The Dual-Factor Model of Mental Health

Abstract

French

Consistent with a positive psychology framework, this study examined the contributions of personality, environmental, and perceived social support variables in classifying adolescents using Greenspoon and Saklofske’s Dual-Factor model of mental health. This model incorporates information about positive subjective well-being (SWB), along with psychopathology (PTH), to identify four groups of adolescents: positive mental health (high SWB, low PTH), vulnerable (low SWB, low PTH), symptomatic but content (high SWB, high PTH) and troubled (low SWB, high PTH). Using multinomial logistic regression analyses, adolescents were accurately classified into the four groups above chance. The contribution of the personality, social support, and stressful life events variables differed across the groups. Differences in perceived parent social support statistically significantly differentiated (p < .05) the vulnerable and troubled groups from the positive mental health group. The experience of stressful life events significantly differentiated the troubled group from the positive mental health group. The personality characteristics of Extraversion and Neuroticism significantly differentiated symptomatic but content and troubled students from the positive mental health group. The study thus identified relatively malleable factors (e.g., parent support) that relate to optimal mental health.

Keywords

dual-factor model positive mental health adolescent life satisfaction subjective well-being

The field of psychology has historically focused on classifying psychological dysfunction to understand and treat mental illness. Over the past 100 years, the field has made tremendous strides in designing relevant classification systems (e.g., the Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text rev. [DSM-IV], American Psychiatric Association, 2000). However, less attention has been given to understanding how positive indicators of mental health, such as happiness, hope, and resiliency may relate to a person’s mental health and development of psychopathology (Seligman & Csikszentmihalyi, 2000).

Studies in the field of positive psychology suggest that positive indicators provide useful information beyond that normally gathered during a traditional psychological assessment. Although the key indicators of positive mental health have been debated, one construct that has been widely used in the positive psychology literature is subjective well-being (SWB). According to Diener (1984), SWB is a higher-order construct comprised of several components, including positive affect, negative affect, and life satisfaction. The former two components refer to emotional experiences, specifically the frequency of positive and negative emotions experienced during a given time period. The latter component, life satisfaction, is the cognitive-evaluative component, which has been defined as “A global assessment of a person’s quality of their life according to his (sic) chosen criteria” (Shin & Johnson, 1978, p. 478). The life satisfaction component is the most stable component and has received the most research attention among children and adolescents (Huebner, Gilman, & Ma, 2012).

Although researchers have increased their focus on examining positive indicators, studies often conceptualize SWB and psychopathology (PTH) as representing two extremes on a single continuum. That is, most research on life satisfaction implicitly or explicitly suggests that individuals experiencing high SWB do not experience PTH, and individuals manifesting the lowest levels of SWB experience some PTH. However, recent variable-centered studies suggest that PTH and SWB are multidimensional. For example, using factor analysis, Wilkinson and Walford (1998) found support for a two-factor model of mental health comprised of distinguishable SWB and PTH factors in a sample of adolescents. Person-centered studies, such as those described as follows, have also supported a multidimensional model of mental health.

Proposing the dual-factor model of mental health, Greenspoon and Saklofske used measures of PTH (i.e., internalizing and externalizing behaviors) and SWB (i.e., life satisfaction) together to classify elementary school students as high or low in SWB and PTH. Using the subsequent nomenclature of Antaramian, Huebner, Hills, and Valois (2010), four meaningful groups of students were identified: (1) “positive mental health” students reporting high SWB and low PTH, (2) “troubled” students reporting low SWB and high PTH, (3) “symptomatic but content” students reporting high SWB and PTH, and (4) “vulnerable” students reporting low SWB and PTH. The Dual-Factor model thus differentiated two groups of children that would not have been differentiated using traditional unidimensional measures of mental health, that is, the positive mental health group and the vulnerable group. By identifying a group of children who display low levels of PTH and SWB, this integrated system of assessment challenges the traditional notion of mental health as simply the absence of PTH (Greenspoon & Saklofske, 2001). Using a wide array of variables, Greenspoon and Saklofske further explored presumed antecedents of group membership. They found that the group membership could be reliably differentiated by various combinations of personality, social, and cognitive variables.

Two subsequent research studies with adolescents have assessed presumed differential “outcomes” associated with group membership in the Dual- Factor model. Suldo and Shaffer (2008) found that the positive mental health group displayed higher scores on measures of reading, social functioning, and physical health. Antaramian et al. (2010) found the positive mental health group demonstrated higher levels of school engagement and school grades. Furthermore, they found that adolescents identified as vulnerable closely resembled the adolescents identified as troubled.

Given that important academic, social, and physical health differences can be identified among the groups, it is important to investigate further the potential antecedents of group membership. For example, understanding how and why some children become vulnerable should yield insight into relevant developmental pathways, leading to improved efforts to promote optimal mental health and prevent the development of clinical levels of PTH.

The purpose of this study was to investigate further the presumed antecedents of group affiliation within the Dual-Factor model. Previous studies have been largely exploratory and atheoretical; thus, Evans’ (1994) conceptual model of subjective quality of life (similar to life satisfaction) was used to guide our work. Evans’ model incorporates personality/temperament (e.g., Extraversion), environmental (e.g., life events), and cognitive variables (e.g., perceived social support) as determinants of subjective quality of life. No studies have sampled presumed determinants from all of these three major sets of antecedent variables. Thus, we included measures of personality/temperament variables (i.e., Extraversion and Neuroticism), environmental variables (i.e., stressful life events), and cognitive variables (i.e., perceived social support), all of which have been shown to be significant correlates of adolescents’ life satisfaction (Huebner et al., 2012). Moreover, Greenspoon and Saklofske’s study was based on a sample of Canadian elementary school students. To assess the generalizability of these findings to a different sample, we employed a sample of US adolescents in middle and high school. Finally, statistical techniques were used that are more robust to violations of assumptions compared to discriminant function analysis.

Method

Participants/Procedure

The data used for this study were part of an extant data set. Analyses from this data base have been reported on elsewhere (e.g., DeSantis-King, Huebner, Suldo, & Valois, 2006; Suldo & Huebner, 2004, 2006); however, the following analyses are new.

Participants in the study included 990 students in Grades 6 to 12 from 3 middle schools and 2 high schools within a Southeastern U.S. school district. The participants mean age was 14.62 years (SD = 2.06). Eight students were dropped from analyses due to missing data. The sample contained 358 (36%) males and 624 (64%) females. Students identified themselves as Caucasian (35%), African American (58%), and “Other” (7%). One student did not report ethnicity. In addition, as a rough indicator of socioeconomic status (SES), 60% of the students reported participation in the free or reduced lunch program (i.e., low SES). Two students did not report lunch status (see Table 1 for descriptive statistics according to group status).

Table 1.

Descriptive Statistics

	Positive mental health	Vulnerable	Symptomatic but content	Troubled
N	626	72	87	197
Parent social support	4.89 (1.02)	3.67 (1.33)	4.29 (1.18)	4.30 (1.29)
Close friend social support	4.43 (1.09)	3.73 (1.17)	3.99 (1.12)	4.00 (1.20)
Peer social support	5.27 (0.86)	4.78 (1.21)	5.05 (1.19)	4.91 (1.18)
Teacher social support	4.72 (1.04)	4.31 (1.17)	4.35 (1.15)	4.37 (1.19)
Extraversion	0.50 (0.21)	0.33 (0.30)	0.34 (0.26)	0.48 (0.22)
Neuroticism	0.31 (0.28)	0.74 (0.22)	0.72 (0.21)	0.37 (0.27)
Stressful life events	3.45 (2.71)	5.19 (2.98)	4.41 (2.77)	3.82 (2.75)

Note: Standard deviations are in parentheses.

Measures

Students’ Life Satisfaction Scale (SLSS: Huebner, 1991)

The SLSS is a 7-item, self-report scale designed to measure global life satisfaction. Participants respond to statements about their perceived quality of life using a 6-point Likert-type scale. The SLSS is designed to measure a child’s global, or overall, life satisfaction. Higher scores are considered an indication of higher levels of life satisfaction. The SLSS has been shown to have good validity and reliability for elementary, middle, and high school students (Bender, 1997). The alpha coefficient has previously been reported in Huebner (1991) as .84. The alpha for this study was .81.

The Youth Self-Report of the Child Behavior Checklist (YSR; Achenbach, 1991)

The YSR is a self-report questionnaire containing 118 items that measures 8 domains of problem behavior. The respondent indicates how well each item represents their behavior using a 3-point scale. For the purposes of this study, only the internalizing and externalizing scores were examined. As a result, 61 items corresponding to the subscales—withdrawal, somatic complaints, anxious/ depressed, delinquent behavior, and aggressive behavior—were analyzed. The overall alpha for the YSR in this study was .91.

Abbreviated Junior Eysenck Personality Questionnaire (JEPQR-A; Francis, 1996)

Students completed an abbreviated version of the Junior Eysenck Personality Questionnaire (JEPQR). Students responded to questions in a “yes/ no” format, and higher scores indicated greater levels of the personality trait to 12 items measuring Extraversion and Neuroticism. The convergent validity of the JEPQR-A is supported by high correlations between the JEPRA and the JEPQR-A Extraversion scale (r = .91) as well as the Neuroticism scale (r = .91) as reported by Francis (1996). Furthermore, the alpha coefficients for the Extraversion and Neuroticism scales in this study were .63 and .71, respectively.

Child and Adolescent Social Support Scale (CASSS; Malecki & Demaray, 2002)

The CASSS is a self-report measure assessing adolescent perceptions of social support provided by a parent, teacher, classmate, close friend, and school administrator. For the purposes of this study, all of the social support domains, except for “school administrator,” were analyzed. Each domain was comprised of 12 items that students rate from 1 (never) to 6 (always) the frequency with which they experience support from the particular domain. Higher scores indicated greater level of social support reported by the adolescent.

The CASSS has been shown to have excellent construct validity as it correlates highly with other measures of social support (Malecki & Elliott, 1999). Malecki and Demaray (2002) reported test–retest reliability of .70 over an 8-week period. In addition, they reported estimates of internal consistency ranging from .60 to .76 for the subscales. Alpha coefficients ranged from .94 to .96 for the CASSS subscales in this study.

Stressful Life Events Scale (Johnson & McCutcheon, 1980)

The Stressful Life Events Scale is a widely used self-report measure in which participants indicate whether they have experienced a particular stressful life event over the past 12 months. This scale includes 28 items that refer to controllable stressful life events and 18 items that refer to uncontrollable stressful life events. For the purpose of this study, only items that referred to uncontrollable stressful life events (e.g., parent loss of job) were used. The dichotomous responses were summed to create a measure of overall stressful life events in the past 12 months.

Procedure

The extant data were originally obtained from secondary schools. Parent consent and student assent were required for participation in the study. Trained graduate students administered the survey with assistance from classroom teachers during a 30-min period in regular classroom settings. Measures were presented in counterbalanced fashion.

Data Analysis

Students were first categorized into one of Greenspoon and Saklofske’s (2001) four Dual-Factor model groups based on levels of PTH and SWB. PTH scores were based on YSR reports of internalizing and externalizing behaviors, and SWB scores were based on SLSS reports of global life satisfaction. Following the cutoff scores used in Antaramian et al. (2010), students who fell 1 standard deviation above the mean on either internalizing or externalizing measures were classified as “high psychopathology.” Students below this cutoff were classified as “low psychopathology.” Likewise, students who fell 1 standard deviation below the mean on life satisfaction were classified as “low SWB,” and students above this cutoff were classified as “high SWB.” The use of predetermined, dichotomized cutoff scores for determining group membership (e.g., absence vs. presence of psychological disorder) is consistent with diagnostic decision-making practices in both school and mental health settings.

Next, a multinomial logistic regression analyses were run using maximum likelihood estimation using the Zelig package in R version 2.10.0 (Imai, King, & Lau, 2007, 2008). In this study, a student was assigned to one of four groups based on their mental health characteristics, so parameters are interpreted as odds ratios where a one-unit change in a variable represents the odds of belonging to one group over another. The four groups were: positive mental health (high SWB combined with low PTH), troubled (low SWB combined with high PTH), symptomatic but content (high SWB combined with high PTH), and vulnerable (low SWB combined with low PTH). The results of these analyses are reported in terms of odds ratios as a measure of effect size and logit (odds) as a test of statistical significance. For the purposes of this study, the odds ratios represent the odds of belonging to one group relative to belonging to the positive mental health group.

Multinomial logistic regression analyses were used to predict group membership from the set of continuous and categorical predictors. Specifically, personality variables (Extraversion and Neuroticism), perceived social support (parent, teacher, friend, and close friend), and stressful life events variables were used to predict membership in one of the four Dual-Factor model groups.

Classification accuracy was evaluated with leave-one-out cross-validation. This method uses N − 1 observations to estimate a regression equation, which is used to obtain a predicted probability for the observation omitted from the analysis (Hastie, Tibshirani, & Friedman, 2001). This process was repeated for all observations in the data set such that three predicted probabilities representing the probability of belonging in one group relative to the referent group were obtained for all observations. To classify observations into one of four groups, the greatest probability was selected, and random binomial distribution was sampled with a probability equal to the maximum probability of each observation. Based on these results, an individual was assigned to the group associated with the maximum probability if the binomial distribution was equal to “true” and assigned to the referent group if the binomial distribution was equal to “false.” Next, each observation’s predicted group was compared to its observed group, and the rate of correct classification was calculated. Finally, a significance test was constructed by running the same cross-validation technique on 1,200 bootstrapped data sets to obtain the standard error of the overall correct classification rate. It was expected that the overall correct classification rate would exceed chance if the model accurately predicted group membership. By training the classification function on the maximum allowable set of data (i.e., N − 1 cases), a precise estimate of an observation’s predicted probability of group membership is obtained, maximizing the likelihood of correct classification.

Although this validation technique represents a relatively novel approach to classifying cases in the field of school psychology, it is widely used in other fields (Hastie et al., 2001). In addition, multinomial logistic regression is more appropriate than other statistical techniques because the technique is more robust to violations of model assumptions (e.g., equality of variance–covariance matrices) than other traditional techniques. Specifically, Press and Wilson (1978) concluded that logistic regression yields more stable parameter estimates when nonnormality and heterogeneity of variance–covariance matrices exist. Initial tests for univariate normality indicated peer social support, stressful life events, and Extraversion were not normally distributed (see Table 2 for values of skew and kurtosis). Therefore, multinomial logistic regression analysis was employed.

Table 2.

Distribution Characterizations and Intercorrelations of Predictor Variables

	Skewness	Kurtosis	1	2	3	4	5	6	7
1. Peer SS	−1.81	3.52	1.00
2. Close friend SS	−0.47	−0.39	0.34	1.00
3. Parent SS	−0.58	−0.59	0.33	0.51	1.00
4. Teacher SS	−0.58	−0.30	0.46	0.35	0.35	1.00
5. Extraversion	−1.16	0.57	0.07	0.24	0.24	−0.03	1.00
6. Neuroticism	−0.09	−1.13	−0.21	−0.13	−0.05	−0.06	−0.26	1.00
7. Stressful life events	1.03	1.37	−0.10	−0.07	−0.01	−0.04	0.04	0.18	1.00

Note: SS = social support.

Results

Group Membership

As described previously, students were classified into one of four groups based on self-reported levels of SWB and psychopathology. The results of this classification procedure indicated that 626 students (64%) fell in the positive mental health group, 72 students fell in the vulnerable group, 87 students fell into the symptomatic but content group, and 197 students fell into the troubled group. In addition, student demographic characteristics (i.e., age, gender, and SES) were approximately equivalent across groups. Descriptive statistics for the major variables are provided in Table 1.

By way of comparison, the majority of students in previous studies using predetermined cutoffs for Dual-Factor classifications have been identified as having positive mental health (usually around 60%), and a significantly smaller proportion of students have comprised the remaining quadrants (around 15% each; see Antaramian et al., 2010; Suldo & Shaffer, 2008). Given the infancy of this research, no widely accepted standards or estimates for quadrant sample sizes exist.

Parametric Tests

As described earlier, tests of statistical significance for multinomial logistic regression reflect a statistically significant change in the probability of belonging to the referent group over the comparison group. As shown in Table 3, tests of statistical significance reflect the comparisons between the positive mental health group and the other three quadrants. As such, three comparisons were made: (1) positive mental health versus vulnerable, (2) positive mental health versus symptomatic but content, and (3) positive mental health versus troubled. Furthermore, (a) comparisons between the positive mental health and vulnerable groups indicated that a one-unit increase in perceived parental support predicted a statistically significant (p < .05) decrease in the odds of belonging to the vulnerable group and that a one-unit increase in Neuroticism predicted an increase that approached statistical significance (p < .10) for the odds of belonging to the vulnerable group (p < .05). No other variable produced a statistically significant change in the odds for this comparison. (b) Comparisons between the positive mental health and symptomatic but content groups indicate that a one-unit increase in Neuroticism predicted an increase in the odds of belonging to the symptomatic but content group (p <. 05). This inverse relationship also held for stressful life events but was marginally significant (p < .10). In addition, a one-unit increase in Extraversion predicted a significant decrease in the odds of belonging to the symptomatic but content group (p < .05). This relationship also held for parental social support, but was marginally significant (p < .10). (c) Comparisons between the positive mental health group and the troubled group indicate that a one-unit increase in Neuroticism and stressful life events predicted a statistically significant increase for the odds of belonging to the troubled group (p < .05). In addition, a one-unit increase in Extraversion and parental social support predicted a decrease in the odds of belonging to the troubled group (p < .05).

Table 3.

Parametric Tests

Comparison	β	SE	t value	Odds ratio—exp. (β)
Positive mental health versus vulnerable
Parental social support	−0.33	0.08	−3.96**	0.72
Close friend social support	−0.13	0.09	−1.48	0.88
Teacher social support	−0.05	0.09	−0.52	0.95
Friends social support	−0.12	0.10	−1.24	0.88
Stressful life events	0.03	0.03	0.80	1.03
Extraversion	0.01	0.42	0.03	1.01
Neuroticism	0.55	0.32	1.74*	1.74
Positive mental health versus symptomatic but content
Parental social support	−0.20	0.12	−1.66*	0.82
Close friend social support	−0.07	0.13	−0.52	0.93
Teacher social support	−0.20	0.13	−1.50	0.82
Friends social support	−0.05	0.15	−0.33	0.95
Stressful life events	0.09	0.05	1.90*	1.09
Extraversion	−1.67	0.54	−3.07**	0.19
Neuroticism	4.80	0.53	8.98**	120.94
Positive mental health versus troubled
Parental social support	−0.57	0.13	−4.44**	0.56
Close friend social support	−0.14	0.15	−0.91	0.87
Teacher social support	−0.01	0.15	−0.09	0.99
Friends social support	−0.16	0.15	−1.03	0.85
Stressful life events	0.19	0.05	3.85**	1.21
Extraversion	−1.72	0.61	−2.83**	0.18
Neuroticism	4.82	0.61	7.90**	123.86

p < .10. **p < .05.

In addition to examining statistical significance, odds ratios are reported in Table 3 as measures of effect size. Odds ratios close to 1 indicate little to no effect, whereas odds ratios that deviate greatly from 1 (either much larger or much smaller) indicate a larger effect size. Extraversion and Neuroticism were associated with the largest effect sizes for two of the three comparisons.

Classification

Using the classification procedure described previously, the model obtained was used to classify cases based on their predicted probability of group membership. Overall, 55.20% of the cases were correctly classified using this model (compared to 25% chance, the probability of falling into one of the four quadrants). That is, new individuals were classified in the correct quadrant within the Dual-Factor model based on the model obtained. Results of the bootstrapping procedure indicate that the overall correct classification rate was significantly different than chance (p < .05).

Discussion

This exploratory study identified important predictors of group membership in Greenspoon and Saklofske’s (2001) Dual-Factor Model of Mental Health, using a sample of U.S. secondary school students. Overall, the findings provided further support for usefulness of the Dual-Factor model by demonstrating the existence of four distinct groups of adolescent students that could be differentiated on the basis of meaningful comparisons. Furthermore, the contribution of specific personality, environmental, and social-cognitive variables to predictions of adolescents’ memberships in one of the four groups of the Dual-Factor model was determined. Although the model is relatively simple, it identifies two additional groups of students (vulnerable and symptomatic but content) that would not be identified using traditional models of mental health based solely on the presence of psychopathological symptoms. Studies of the determination of the origins of the differences in group membership are thus essential to understanding the nature and implications of group membership using this framework.

The specific findings of this study were threefold. First, the personality variables of Extraversion and Neuroticism had a statistically significant relationship with membership in the groups of adolescents experiencing psychopathology (troubled and symptomatic but content) but were not significantly related to membership in the vulnerable group. The finding for Neuroticism was consistent with Greenspoon and Saklofske’s (2001) study in which Neuroticism significantly predicted membership in the troubled group but not the vulnerable group. However, the finding for Extraversion was inconsistent with Greenspoon and Saklofske’s study in which Extraversion did not differentiate among the groups. The differences in the findings related to Extraversion may reflect cultural or developmental differences and warrant further research. Second, perceived parental social support contributed significantly to group membership in the vulnerable and troubled groups whereas other sources of social support (i.e., peer, close friend, and teacher) did not significantly discriminate between groups. The results of Greenspoon and Saklofske’s study also underscored the importance of parental social support to group membership. Third, going beyond the findings of Greenspoon and Saklofske, the results of this study identified the experience of acute stressful life events as a significant predictor for the troubled group (i.e., the group associated with high PTH and low SWB) and approached significance for the symptomatic but content group. This study also revealed that the magnitude of the effect sizes of predictors differed across the four groups of the Dual-Factor model. For example, personality variables were robust predictors for the symptomatic but content and the troubled groups relative to the positive mental health group. However, the magnitude of the effect sizes of the personality variables was not as large for the vulnerable group relative to the positive mental health group. Likewise, parental social support was a stronger predictor for troubled and vulnerable students compared to students in the symptomatic but content group.

This study goes beyond previous studies related to the Dual-Factor model in several ways. First, this study employed a different sample in regard to the nationality, age, and ethnicity of the participants, thus extending the results of Greenspoon and Saklofske (2001). Second, the statistical approach used in this study was superior to the previously employed approach to classifying categorical outcomes because multinomial logistic regression is more robust to violations of model assumptions (namely, homogeneity of variance–covariance matrices) and provides parametric tests for each individual parameter. Finally, the N − 1 cross-validation classification procedure for multinomial logistic regression represented a novel and statistically sophisticated method of assessing the predictive accuracy of the proposed model. This procedure allowed the most precise parameters to be estimated and maximized the model’s ability to correctly classify new cases. In addition, bootstrapping 1,200 new samples provided an empirical estimation of variability of overall classification rates and allowed a 95% confidence interval to be estimated for the model’s overall accuracy. This result suggested that the classification accuracy of the overall model was statistically greater than chance.

Limitations of the study should be noted. First, the sample was relatively homogeneous. Although the characteristics of this sample differed from that of Greenspoon and Saklofske (2001), future research should be conducted with children and youth spanning a wider variety of different ages, cultures, and nations to assess generalizability. Furthermore, this research relied on an extant database; the variables selected for inclusion in the model were thus limited to those in the existing data set. Future studies may benefit from testing even more comprehensive models of antecedents. Finally, the study was cross-sectional in nature. Longitudinal studies are needed to assess directionality of relationships.

The study suggests implications for professionals who design programs to promote adolescents’ positive mental health. Because the nature and strength of predictors differed across groups, interventions aimed at improving the mental health of adolescents should target the antecedent variables with the greatest predictive power and potential for change (Greenspoon & Saklofske, 2001). For example, given that personality characteristics (e.g., Neuroticism) are relatively resistant to change, efforts by professionals to improve parental social support would be more likely to increase an adolescent’s odds of moving from the vulnerable group to the positive mental health group. The finding of differential magnitudes of predictors (e.g., parent vs. friend support) is significant not only because it highlights environmental variables that help clarify possible origins of the group differences (e.g., stressful life events) but also because it highlights environmental variables (e.g., parental support) that are potentially more amenable to intervention than personality variables.

Footnotes

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The authors received no financial support for the research, authorship, and/or publication of this article.

Bios

Michael D. Lyons, MA, is an advanced doctoral student in the School Psychology program at the University of South Carolina. His research interests include adolescent positive psychology, prevention, and quantitative psychology.

E. Scott Huebner, PhD, is professor and director of the School Psychology program at the University of South Carolina. His research interests involve the conceptualization, measurement, and applications of positive psychological well-being constructs for school psychology practice. He is a fellow of Division 16 of the APA and the International Society for Quality of Life Studies.

Kimberly J. Hills, PhD, is clinical assistant professor in the psychology department at the University of South Carolina. Her major research interests and foci include the biopsychosocial factors associated with well-being in children and adolescents. Her interests also include teacher well-being, assessment of autism spectrum disorders, and school psychology training.

Svetlana V. Shinkareva, PhD, received her doctoral training from the University of Illinois at Urbana–Champaign and completed her postdoctoral training in cognitive psychology and machine learning at Carnegie Mellon University. She is currently an assistant professor of psychology at the University of South Carolina. Her research interests focus on the development and application of quantitative methods to neuroimaging data.

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