injury (NSSI) is prevalent in adolescents, structured assessment is an essential tool to guide treatment interventions. The Functional Assessment of Self-Mutilation (FASM) is a self-report scale that assesses frequency, methods, and functions of NSSI. FASM was administered to 3,097 Swedish adolescents in a community sample. With the aim of examining the underlying factor structure of the functions of FASM in this sample, the adolescents with NSSI who completed all function items (n = 836) were randomly divided into 2 subsamples for cross-validation purposes. An exploratory factor analysis (EFA) was followed by a confirmatory factor analysis (CFA) using the mean and variance adjusted weighted least squares (WLSMV) estimator in the Mplus statistical modeling program. The results of the EFA suggested a 3-factor model (social influence, automatic functions, and nonconformist peer identification), which was supported by a good fit in the CFA. Factors differentiated between social/interpersonal and automatic/intrapersonal functions. Based on learning theory and the specific concepts of negative and positive reinforcement, the nonconformist peer identification factor was then split into 2 factors (peer identification and avoiding demands). The resulting 4-factor model showed an excellent fit. Dividing social functions into separate factors (social influence, peer identification, and avoiding demands) can be helpful in clinical practice, where the assessment of NSSI functions is an important tool with direct implications for treatment. Keywords: nonsuicidal self-injury, assessment, adolescents, functions, factor analysis Supplemental materials: http://dx.doi.org/10.1037/pas0000034.supp

Bjarehed, 2011; Zetterqvist, Lundh, Dahlstrom, & Svedin, 2013), while repeated NSSI (defined as at least five instances) was reported by —15-20% of adolescents. These results are based on checklist questionnaires, such as the Functional Assessment of Self-Mutilation (Lloyd, Kelley, & Hope, 1997) and the Deliberate Self-Harm Inven­ tory (Gratz, 2001), with examples of both minor and severe types of NSSI. High prevalence rates of NSSI in adolescent community sam­ ples in other countries have also been found with similar methods (e.g., Cerutti, Manca, Presaghi, & Gratz, 2011; Lloyd-Richardson, Penine, Dierker, & Kelley, 2007). With such high rates, adolescent NSSI presents a challenge to health care professionals as well as staff working in school settings who need to know how best to identify, assess, prevent, and treat the behavior. This is clearly shown in a recent study by Taliaferro et al. (2013), in which almost 50% of primary care providers felt insufficiently prepared to ask about NSSI, and more than 70% wanted more training. One important aspect in assessing NSSI is the use of structured, psychometrically tested in­ struments. In making assessments, it is essential to include informa­ tion on the functions of self-injury, because this has direct implica­ tions for treatment (Lloyd-Richardson, Nock, & Prinstein, 2009).

Nonsuicidal self-injury (NSSI), to deliberately inflict damage to one’s own body tissue without suicidal intent (Nock, 2009), is cur­ rently a topic of special interest because of its inclusion in Section HI of the Diagnostic and Statistical Manual o f Mental Disorders-Fifth Edition (DSM-5\ American Psychiatric Association, 2013) as a con­ dition that warrants further study. Research on NSSI during the last decade has shown it to be a prevalent behavior, especially among the adolescent population. In recent Swedish studies, 34-42% of adoles­ cents in community samples reported having engaged in NSSI at least once (Jutengren, Kerr, & Stattin, 2011; Lundh, Wangby-Lundh, &

This article was published Online First January 5, 2015. Orjan Dahlstrom, Swedish Institute for Disability Research, Department of Behavioural Sciences and Learning, Linkoping University; Maria Zetterqvist, Department of Clinical and Experimental Medicine, Child and Adolescent Psychiatry, Linkoping University and Child and Adolescent Psychiatric Clinic, University Hospital; Lars-Gunnar Lundh, Department of Psychology, Lund University; Carl Goran Svedin, Department of Clin­ ical and Experimental Medicine, Child and Adolescent Psychiatry, Linkoping University. Correspondence concerning this article should be addressed to Maria Zetterqvist, Department of Clinical and Experimental Medicine, Child and Adolescent Psychiatry, Linkoping University, SE-581 85 Linkoping, Swe­ den. E-mail: [email protected]

The Functions of NSSI According to learning theory, a functional approach to NSSI requires the behavior to be analyzed and treated according to its 302

FACTOR ANALYSES OF THE FUNCTIONS OF NSSI

immediate internal and external contingencies (Nock & Prinstein, 2004, 2005). The antecedents and consequences of NSSI are analyzed to examine in which context the behavior is likely to be reinforced. Theories on the functions of NSSI have emphasized both intrapersonal and interpersonal functions. The term intraper­ sonal refers to functions aimed at altering an individual’s internal state (emotions, thoughts, and physical sensations), whereas the term interpersonal or social refers to functions that aim to change the external environment (Turner, Chapman, & Layden, 2012), for example, withdrawal of demands or increased social support. NSSI is maintained by several reinforcing processes (Nock, 2009), and in all likelihood serves multiple functions simultaneously (Muehlenkamp, Brausch, Quigley, & Whitlock, 2013). Empirical support for the functions of NSSI has been provided by self-report and laboratory-based studies (Klonsky, 2007), as well as by eco­ logical momentary assessment (Nock, Prinstein, & Sterba, 2009). The evidence suggests that the primary purpose in engaging in NSSI is the regulation of emotional or physiological experiences (e.g., Gratz, 2003; Klonsky, 2007, 2009). In addition to the regu­ lation of affective and dissociative experiences, NSSI can also serve the intrapersonal function of expressing self-hate/punishment (Klonsky, 2007). However, social reinforcing functions (e.g., “to get someone to notice me”) are not uncommon, especially in adolescents (Lloyd-Richardson et al., 2007; Nock & Prinstein, 2005; Zetterqvist et al., 2013). It is worth noting that socially reinforced NSSI is not synonymous with the absence of psycho­ pathology (Nock & Prinstein, 2005). When discussing the func­ tions of NSSI it is important not to make generalizations about all populations. Functions may also change at different stages of development (Lloyd-Richardson et al., 2009).

The Underlying Factor Structure of the Functions of NSSI The Functional Assessment of Self-Mutilation (FASM) assesses a range of functions of NSSI (Lloyd et al., 1997). Psychometric studies of FASM in clinical and nonclinical populations have demonstrated test scores of adequate internal consistency, good test-retest reliability, and good construct and concurrent validity (see under “measures” below). Nock and Prinstein (2004) con­ ducted a confirmatory factor analysis (CFA) based on the frame­ work of learning theory in a clinical sample of 108 adolescents, age 12-17 years, and suggested a four-factor model with good fit: contingencies that are automatic (intrapersonal, or reinforced by oneself) versus social (interpersonal, or reinforced by others), and reinforcement that is positive versus negative. In their study they showed that adolescents engaged in NSSI for a variety of reasons that are consistent with learning theory, providing empirical sup­ port for a functional model of NSSI. A positive reinforcement contingency means that a certain behavior (NSSI) is followed by a favorable stimulus, thus increasing the likelihood of repeated NSSI in a similar context. Negative reinforcement describes a contin­ gency where NSSI is performed in the presence of an aversive stimulus and engaging in NSSI decreases the aversive state, in­ creasing the likelihood of the behavior reoccurring. The results of Nock and Prinstein’s (2004) four-function model (FFM) were later confirmed by a CFA by Lloyd-Richardson et al. (2007) in a community sample of adolescents (n = 261). Other factor analyses on the functions of NSSI have also shown support for underlying

303

interpersonal/social and intrapersonal/automatic factors (Klonsky & Glenn, 2009). Previous research has found support for specific relationships between the factor functions of NSSI and clinical correlates. Depression and posttraumatic stress, for example, have shown to be related to automatic functions, whereas peer victim­ ization and socially prescribed perfectionism have been found to correlate with interpersonal/social functions (e.g., Hilt, Cha, & Nolen-Hoeksema, 2008; Nock & Prinstein, 2005). This research has advanced our understanding of why adolescents engage in NSSI, as well as adding strength to the construct validity of the FASM functional model. There have been more recent attempts to perform factor analy­ ses on the functions of FASM. A previous study (Zetterqvist et al., 2013) that used a CFA in an attempt to replicate the FFM of positive and negative reinforcement (Nock & Prinstein, 2004) in a large community sample of Swedish adolescents, only found sup­ port for a “close to acceptable fit” on both the four-factor and the two-factor (automatic and social) model, implying that there was room for improvement of the factor analysis in this sample. In a study by Leong, Wu, and Poon (2014), FASM was administered to Chinese adolescents ( n = 345), and a CFA was performed to confirm Nock and Prinstein’s (2004) FFM. The original FFM did not reach adequate fit and, therefore, a latent C-FASM factor was added to the model, and 11 residuals were set to correlate to improve model fit. Another study on the underlying factors of FASM functions of NSSI, conducted by Kaess et al. (2013) on clinical adolescents ( n — 65) in Germany, was unable to confirm a differentiation between positive and negative reinforcement as postulated by Nock and Prinstein (2004). Kaess et al. (2013) instead found support for a three-factor solution: “interpersonal influence,” “automatic functions,” and “peer identification.” In the light of recent recognition of peer influence in adolescent NSSI (Prinstein, Guerry, Browne, & Rancourt, 2009), a separate peer identification factor is an interesting finding. Mounting evi­ dence suggests that adolescent NSSI is a behavior susceptible to peer influence, particularly among vulnerable individuals (Jarvi, Jackson, Swenson, & Crawford, 2013). Studies on community samples of adolescents have shown that adolescents’ NSSI could be predicted by their best friend’s engagement in NSSI, even when the effects of depressive symptoms were controlled for (Prinstein et al., 2010; You, Lin, Fu, & Leung, 2013). The results in the study by Prinstein et al. (2010) suggested that socialization effects were most common among girls and younger adolescents. Nock and Prinstein (2005) further showed that more than 82% of psychiatric inpatient adolescents with NSSI reported having a close friend who also engaged in similar behaviors. It seems as though engag­ ing in the same sort of health risk behaviors as friends or highstatus peers may result in feelings of affiliation with others who engage in NSSI, which can serve as contingencies that provide social reinforcement for NSSI among certain individuals. Thus, research suggests that peer identification is a factor that also needs to be taken into account when assessing functions of NSSI among adolescents. In the literature on NSSI functions, two, three, and four under­ lying factors have all been discussed. One of the issues at hand concerns whether the automatic functions are distinct (Bentley, Nock, & Barlow, 2014; Selby, Nock, & Kranzler, 2014) and can be divided into separate automatic positive reinforcement (APR) and automatic negative reinforcement (ANR). Klonsky (2009), for

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example, postulates that self-injury is mainly associated with re­ ductions in negative affect rather than increases in positive affect, and more likely to be negatively reinforced, a perspective also endorsed by Chapman, Gratz, and Brown (2006). Another subject for discussion is whether a two-factor model represents a more parsimonious explanation of the mechanisms involved when an individual engages in NSSI, compared with models with several factors. Furthermore, there are differences in the way functions are defined: as specific antecedents and consequences that cause and maintain NSSI within the framework of learning theory or by using a broader concept of reasons and motives. Researchers have also placed a slightly different emphasis on the role that social functions play in NSSI (Bentley et al., 2014; Chapman et al., 2006; Klonsky, 2007, 2009; Nock & Prinstein, 2004). Some view interpersonal/social functions not as a primary goal of NSSI, but that NSSI can be reinforced, for example, by the elicitation of caring responses (e.g., Gratz, 2003). Despite these ongoing discussions, there is general agreement that functions carry crucial information concerning the mechanism of NSSI and warrant further investiga­ tion. However, previous studies that have examined the underlying factors of the functions of NSSI have used fairly small samples, limiting the analyses. Earlier studies have also either used datadriven approaches with explorative analyses to identify factors, or have confirmed theory-driven models. Combining both ap­ proaches to generate and cross-validate the function models on different subsets of samples would strengthen results.

The Current Study In view of these previous discussions and limitations, the current study aims to examine further the underlying factor structure of the functions of NSSI in FASM in a community sample of Swedish adolescents. This study uses a large sample of self-injuring ado­ lescents, which is especially meaningful because NSSI is particu­ larly salient in this age group. Apart from this project (Zetterqvist et al., 2013), FASM has not previously been psychometrically tested in a Scandinavian context. This motivated an empirically driven generation of underlying factors (EFA), followed by a confirmation of the generated factors (CFA), on different subsets of the sample for cross-validation purposes. As a follow-up, and given our aspiration to adhere to learning theory and positive and negative reinforcement, theory-driven models were also examined (using CFAs) on both the derivation and the validation sample. The large sample used in the present sample enables crossvalidation, which, to our knowledge, has not previously been done on factors of NSSI functions.

Method Participants The participants consisted of 3,097 adolescents age 15-17 years in their first year of high school. The sample was a community sample taken from the county of Ostergotland in the south east of Sweden, which on March 21st 2011 had 429,852 inhabitants (Statistics Sweden, 2011). Data from Statistics Sweden (2011) indicated that Ostergotland in the year 2010 was fairly represen­ tative of Sweden as a whole in terms of gender distribution, education level, proportion of inhabitants under 17 years of age

and proportions of children living with both parents, but the income level and proportion of inhabitants with foreign back­ ground was slightly lower. The county of Ostergotland was chosen for reasons of convenience. Special classes for students with pervasive developmental disorders, such as autism and mental retardation, were excluded from the study, as were classes of adolescents who had recently come to Sweden as refugees or immigrants. The former were excluded to ensure that the behavior was not part of a pattern of repetitive stereotypes, or could not be better explained by another mental disorder, such as autism spec­ trum disorder or intellectual disability, in accordance with the proposed nonsuicidal self-injury criteria (American Psychiatric Association, 2013). The latter group was excluded because of expected language barriers, preventing them from successfully filling out the questionnaire. A conditioned randomized sampling process was used to ame­ liorate the possibility of selecting students from large and small municipalities, urban and rural areas, and larger and smaller schools as well as state and private schools. Students from both vocational education programs (e.g., motor mechanics, electronics, hairdressing, and preparing for a trade) and theoretical education programs (e.g., science, social studies, and preparing for college/ university) were included in the sample to provide diversity in socioeconomic status, ethnicity, and gender. To achieve a sample size of 3,000,70% of the —6,000 students in their first year of high school in Ostergotland county in each of the 17 national education programs (The National Agency for Education, 2010) and the so-called individual program (adolescents who lacked formal com­ petence to begin high school) were selected through a randomiza­ tion process of school classes (expecting a drop-out rate of —20%). Randomization was performed using random.org (Haahr, 2010). When a selected school class or school declined to participate in the study, the next school class in the order given by randomization was contacted until a sufficient sample size had been reached. There were a total of 294 eligible school classes, of which 206 were chosen by randomization, resulting in 3,960 students. Of these, 3,097 students filled in the questionnaires, resulting in a response rate of 78.2%. The sample was representative compared with national data for 16-year olds regarding gender, education, ethnicity, and family structure (Statistics Sweden, 2011; The Na­ tional Agency for Education, 2010). There were 1088 adolescents who confirmed at least one episode of NSSI during the past year (the item “picked at a wound” in FASM was excluded because of its assumed trivial nature, unlikely to induce bleeding, bruising, or pain [American Psychiatric Association, 2013]). The negligible nature of this behavior was supported by a previous study by Zetterqvist et al. (2013), which showed that “picked at a wound” was endorsed by 65.3% of adolescents who confirmed NSSI. There were 836 adolescents who completed all the function items in FASM and were included in the analysis for the present study, thus excluding those with any missing data. Of the 836 adoles­ cents, 60% (n = 502) were randomly included in a derivation cohort and the remaining 40% (n = 334) were included in a validation cohort. Both cohorts were balanced over gender and age. Descriptives for the total sample are presented in Table 1. E xcluded cases. The 252 adolescents that were excluded be­ cause of missing items on FASM functions did not differ signifi­ cantly from those included (n = 836) concerning background demographics such as gender, parents’ or own country of origin,

FACTOR ANALYSES OF THE FUNCTIONS OF NSSI

305

Table 1 Frequencies and Percentages Regarding Demographics and Health-Related Variables fo r a Community Sample o f Adolescents With NSSI

Demographic variables Girls Boys Born in Sweden Both parents bom in Sweden Theoretical education Both parents working Living with both parents15 Health-related variables Ever used drugs Ever smoked Ever tried alcohol Has friend who has self-injured Ever had professional contact3

Overall sample (.n = 789-836)

Derivation cohort (n = 481-502)

Validation cohort (n = 308-334)

474 (56.9) 359(43.1) 763 (91.8) 619(74.1) 363 (43.4) 587 (74.4) 569 (68.4)

284 (56.9) 215(43.1) 465 (93.2) 385 (76.7) 218(43.4) 361 (75.1) 344(69.1)

190 (56.9) 144(43.1) 298 (89.8) 234 (70.3) 145 (43.4) 226 (73.4) 225 (67.4)

132(15.9) 475 (57.4) 665 (79.8) 573 (69.3) 386 (46.7)

73 (14.7) 292 (58.9) 416 (83.2) 359 (72.4) 230 (46.4)

59(17.7) 183 (55.1) 249 (74.8) 214 (64.7) 156 (47.3)

P

.046

.004 .022

Note. NSSI = nonsuicidal self-injury; EFA = exploratory factor analysis; CFA = confirmatory factor analysis. 3 Not specifically for self-injury. b Jointly or separately.

education, perception of family’s financial situation, or parents’ occupational status. However, there were significant differences concerning living conditions as well as in self-injury status. More adolescents in the excluded group reported living with both their parents (jointly or separately). The excluded group also reported less frequent NSSI, as well as less moderate/severe NSSI as defined by Lloyd et al. (1997).

Procedure The headmaster/headmistress of each school was given infor­ mation about the study and they gave their consent for the school to participate. One week before the test session in the classroom, teachers distributed written information about the study. Students and parents were informed that participation was voluntary, and if the students wished to participate in the study they should show up in class the following week when the data collection would take place. According to The Ethical Review Act (2003) of Sweden, active consent is not required from parents when adolescents are 15 years of age or older. Parents were informed that they were welcome to contact the research group if they had any questions or did not want their child to participate. Data collection was per­ formed in the classroom, with desks placed sufficiently far apart to ensure anonymity. The questionnaires consisted of 12 pages and took ~ 2 5 -30 min to complete. The questions on the first two pages were demographic in character, followed by five pages of questions on self-injurious behaviors, which included FASM. The last five pages consisted of questions on adverse life events and trauma symptoms, not used in the present study. The method section has been described in detail in a previous study by Zetterqvist et al. (2013).

Ethical Issues The study was approved by the Regional Ethical Review Board of Linkoping. During the data collection, students were encour­ aged to seek professional help if needed. Additionally, every

student was given written information to take home listing contact information to several counseling alternatives in their home town.

Measures Nonsuicidal self-injury. The FASM (Lloyd et al., 1997) as­ sesses the methods, frequency, and function of self-reported de­ liberate NSSI. Respondents are asked whether they have engaged in any of 11 different forms of NSSI during the past year or at any time previously. The frequency of NSSI and whether medical treatment was received is also assessed. Participants are also asked the length of time they had contemplated the behavior(s), at what age their NSSI first began, whether any of the NSSI was performed under the influence of drugs or alcohol, the degree of physical pain experienced during NSSI, and whether any of these behaviors was a suicide attempt. FASM contains 22 statements assessing the functions of NSSI, which respondents rate on a 4-point Likert scale, covering the categories never, rarely, some, and often. FASM has previously been used in normative (Lloyd et al., 1997) and psychiatric samples (Guertin, Lloyd-Richardson, Spirito, Don­ aldson, & Boergers, 2001), with test scores showing acceptable psychometric properties in adolescent samples (Esposito, Spirito, Boergers, & Donaldson, 2003; Guertin et al., 2001; Penn, Es­ posito, Schaeffer, Fritz, & Spirito, 2003), and adequate internal consistency for both minor and moderate/severe forms of NSSI (a = .65 to .66). Test scores from FASM also support concurrent validity, demonstrating significant associations with measures of recent suicide attempts, hopelessness, and depressive symptoms (Nock & Prinstein, 2005). The Swedish version of FASM was translated into Swedish using a back-translation procedure and tested in a pilot study. The psychometric properties of the Swedish version, administered to a community sample of adolescents, have been fully described in another article by Zetterqvist et al. (2013). Reliability scores of the Swedish version of FASM for the present sample, was tested with acceptable/good internal consistency. Cronbach’s a for the present sample (n = 836) on all NSSI items was a = .80. Results for the subscales referred to in Lloyd et al.

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(1997) and Guertin et al. (2001) for both minor and moderate/ severe forms of NSSI was et = .64 to .70. Cronbach’s a for the FASM functions for the present sample was a = .88. Demographic information. A demographic questionnaire was created for the purpose of the study assessing demographic characteristics such as gender, type of education, own and parents’ country of origin, perception of family’s economy, living condi­ tions, and parents’ occupation. Health-related behaviors were as­ sessed with questions such as “Do you smoke?”, “How often do you drink alcohol?”, and “Have you ever used drugs?” Adoles­ cents self-reported demographic information and health-related behaviors in fixed answer categories.

Data Analysis The derivation cohort (60% of the study sample, n = 502) was used to generate a factor model using EFA and the validation cohort (40% of the study sample, n = 334) was used to confirm the model using CFA. First, an EFA was performed to come up with a data-driven model, which was then validated with a CFA. Sec­ ond, two separate CFAs were performed on the theory-driven models, using both the derivation cohort and the validation cohort to cross-validate results. EFA as well as CFA were carried out with Mplus, Version 7 (Muthen & Muthen, 1998-2012), using the robust mean and variance adjusted weighted least squares (WLSMV) estimator method based on polychoric correlations and the diagonal of the weight matrix, because FASM functions use an ordinal Likert scale 0 -3 for its items. In the EFA, oblique rotation was used to allow for correlation between the factors. The decision regarding the number of factors to retain was based on Kaiser Criteria (eigenvalue >1.0) and inspection of scree plot, in the absence of parallel analysis on (ordered) categorical data in Mplus. Overall model fit was tested by x2 statistics and fit indices pro­ vided by the Mplus output: Root Mean Square Error of Approxi­ mation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI, also known as the Non-Normed Fit Index [NNFI]), and Standardized Root Mean Square Residual (SRMR). In the CFA, the Weighted Root Mean Square Residual (WRMR) was also used instead of SRMR. Using guiding principles by Brown (2006), several different fit indices were used. For convenience of reporting, a model was judged as having good fit when the overall picture of fit indices indicated good fit and excellent if all of them indicated good fit: RMSEA s .05, CFI and TLI £ .95, and SRMR < .08 or WRMR < .90 (see, e.g., Hu & Bender, 1999; Yu, 2002). Discriminant validity was tested using the procedure sug­ gested by Fomell and Larcker (1981), that is, that the average extracted variance for each factor should be at least .50 and exceed the squared correlations with the other factors. Cronbach’s a was used to test the internal consistency of the different factors. Cat­ egorical data were analyzed with descriptive statistics using fre­ quencies and cross-tabulation with x2 statistics, performed in IBM SPSS Statistics (version 20.0).

Results Study Sample Characteristics Of the 836 adolescents who reported NSSI during the past year, and had completed all function items on FASM without missing

data, 289 (34.6%) reported having engaged in NSSI 1 -4 times, 169 (20.2%) reported 5-10 times and the remaining 378 (45.2%) adolescents reported NSSI >11 times. Regarding type of NSSI reported in the sample, 591 (70.7%) reported at least one episode of NSSI that Lloyd et al. (1997) referred to as “moderate/severe,” such as cutting/carving, burning, self-tattooing, scraping and eras­ ing skin. Of these, 277 (46.9%) reported a:5 of more moderate/ severe types of NSSI. Demographics and health-related variables of all the participants, as well as the derivation and validation cohort, are presented in Table 1. Significantly more adolescents in the derivation cohort reported having tried alcohol, x2( L N = 833) = 8.30, p = .004, had a friend who had self-injured, x2(l> A = 827) = 5.21, p = .02 and had parents who were both born in Sweden, x2(U N = 835) = 3.98, p = .046. The other demographics and health-related variables did not differ significantly between the derivation and the validation cohort. There was no significant difference with regard to NSSI frequency. However, a higher proportion of adolescents in the derivation cohort (73.9%, n = 371) reported at least one episode of moderate/severe NSSI compared with the validation cohort (65.9%, n = 220), x2(E N = 836) = 5.87, p = .02.

Generation of Underlying Factors—EFA The EFA found a three-factor structure with eigenvalues of 10.92, 2.89 and 1.64, with all remaining eigenvalues less than 1.00. Inspection of the scree plot also lent support to a three-factor model. A figure of the scree plot is available as a supplement online. The standardized factor loadings were all significant and varied between .696 to .947 for Factor 1, .712 to .935 for Factor 2 and .532 to .971 for Factor 3 (see Table 2), indicating high factor loadings (Brown, 2006). The fit indices for the exploratory model were x2 = 279.54 (df = 168) p < .0001, CFI = .981, TLI = .973, RMSEA = .036 (90% Cl = [.029, .044]), and SRMR = .051. x2 was significant, but it is sensitive to sample size (Brown, 2006). It was, however, less than two times (1.66) the degrees of freedom, indicating a good-fitting model (Tabachnick & Fidell, 2013). The other fit indices indicated excellent fit, CFI and TLI > .95 (Hu & Bentler, 1999), RMSEA < .05 and SRMR = .05. The first factor referred to social functions and only included functions that imply that NSSI was performed to receive help and to be noticed. The second factor mainly addressed emotion regulation functions and the attempt to decrease or increase affect by engaging in NSSI (auto­ matic functions). The EFA also identified a separate third factor including functions such as “to be like someone you respect,” “to feel more a part of a group,” and seemed to refer to social functions focusing on the relationships with peers. In addition to the peer items, the third factor also included other social items that aimed at avoiding demands such as school or paying the consequences, as well as making others angry. This factor was named “nonconform­ ist peer identification,” suggesting that the behavior is reinforced in the social context of peer affiliation and identification with a lifestyle that does not conform to common rules and the demands of society in general. Each of the three factors identified in the EFA differentiated between social (social influence and noncon­ formist peer identification) and automatic functions (emotion reg­ ulation).

FACTOR ANALYSES OF THE FUNCTIONS OF NSSI

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Table 2 Standardized Factor Loadings fo r the Three-Factor Model Derived by EFA in Derivation Cohort (n = 502) and Confirmed by CFA in Validation Cohort (n = 334) EFA 1 Social influence 8. To receive more attention from your parents or friends 3. To get attention 7. To try to get a reaction from someone, even if it’s a negative reaction 17. To get your parents to understand or notice you 15. To let others know how desperate you were 11. To get other people to act differently or change 20. To get help Automatic functions 2. To relieve feeling numb or empty 14. To stop bad feelings 22. To feel relaxed 4. To feel something, even if it was pain 10. To punish yourself 6. To get control of a situation Nonconformist peer identification 19. To give yourself something to do when with others 16. To feel more a part of a group 5. To avoid having to do something unpleasant you don’t want to do 21. To make others angry 12. To be like someone you respect 9. To avoid being with people 13. To avoid punishment or paying the consequences 1. To avoid school, work or other activities 18. To give yourself something to do when alone Eigenvalue Note.

2

3

CFA

.947 .850 .842 .825 .799 .726 .696

.359 .330 .584 .449 .591 .554 .677

.538 .379 .406 .542 .545 .571 .359

.871 .709 .815 .821 .683 .815 .865

.505 .444 .321 .384 .391 .402

.935 .848 .822 .783 .768 .712

.147 .288 .405 .219 .251 .342

.848 .851 .835 .869 .760 .725

.470 .452 .519 .701 .378 .511 .498 .603 .325 10.92

.260 .012 .506 .336 .137 .517 .484 .495 .412 2.89

.971 .760 .720 .712 .710 .708 .679 .679 .532

.659 .721 .834 .768 .720 .807 .816 .753 .686

1.64

Values in boldface indicate the highest factor loading for each item. EFA = exploratory factor analysis; CFA = confirmatory factor analysis.

Validation of Underlying Factors—CFA Model 1: Three-factor model. We let the data guide our choice of factors and the three-factor model was passed on for validation on the validation cohort. A CFA was conducted on the validation cohort using the three-factor FASM model, suggested by the EFA. The results of the three-factor model in the CFA (see Table 2) showed good model fit indices (see Table 3). No func­ tions were only reported “never” or “rarely.” The functions in the automatic factor were most commonly reported by adolescents,

Table 3 CFA Fit Indices fo r the Three-Factor and Four-Factor Models o f NSSI Functions fo r the Functional Assessment o f SelfMutilation (n = 334) Statistic

Three-factor model

Four-factor model

X2 model fit df p-value RMSEA (90% Cl) CFI TLI WRMR

358.66 206 5 episodes of NSSI to examine whether the findings would be affected if the sample consisted of only those with repetitive NSSI. However, this did not alter the factor structure and the differences in factor loadings and model fit were minor. We also used two missing value imputation proce­ dures in which missing values were randomly imputed by (a) any possible values, and (b) a randomly selected value among those values existing for that variable in the items excluded in the factor analyses. None of these analyses showed any meaningful differ­ ence to the results reported (concerning factor structure, factor loadings, or model fit).

Discussion The purpose of the present study was to examine the underlying factor structure of the functions of NSSI. To our knowledge, this is the largest study to date using FASM in an adolescent sample, and the first to use cross-validation of samples with the WLSMV estimator in Mplus, suitable for factor analysis on ordinal data, when investigating underlying factor structure. Analysis revealed support for both a three-factor and a four-factor solution, and to a somewhat lesser degree a two-factor model. The first two factors were virtually identical in both the three- and four-factor models. The first factor can be described as “social influence,” and the second, “automatic functions,” refers to the regulation of affective and dissociative experiences as well as punishing oneself. In the three-factor model the third factor was named “nonconformist peer identification.” To adhere more closely to learning theory and the concept of negative and positive reinforcement, the third factor in the three-factor model was then split into two factors, resulting in a four-factor model with peer identification as the third factor and avoiding demands as the fourth. This four-factor model showed excellent fit to the data. Each of the factors in the analysis differ­ entiated between interpersonal/social and automatic/intrapersonal functions. Thus, a two-factor (automatic and social) model was also cross-validated against the derivation cohort, showing a good fit in the validation cohort, although not as good as the excellent fit of our four-factor model in this study. Our four-factor model was preferred in this community sample of adolescents, due both to its better fit and adherence to learning theory, as well as the clinical utility of dividing the social functions into separate meaningful factors, such as a separate peer identification, which probably is especially salient for this age group.

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The Three-Factor and Four-Factor Models Factor 1: Social influence. To some extent this factor is congruent with the social positive reinforcement factor in Nock and Prinstein’s (2004) original FFM. However, the EFA in this study identified a clearer help-seeking factor, consisting only of items where engaging in NSSI is anticipated to influence others so as to increase their support and commitment, which is likely to reinforce the behavior (e.g., “to get help,” “to get others to notice you,” “to get other people to act differently,” or “to get attention from parents”). In our study, this factor thus distinguished between social influence items that refer to help-seeking, and peer identi­ fication items, which were not included. The first factor in our analysis closely resembles the interpersonal influence factor found in Kaess et al. (2013). This further supports the supposition that NSSI in adolescents is also motivated by social functions (LloydRichardson et al., 2007; Nock & Prinstein, 2004, 2005) and the anticipation of consequences, such as receiving help and being noticed, possibly in a context where there has been insufficient response to previous attempts (Nock, 2008). Although it is perhaps secondary to the emotion regulation functions and not as com­ monly reported, this social influence factor included functions that were relatively prevalent in this sample. Although they represent distinguishable factors, it is important to bear in mind that the social and automatic factors are also correlated. It is not uncom­ mon that aversive internal experiences are triggered by interper­ sonal stress (Chapman et al., 2006). It is also likely that positive changes in close relationships, such as increased warmth, attention, and help, can alleviate negative states and help regulate affect. Factor 2: Automatic functions. The automatic factor in­ cluded the items with functions that were most often reported by adolescents in this study. This is congruent with earlier literature on the functions of NSSI (e.g., Gratz, 2003; Klonsky, 2007, 2009) and converges with evidence that emotion regulation is the main function of NSSI. However, we did not find support for dividing this factor into two factors with positive and negative reinforce­ ment as in Nock and Prinstein (2004). Instead, the factor consisted of items that aimed at both generating feelings and decreasing arousal, which again is consistent with the results from the factor analysis performed by Kaess et al. (2013) on FASM administered to clinical adolescents in Germany. Nock, Holmberg, Photos, and Michel (2007) have pointed out that some items in the ANR and APR factors are theoretically similar. The fact that the ANR factor in the original FFM (Nock & Prinstein, 2004) only consists of two items could possibly contribute to its difficulty in being replicated in our study, because factors with less than three variables are difficult to interpret (Velicer & Fava, 1998). In FASM, there are more items that represent social functions and fewer that repre­ sents automatic functions and the relative sparsity of automatic functions may influence the factor structure. It could be argued that in theory all items in the automatic factor represent some form of experiential avoidance as a general func­ tional class of behavior, regardless of whether it be the removal of an aversive, or the presentation of an appetitive, stimulus that reinforces the behavior. The aversive state becomes the stimulus for performing NSSI, which is congruent with the theory of expe­ riential avoidance (Chapman et al., 2006). The positive reinforce­ ment of feeling relaxed, for example, could be preceded by a sense of discomfort that is reduced by performing NSSI. The question of

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negative and positive reinforcement with regard to emotion regu­ lation in NSSI has been explored empirically, with some incon­ sistent results. A study by Muehlenkamp et al. (2009) using a sample of women with bulimia nervosa, for example, showed that NSSI was followed by a significant increase in positive affect, but no decrease in negative affect. Klonsky (2009) found that higharousal negative affect (e.g., anxiety) decreased after NSSI and low-arousal positive affect (e.g., relief) increased. Thus, it is likely that NSSI is reinforced both by the reduction of an unwanted state as well as the positive sensation of feeling calmer and more relaxed when the negative aversive stimulus is reduced. Selby et al. (2014) have discussed this issue, reporting that semantically, adolescents may interpret relief from unbearable emotions as re­ laxing, for example. For some individuals, both APR and ANR motivations could be involved in maintaining NSSI simultane­ ously, and that it is a matter of perspective. This could be one possible explanation as to why the automatic factor loaded on all the intrapersonal items in the present study, regardless of whether NSSI was positively or negatively reinforced. Selby et al. (2014) recommends more precise definitions and understanding of the APR construct to further understand where the APR and ANR functions overlap or are distinguishable. The automatic factor loaded higher on Item 6, “to get control of a situation,” than did the social factors. This implies that in our Swedish sample, Item 6 was not considered to be a social factor, as is the case in Nock and Prinstein (2004) and Lloyd-Richardson et al. (2007), but instead probably referred to self-control of internal states. Our results regarding Item 6 replicated the German study by Kaess et al. (2013), in which the automatic factor also loaded on Item 6. This inconsistency is most probably a result of the analyses used. In Nock and Prinstein’s (2004) study, Item 6 was placed in the social positive reinforcement factor based on a consensus discussion and then confirmed with a CFA, while when using explorative analysis based on data, as in Kaess et al. (2013) and the present study, the automatic factor loaded on Item 6, as it correlated more with other automatic items than with social items. In the EFA, the automatic factor had high loadings on Item 20, “to get help,” meaning that it also correlated with items mainly refer­ ring to internal emotion regulation, perhaps using NSSI to regulate internal emotional states as a way of helping oneself. However, to be congruent with the social and automatic perspective, the item was included in the social influence factor where it in fact had the highest loading.

The Three-Factor Model Factor 3: Nonconformist peer identification. In addition to the first and second factors above, the EFA also identified a third factor, “nonconformist peer identification,” which was confirmed with a good fit in the CFA. Some items included in this factor can be viewed as peer identification, such as “to be like someone you respect,” “to be a part of a group,” and “to give yourself something to do with others.” Additionally, the factor also had strong load­ ings on items such as “to make others angry,” “to avoid paying the consequences,” “to avoid school or other activities,” and “to avoid other people.” Taken as a whole, this caused us to view the factor as perhaps representing a somewhat socially deviant lifestyle, where the attention is directed toward affiliation with the peer group. In this factor, NSSI is reinforced in the social context of the

peers, perhaps by strengthening a feeling of belonging with the reinforcing properties of being part of a group. As Prinstein et al. (2010) point out, for some subgroups of peers, as for example nonconformist peer crowds, NSSI may be associated with high status. Peer identification was also identified as a third factor in Kaess et al. (2013) three-factor model, but in their study the factor did not load on the other “avoiding” items, as was the case in our study. The reinforcing properties of peers during adolescence have previously been shown in empirical research on NSSI (see, e.g., Jarvi et al., 2013; Prinstein et al., 2010; You et al., 2013).

The Four-Factor Model In addition to the first two factors (the social influence and automatic factor), two additional factors were decided upon and tested in the CFA. This was done with the aim of adhering to Nock and Prinstein’s (2004) original four-factor function model based on learning theory with negative and positive reinforcement, as well as integrating the results from Kaess et al. (2013) regarding the “peer identification” factor. As mentioned above, the automatic factor could not be differentiated into negative and positive rein­ forcement, but it was possible to apply the distinction to the social functions. According to Bentley et al. (2014), it is easier to see a distinction between negative and positive reinforcement in social functions, where the contingencies are more readily distinguish­ able. This is confirmed in our study where the social functions could be separated into negative and positive reinforcement. The items in the nonconformist peer identification factor were divided into two social factors (negative and positive reinforcement). Factor 3: Peer identification. In the four-factor model, the third factor, “peer identification,” consisted only of social positive reinforcement items referring to peer identification, similar to the peer factor identified in Kaess et al. (2013). Although empirical evidence has shown peer identification/influence to be salient in adolescent NSSI (Jarvi et al., 2013; Prinstein et al., 2010; You et al., 2013), and therefore, needs to be taken into account when assessing functions of NSSI, these functions were not commonly reported by adolescents in the current self-report study. The peer identification factor contained the least commonly reported func­ tions, confirmed only by a small percentage, and was reported far more seldom than automatic or social influence functions. In our sample, 69.3% of the adolescents reported having a friend who had also engaged in NSSI. Although this is a considerable number, it is not as high as that reported in Nock and Prinstein’s (2005) study with a clinical sample of adolescents. It is probable that the observed difference in prevalence rates can be explained by the difference in samples (clinical vs. community). Perhaps this peer identification factor can be useful in understanding mechanisms behind the social contagion of NSSI that has been reported in adolescents in both community and clinical samples previously (Jarvi et al., 2013; Nock & Prinstein, 2005; Prinstein et al., 2010). Factor 4: Avoiding demands. The fourth factor in our fourfactor model refers to avoiding demands and is identical to the social negative reinforcement factor in Nock and Prinstein’s (2004) original FFM. The function “to avoid school, work or other activities” was relatively commonly reported by adolescents in this sample (17.9%). Individualized assessment of the specific func­ tions can be helpful in clarifying whether the avoidance is a consequence of too high demands on the adolescent, lack of skills

FACTOR ANALYSES OF THE FUNCTIONS OF NSSI

and/or necessary support to deal with challenging situations, knowledge that is essential to tailor interventions. Learning adap­ tive skills to deal with perceived overwhelming social demands, for instance by asking for help, as well as increasing social support and adjusting demands would hopefully decrease the need for future NSSI to avoid demands. The four-factor model had the strongest support in the CFA (see Table 3). The excellent model fit was achieved without correlation of item residuals, adding strength to the model. The four-factor model in our study seems more logical and closer to learning theory, with social positive and negative reinforcement factors and one automatic factor referring mainly to emotional regulation. According to Brown (2006), aspects such as clinical benefit should also be considered when deciding on a suitable model, in addition to goodness-of-fit. The strength of this model is that is combines both empirically driven and theory-driven approaches. The factor structures found in the current study share both differences and similarities with previous factor analyses of FASM, which, how­ ever, have been conducted on smaller samples. The large sample and the use of both EFA and CFA in the Mplus statistical program with the WLSMV estimator (that is suitable for factor analysis on ordinal data) add to the importance of the contribution of this study. As in previous research, our results showed that factors seem to be differentiated by “social/interpersonal” and “automatic/ intrapersonal” functions. At the same time, the results contribute further information, with social functions being divided into three separate factors: social influence, peer identification, and avoiding demands, a distinction that can be of importance in clinical prac­ tice.

Methodological Considerations When using CFA, a recommended sample size of 100 to 200 observations or a ratio of 5 to 20 observations per variable is often used as a minimum criterion (Schumacker & Lomax, 2004). The CFA in the present study was based on a sample size (n = 334) greater than 200 observations and the ratio of observations for each item was approximately 16:1. From this perspective, our sample size is sufficient. Both sample sizes exceeded the minimal size requirements necessary for factor analysis (Flora & Curran, 2004; Muthen, du Toit, & Spisic, 1997). There is growing consensus that the best approach to analyzing categorical variables (with few categories) is the robust weighted least squares (WLS) approach (estimator = WLSMV or WLSM in Mplus). This seems to work well if the sample size is 200 or larger (Flora & Curran, 2004; Muthen et al., 1997). We chose to exclude the 252 cases with incomplete FASM function data, that is, to analyze using listwise deletion, because it is preferable to base analysis on actual data rather than estimates and imputation for missing values (Brown, 2006). This decreases power for analysis, but was possible because of our large sample. In addition, we also tested for the effect of the excluded participants by rerunning the analyses several times using (nonfavorable, when trying to replicate results) imputation methods, but results showed only minor deviations from the results in the original analyses. All analyses in the present study were performed on samples from the same population. Further studies should include samples from other populations. We could not compare the fit between our models, because they were not nested and there is no established method to evaluate the relative fit of

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nonnested models under the WLSMV estimator in Mplus. The x2 goodness-of-fit was significant in our models. Other fit indices were also used. The RMSEA, which is less sensitive to sample size, showed a very good fit between both the three-factor and four-factor models and the data in our analyses, as did the other fit indices. In the EFA, several factors loaded onto several items (see Table 2). In psychological research it is common that items also have secondary loadings (Brown, 2006). There were some differ­ ences between the derivation and the validation cohorts (see Table 1). After (Bonferroni) correction for multiple testing the only significant difference remaining is that the proportion who had ever tried alcohol was 8.4% higher in the derivation cohort. Given a proportion of 79.8% in the study sample, an 8.4% difference was not considered particularly large. Comparison of the correlation matrices from the cohorts showed that the differences in correla­ tions approximately followed a normal distribution (the deviation from normal distribution of these differences was minimal with a Kolmogorov-Smirnov test resulting in p = .92).

Study Limitations There are some limitations in this study that warrant discussion. The 252 adolescents that were excluded from analysis because of missing items on FASM functions did not differ from those in­ cluded regarding background demographics such as gender, par­ ents’ or own country of origin, education, parental occupational status, or perception of the family’s financial situation. However, there were significant differences regarding living conditions as well as self-injury status. Those excluded reported less frequent NSSI, as well as less moderate/severe NSSI. Many of those with less frequent and minor NSSI had not completed any of the function items. It is possible that they constituted a group that had experimented with NSSI once or twice and did not regard the specified functions as applicable. This needs to be taken into account when discussing the generalizability of the results beyond the study sample. Because the findings are based on data from a community sample of adolescents, they might be less generalizable to clinical samples with more severe psychopathology. Data was gathered through retrospective self-report, which has well-known limitations, for example with recall bias. One can also question whether psychological processes involved in behaviors such as NSSI, as for example antecedents and consequences, are totally within an individual’s awareness. NSSI may very well be rein­ forced by contingencies that are outside conscious awareness, limiting self-report as a method (Nock et al., 2009).

Implications and Future Research The results in the current study have implications that are useful both for clinical work and future research. Clinicians can benefit by having access to easily administered and structured instruments to assess NSSI and its functions. Knowledge of why an individual chooses to engage in NSSI, by examining its reinforcing mecha­ nisms, is a crucial component in treatment. As of yet, evidence for specific psychological treatment of NSSI is lacking. This must have high priority for future research, which would benefit from having functionally guided research questions (Bentley et al., 2014). Longitudinal research is needed, for example, by using the factor structure to investigate changes of functions of NSSI over

DAHLSTROM, z e t t e r q v is t , l u n d h , a n d s v e d in

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time. A fruitful way to use the factor structure would be to evaluate interventions by measuring the change in the need to use NSSI to regulate affect and to influence social relationships. These inter­ ventions can for example, be aimed at increasing emotional aware­ ness and regulation skills as well as improving communication and encouraging caregivers to respond to less intense forms of com­ munication (Nock, 2008). Future research that provides evidence of function-specific risk factors would also be of importance in prevention and functionally relevant interventions. Using the underlying factor structure, pos­ sible subgroups of adolescents could be identified for whom the behavior of NSSI serves different functions, hopefully leading the way to individualized treatment. Future research of factor structure needs to be extended to large clinical samples before results can be generalized to clinical practice.

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Received September 27, 2013 Revision received August 15, 2014 Accepted September 3, 2014 ■

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Functions of nonsuicidal self-injury: exploratory and confirmatory factor analyses in a large community sample of adolescents.

Given that nonsuicidal self-injury (NSSI) is prevalent in adolescents, structured assessment is an essential tool to guide treatment interventions. Th...
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