Personality Beyond the Big Five
Creators: name: Lundy, Zachary
name: Lodi-Smith, Jennifer
name: Rodgers, Jonathan
name: Luna, Martha
name: Long, Caleb
name: Khan, Sarah
name: Hill, Patrick
name: Cho, An-Chuen
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Personality and Social Contexts
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Preregistration: Personality Beyond the Big Five This preregistration presents a secondary data analysis (Weston et al., 2019) of pooled data sets to test the relationship between autism characteristics as measured by the Autism Quotient Scale (AQ; Baron-Cohen et al., 2001) and measurements of personality beyond the Big Five in a large, age-diverse sample of individuals. Description Since Leo Kanner’s report in 1943, we have learned a great deal about autism. Most recently in the area, DSM-V brought about many changes to the diagnostic criteria and categorization of individuals with autism (American Psychiatric Association, 2013). Autism Spectrum Disorder (ASD) and Social Communication Disorder (SCD) came to replace categories formerly named Autistic Disorder, Asperger’s Syndrome, and Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS). Despite the change in classification, we continue to struggle with the immense heterogeneity of autism and the problems that presents for treatment. Such heterogeneity impedes the progress of clinical trials and evidence-based treatments (Masi et al., 2017). Suggestions have been made to create sub-groups in order to provide the best treatment for these individuals. A study utilized a two factor/three class approach to stratify such a diverse population. Despite being able to create three distinct subgroups, there was still heterogeneity within the subgroups (Georgiades et al., 2013). Another study identified four distinct subgroups of adults with elevated ASD characteristics using Big Five personality traits (Schwartzman et al., 2016). While these findings point towards evidence of multiple autisms with distinct phenotypic profiles (Tordjman et al., 2018), the authors discovered that a great deal of variability remained within the subgroups. Instead of seeing the variability as an impediment to research, it could be argued that variability could be used as leverage when focusing on the individual. In looking at the individual personality trait profiles, there may be patterns and groups that can be identified rather than predefining clusters and groups. This allows for the opportunity to analyze individual differences to create better treatment options for those with autism characteristics. Personality psychology provides the necessary framework to better understand the complex heterogeneity of autism characteristics. A targeted trait therapy could be beneficial for both clinicians and their clients. This focus on unique traits allows for a more personable and individualized treatment as opposed to wholesale, “one-size-fits-all” approaches. Rather than treating a generic diagnosis, clinicians would be able to treat their client for specific behaviors or symptoms to provide optimal, customizable care (Lengel et al., 2016). In the realm of personality psychology, many tend to solely rely on the traits known as the Big Five. Current research shows that ASD characteristics are associated with lower levels of the Big Five personality traits. However, despite seeing lower levels, that does not imply that the trait levels are homogenous in those with ASD. Further, modern personality psychology conceptualizes personality as far more than just the Big Five. Personality beyond the Big Five must be considered to comprehensively articulate the relationship between autism and the complex concept of personality. Such concepts include; attachment, Dark Triad, grit, alexithymia, goals, self-concept clarity, self-esteem, resilience, and purpose (Lodi-Smith et al., 2019). Understanding that personality is more than just the Big Five creates, “an integrative framework for understanding how every person is like all other persons, like some persons, and like no person” (McAdams &amp; Pals, 2006, p. 215). The characteristic adaptations like the aforementioned ones contribute to our uniqueness, further exemplified through our personal narratives and culture (McAdams &amp; Pals, 2006). Hypothesis Due to the lack of research in the area and where research does exist, conflicting findings, we will not create independent hypotheses for each of the personality measures. As the Big Five solely has dominated personality psychology, it is pertinent to explore other measures of personality. In doing so, we hope to help capture the nomological net of personality in the context of autism. Data The planned analyses will be conducted across pooled data (N = 739) from two online samples in which participants completed the AQ. Sample 1 is primarily comprised of undergraduate students and their family members (n = 372, Mage = 23.04, SDage = 23.04, age range = 18 - 63) who completed the AQ for course credit as part of studies by our research team investigating personality in the context of ASD. Sample 2 is primarily older adults (n = 367, Mage = 65.53, SDage = 14.82, age range = 18 - 97) who participated in a study of autism and aging funded by NIH Grant #R21 AG059051-01. Sample 2 participants were primarily recruited through paid advertisements and mailers and entered into a monthly drawing for $100 for their participation. All data was collected with approval of our institution’s IRB and met the APA ethical guidelines for human subjects research. All data is recorded and stored online via REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Canisius College. REDCap is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources (Harris et al., 2009). While the primary recruitment effort for the sample is complete, the surveys are not closed to the public at the point of preregistration and the final sample size included in the analyses may vary slightly from this estimate if new individuals participate or an individual skipped a target questionnaire. These data sets are not publically available at the time of preregistration. Data and code for the analyses described below will be posted to the parent OSF for this registration when presenting and submitting findings. Prior Knowledge of the Data &amp; Prior Research Activity The authors have presented findings from this sample in the following manuscripts and at the following professional conferences: -Lodi-Smith, J., Rodgers, J.D., Kozlowski, K.F., Khan, S., Marquez Luna, V., Long, C., Donnelly, J.P., Lopata, C., &amp; Thomeer, M.L. (under review). Autism characteristics and self-reported health in older adulthood. Under review at The Journals of Gerontology, Series B. -Lodi-Smith, J., Rodgers, J.D., Marquez Luna, V., Khan, S., Long, C., Kozlowski, K.F., Donnelly, J.P., Lopata, C., &amp; Thomeer, M.L. (in press). The relationship of age to the Autism-Spectrum Quotient Scale in a large sample of adults. In press at Autism in Adulthood. -Rodgers, J.D., Lodi-Smith, J., Hill, P.H., Spain, S.M., Lopata, C., &amp; Thomeer, M.L. (2018). Personality traits and self-concept clarity mediate the relationship between autism spectrum disorder characteristics and well-being. Journal of Autism and Developmental Disorders, 48, 307 – 315. -Lodi-Smith, J. &amp; Rodgers, J.D. (2018, March). Autism Spectrum Disorder and Functional Personality Maturation across the Lifespan. Talk presented at the Personality Dynamics, Processes, and Functioning Preconference at the Society for Personality and Social Psychology, Atlanta, GA. -Lodi-Smith, J. &amp; Rodgers, J.D. (2020, November). Desired personality trait change and autism spectrum disorder. Poster presented at the 2020 Geneva Centre for Autism Symposium (Virtual Symposium), Montreal. -Lodi-Smith, J., Rodgers, J.D., Khan, S., Long, C., Marquez Luna, V., Kozlowski, K., Donnelly, J.P., Lopata, C., &amp; Thomeer, M.L. (2020, November). Autism characteristics and self-reported health in older adulthood. Poster presented at the 2020 Geneva Centre for Autism Symposium (Virtual Symposium), Montreal. -Lodi-Smith, J., Rodgers, J.D., Marquez Luna, V., Khan, S., Long, C., Kozlowski, K., Donnelly, J.P., Lopata, C., &amp; Thomeer, M.L. (2020, November). The relationship of age to the Autism-Spectrum Quotient scale in a large sample of adults. Poster presented at the 2020 Geneva Centre for Autism Symposium (Virtual Symposium), Montreal. -Virginia, H.J., Brennan, S., Lodi-Smith, J., &amp; Rodgers, J.D. (2018, April). The Relationship of Personal Variability to Symptoms and Outcomes in Autism Spectrum Disorder. Poster presented at the Canisius College 2018 Ignatian Scholarship Day, Buffalo, NY. -DiMayo, S., Rosenthal, M., Stoll, M.M., Virginia, H.J., Lodi-Smith, J., Rodgers, J.D., &amp; the Canisius Identity Development Lab (2017, April). Individuals with high autism spectrum disorder characteristics evidence differences in self-defining memories. Poster presented at the Canisius College 2017 Ignatian Scholarship Day, Buffalo, NY. We have worked with data from these samples on the relationship between the AQ and age (https://osf.io/evh6f/); AQ and healthy aging (https://osf.io/nc3m7); and AQ, self-concept clarity, self-esteem, purpose, and Big Five traits in some of the student data (Rodgers et al., 2018). We are aware of the descriptive statistics for and relationship between variables used in these studies but not of the relationship between these variables and other measures of personality beyond the Big Five. Data already included in our prior work (Rodgers et al., 2018) will not be included in the present manuscript. A research assistant blind to study hypotheses and not involved with this preregistration or analyses has developed R scripts to extract data and descriptive statistics from REDCap but these have not been shared with the authors of this preregistration and no analyses beyond descriptive statistics have been conducted. The planned analyses are, therefore, novel. Moment of Preregistration All computations and analyses described below will occur after the submission of this preregistration. Measured Variables Demographic variables. Participant reports of age, gender, and ASD diagnostic status are included in the analysis plan as exploratory moderator variables. The lack of racial and ethnic diversity in the sample prohibits the inclusion of these demographic variables in moderation analyses. Racial and ethnicity data will be reported in the manuscript but will not be included in the final data set uploaded to OSF to protect the identity of the few minority participants in the samples. Autism Quotient Scale. The Autism-Spectrum Quotient scale (AQ; Baron-Cohen et al., 2001) quantified participants’ ASD characteristics. The AQ consists of 50 statements such as “I tend to have very strong interests, which I get upset about if I can’t pursue” and “I find social situations easy”. Participants responded to each statement from “Definitely Agree” (1) to “Definitely Disagree” (4). Following the AQ-Short scoring (Hoekstra et al., 2011), 28 items from the AQ will be used to compute five subscales with four of these subscales (social skills, routines, switching, and imagination) then being used to compute a higher-order index of social interaction and the remaining subscale indicating attention to detail. Alternative scoring may be requested by reviewers and so may eventually be included in analyses or supplementary results. The Experience in Close Relationships-Revised. Participants in the aging sample took the Experiences in Close Relationships-Revised (ECR-R; Fraley et al., 2000) to quantify their individual difference with respect to attachment-related anxiety and attachment-related avoidance. The ECR-R is a 36-item assessment that measures your level of anxiety and avoidance. For each item, the participant answered on a scale from “Strongly Disagree” (1) to “Strongly Agree” (7) to statements such as "I'm afraid that I will lose my partner's love". Dark Triad Scale. Participants in both the aging and student samples took the Short Dark Triad Scale (SD3; Jones &amp; Paulhus, 2014) to quantify their Machiavellianism, Narcissism, and Psychopathy. The SD3 is a 27-item assessment that measures characteristics of the Dark Triad. For each item, the participant answered on a scale from “Strongly Disagree” (1) to “Strongly Agree” (5) to statements such as "It's not wise to tell your secrets". The Grit Scale (Duckworth &amp; Quinn, 2009). Participants in the aging sample took the Grit Scale to quantify the tendency to sustain interest in and effort toward very long-term goals. The Grit is an eight-item assessment that measures your resiliency. For each item, the participant answered on a scale from “Not Like Me at All” (1) to “Very Much Like Me” (5) to statements such as "New ideas and projects sometimes distract me from previous ones." Toronto Alexithymia Scale (TAS-20). Participants in the aging sample took the Toronto Alexithymia Scale (TAS-20; Taylor et al., 1988) to test their Alexithymia levels. Alexithymia refers to people who have trouble identifying and describing emotions and tend to minimize emotional experiences and focus attention externally. The TAS-20 is a twenty-item assessment separated into three subsections that measure alexithymia in areas such as difficulty describing emotions, difficulty identifying emotions, and tendency to focus on their attention externally. For each item, the participant answered on a scale from “Strongly Disagree” (1) to “Strongly Agree” (5) to statements such as "I am often confused about what emotion I am feeling". HERI Life-goal Scale (Hill et al., 2010). Participants in both samples took the HERI life-goal scale to quantify their personal importance of 17 different life-goals ranging from prosocial, financial, creative, and personal recognition goals. The HERI life-goal scale is a seventeen-item assessment that measures how important you find these different goals. For each item, the participant answered on a scale from “Not Important” (1) to “Highly Important” (4) to statements such as "Participating in a community service program". Self-Concept Clarity Scale. Participants in the student and aging samples took the Self-Concept Clarity Scale (SCCS; Campbell et al., 1996) to quantify whether their self-beliefs are defined, consistent, and stable. The SCCS is a 12-item assessment that measures self-belief confidence. For each item, the participant answered on a scale from “Strongly Disagree” (1) to “Strongly Agree” (5) to statements such as "My beliefs about myself often conflict with one another.". Some student self-concept clarity data was included in prior work (Rodgers et al., 2018) and will not be included in the present analyses. Because the self-concept clarity scale is known to have some items that do not function well in all samples (Lodi-Smith &amp; Roberts, 2010), principal components analysis will confirm whether or not all items will be included in the self-concept clarity scale. Rosenberg's Self-Esteem Scale. Participants in the student and aging samples took the Rosenberg's Self-Esteem Scale (Rosenberg, 1965) to quantify their self-esteem. The Rosenberg's Self-Esteem Scale is a 10-item assessment that measures a person's self-esteem. For each item, the participant answered on a scale from “Strongly Disagree” (0) to “Strongly Agree” (3) to statements such as "I feel that I have a number of good qualities". Some student self-esteem data was included in prior work (Rodgers et al., 2018) and will not be included in the present analyses. Resilience. Participants in the aging sample took the Brief Resilience Scale (BRS, Smith et al., 2008). The BRS is a six-item assessment that measures participant’s ability to adapt quickly in the face of stressors. For each item, the participant answered on a scale from “Strongly Disagree” (1) to “Strongly Agree” (5) to statements such as "I tend to bounce back quickly after hard times". Scheier Purpose Measure. Participants in the student and aging samples took the Scheier Purpose Measure (Scheier et al., 2006) to quantify their purpose in life. The Scheier Purpose Measure is a six-item assessment that measures a person's purpose in life based on the person's engagement in activities that are personally valued. For each item, the participant answered on a scale from “Strongly Disagree” (1) to “Strongly Agree” (5) to statements such as "There is not enough purpose in my life". Some student purpose data was included in prior work (Rodgers et al., 2018) and will not be included in the present analyses. The Personality Inventory for DSM-5 (PID-5). Participants in the aging sample took the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012) to evaluate personality and related disorders based on the dimensional trait model. The PID-5 is a 25-item assessment separated into five domains that measure a person’s personality and related disorders. For each item, the participant answers on a scale from “Very False or Often False” (0) to “Very True or Often True” (3) to statements such as “I am often confused about what emotion I am feeling”. Data Inclusion/Exclusion Data that has been previously published will not be included in the present analyses. The only inclusion criterion is participation in one of the samples described above and completion of the AQ alongside the target personality measures. Sample Size &amp; Power As we have yet to analyze the data at the writing of this pre-registration, we do not have an anticipated effect size targeted for power analyses. Based on known response rates in the samples, it is anticipated that the number of responses included in our study will be between 100 and 700 participants depending on the target personality measure. Two separate power analyses consisting of a significance of 0.05 and a power of .9, indicate that we can detect relationships of r = .32 and r = .12 respectively. Missing Data Complete data will be used pairwise with missing data excluded for those participants who did not respond to a specific questionnaire. Item-level missing data will be ignored by computing scores as the mean of scales rather than the sum. Analysis Plan All analyses will be conducted in R (R Core Team, 2018). Significance values will be reported for all analyses but no explicit inferences will be based on these values. Instead, judgments of effect will be based on the effect size estimates themselves using guidelines from Funder &amp; Ozer (2019). Our exploratory analysis will first quantify the relationship between autism characteristics and personality measures. Exploratory analyses will test the extent to which age moderates these relationships. All variables will be standardized with AQ, age, and the interaction term of AQ and age entered into a multiple regression predicting each personality measure. We also plan to conduct a series of exploratory analyses to test the extent to which the relationship between autism characteristics and personality measures is moderated by the following demographic variables and sample characteristics: -Individuals who self-identify as having an ASD diagnosis compared to those who do not -Individuals with elevated AQ scores (≥ 26; Booth et al., 2013; Woodbury-Smith et al., 2005) compared to those with lower AQ scores -Gender These analyses will be conducted by computing the correlations within groups and comparing the magnitude of the correlation between groups using Fisher’s r-to-z transformations within psych::paired.r (Revelle, 2018). Sample size permitting, we may also compare the correlation matrix among personality measures between ASD and AQ groups including the correlation of target personality measures with Big Five measures. **N.B. 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