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Nomothetic, Differential, and Idiographic Assessment of Character Development among South African Youth

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Title: Nomothetic, Differential, and Idiographic Assessment of Character Development among South African Youth
Language: English
Authors: Roya Abbasi-Asl (ORCID 0000-0003-1186-5424), Natasha Keces, Richard M. Lerner, Margaret Mackin, Dian Yu, Elizabeth M. Dowling, Jonathan M. Tirrell, Alexa Hasse, Kirsten Olander, Angela Larkan, Chuma Mashita, Raah Msimango, Sinenhlanhla Mkhithi, Tyler Howard
Source: Child & Youth Care Forum. 2025 54(5):1203-1234.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 32
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Foreign Countries, Moral Development, Youth, Individual Development, Personality Traits, Longitudinal Studies, Scores, Individual Differences
Geographic Terms: South Africa
DOI: 10.1007/s10566-025-09856-6
ISSN: 1053-1890
1573-3319
Abstract: Background: Nomothetic, group differential, and idiographic approaches are all needed to fully understand youth development. However, most research on youth character development has traditionally relied on either the nomothetic approach to study whole sample changes or the differential approach to study changes in predefined groups (e.g., involving gender or age). Objective: In this study, we used data from the Thanda Study of Character and Health to assess nomothetic, differential, and idiographic development of character attributes (generosity, curiosity) among South African youth. Methods: We measured character attributes using a traditional longitudinal design and an intensive longitudinal design (ILD). The traditional longitudinal design involved 222 youth (50% female; M[subscript age] = 9.82, SD[subscript age] = 1.39), and their scores of character attributes for three assessments separated by about half a year. A subset of 35 youth from this sample (48% female, M[subscript age] = 11.6, SD[subscript age] = 0.62) participated in the ILD, completing a survey of character attributes across 16 weekly assessments. Using Growth Mixture Modeling and person-specific pathways plots, we examined group-based heterogeneity and specificity in youth character development. Results: Results from both designs confirmed that there is group-based heterogeneity in character development. Findings from the ILD additionally pointed to the specificity of character development and the fact that person-specific trajectories of the character attributes cannot be captured by the estimated mean trajectories of groups. Conclusions: These findings underscore the importance of identifying both group-based and idiographic trajectories rather than relying solely on nomothetic or differential approaches on predefined groups when studying character development.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1488792
Database: ERIC
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  Value: <anid>AN0188240007;5jr01oct.25;2025Sep29.05:57;v2.2.500</anid> <title id="AN0188240007-1">Nomothetic, Differential, and Idiographic Assessment of Character Development Among South African Youth </title> <p>Background: Nomothetic, group differential, and idiographic approaches are all needed to fully understand youth development. However, most research on youth character development has traditionally relied on either the nomothetic approach to study whole sample changes or the differential approach to study changes in predefined groups (e.g., involving gender or age). Objective: In this study, we used data from the Thanda Study of Character and Health to assess nomothetic, differential, and idiographic development of character attributes (generosity, curiosity) among South African youth. Methods: We measured character attributes using a traditional longitudinal design and an intensive longitudinal design (ILD). The traditional longitudinal design involved 222 youth (50% female; M<sub>age</sub> = 9.82, SD<sub>age</sub> = 1.39), and their scores of character attributes for three assessments separated by about half a year. A subset of 35 youth from this sample (48% female, M<sub>age</sub> = 11.6, SD<sub>age</sub> = 0.62) participated in the ILD, completing a survey of character attributes across 16 weekly assessments. Using Growth Mixture Modeling and person-specific pathways plots, we examined group-based heterogeneity and specificity in youth character development. Results: Results from both designs confirmed that there is group-based heterogeneity in character development. Findings from the ILD additionally pointed to the specificity of character development and the fact that person-specific trajectories of the character attributes cannot be captured by the estimated mean trajectories of groups. Conclusions: These findings underscore the importance of identifying both group-based and idiographic trajectories rather than relying solely on nomothetic or differential approaches on predefined groups when studying character development.</p> <p>Keywords: Character development; Traditional longitudinal design; Intensive longitudinal design; Variable-focused; Person-specific; Psychology and Cognitive Sciences Psychology</p> <p>Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s10566-025-09856-6.</p> <hd id="AN0188240007-2">Introduction</hd> <p>Since the third decade of the twentieth century, scholars have been concerned with how best to study key facets of human development, such as personality (e.g., Allport, [<reflink idref="bib1" id="ref1">1</reflink>]; Costa & McCrae, [<reflink idref="bib18" id="ref2">18</reflink>]; Kluckhohn & Murray, [<reflink idref="bib37" id="ref3">37</reflink>]) or cognitive skills and academic achievement (e.g., Baltes & Schaie, [<reflink idref="bib3" id="ref4">3</reflink>]; Cantor et al., [<reflink idref="bib14" id="ref5">14</reflink>]; Herrnstein & Murray, [<reflink idref="bib30" id="ref6">30</reflink>]; Horn, [<reflink idref="bib32" id="ref7">32</reflink>]; Horn & Donaldson, [<reflink idref="bib33" id="ref8">33</reflink>]; Immordino-Yang et al., [<reflink idref="bib34" id="ref9">34</reflink>]; Piaget, [<reflink idref="bib74" id="ref10">74</reflink>]). These discussions have often involved diverse theoretical ideas and methodological issues (e.g., issues of nature vs. nurture, continuity vs. discontinuity, and stability vs. instability; see Lerner, [<reflink idref="bib40" id="ref11">40</reflink>]).</p> <p>However, a key theme—and arguably the key theme—has been whether nomothetic, group differential, or idiographic approaches should be the focal method for understanding human development (Emmerich, [<reflink idref="bib22" id="ref12">22</reflink>]; Molenaar, [<reflink idref="bib56" id="ref13">56</reflink>]). The first two of these approaches focus on sample (or subsample) parameters (e.g., average scores for and/or standard deviations of a variable), and thus present a variable-focused understanding of the construct of interest. These approaches assume that the developmental processes illuminated by nomothetic or differential analyses reflect the course of development for all individuals in the sample (or subsample). In turn, idiographic approaches focus on an individual's scores on a variable and assess within-person changes to study person-specific trajectories (e.g., McNeish & Hamaker, [<reflink idref="bib54" id="ref14">54</reflink>]).</p> <p>The present article is derived from an interest in this ongoing discussion. We used data from the Thanda Study of Character and Health—a research-practitioner partnership aimed at evaluating the character development programs of Thanda, in rural South Africa—to assess the use of nomothetic, differential, and idiographic approaches in studying the character development of South African youth.</p> <hd id="AN0188240007-3">Insights into Theorical Frameworks</hd> <p>Our work was framed by, first, the holistic and integrated ideas found within dynamic, relational developmental systems (RDS)-based models of human development (Lerner, [<reflink idref="bib40" id="ref15">40</reflink>]; Overton, [<reflink idref="bib71" id="ref16">71</reflink>]), which take an integrated approach to all levels of organization comprising the bioecology of human development (e.g., Bronfenbrenner & Morris, [<reflink idref="bib11" id="ref17">11</reflink>]; Mascolo & Bidell, [<reflink idref="bib50" id="ref18">50</reflink>]; Witherington & McCready, [<reflink idref="bib98" id="ref19">98</reflink>]). There are several quite prominent RDS-based models of character development (e.g., Bornstein, [<reflink idref="bib9" id="ref20">9</reflink>]; Lerner, [<reflink idref="bib41" id="ref21">41</reflink>]; Lerner & Callina, [<reflink idref="bib43" id="ref22">43</reflink>]; Lerner et al., [<reflink idref="bib45" id="ref23">45</reflink>]; Lerner & Matthews, [<reflink idref="bib44" id="ref24">44</reflink>]; Nucci, [<reflink idref="bib67" id="ref25">67</reflink>], [<reflink idref="bib68" id="ref26">68</reflink>]) that indicate character is developed through specific, mutually influential relations (coactions) between a specific individual and the specific settings within which individual develops. The specifics of the times and places of coactions for each individual create person-specific aspects of each person's developmental pathway (Elder et al., [<reflink idref="bib21" id="ref27">21</reflink>]). Accordingly, character development has specificity, as no two individuals have experienced the same individual-context coactions across time and place (Bornstein, [<reflink idref="bib8" id="ref28">8</reflink>]; Elder et al., [<reflink idref="bib21" id="ref29">21</reflink>]; Parke & Elder, [<reflink idref="bib73" id="ref30">73</reflink>]). As Bornstein ([<reflink idref="bib7" id="ref31">7</reflink>], p. 5) indicated, "the life-span development of specific characteristics in specific individuals is affected by specific experiences in specific ways at specific times—this is the specificity principle."</p> <p>Second, our work was framed by the wisdom in Kluckhohn and Murray's ([<reflink idref="bib37" id="ref32">37</reflink>]) observation, which underscores the RDS-based approach. Kluckhohn and Murray ([<reflink idref="bib37" id="ref33">37</reflink>]) stated that, in some respects each person is like all people (a nomothetic concept), each person is like only some other people (a differential concept), and each person is like no other person (an idiographic concept). This holistic idea led us to expect that the specificity of character development, or the coherence of a specific individual's character enactment, does not preclude the possibility that an individual's repertoire of character attributes may be similar in some instances to those of other individuals. Accordingly, we believe in the usefulness of studying the specificity and heterogeneity of character development while also identifying meaningful groups or classes of individuals, or in other words, homogeneous groups of participants within a larger heterogeneous sample or subsamples of participants.</p> <p>Finally, third, our work was framed by the growing interest in using statistical approaches to model the distinct developmental trajectories of individuals, such as growth mixture modeling (GMM; see Jung & Wickrama, [<reflink idref="bib36" id="ref34">36</reflink>]; Ram & Grimm, [<reflink idref="bib75" id="ref35">75</reflink>]), latent profile transition analyses (LPTA; see Johnson, [<reflink idref="bib35" id="ref36">35</reflink>]) or latent class growth analysis (LCGA; see Nagin, [<reflink idref="bib65" id="ref37">65</reflink>], [<reflink idref="bib63" id="ref38">63</reflink>]; Nagin & Tremblay, [<reflink idref="bib64" id="ref39">64</reflink>]). These models are recognized for their ability to identify homogeneous groups of participants within a larger heterogeneous sample or subsample of participants and meaningful information about interindividual (between-person) differences in intraindividual (within-person) change over time (Jung & Wickrama, [<reflink idref="bib36" id="ref40">36</reflink>]; Nesselroade, [<reflink idref="bib66" id="ref41">66</reflink>]). The identification of meaningful groups or classes of individuals and studying changes in these groups represents a true differential approach, as articulated by Kluckhohn and Murray's ([<reflink idref="bib37" id="ref42">37</reflink>]), compared to an application of this approach wherein a study of predefined groups or subsamples changes (e.g., involving gender or age) occurs (see Johnson, [<reflink idref="bib35" id="ref43">35</reflink>]).</p> <p>Admittedly, the predominant quantitative approaches to studying character development or, in fact, any facet of human development (Hamaker et al., [<reflink idref="bib28" id="ref44">28</reflink>]; Lerner, [<reflink idref="bib40" id="ref45">40</reflink>]) have historically been based on the nomothetic approach to study sample changes or on the application of differential approach on subsamples (e.g., gender, age) to study predefined group changes. These approaches assume that a single growth trajectory from a sample or from predefined groups can accurately represent the developmental trajectories of an entire population or of subpopulations (Lerner et al., [<reflink idref="bib46" id="ref46">46</reflink>]; Molenaar & Nesselroade, [<reflink idref="bib57" id="ref47">57</reflink>], [<reflink idref="bib58" id="ref48">58</reflink>], [<reflink idref="bib59" id="ref49">59</reflink>]; Ram & Grimm, [<reflink idref="bib75" id="ref50">75</reflink>]; Rose, [<reflink idref="bib79" id="ref51">79</reflink>]; Rose et al., [<reflink idref="bib80" id="ref52">80</reflink>]). To our knowledge, the present research is the first to study overall/single-group trajectories (nomothetic development), distinct groups of individual trajectories (differential development), and person-specific trajectories (idiographic development) of character attributes.</p> <p>We obtained our data on character development by recruiting a sample of South African youth enrolled in the character development programs of Thanda and collecting data through the use of an integrated traditional and intensive longitudinal design (ILD) (e.g., see Ram et al., [<reflink idref="bib76" id="ref53">76</reflink>]). Accordingly, it is useful to describe the Thanda character development programs for South African youth to provide an understanding of a key feature of the context of the youth we studied, and as such, facets of our research that affect the generalizability of our work.</p> <hd id="AN0188240007-4">Thanda Character Development Programs Among South African Youth</hd> <p>Founded in 2008, Thanda is a non-governmental organization (NGO) working in the Umzumbe Municipality in rural KwaZulu-Natal, South Africa, a historically underserved and impoverished community. According to data collected from the 2016 Community Survey, the most recent municipality-level data at the time of this writing, the average annual household family income within this community is equivalent to approximately $803 U.S. dollars and the adult employment rate is only 14% (Statistics South Africa, [<reflink idref="bib88" id="ref54">88</reflink>], [<reflink idref="bib87" id="ref55">87</reflink>]). In addition, the majority of community residents live without access to piped water (Statistics South Africa, [<reflink idref="bib89" id="ref56">89</reflink>]). Whereas KwaZulu-Natal had the largest number of students attending school in South Africa as of 2023, these students largely range from 7 to 14 years old. Only 23.8% of children ages 0–4 attend early childhood development programs in KwaZulu-Natal, and across South Africa, school attendance after 14 years of age drastically decreases (Statistics South Africa, [<reflink idref="bib90" id="ref57">90</reflink>]).</p> <p>In addition, schools are not necessarily safe environments for youth to learn and grow. Although corporal punishment in schools has decreased in South Africa in the past fifteen years, it is still most prevalent in KwaZulu-Natal (Statistics South Africa, [<reflink idref="bib90" id="ref58">90</reflink>]). Within the Umzumbe Municipality, there are limited resources and opportunities to support healthy youth development. Close to 29% of youth suffer from stunting[<reflink idref="bib1" id="ref59">1</reflink>] (Children's Institute, [<reflink idref="bib16" id="ref60">16</reflink>]; Thanda, [<reflink idref="bib92" id="ref61">92</reflink>]). Less than 16% of children under Age 5 have access to early learning opportunities. Youth are also taught to repress emotions, and their opinions are not often considered or welcomed (Thanda, [<reflink idref="bib92" id="ref62">92</reflink>]).</p> <p>Thanda, which means "to love" in the local language of Zulu, strives to create safer, stronger communities for youth in the region it serves by especially offering after-school (AS) programming and community-based initiatives (e.g., home-visiting, farming). The AS program—framed on the sub-Saharan African principle of Ubuntu, or "I am what I am because we all are" —aims to improve youth academic foundations (e.g., literacy, science, math) as well as to strengthen their character attributes (e.g., curiosity, generosity) by providing support from positive and caring adults and employing collaborative and activity-based curricula. For instance, Thanda's science curriculum prompts youth to ask questions and test hypotheses, encouraging curiosity. Similarly, generosity is bolstered through activities related to building literacy. In such activities, youth learn about characters and stories that differ from their own and are directed to reflect on how they can contribute to the needs of others and the world at large. Adults also serve as role models, demonstrating generosity and promoting a sense of self-confidence to be curious.</p> <p>Taken together, Thanda envisions that creating mutually beneficial relationships between adults and children can provide programs with the foundation needed to equip youth with the character attributes necessary to succeed in life (e.g., Lerner, in press). It is important to emphasize, however, that all parties engaged in these relationships bring with them specific histories of experience that shape the specific thoughts, knowledge, emotions, and social skills they possess and can contribute to these relationships (Bornstein, [<reflink idref="bib7" id="ref63">7</reflink>], [<reflink idref="bib8" id="ref64">8</reflink>]). Accordingly, Thanda aims to align with this specificity by evaluating the effectiveness of their character development programs through both person-specific (idiographic) and variable-focused (nomothetic and differential) assessments. This emphasis on gaining a comprehensive understanding of character development among youth enrolled in Thanda was the basis for the design and launch of the current study.</p> <hd id="AN0188240007-5">Developmental Trajectories of the Character Attributes of Generosity and Curiosity</hd> <p>Much like other socially relevant aspects of human development, the development of character attributes such as generosity and curiosity are also likely to demonstrate systematic variations across adolescence before stabilizing in adulthood (e.g., Bornstein, [<reflink idref="bib9" id="ref65">9</reflink>]; Lerner & Matthews, [<reflink idref="bib44" id="ref66">44</reflink>]; Nucci, [<reflink idref="bib68" id="ref67">68</reflink>]; Witherington & McCready, [<reflink idref="bib98" id="ref68">98</reflink>]). Indeed, studies indicate that even adults exhibit continuing plasticity in character attributes (e.g., Gander et al., [<reflink idref="bib24" id="ref69">24</reflink>]; Wagner & Gander, [<reflink idref="bib96" id="ref70">96</reflink>]). In recent years, the overall desirability of character attributes)—due to their associations with multiple positive outcomes such as well-being (e.g., curiosity: Lydon-Staley et al., [<reflink idref="bib48" id="ref71">48</reflink>]; Mishra, [<reflink idref="bib55" id="ref72">55</reflink>]; generosity: Malik et al., [<reflink idref="bib49" id="ref73">49</reflink>]), greater meaning and satisfaction in life (e.g., curiosity: Garrosa et al., [<reflink idref="bib25" id="ref74">25</reflink>]; generosity: Wang et al., [<reflink idref="bib97" id="ref75">97</reflink>]), and increased social engagement and integration (e.g., curiosity: Begus & Southgate, [<reflink idref="bib4" id="ref76">4</reflink>]; generosity: Sim et al., [<reflink idref="bib86" id="ref77">86</reflink>]; Steinebach et al., [<reflink idref="bib91" id="ref78">91</reflink>])—has spurred more research into their foundations in youth (e.g., Brown et al., [<reflink idref="bib12" id="ref79">12</reflink>]; Ferragut et al., [<reflink idref="bib23" id="ref80">23</reflink>]; Shubert et al., [<reflink idref="bib84" id="ref81">84</reflink>]). However, despite growing interest, there remains a relative lack of literature on developmental trajectories of character attributes in general and of the character attributes of generosity and curiosity in particular.</p> <p>The first and primary limitation of the current studies is their extensive reliance on a cross-sectional, between-person design rather than a longitudinal, within-person design (see Brown et al., [<reflink idref="bib12" id="ref82">12</reflink>]; McGrath & Walker, [<reflink idref="bib52" id="ref83">52</reflink>]; Shubert et al., [<reflink idref="bib84" id="ref84">84</reflink>]). Because one-time scores do not provide information about temporal or predominant changes in character attributes, it remains unclear whether the developmental trends observed in cross-sectional findings would also appear in longitudinal studies.</p> <p>Second, although the few available studies have provided important information about changes in character attributes as a function of age, these studies were limited to examining average changes. For example, a longitudinal study by Ferragut et al. ([<reflink idref="bib23" id="ref85">23</reflink>]) showed that character attributes remained relatively stable between the ages of 12 and 14 years. In another study, Brown et al. ([<reflink idref="bib12" id="ref86">12</reflink>]) found that the character attributes of curiosity and kindness decreased with age in a sample of youth aged 11–17 years, with boys scoring higher in curiosity and girls scoring higher in kindness. However, as specifically noted in previous discussions of theoretical frameworks, mean-level changes do not account for the changes within an individual or group of individuals in a given character attribute over time. Subgroups of youth might show substantial changes in a character attribute, such as curiosity, even if the mean-level score of the full sample for that attribute remains stable. Conversely, some individuals may show no change or even an increase in their scores, whereas the observed mean-level score decreases.</p> <p>Finally, both generosity and curiosity have been widely conceptualized from various perspectives in the scientific literature and are often used interchangeably with related concepts. For instance, generosity has been examined through constructs such as prosocial behavior (Dickerson & Quas, [<reflink idref="bib20" id="ref87">20</reflink>]), volunteering (Glanville et al., [<reflink idref="bib26" id="ref88">26</reflink>]), and acts of kindness (Ko et al., [<reflink idref="bib38" id="ref89">38</reflink>]). Similarly, curiosity has been explored in relation to concepts such as interest (Bowler, [<reflink idref="bib10" id="ref90">10</reflink>]), the need for knowledge (Arnone et al., [<reflink idref="bib2" id="ref91">2</reflink>]), openness to experience (Mussel et al., [<reflink idref="bib61" id="ref92">61</reflink>]), sensation seeking (Byman, [<reflink idref="bib13" id="ref93">13</reflink>]), wonder (Schmitt & Lahroodi, [<reflink idref="bib81" id="ref94">81</reflink>]), and intellectual engagement (Mussel, [<reflink idref="bib60" id="ref95">60</reflink>]). Therefore, further research is needed to deliberately focus on the conceptualization and operationalization of these constructs in specific contexts, perhaps in particular relatively understudied ones in regard to character development research—such as South Africa in the current study—to achieve a more accurate and deeper understanding of their developmental trajectories (see Sim et al., [<reflink idref="bib86" id="ref96">86</reflink>]). Relying solely on findings based on the use of VIA-Youth measure (Values in Action Inventory-Youth; Park & Peterson, [<reflink idref="bib72" id="ref97">72</reflink>]), a relatively reliable index of character attributes but predominantly used in minority-world nations (see Brown et al., [<reflink idref="bib12" id="ref98">12</reflink>]), may limit efforts to fully understand the developmental trajectories of these character attributes in majority-world settings.</p> <hd id="AN0188240007-6">The Present Study</hd> <p>Although current literature on the developmental trajectories of character constructs among youth suggests general stability or a decline with age (see Brown et al., [<reflink idref="bib12" id="ref99">12</reflink>]; Ferragut, et al., [<reflink idref="bib23" id="ref100">23</reflink>]), involvement in a character development program may potentially disrupt these trajectories in youth, specific subgroups of youth, or individual youth (see Johnson, [<reflink idref="bib35" id="ref101">35</reflink>]; Hilliard et al., [<reflink idref="bib31" id="ref102">31</reflink>]; Lerner et al., [<reflink idref="bib46" id="ref103">46</reflink>]). Therefore, in the current study, we were not focused on examining the developmental trajectories of generosity and curiosity—the character attributes emphasized in Thanda's character development programs—in a specific direction. Instead, we aimed to explore these trajectories regardless of whether they are positive (i.e., an increase in attributes), negative (i.e., a decrease in attributes), or stable (i.e., no significant changes in attributes) with particular attention to group-based heterogeneity and specificity in the developmental trajectories of these attributes.</p> <p>Accordingly, using two longitudinal data sets, and taking an integrated approach, we studied the overall/single-group trajectories (nomothetic), distinct groups of individual trajectories (differential), and person-specific trajectories (idiographic) of the character attributes of generosity and curiosity among South African youth. We first report our findings using a traditional longitudinal design, wherein data are sparsely collected across time (Ram et al., [<reflink idref="bib76" id="ref104">76</reflink>]). We then report findings derived from an ILD, wherein data are collected across densely spaced time points (Bolger & Laurenceau, [<reflink idref="bib6" id="ref105">6</reflink>]; Ram et al., [<reflink idref="bib76" id="ref106">76</reflink>]).</p> <hd id="AN0188240007-7">Research Using a Traditional Longitudinal Design</hd> <p>In the traditional longitudinal design, our main goal was to compare nomothetic findings (overall/single-group trajectories) with group differential findings (distinct groups of individual trajectories) regarding the developmental trajectories of character attributes (generosity and curiosity) in the full sample of participants. In addition, we aimed to compare findings from developmental trajectories of predefined groups (i.e., girls and boys, within the full sample disaggregated by gender) with the trajectories of groups or classes that emerge from the analyses within these predefined groups. We hypothesized that, first, the findings from the full sample of participants, and second, the findings from subsamples of boys and girls, would demonstrate group-based heterogeneity in the developmental trajectories of character constructs, both in terms of the number (e.g., two, three, or four) and the nature (e.g., increasing, decreasing, or stable) of trajectories. In other words, we anticipated that, in both the full sample and the subsamples of boys and girls, there would be distinct subgroups of youth who would follow different developmental trajectories for the character constructs of generosity and curiosity across the three waves of the study.</p> <hd id="AN0188240007-8">Method</hd> <p></p> <hd id="AN0188240007-9">Participants</hd> <p>Participants within the traditional longitudinal design were 222 Thanda-enrolled youth. At the time of Wave 1, data were collected from 221 participants aged 8 to 13 (50% female, <emph>M</emph><subs><emph>age</emph></subs> = 9.82, <emph>SD</emph><subs><emph>age</emph></subs> = 1.39). In the second wave of the study, data were collected from 163 (55% female; <emph>M</emph><subs><emph>age</emph></subs> = 10.87, <emph>SD</emph><subs><emph>age</emph></subs> = 1.32) participating youth ages 9 to 14. Of the Wave 2 analytic sample, 162 were returning participants (i.e., 27% attrition) and one was new to the study (male; <emph>age</emph> = 10), having not participated in Wave 1. In the Wave 3 of the study, data were collected from 157 (54% female; <emph>M</emph><subs><emph>age</emph></subs> = 11.18, <emph>SD</emph><subs><emph>age</emph></subs> = 1.38) participating youth also ranging in age from 9 to 14. Overall, there was a 4% attrition rate between Waves 2 and 3 and 29% attrition between Waves 1 and 3. Chi-square tests and two-sample <emph>t</emph>-tests were conducted to examine differences in gender, age, and Wave 1 values of generosity and curiosity related to participant attrition. Chi-square tests indicated a statistically significant association between gender and attrition rates between Wave 1 and Wave 2 (<emph>χ</emph><sups>2</sups>(<reflink idref="bib1" id="ref107">1</reflink>) = 5.39, <emph>p</emph> = 0.020). Specifically, there was an overrepresentation of females among youth who participated in both Wave 1 and Wave 2 as compared to the attrition group. There was no association between gender and attrition rates between Wave 1 and Wave 3. There was a statistically significant relation between age and attrition rates for participants from Wave 1 to Wave 2 (<emph>t</emph>(104.9) = − 2.72, <emph>p</emph> = 0.008) as well as from Wave 1 to Wave 3 (<emph>t</emph>(119.4) = − 2.38, <emph>p</emph> = 0.019). In both cases, the average age of participants that responded to the two waves was higher than those that only responded to Wave 1. There were no statistically significant differences in baseline curiosity or generosity at Wave 1 related to attrition across the waves.</p> <p>The analysis was conducted on the full sample of participants (<emph>N</emph> = 222), as well as disaggregated based on gender (111 girls, <emph>M</emph><subs><emph>age</emph></subs> = 9.88, <emph>SD</emph><subs><emph>age</emph></subs> = 1.39; 111 boys, <emph>M</emph><subs><emph>age</emph></subs> = 9.76, <emph>SD</emph><subs><emph>age</emph></subs> = 1.39).</p> <hd id="AN0188240007-10">Procedure</hd> <p>Data collection included three waves. Wave 1 data collection occurred between July and September 2022, Wave 2 data collection took between April and May 2023, and Wave 3 data collection spanned October to December 2023. Data were collected using a quantitative self-report online Qualtrics survey presented to youth on tablets. Due to the participants' age range (8 to 13 at the time of Wave 1), and their varying levels of reading, experience using tablets, or completing surveys with a visual analog scale (see Rioux & Little, [<reflink idref="bib78" id="ref108">78</reflink>]), the surveys were administered via one-on-one, in-person sessions by independent data collectors hired by Thanda.</p> <p>Prior to the first wave of data collection, data collectors attended a two-day training where they familiarized themselves with the survey, were educated on survey administration, and piloted the survey. The survey was originally written in English and was translated from English to Zulu (the local language in Umzumbe, KwaZulu-Natal, South Africa) and back-translated to English to check for accuracy. As a part of this process, data collectors were also asked to assist with survey translations and provided feedback on phrasing or concepts that would not be understood by South African youth; this step helped to ensure that the survey was culturally and developmentally appropriate. Accordingly, concerns noted by the data collectors were incorporated into the survey in advance of data collection. Surveys were administered at the Thanda site during Thanda programming by data collectors reading aloud the survey questions in an interview-style format and recording youth responses on tablets using an offline version of the Qualtrics survey operating system. When administering the survey, data collectors asked participants to indicate how much they related to a series of statements by moving a slider to the left or right on a visual analog scale of 0 to 100, with 100 reflecting high agreement and 0 reflecting low agreement. Completion of the survey was expected to take roughly 30–45 min which included also other constructs than the main constructs of this study.</p> <p>Thanda youth were given thank-you gifts as determined by the program staff, including gift baskets, shoes, and/or clothing after data collection. Finally, the U.S.-based research team received the de-identified dataset for the analyses, which was also granted exempt status for secondary data analysis by the local University's Institutional Review Board (IRB). The research study and protocol were also approved by the South Africa National Council for Science and Technology.</p> <hd id="AN0188240007-11">Measures</hd> <p>The traditional longitudinal design involved testing character attributes using two scales: generosity and curiosity, developed by the research team in collaboration with Thanda partners. In selecting and identifying items for the current measures of character attributes, the U.S.-based research team and Thanda leadership and staff tried to reach a mutual understanding about the theoretical conceptualization of these attributes and how best they can fit within the South African context and be understood by South African youth.</p> <p> <emph>Generosity</emph>. To assess generosity, we used five items. Example items include "I am good at sharing" and "I am helpful to others." Cronbach's alpha coefficient calculated in the current study for generosity in Waves 1, 2, and 3 were 0.76, 0.79, and 0.72, respectively.</p> <p> <emph>Curiosity</emph>. Curiosity was assessed with eight items. Example items include "You are curious about the world around you" and "When you ask questions, the answer leads you to have more questions." Cronbach's alpha coefficient calculated in the current study for curiosity in Waves 1, 2, and 3 were 0.63, 0.69, and 0.63, respectively.</p> <hd id="AN0188240007-12">Analysis Plan</hd> <p>Before conducting main analyses, we performed preliminary descriptive analyses for all study variables. This involved calculating the means and standard deviations, as well as zero-order bivariate correlations among the scores of the character attributes of generosity and curiosity across the three waves, to illustrate the preliminary relationships among them throughout the data collection process.</p> <p>Then, to test our research hypotheses, we conducted a series of GMMs on each of the attributes of generosity and curiosity. Similar to other types of latent variable analyses, GMM involves testing several models to determine which one best fits the data. The variations in these models reflect the heterogeneity of the initial scores (intercepts), and the types and rates of changes (slopes). Through the GMM process, we aimed to identify the optimal number of distinct developmental trajectory groups for each attribute. This determination was based on statistical considerations of the available fit indices, the proportion of participants likely to be classified into each group (typically a minimum of 5% or more in each group, though groups with less than 5% can be chosen if the model meets the theoretical expectation and fit indices; see Johnson, [<reflink idref="bib35" id="ref109">35</reflink>]), as well as the parsimony and interpretability of the chosen model and theoretical justification (see Johnson, [<reflink idref="bib35" id="ref110">35</reflink>]; Jung & Wickrama, [<reflink idref="bib36" id="ref111">36</reflink>]).</p> <p>As of this writing, methodological research has not identified a universally recommended fit index for GMM. Therefore, it is advised to use a combination of indices (e.g., Grimm et al., [<reflink idref="bib27" id="ref112">27</reflink>]; Johnson, [<reflink idref="bib35" id="ref113">35</reflink>]; Nylund-Gibson & Choi, [<reflink idref="bib70" id="ref114">70</reflink>]; Ram & Grimm, [<reflink idref="bib75" id="ref115">75</reflink>]). We used the values of Akaike information criteria (AIC), Bayesian information criteria (BIC; Schwarz, [<reflink idref="bib82" id="ref116">82</reflink>]), and sample-size adjusted Bayesian information criteria (Adj BIC; Sclove, [<reflink idref="bib83" id="ref117">83</reflink>]), where smaller values indicate a better fit. In addition, we considered the significance of the adjusted Lo-Mendell-Rubin likelihood test (Adj-LMR-LRT; Lo et al., [<reflink idref="bib47" id="ref118">47</reflink>]) and the bootstrapped likelihood ratio test (BLRT; McLachlan & Peel, [<reflink idref="bib53" id="ref119">53</reflink>]). We also considered the value of entropy, which ranges from 0.00 to 1.00. High values of entropy (> 0.80; Ram & Grimm, [<reflink idref="bib75" id="ref120">75</reflink>]) indicate confident classification of individuals and adequate separation between latent classes. Models with higher entropy are preferred when selecting among models with similar relative fit indices (e.g., BIC; see Ram & Grimm, [<reflink idref="bib75" id="ref121">75</reflink>]).</p> <p>To address our first research hypothesis, using the full sample of participants, we first conducted a one-group GMM to explore the overall/single-group trajectory of youth character development regarding the attribute of generosity. This one-group model also served as the basis for further comparison of the fits of models with more developmental trajectory groups. Next, we used GMM with two- to five-class solutions (see Sijbrandij et al., [<reflink idref="bib85" id="ref122">85</reflink>]) to model possible group-based heterogeneity in developmental trajectories of the character attributes of generosity. The same process was applied to the attribute of curiosity.</p> <p>To test our second hypothesis, using the full sample disaggregated by gender (girls and boys), we again, first conducted a one-group GMM for boys and girls separately to explore the overall/single-group trajectory of character development in each of these subsamples for the attribute of generosity. Then, we used GMM with two- to five-class solutions to model potential group-based heterogeneity in the developmental trajectories of the character attribute of generosity within these subsamples. The same process was applied to the attribute of curiosity.</p> <p>All analyses were conducted using Mplus Version 8.11 (Muthén & Muthén, [<reflink idref="bib62" id="ref123">62</reflink>]–[<reflink idref="bib62" id="ref124">62</reflink>]). The MLR estimator, a procedure of maximum likelihood estimation that is robust to observation of non-normality was employed in all analyses. In all models, we fixed the factor loadings of the slope estimate to 0, 1, 2, corresponding to Wave 1 (Fall 2022), Wave 2 (Spring 2023), and Wave 3 (Fall 2023), respectively, to account for differences in the time between assessments. Given that the generosity and curiosity scales were multiple-item scales, the composite scores of the items were used in the GMM analyses. In addition, both scales included construct-level missing data. Accordingly, we used Full Information Maximum Likelihood (FIML) to handle missing data in Mplus 8.11.</p> <hd id="AN0188240007-13">Results</hd> <p></p> <hd id="AN0188240007-14">Preliminary Analyses</hd> <p>Table 1 presents descriptive statistics and correlations among the scores of the main variables across the three waves of data collection. Zero-order bivariate correlations between the scores of the character attributes, except for a few cases of non-significant correlations, indicated a significant positive association in the full sample as well as in the boys' and girls' subsamples. These correlations ranged from 0.257 (girls; Wave 1 and Wave 2) to 0.499 (girls; Wave 1 and Wave 3) for the attribute of generosity and from 0.180 (full sample; Wave 2 and Wave 3) to 0.412 (boys; Wave 1 and Wave 2) for the attribute of curiosity.</p> <p>Table 1 Descriptive statistics and correlations among character attributes (generosity, curiosity) through the three waves of data collection in the traditional longitudinal design</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>M</p></th><th align="left"><p>SD</p></th><th align="left"><p>1</p></th><th align="left"><p>2</p></th><th align="left"><p>3</p></th></tr></thead><tbody><tr><td align="left" colspan="6"><p><italic>Generosity (Full sample, N</italic> = <italic>222)</italic></p></td></tr><tr><td align="left"><p>Wave 1</p></td><td char="." align="char"><p>90.996</p></td><td char="." align="char"><p>11.839</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 2</p></td><td char="." align="char"><p>88.361</p></td><td char="." align="char"><p>12.300</p></td><td align="left"><p>0.264**</p></td><td align="left"><p>1</p></td><td align="left" /></tr><tr><td align="left"><p>Wave 3</p></td><td char="." align="char"><p>88.490</p></td><td char="." align="char"><p>11.818</p></td><td align="left"><p>0.332**</p></td><td align="left"><p>0.388**</p></td><td align="left"><p>1</p></td></tr><tr><td align="left" colspan="6"><p><italic>Generosity (Girls, N</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>Wave 1</p></td><td char="." align="char"><p>92.378</p></td><td char="." align="char"><p>11.353</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 2</p></td><td char="." align="char"><p>88.056</p></td><td char="." align="char"><p>12.395</p></td><td align="left"><p>0.257*</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 3</p></td><td char="." align="char"><p>87.476</p></td><td char="." align="char"><p>13.730</p></td><td align="left"><p>0.499**</p></td><td align="left"><p>0.411**</p></td><td align="left" /></tr><tr><td align="left" colspan="6"><p><italic>Generosity (Boys, N</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>Wave 1</p></td><td char="." align="char"><p>89.602</p></td><td char="." align="char"><p>12.152</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 2</p></td><td char="." align="char"><p>88.727</p></td><td char="." align="char"><p>12.174</p></td><td align="left"><p>0.282**</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 3</p></td><td char="." align="char"><p>89.658</p></td><td char="." align="char"><p>8.995</p></td><td align="left"><p>0.133</p></td><td align="left"><p>0.295*</p></td><td align="left" /></tr><tr><td align="left" colspan="6"><p><italic>Curiosity (Full sample, N</italic> = <italic>222)</italic></p></td></tr><tr><td align="left"><p>Wave 1</p></td><td char="." align="char"><p>89.500</p></td><td char="." align="char"><p>10.473</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 2</p></td><td char="." align="char"><p>89.090</p></td><td char="." align="char"><p>10.679</p></td><td align="left"><p>0.343**</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 3</p></td><td char="." align="char"><p>88.484</p></td><td char="." align="char"><p>10.321</p></td><td align="left"><p>0.168</p></td><td align="left"><p>0.180*</p></td><td align="left" /></tr><tr><td align="left" colspan="6"><p><italic>Curiosity (Girls, N</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>Wave 1</p></td><td char="." align="char"><p>90.333</p></td><td char="." align="char"><p>10.056</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 2</p></td><td char="." align="char"><p>89.306</p></td><td char="." align="char"><p>9.591</p></td><td align="left"><p>0.261*</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 3</p></td><td char="." align="char"><p>87.475</p></td><td char="." align="char"><p>11.045</p></td><td align="left"><p>0.201*</p></td><td align="left"><p>0.192</p></td><td align="left" /></tr><tr><td align="left" colspan="6"><p><italic>Curiosity (Boys, N</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>Wave 1</p></td><td char="." align="char"><p>88.659</p></td><td char="." align="char"><p>10.813</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 2</p></td><td char="." align="char"><p>88.829</p></td><td char="." align="char"><p>11.851</p></td><td align="left"><p>0.412**</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Wave 3</p></td><td char="." align="char"><p>89.646</p></td><td char="." align="char"><p>9.285</p></td><td align="left"><p>0.138</p></td><td align="left"><p>0.162</p></td><td align="left" /></tr></tbody></table> </ephtml> </p> <p> <emph>M</emph> and <emph>SD</emph> are used to represent mean and standard deviation, respectively <sups>*</sups>Indicates <emph>p</emph> <.05. ** indicates <emph>p</emph> <.01</p> <hd id="AN0188240007-15">GMMs of the Attributes of Generosity and Curiosity in Full Sample of Participants</hd> <p>Analyses of the first GMM with a single-group for the attribute of generosity showed that the means of the intercept (90.736, <emph>SE</emph> = 0.770, <emph>p</emph> < 0.001) and the slope (− 1.364, <emph>SE</emph> = 0.540, <emph>p</emph> = 0.012) were both statistically significant. This statistical significance indicates that, on average, youth in the program reported a very high level of generosity, although this level slightly decreased over the waves compared to the initial level.[<reflink idref="bib2" id="ref125">2</reflink>] Table 2, first section, shows the fit indices for all tested models concerning the attribute of generosity in the full sample. Although the AIC, BIC and Adj BIC indicated a superior fit for models with more classes, considering the significance of the Adj-LMR-LRT and BLRT tests, and better class sizes (i.e., a minimum of 5% or more in each class; see Fig. 1), a model with three classes seemed to fit the data best, including: Class 1, Moderate (intercept)-Stable (slope) (intercept: 80.938, <emph>SE</emph> = 0.990, <emph>p</emph> < 0.001; slope: 1.055, <emph>SE</emph> = 1.251, <emph>p</emph> = 0.399; 21.1%), Class 2, High-Decreasing (intercept: 97.535, <emph>SE</emph> = 0.347, <emph>p</emph> < 0.001; slope: − 4.014, <emph>SE</emph> = 0.520, <emph>p</emph> < 0.001; 70.2%), and Class 3, Low-Increasing (intercept: 62.450, <emph>SE</emph> = 1.713, <emph>p</emph> < 0.001; slope: 12.927, <emph>SE</emph> = 1.280, <emph>p</emph> < 0.001; 8.6%).</p> <p>Table 2 Model fit indices for GMMs of the character attribute of generosity in the traditional longitudinal design</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Classes</p></th><th align="left"><p>AIC</p></th><th align="left"><p>BIC</p></th><th align="left"><p>Adj BIC</p></th><th align="left"><p>Entropy</p></th><th align="left"><p>Adj-LMR-LRT</p><p>(<italic>p</italic> value)</p></th><th align="left"><p>BLRT</p><p>(<italic>p</italic> value)</p></th><th align="left"><p>SC (%) of <italic>N</italic></p></th></tr></thead><tbody><tr><td align="left" colspan="8"><p><italic>Generosity (Full sample, N</italic> = <italic>222)</italic></p></td></tr><tr><td align="left"><p>1<sup>a</sup></p></td><td char="." align="char"><p>4190.324</p></td><td char="." align="char"><p>4210.740</p></td><td char="." align="char"><p>4191.725</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>2<sup>a</sup></p></td><td char="." align="char"><p>4124.754</p></td><td char="." align="char"><p>4155.379</p></td><td char="." align="char"><p>4126.857</p></td><td char="." align="char"><p>0.889</p></td><td char="." align="char"><p>0.0113</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.180</p></td></tr><tr><td align="left"><p><bold>3</bold><sup><bold>a</bold></sup></p></td><td char="." align="char"><p><bold>4073.424</bold></p></td><td char="." align="char"><p><bold>4114.256</bold></p></td><td char="." align="char"><p><bold>4076.227</bold></p></td><td char="." align="char"><p><bold>0.939</bold></p></td><td char="." align="char"><p><bold>0.0038</bold></p></td><td char="." align="char"><p><bold>0.0000</bold></p></td><td char="." align="char"><p><bold>0.085</bold></p></td></tr><tr><td align="left"><p>4</p></td><td char="." align="char"><p>4051.731</p></td><td char="." align="char"><p>4109.577</p></td><td char="." align="char"><p>4055.702</p></td><td char="." align="char"><p>0.953</p></td><td char="." align="char"><p>0.0771</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.016</p></td></tr><tr><td align="left"><p>5<sup>a</sup></p></td><td char="." align="char"><p>3996.524</p></td><td char="." align="char"><p>4057.772</p></td><td char="." align="char"><p>4000.728</p></td><td char="." align="char"><p>0.954</p></td><td char="." align="char"><p>0.0069</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.018</p></td></tr><tr><td align="left" colspan="8"><p><italic>Generosity (Girls, n</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>1<sup>a</sup></p></td><td char="." align="char"><p>2231.039</p></td><td char="." align="char"><p>2247.296</p></td><td char="." align="char"><p>2228.335</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>2<sup>a</sup></p></td><td char="." align="char"><p>2177.816</p></td><td char="." align="char"><p>2202.202</p></td><td char="." align="char"><p>2173.760</p></td><td char="." align="char"><p>0.927</p></td><td char="." align="char"><p>0.0951</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.162</p></td></tr><tr><td align="left"><p>3<sup>a</sup></p></td><td char="." align="char"><p>2153.999</p></td><td char="." align="char"><p>2186.513</p></td><td char="." align="char"><p>2148.591</p></td><td char="." align="char"><p>0.936</p></td><td char="." align="char"><p>0.3946</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.063</p></td></tr><tr><td align="left"><p>4<sup>a</sup></p></td><td char="." align="char"><p>2139.016</p></td><td char="." align="char"><p>2179.658</p></td><td char="." align="char"><p>2132.256</p></td><td char="." align="char"><p>0.941</p></td><td char="." align="char"><p>0.3541</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.027</p></td></tr><tr><td align="left"><p><bold>5</bold><sup><bold>a</bold></sup></p></td><td char="." align="char"><p><bold>2101.676</bold></p></td><td char="." align="char"><p><bold>2150.447</bold></p></td><td char="." align="char"><p><bold>2093.564</bold></p></td><td char="." align="char"><p><bold>0.967</bold></p></td><td char="." align="char"><p><bold>0.0479</bold></p></td><td char="." align="char"><p><bold>0.0000</bold></p></td><td char="." align="char"><p><bold>0.027</bold></p></td></tr><tr><td align="left" colspan="8"><p><italic>Generosity (Boys, n</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>1</p></td><td char="." align="char"><p>1973.853</p></td><td char="." align="char"><p>1995.529</p></td><td char="." align="char"><p>1970.247</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>2<sup>a</sup></p></td><td char="." align="char"><p>1944.117</p></td><td char="." align="char"><p>1968.503</p></td><td char="." align="char"><p>1940.061</p></td><td char="." align="char"><p>0.850</p></td><td char="." align="char"><p>0.0345</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.189</p></td></tr><tr><td align="left"><p><bold>3</bold><sup><bold>a</bold></sup></p></td><td char="." align="char"><p><bold>1919.032</bold></p></td><td char="." align="char"><p><bold>1951.546</bold></p></td><td char="." align="char"><p><bold>1913.624</bold></p></td><td char="." align="char"><p><bold>0.937</bold></p></td><td char="." align="char"><p><bold>0.0230</bold></p></td><td char="." align="char"><p><bold>0.0000</bold></p></td><td char="." align="char"><p><bold>0.117</bold></p></td></tr><tr><td align="left"><p>4<sup>a</sup></p></td><td char="." align="char"><p>1914.600</p></td><td char="." align="char"><p>1955.243</p></td><td char="." align="char"><p>1907.840</p></td><td char="." align="char"><p>0.959</p></td><td char="." align="char"><p>0.0165</p></td><td char="." align="char"><p>0.1200</p></td><td char="." align="char"><p>0.001</p></td></tr><tr><td align="left"><p>5<sup>a</sup></p></td><td char="." align="char"><p>1901.653</p></td><td char="." align="char"><p>1950.425</p></td><td char="." align="char"><p>1893.541</p></td><td char="." align="char"><p>0.941</p></td><td char="." align="char"><p>0.3024</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.009</p></td></tr></tbody></table> </ephtml> </p> <p>The final class solution is <bold>bolded</bold><emph>AIC</emph> Akaike information criteria, <emph>BIC</emph> Bayesian information criteria, <emph>Adj BIC</emph> Sample-size adjusted Bayes information criteria, <emph>Adj LMR-LRT</emph> Adjusted Lo-Mendell-Rubin likelihood test, <emph>BLRT</emph> Bootstrapped likelihood ratio test, <emph>SC</emph> Smallest class a = Several models encountered the "matrix (psi) is not positive definite" warning, indicating a negative variance/residual variance for a latent variable. Since the negative variance wasn't significant in these models, we fixed the variance to zero for either the intercept or slope of one or more classes (see Johnson, [<reflink idref="bib35" id="ref126">35</reflink>]; Ram & Grimm, [<reflink idref="bib75" id="ref127">75</reflink>])</p> <p>Graph: Fig. 1 Three latent trajectory classes of the character attribute of generosity versus the single-group trajectory in the full sample of traditional longitudinal design (N = 222). Note: The Red line indicates the class with the highest number of participants, followed by Blue and then Green (Color figure online)</p> <p>Analyses of the first GMM with a single-group for the attribute of curiosity showed that the mean of the intercept (89.535, <emph>SE</emph> = 0.692, <emph>p</emph> < 0.001) was statistically significant but the mean of the slope (− 0.538, <emph>SE</emph> = 0.495, <emph>p</emph> = 0.277) was not. This indicates that, on average, youth in the program reported a very high level of curiosity, which remained stable over the waves. Based on the fit indices for all tested models concerning the attribute of curiosity in the full sample, the model with five classes was chosen for the developmental trajectories of curiosity in youth. Although the significance of the Adj-LMR-LRT test was better in the model with two classes, all the other fit indices indicated a superior fit for the model with five classes (see Table 3), including: Class 1, Moderate-Increasing (intercept: 65.855, <emph>SE</emph> = 0.948, <emph>p</emph> < 0.001; slope: 8.644, <emph>SE</emph> = 2.080, <emph>p</emph> < 0.001; 5.8%), Class 2, Low-Increasing (intercept: 56.250, <emph>SE</emph> = 2.305, <emph>p</emph> < 0.001; slope: 19.255, <emph>SE</emph> = 5.508, <emph>p</emph> < 0.001; 1.4%), Class 3, High-Stable (intercept: 88.177, <emph>SE</emph> = 0.426, <emph>p</emph> < 0.001; slope: − 0.115, <emph>SE</emph> = 0.612, <emph>p</emph> = 0.851; 35.6%), Class 4, High-Decreasing (intercept: 98.392, <emph>SE</emph> = 0.266, <emph>p</emph> < 0.001; slope: − 4.6 34, <emph>SE</emph> = 0.570, <emph>p</emph> < 0.001; 43.9%), and Class 5, High-Increasing (intercept: 77.520, <emph>SE</emph> = 0.723, <emph>p</emph> < 0.001; slope: 6.619, <emph>SE</emph> = 1.106, <emph>p</emph> < 0.001; 13.3%) (see Fig. 2).</p> <p>Table 3 Model fit indices for GMMs of the character attribute of curiosity in the traditional longitudinal design</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Classes</p></th><th align="left"><p>AIC</p></th><th align="left"><p>BIC</p></th><th align="left"><p>Adj BIC</p></th><th align="left"><p>Entropy</p></th><th align="left"><p>Adj-LMR-LRT</p><p>(<italic>p</italic> value)</p></th><th align="left"><p>BLRT</p><p>(<italic>p</italic> value)</p></th><th align="left"><p>SC (%) of <italic>N</italic></p></th></tr></thead><tbody><tr><td align="left" colspan="8"><p><italic>Curiosity (Full sample, N</italic> = <italic>222)</italic></p></td></tr><tr><td align="left"><p>1</p></td><td char="." align="char"><p>4067.658</p></td><td char="." align="char"><p>4094.879</p></td><td char="." align="char"><p>4069.526</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>2</p></td><td char="." align="char"><p>4029.119</p></td><td char="." align="char"><p>4066.549</p></td><td char="." align="char"><p>4031.689</p></td><td char="." align="char"><p>0.856</p></td><td char="." align="char"><p>0.0005</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.153</p></td></tr><tr><td align="left"><p>3<sup>a</sup></p></td><td char="." align="char"><p>4019.405</p></td><td char="." align="char"><p>4060.237</p></td><td char="." align="char"><p>4022.208</p></td><td char="." align="char"><p>0.767</p></td><td char="." align="char"><p>0.4776</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.072</p></td></tr><tr><td align="left"><p>4<sup>a</sup></p></td><td char="." align="char"><p>3976.901</p></td><td char="." align="char"><p>4027.941</p></td><td char="." align="char"><p>3980.404</p></td><td char="." align="char"><p>0.910</p></td><td char="." align="char"><p>0.1000</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.072</p></td></tr><tr><td align="left"><p><bold>5</bold><sup><bold>a</bold></sup></p></td><td char="." align="char"><p><bold>3964.278</bold></p></td><td char="." align="char"><p><bold>4025.526</bold></p></td><td char="." align="char"><p><bold>3968.483</bold></p></td><td char="." align="char"><p><bold>0.939</bold></p></td><td char="." align="char"><p><bold>0.0223</bold></p></td><td char="." align="char"><p><bold>0.0000</bold></p></td><td char="." align="char"><p><bold>0.058</bold></p></td></tr><tr><td align="left" colspan="8"><p><italic>Curiosity (Girls, n</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>1<sup>a</sup></p></td><td char="." align="char"><p>2125.389</p></td><td char="." align="char"><p>2141.646</p></td><td char="." align="char"><p>2122.685</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p><bold>2</bold><sup><bold>a</bold></sup></p></td><td char="." align="char"><p><bold>2112.727</bold></p></td><td char="." align="char"><p><bold>2137.113</bold></p></td><td char="." align="char"><p><bold>2108.671</bold></p></td><td char="." align="char"><p><bold>0.885</bold></p></td><td char="." align="char"><p><bold>0.0365</bold></p></td><td char="." align="char"><p><bold>0.0000</bold></p></td><td char="." align="char"><p><bold>0.144</bold></p></td></tr><tr><td align="left"><p>3<sup>a</sup></p></td><td char="." align="char"><p>2109.242</p></td><td char="." align="char"><p>2139.047</p></td><td char="." align="char"><p>2104.285</p></td><td char="." align="char"><p>0.902</p></td><td char="." align="char"><p>0.0687</p></td><td char="." align="char"><p>0.0619</p></td><td char="." align="char"><p>0.018</p></td></tr><tr><td align="left"><p>4<sup>a</sup></p></td><td char="." align="char"><p>2081.886</p></td><td char="." align="char"><p>2122.529</p></td><td char="." align="char"><p>2075.126</p></td><td char="." align="char"><p>0.945</p></td><td char="." align="char"><p>0.2902</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.027</p></td></tr><tr><td align="left"><p>5<sup>a</sup></p></td><td char="." align="char"><p>2074.501</p></td><td char="." align="char"><p>2123.273</p></td><td char="." align="char"><p>2066.389</p></td><td char="." align="char"><p>0.955</p></td><td char="." align="char"><p>0.0816</p></td><td char="." align="char"><p>0.0204</p></td><td char="." align="char"><p>0.009</p></td></tr><tr><td align="left" colspan="8"><p><italic>Curiosity (Boys, n</italic> = <italic>111)</italic></p></td></tr><tr><td align="left"><p>1</p></td><td char="." align="char"><p>1944.116</p></td><td char="." align="char"><p>1965.793</p></td><td char="." align="char"><p>1940.511</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p><bold>2</bold></p></td><td char="." align="char"><p><bold>1926.722</bold></p></td><td char="." align="char"><p><bold>1956.527</bold></p></td><td char="." align="char"><p><bold>1921.765</bold></p></td><td char="." align="char"><p><bold>0.836</bold></p></td><td char="." align="char"><p><bold>0.0060</bold></p></td><td char="." align="char"><p><bold>0.0000</bold></p></td><td char="." align="char"><p><bold>0.162</bold></p></td></tr><tr><td align="left"><p>3<sup>a</sup></p></td><td char="." align="char"><p>1918.436</p></td><td char="." align="char"><p>1950.951</p></td><td char="." align="char"><p>1913.028</p></td><td char="." align="char"><p>0.823</p></td><td char="." align="char"><p>0.0607</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.090</p></td></tr><tr><td align="left"><p>4<sup>a</sup></p></td><td char="." align="char"><p>1904.532</p></td><td char="." align="char"><p>1945.174</p></td><td char="." align="char"><p>1897.772</p></td><td char="." align="char"><p>0.906</p></td><td char="." align="char"><p>0.0528</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.090</p></td></tr><tr><td align="left"><p>5</p></td><td char="." align="char"><p>1905.301</p></td><td char="." align="char"><p>1959.492</p></td><td char="." align="char"><p>1896.288</p></td><td char="." align="char"><p>0.897</p></td><td char="." align="char"><p>0.4758</p></td><td char="." align="char"><p>0.2000</p></td><td char="." align="char"><p>0.072</p></td></tr></tbody></table> </ephtml> </p> <p>The final class solution is <bold>bolded</bold><emph>AIC</emph> Akaike information criteria, <emph>BIC</emph> Bayesian information criteria, <emph>Adj BIC</emph> Sample-size adjusted bayes information criteria, <emph>Adj LMR-LRT</emph> Adjusted Lo-Mendell-Rubin likelihood test, <emph>BLRT</emph> Bootstrapped likelihood ratio test, <emph>SC</emph> Smallest class a = Several models encountered the 'matrix (psi) is not positive definite' warning, indicating a negative variance/residual variance for a latent variable. Since the negative variance wasn't significant in these models, we fixed the variance to zero for either the intercept or slope of one or more classes (see Johnson, [<reflink idref="bib35" id="ref128">35</reflink>]; Ram & Grimm, [<reflink idref="bib75" id="ref129">75</reflink>])</p> <p>Graph: Fig. 2 Five latent trajectory classes of the character attribute of curiosity versus the single-group trajectory in the full sample of traditional longitudinal design (N = 222). Note: The Red line indicates the class with the highest number of participants, followed by Blue, Green, Purple, and Magenta (Color figure online)</p> <hd id="AN0188240007-16">GMMs of the Attributes of Generosity and Curiosity in Boys and Girls Groups (Full Sample Disa...</hd> <p>Analyses of the first GMMs with a single-group for the attributes of generosity and curiosity in the girls' subsample showed that the mean of the intercepts (generosity: 91.818, <emph>SE</emph> = 1.032, <emph>p</emph> < 0.001; curiosity: 90.496, <emph>SE</emph> = 0.895, <emph>p</emph> < 0.001) and the mean of slopes (generosity: − 2.355, <emph>SE</emph> = 0.540, <emph>p</emph> = 0.002; curiosity: − 1.388, <emph>SE</emph> = 0.667, <emph>p</emph> = 0.038) for both attributes were all statistically significant. This indicates that, on average, girls in the Thanda program reported very high levels of generosity and curiosity initially, but these scores slightly decreased over the waves. GMMs with two- to five-class solutions suggested a potential five-class developmental trajectory pattern for the attribute of generosity and two-class developmental trajectory pattern for the attributes of curiosity in the girls' subsample.</p> <p>Table 2, second section, shows the fit indices for all tested models concerning the attribute of generosity in the girls' subsample. All fit indices indicated a superior fit for the model with five classes, including: Class 1, Low-Stable; (intercept: 52.281, <emph>SE</emph> = 2.098, <emph>p</emph> < 0.001; slope: 3.315, <emph>SE</emph> = 3.710, <emph>p</emph> = 0.372; 2.7%), Class 2, High-Decreasing (intercept: 99.266, <emph>SE</emph> = 0.208, <emph>p</emph> < 0.001; slope: − 4.258, <emph>SE</emph> = 0.837, <emph>p</emph> < 0.001; 61.6%), Class 3, Moderate-Increasing (intercept: 66.983, <emph>SE</emph> = 1.276, <emph>p</emph> < 0.001; slope: 8.678, <emph>SE</emph> = 4.342, <emph>p</emph> = 0.046; 4.5%), Class 4, High-Stable (intercept: 90.373, <emph>SE</emph> = 1.075, <emph>p</emph> < 0.001; slope: − 1.187, <emph>SE</emph> = 1.504, <emph>p</emph> = 0.430; 17.5%), and, Class 5, Moderate-Stable (intercept: 80.084, <emph>SE</emph> = 1.161, <emph>p</emph> < 0.001; slope: − 1.560, <emph>SE</emph> = 2.177, <emph>p</emph> = 0.474; 13.7%) (see Fig. 3).</p> <p>Graph: Fig. 3 Distinct latent trajectory classes of the character attributes of generosity and curiosity versus single-group trajectories in the girls (n = 111) and Boys (n = 111) Subsamples of traditional longitudinal design. Note: The Red line indicates the class with the highest number of participants, followed by Blue, Green, Purple, and Magenta (Color figure online)</p> <p>Table 3, second section, shows the fit indices for all tested models concerning the attribute of curiosity in the girls' subsample. Considering the significance of the Adj-LMR-LRT and BLRT tests, the value of BIC, and better class sizes, a model with two classes seemed to fit the data best. Class 1, which was labeled as High-Decreasing; (intercept: 93.408, <emph>SE</emph> = 0.775, <emph>p</emph> < 0.001; slope: -2.615, <emph>SE</emph> = 0.683, <emph>p</emph> < 0.001), comprised 85.7% of the girls in the program, and Class 2, which was referred as Moderate-Increasing (intercept: 72.596, <emph>SE</emph> = 3.074, <emph>p</emph> < 0.001; slope: 6.391, <emph>SE</emph> = 2.356, <emph>p</emph> = 0.007), comprised 14.03% of the girls in the program (see Fig. 3).</p> <p>Analyses of the first GMMs with a single-group for the attributes of generosity and curiosity in the boys' subsample showed that the means of the intercepts (generosity: 89.507, <emph>SE</emph> = 1.132, <emph>p</emph> < 0.001; curiosity: 88.631, <emph>SE</emph> = 1.021, <emph>p</emph> < 0.001) were statistically significant, but the means of slopes (generosity: − 2.355, <emph>SE</emph> = -0.076, <emph>p</emph> = 0.918; curiosity: 0.390, <emph>SE</emph> = 0.699, <emph>p</emph> = 0.577) were not. This finding indicates that, on average, boys in the Thanda program reported very high levels of generosity and curiosity initially and these scores remained stable over the waves. GMMs with two- to five-class solutions suggested a potential three-class developmental trajectory pattern for the attribute of generosity, and two-class developmental trajectory pattern for the attributes of curiosity in the boys' subsample.</p> <p>Table 2, section three, shows the fit indices for all tested models concerning the attribute of generosity in the boys' subsample. Considering the significance of the Adj-LMR-LRT and BLRT tests, the value of BIC, and better class sizes, a model with three classes seemed to fit the data best; including: Class 1, High-Decreasing; (intercept: 97.268, <emph>SE</emph> = 0.489, <emph>p</emph> < 0.001; slope: − 3.625, <emph>SE</emph> = 0.734, <emph>p</emph> < 0.001; 64.5%), Class 2, Moderate-Increasing (intercept: 64.284, <emph>SE</emph> = 1.680, <emph>p</emph> < 0.001; slope: 12.300, <emph>SE</emph> = 1.411, <emph>p</emph> < 0.001; 11.7%), and Class 3, High-Increasing (intercept: 81.266, <emph>SE</emph> = 1.087, <emph>p</emph> < 0.001; slope: 8.678, <emph>SE</emph> = 3.373, <emph>p</emph> = 0.009; 23.8%) (see Fig. 3).</p> <p>Table 3, section three, shows the fit indices for all tested models concerning the attribute of curiosity in the boys' subsample. Considering the significance of the Adj-LMR-LRT and BLRT tests, a model with two classes seemed to fit the data best, including Class 1, High-Decreasing (intercept: 92.627, <emph>SE</emph> = 0.933, <emph>p</emph> < 0.001; slope: − 1.798, <emph>SE</emph> = 0.679, <emph>p</emph> = 0.008; 81.3%), and Class 2, High-Increasing (intercept: 71.291, <emph>SE</emph> = 2.623, <emph>p</emph> < 0.001; slope: 9.932, <emph>SE</emph> = 1.559, <emph>p</emph> < 0.001; 18.7%) (see Fig. 3).</p> <hd id="AN0188240007-17">Discussion</hd> <p>Findings from the traditional longitudinal design, using the nomothetic approach, indicated that, on average, youth in the program reported very high levels of generosity and curiosity. Over time, generosity slightly decreased, whereas curiosity remained stable. In addition, findings from the application of differential approach to predefined groups, comparing the overall developmental trajectories of character attributes in girls' and boys' subsamples, indicated that both boys and girls in the program initially reported very high levels of generosity and curiosity. For girls, these scores slightly decreased over time, whereas for boys stayed the same. Present findings are consistent with previous studies that suggested general stability or a decline with age for character constructs (see Brown et al., [<reflink idref="bib12" id="ref130">12</reflink>]; Ferragut, et al., [<reflink idref="bib23" id="ref131">23</reflink>]). However, GMMs with more than one class revealed unique subgroups of youth in the full sample (generosity: three-class, curiosity: five-class), as well as girls' (generosity: five-class, curiosity: two-class) and boys' (generosity: three-class, curiosity: two-class) subsamples, who followed different developmental trajectories of character for the attributes of generosity and curiosity.</p> <p>These findings challenge the underlying assumptions of the nomothetic approach and the application of differential approach on predefined groups that assume homogeneity across the sample and subsamples of participants. Such approaches believe that 1. a single-group trajectory can adequately describe the developmental trajectories of character for youth in the program, or 2. by comparing single-group trajectories of subsamples, researchers can adequately describe the differences in the developmental trajectories of character in boys and girls in the program. Accordingly, based on these findings, our two research hypotheses, that suggested group-based heterogeneity in the developmental trajectories of character constructs, both in terms of the number (e.g., two, three, or four) and the nature (e.g., increasing, decreasing, or stable) in the full sample of participants and in the subsamples of boys and girls, were confirmed.</p> <p>Overall, these findings align with the recent theoretical frameworks (e.g., Bornstein, [<reflink idref="bib9" id="ref132">9</reflink>]; Lerner & Matthews, [<reflink idref="bib44" id="ref133">44</reflink>]; Nucci, [<reflink idref="bib68" id="ref134">68</reflink>]; Witherington & McCready, [<reflink idref="bib98" id="ref135">98</reflink>]) that point to the heterogeneity in character development. In addition, although no empirical evidence prior to the current study supported group-based heterogeneity in the developmental trajectories of the two character constructs, our findings are consistent with previous studies utilizing GMMs to test group-based heterogeneity in youth general character (moral, performance, and civic character components, Hilliard et al., 2014) and positive development (e.g., the Five Cs model of positive youth development, Johnson, [<reflink idref="bib35" id="ref136">35</reflink>]; prosocial behavior, Lee et al., [<reflink idref="bib39" id="ref137">39</reflink>]). Furthermore, our findings align with classic differential development work in personality development by Block ([<reflink idref="bib5" id="ref138">5</reflink>]). Following these findings, researchers and practitioners in the fields of character development and education should anticipate that the specific instances of thriving relevant to one population or subpopulation (e.g., boys versus girls) of youth may differ from those relevant to a different population or subpopulation.</p> <hd id="AN0188240007-18">Research Using an Intensive Longitudinal Design</hd> <p>In the ILD, we aimed to study overall/single-group trajectories for character attributes (nomothetic approach), as well as distinct groups of individual trajectories (group differential approach). In addition, using the idiographic approach, we aimed to study the person-specific (idiographic) trajectories of character development.</p> <p>Accordingly, we tested two overarching research hypotheses: First, we hypothesized that findings from the ILD would further confirm group-based heterogeneity in the developmental trajectories of character constructs, both in terms of the number (e.g., two, three, or four) and the nature (e.g., increasing, decreasing, or stable) of these trajectories across the full sample of participants. Second, we hypothesized that the developmental trajectories of character constructs would show specificity for youth in the program that could not be captured by the estimated mean trajectories of groups or classes.</p> <hd id="AN0188240007-19">Method</hd> <p></p> <hd id="AN0188240007-20">Participants</hd> <p>A subsample of 36 youth (18 girls,<emph> M</emph><subs><emph>age</emph></subs> = 11.58, <emph>SD</emph><subs><emph>age</emph></subs> = 0.59; 18 boys,<emph> M</emph><subs><emph>age</emph></subs> = 11.55, <emph>SD</emph><subs><emph>age</emph></subs> = 0.64) from the traditional longitudinal design participated in the ILD. Participants were selected based on their availability for intensive data collection and completed a short survey of character attributes (generosity and curiosity) across sixteen weeks. To have sufficient time points to account for within-person variability, we restricted our analytic sample to participants who provided data on at least 5 measurement occasions (see Bolger & Laurenceau, [<reflink idref="bib6" id="ref139">6</reflink>]). Therefore, data from one participant were removed, and the final analytic sample included 525 observations embedded in 35 participants who provided data on five or more measurement occasions (<emph>M</emph> = 13.8, <emph>SD</emph> = 1.3). In the final analytic sample, youth age ranged from 11 to 13 years old (17 girls, 18 boys, <emph>M</emph><subs><emph>age</emph></subs> = 11.6, <emph>SD</emph><subs><emph>age</emph></subs> = 0.62).</p> <hd id="AN0188240007-21">Procedure</hd> <p>Data collection for the ILD occurred every Friday across sixteen weeks from mid-August through the first week of December 2022. It should be noted that data collection was halted between Weeks 8 and 9 due to operational issues at the Thanda site, which required the cancellation of Thanda programming. In addition, although participants were surveyed in Week 9, Thanda programming did not fully resume until Week 10.</p> <p>Data collection for the ILD followed the exact same steps as traditional longitudinal design. Data collectors met with Thanda-enrolled youth at the Thanda programs site every Friday and collected data via one-on-one in-person sessions, administering an offline version of the quantitative self-report Qualtrics survey.</p> <p>The short survey used in ILD, which included also other constructs than the main constructs of this study, was expected to take three to seven minutes to complete. Despite the challenges that can occur in the ILD data collection (e.g., participants being absent or unavailable, or getting bored with taking the same survey), the majority of participants completed the survey at least 10 times. This retention was due to data collectors returning to survey participants if they were missing at the time of data collection. For instance, if a data collector could not find a child on Friday, they would find them on Saturday. However, data collection consistently occurred on designated days (Fridays) to ensure equal visualization along the x-axis for observations across all measurement occasions for each participant.</p> <hd id="AN0188240007-22">Measures</hd> <p>The short survey was comprised of a subset of items that appeared in the longer survey in traditional longitudinal design (please refer to Table 1 in the supplementary material document for the full set of items featured in both designs). To alleviate the burden on participants, we reduced the number of items for each attribute to a number more suitable for densely-spaced assessments—typically three or four items for each factor (see van Alphen et al., [<reflink idref="bib94" id="ref140">94</reflink>]). In addition, the format of items was also adapted to better fit an ILD––each item began with, "today," followed by a statement. Generosity and curiosity were each assessed by three items. An example item for generosity is "Today, I was kind" and an example item for curiosity is "Today, I enjoyed learning new things". The response scale for the items in the short survey also ranged from 0 (completely disagree) to 100 (completely agree).</p> <hd id="AN0188240007-23">Analysis Plan</hd> <p>Before conducting main analyses, we performed preliminary descriptive analyses, including calculating the means and standard deviations, as well as zero-order bivariate correlations among the scores of the character attributes of generosity and curiosity across the fifteen waves. These analyses were undertaken to illustrate the preliminary relationships among scores throughout the data collection process.</p> <p>Then, to test our first research hypothesis, we conducted a series of GMMs on the intensive longitudinal dataset for each of the attributes of generosity and curiosity. Similar to the traditional longitudinal design, we began with a single-group trajectory to explore the overall group trajectory of youth character development for these attributes. Subsequently, we applied two- to five-class model solutions to explore the potential group-based heterogeneity in developmental trajectories of these attributes. If the models with two- to five-class did not meet the optimal groups determination criteria, we continued GMMs with more classes until both the BLRT and the LMR-LRT tests were non-significant (see Jung & Wickrama, [<reflink idref="bib36" id="ref141">36</reflink>]).</p> <p>Finally, to address our second research hypothesis and to highlight the specificity in character development, we developed person-specific pathway plots. These plots depicted the trajectories of each participant in each of the character attributes across all measurement occasions, along with the estimated average fluctuations for latent classes.[<reflink idref="bib3" id="ref142">3</reflink>] In addition, for each character attribute, we randomly selected one of the latent classes and three participants grouped within that class. We then compared their person-specific trajectories with the trajectory of the latent class into which they were grouped.</p> <p>In all the ILD models, we fixed the factor loadings of the slope estimates, from 0 to 14, corresponding to Waves 1 to 15—Wave 1 (August 2022) to Wave 15 (November 2022)—to account for differences in the time between assessments. All analyses were conducted using Mplus Version 8.11 (Muthén & Muthén, [<reflink idref="bib62" id="ref143">62</reflink>]–[<reflink idref="bib62" id="ref144">62</reflink>]), employing the MLR estimator. Similar to the traditional longitudinal design, because both the generosity and curiosity scales were multiple-item scales (3 items each), we used the composite scores of the items. Moreover, because both scales included construct-level missing data, we used FIML to handle missing data in Mplus 8.11.</p> <hd id="AN0188240007-24">Results</hd> <p></p> <hd id="AN0188240007-25">Preliminary Analyses</hd> <p>Table 4 presents descriptive statistics and correlations among the scores of the main variables across the fifteen waves of data collection. Zero-order bivariate correlations between the scores of the character attributes, except for a few cases of negative correlation, indicated positive associations among them through the data collection process.</p> <p>Table 4 Descriptive statistics and correlations among character attributes (generosity, curiosity) through the fifteen waves of data collection in the intensive longitudinal design</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>M</p></th><th align="left"><p>SD</p></th><th align="left"><p>1</p></th><th align="left"><p>2</p></th><th align="left"><p>3</p></th><th align="left"><p>4</p></th><th align="left"><p>5</p></th><th align="left"><p>6</p></th><th align="left"><p>7</p></th><th align="left"><p>8</p></th><th align="left"><p>9</p></th><th align="left"><p>10</p></th><th align="left"><p>11</p></th><th align="left"><p>12</p></th><th align="left"><p>13</p></th><th align="left"><p>14</p></th></tr></thead><tbody><tr><td align="left" colspan="17"><p><italic>Generosity (N</italic> = <italic>35)</italic></p></td></tr><tr><td align="left"><p>W1</p></td><td char="." align="char"><p>86.782</p></td><td char="." align="char"><p>15.930</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W2</p></td><td char="." align="char"><p>89.744</p></td><td char="." align="char"><p>16.692</p></td><td align="left"><p>-0.09</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W3</p></td><td char="." align="char"><p>89.781</p></td><td char="." align="char"><p>16.449</p></td><td align="left"><p>-0.13</p></td><td align="left"><p>0.49*</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W4</p></td><td char="." align="char"><p>93.500</p></td><td char="." align="char"><p>14.676</p></td><td align="left"><p>0.19</p></td><td align="left"><p>0.43*</p></td><td align="left"><p>0.28</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W5</p></td><td char="." align="char"><p>92.177</p></td><td char="." align="char"><p>17.804</p></td><td align="left"><p>0.07</p></td><td align="left"><p>0.59**</p></td><td align="left"><p>0.38*</p></td><td align="left"><p>0.86**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W6</p></td><td char="." align="char"><p>87.863</p></td><td char="." align="char"><p>18.869</p></td><td align="left"><p>0.57**</p></td><td align="left"><p>0.43*</p></td><td align="left"><p>0.28</p></td><td align="left"><p>0.63**</p></td><td align="left"><p>0.70**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W7</p></td><td char="." align="char"><p>87.333</p></td><td char="." align="char"><p>21.432</p></td><td align="left"><p>0.15</p></td><td align="left"><p>0.23</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.24</p></td><td align="left"><p>0.07</p></td><td align="left"><p>0.32</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W8</p></td><td char="." align="char"><p>89.278</p></td><td char="." align="char"><p>22.285</p></td><td align="left"><p>-0.00</p></td><td align="left"><p>0.05</p></td><td align="left"><p>0.24</p></td><td align="left"><p>0.15</p></td><td align="left"><p>0.36</p></td><td align="left"><p>0.46*</p></td><td align="left"><p>0.27</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W9</p></td><td char="." align="char"><p>84.778</p></td><td char="." align="char"><p>28.955</p></td><td align="left"><p>0.07</p></td><td align="left"><p>-0.14</p></td><td align="left"><p>0.61**</p></td><td align="left"><p>0.03</p></td><td align="left"><p>0.16</p></td><td align="left"><p>0.37*</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.66**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W10</p></td><td char="." align="char"><p>89.323</p></td><td char="." align="char"><p>19.949</p></td><td align="left"><p>-0.06</p></td><td align="left"><p>0.35</p></td><td align="left"><p>0.49**</p></td><td align="left"><p>0.45*</p></td><td align="left"><p>0.52**</p></td><td align="left"><p>0.36*</p></td><td align="left"><p>0.17</p></td><td align="left"><p>0.57**</p></td><td align="left"><p>0.32</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W11</p></td><td char="." align="char"><p>88.177</p></td><td char="." align="char"><p>21.269</p></td><td align="left"><p>0.07</p></td><td align="left"><p>0.02</p></td><td align="left"><p>0.29</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.23</p></td><td align="left"><p>0.35</p></td><td align="left"><p>0.07</p></td><td align="left"><p>0.83**</p></td><td align="left"><p>0.68**</p></td><td align="left"><p>0.63**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W12</p></td><td char="." align="char"><p>86.222</p></td><td char="." align="char"><p>26.423</p></td><td align="left"><p>0.65**</p></td><td align="left"><p>-0.12</p></td><td align="left"><p>-0.20</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.03</p></td><td align="left"><p>0.33</p></td><td align="left"><p>-0.11</p></td><td align="left"><p>-0.03</p></td><td align="left"><p>0.26</p></td><td align="left"><p>-0.12</p></td><td align="left"><p>0.20</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W13</p></td><td char="." align="char"><p>85.510</p></td><td char="." align="char"><p>26.854</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.26</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.43*</p></td><td align="left"><p>0.57**</p></td><td align="left"><p>0.04</p></td><td align="left"><p>0.74**</p></td><td align="left"><p>0.60**</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.57**</p></td><td align="left"><p>0.36*</p></td><td align="left"><p>1</p></td><td align="left" /></tr><tr><td align="left"><p>W14</p></td><td char="." align="char"><p>86.160</p></td><td char="." align="char"><p>23.583</p></td><td align="left"><p>0.25</p></td><td align="left"><p>-0.03</p></td><td align="left"><p>-0.05</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.12</p></td><td align="left"><p>0.32</p></td><td align="left"><p>0.19</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.23</p></td><td align="left"><p>0.17</p></td><td align="left"><p>0.33</p></td><td align="left"><p>0.51**</p></td><td align="left"><p>0.35</p></td><td align="left"><p>1</p></td></tr><tr><td align="left"><p>W15</p></td><td char="." align="char"><p>91.444</p></td><td char="." align="char"><p>17.765</p></td><td align="left"><p>-0.08</p></td><td align="left"><p>-0.20</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.11</p></td><td align="left"><p>0.01</p></td><td align="left"><p>-0.05</p></td><td align="left"><p>-0.05</p></td><td align="left"><p>-0.07</p></td><td align="left"><p>0.24</p></td><td align="left"><p>-0.07</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.41*</p></td><td align="left"><p>0.06</p></td><td align="left"><p>-0.04</p></td></tr><tr><td align="left" colspan="17"><p><italic>Curiosity (N</italic> = <italic>35)</italic></p></td></tr><tr><td align="left"><p>W1</p></td><td char="." align="char"><p>86.805</p></td><td char="." align="char"><p>15.875</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W2</p></td><td char="." align="char"><p>88.167</p></td><td char="." align="char"><p>18.040</p></td><td align="left"><p>0.60**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W3</p></td><td char="." align="char"><p>93.937</p></td><td char="." align="char"><p>13.898</p></td><td align="left"><p>0.72**</p></td><td align="left"><p>0.58**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W4</p></td><td char="." align="char"><p>94.083</p></td><td char="." align="char"><p>13.830</p></td><td align="left"><p>0.62**</p></td><td align="left"><p>0.63**</p></td><td align="left"><p>0.79**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W5</p></td><td char="." align="char"><p>93.177</p></td><td char="." align="char"><p>14.365</p></td><td align="left"><p>0.69**</p></td><td align="left"><p>0.66**</p></td><td align="left"><p>0.67**</p></td><td align="left"><p>0.72**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W6</p></td><td char="." align="char"><p>90.716</p></td><td char="." align="char"><p>21.132</p></td><td align="left"><p>0.72**</p></td><td align="left"><p>0.76**</p></td><td align="left"><p>0.69**</p></td><td align="left"><p>0.71**</p></td><td align="left"><p>0.84**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W7</p></td><td char="." align="char"><p>86.312</p></td><td char="." align="char"><p>21.824</p></td><td align="left"><p>0.53**</p></td><td align="left"><p>0.65**</p></td><td align="left"><p>0.67**</p></td><td align="left"><p>0.71**</p></td><td align="left"><p>0.58**</p></td><td align="left"><p>0.58**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W8</p></td><td char="." align="char"><p>84.389</p></td><td char="." align="char"><p>18.851</p></td><td align="left"><p>0.42</p></td><td align="left"><p>0.35</p></td><td align="left"><p>-0.05</p></td><td align="left"><p>0.09</p></td><td align="left"><p>0.38</p></td><td align="left"><p>0.34</p></td><td align="left"><p>0.25</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W9</p></td><td char="." align="char"><p>88.172</p></td><td char="." align="char"><p>17.114</p></td><td align="left"><p>0.41*</p></td><td align="left"><p>0.54**</p></td><td align="left"><p>0.33</p></td><td align="left"><p>0.32</p></td><td align="left"><p>0.66**</p></td><td align="left"><p>0.61**</p></td><td align="left"><p>0.44*</p></td><td align="left"><p>0.45*</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W10</p></td><td char="." align="char"><p>89.343</p></td><td char="." align="char"><p>19.047</p></td><td align="left"><p>0.49**</p></td><td align="left"><p>0.68**</p></td><td align="left"><p>0.51**</p></td><td align="left"><p>0.56**</p></td><td align="left"><p>0.76**</p></td><td align="left"><p>0.81**</p></td><td align="left"><p>0.52**</p></td><td align="left"><p>0.66**</p></td><td align="left"><p>0.72**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W11</p></td><td char="." align="char"><p>88.406</p></td><td char="." align="char"><p>18.887</p></td><td align="left"><p>0.44*</p></td><td align="left"><p>0.38</p></td><td align="left"><p>0.37*</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.45*</p></td><td align="left"><p>0.52**</p></td><td align="left"><p>0.44*</p></td><td align="left"><p>0.32</p></td><td align="left"><p>0.83**</p></td><td align="left"><p>0.60**</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W12</p></td><td char="." align="char"><p>83.556</p></td><td char="." align="char"><p>24.303</p></td><td align="left"><p>0.56**</p></td><td align="left"><p>0.39</p></td><td align="left"><p>0.41*</p></td><td align="left"><p>0.40*</p></td><td align="left"><p>0.65**</p></td><td align="left"><p>0.65**</p></td><td align="left"><p>0.23</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.35</p></td><td align="left"><p>0.53**</p></td><td align="left"><p>0.33</p></td><td align="left"><p>1</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>W13</p></td><td char="." align="char"><p>83.719</p></td><td char="." align="char"><p>25.858</p></td><td align="left"><p>0.72**</p></td><td align="left"><p>0.45*</p></td><td align="left"><p>0.50**</p></td><td align="left"><p>0.49**</p></td><td align="left"><p>0.57**</p></td><td align="left"><p>0.68**</p></td><td align="left"><p>0.67**</p></td><td align="left"><p>0.26</p></td><td align="left"><p>0.65**</p></td><td align="left"><p>0.61**</p></td><td align="left"><p>0.69**</p></td><td align="left"><p>0.49**</p></td><td align="left"><p>1</p></td><td align="left" /></tr><tr><td align="left"><p>W14</p></td><td char="." align="char"><p>87.086</p></td><td char="." align="char"><p>20.248</p></td><td align="left"><p>0.46*</p></td><td align="left"><p>0.49*</p></td><td align="left"><p>0.45*</p></td><td align="left"><p>0.44*</p></td><td align="left"><p>0.63**</p></td><td align="left"><p>0.74**</p></td><td align="left"><p>0.70**</p></td><td align="left"><p>0.44</p></td><td align="left"><p>0.67**</p></td><td align="left"><p>0.86**</p></td><td align="left"><p>0.67**</p></td><td align="left"><p>0.49*</p></td><td align="left"><p>0.84**</p></td><td align="left"><p>1</p></td></tr><tr><td align="left"><p>W15</p></td><td char="." align="char"><p>80.970</p></td><td char="." align="char"><p>31.593</p></td><td align="left"><p>0.60**</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.34</p></td><td align="left"><p>0.58**</p></td><td align="left"><p>0.46*</p></td><td align="left"><p>0.48**</p></td><td align="left"><p>0.49**</p></td><td align="left"><p>0.61**</p></td><td align="left"><p>0.47**</p></td><td align="left"><p>0.55**</p></td><td align="left"><p>0.37*</p></td><td align="left"><p>0.20</p></td><td align="left"><p>0.69**</p></td><td align="left"><p>.62**</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>M</emph> and <emph>SD</emph> are used to represent mean and standard deviation, respectively <sups>*</sups>Indicates <emph>p</emph> <.05. ** indicates <emph>p</emph> <.01</p> <hd id="AN0188240007-26">Distinct Group-Based Trajectories of Character Development in Intensive Longitudinal Design</hd> <p>The fit indices of GMMs for the attributes of generosity and curiosity within the intensive longitudinal data set are displayed in Table 5. Analyses of the GMMs using a single-group for the attribute of generosity showed that the mean of intercept (91.305, <emph>SE</emph> = 2.940, <emph>p</emph> < 0.001) was statistically significant, but the mean of slope (− 0.283, <emph>SE</emph> = 0.312, <emph>p</emph> = 0.363) was not. This finding indicates that the 35 participants who took part in ILD reported very high levels of generosity initially, and these scores remained stable over time. However, the GMMs with more than one-class solution suggested five-class developmental trajectories for these participants. The BLRT test results indicated no significance for models with more than five classes, suggesting there is no significant difference between models with five classes or more. Accordingly, a model with five classes seemed to fit the data best, including: Class 1, Very Low-Increasing (intercept: 22.906, <emph>SE</emph> = 2.046, <emph>p</emph> < 0.001; slope: 5.180, <emph>SE</emph> = 0.472, <emph>p</emph> < 0.001; 2.9%), Class 2, High-Increasing (intercept: 77.145, <emph>SE</emph> = 0.679, <emph>p</emph> < 0.001; slope: 1.046, <emph>SE</emph> = 0.167, <emph>p</emph> < 0.001; 2.9%), Class 3, High-Decreasing (intercept: 99.584, <emph>SE</emph> = 0.978, <emph>p</emph> < 0.001; slope: − 0.410, <emph>SE</emph> = 0.134, <emph>p</emph> = 0.002; 74.2%), Class 4, Very High-Decreasing (intercept: 114.351, <emph>SE</emph> = 2.036, <emph>p</emph> < 0.001; slope: − 5.641, <emph>SE</emph> = 0.472, <emph>p</emph> < 0.001; 11.4%), and, Class 5, Moderate-Stable (intercept: 63.665, <emph>SE</emph> = 4.767, <emph>p</emph> < 0.001; slope: 0.746, <emph>SE</emph> = 0.526, <emph>p</emph> = 0.156; 8.6%) (ee Fig. 4).</p> <p>Table 5 Model fit indices for GMMs of the character attributes of generosity and curiosity in the intensive longitudinal design</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Classes</p></th><th align="left"><p>AIC</p></th><th align="left"><p>BIC</p></th><th align="left"><p>Adj BIC</p></th><th align="left"><p>Entropy</p></th><th align="left"><p>Adj-LMR-LRT</p><p>(<italic>p</italic> value)</p></th><th align="left"><p>BLRT</p><p>(<italic>p</italic> value)</p></th><th align="left"><p>SC (%) of <italic>N</italic></p></th></tr></thead><tbody><tr><td align="left" colspan="8"><p><italic>Generosity (Full sample, N</italic> = <italic>35)</italic></p></td></tr><tr><td align="left"><p>1<sup>a</sup></p></td><td char="." align="char"><p>4059.201</p></td><td char="." align="char"><p>4090.308</p></td><td char="." align="char"><p>4027.858</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>2<sup>a</sup></p></td><td char="." align="char"><p>4044.484</p></td><td char="." align="char"><p>4077.147</p></td><td char="." align="char"><p>4011.574</p></td><td char="." align="char"><p>0.990</p></td><td char="." align="char"><p>0.6694</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.114</p></td></tr><tr><td align="left"><p>3<sup>a</sup></p></td><td char="." align="char"><p>4028.355</p></td><td char="." align="char"><p>4065.683</p></td><td char="." align="char"><p>3990.744</p></td><td char="." align="char"><p>0.993</p></td><td char="." align="char"><p>0.6417</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.114</p></td></tr><tr><td align="left"><p>4<sup>a</sup></p></td><td char="." align="char"><p>4020.382</p></td><td char="." align="char"><p>4060.821</p></td><td char="." align="char"><p>3979.637</p></td><td char="." align="char"><p>0.998</p></td><td char="." align="char"><p>0.3928</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.057</p></td></tr><tr><td align="left"><p><bold>5</bold><sup><bold>a</bold></sup></p></td><td char="." align="char"><p><bold>4010.274</bold></p></td><td char="." align="char"><p><bold>4055.379</bold></p></td><td char="." align="char"><p><bold>3964.827</bold></p></td><td char="." align="char"><p><bold>0.997</bold></p></td><td char="." align="char"><p><bold>0.4541</bold></p></td><td char="." align="char"><p><bold>0.0300</bold></p></td><td char="." align="char"><p><bold>0.028</bold></p></td></tr><tr><td align="left"><p>6<sup>a</sup></p></td><td char="." align="char"><p>4006.020</p></td><td char="." align="char"><p>4055.791</p></td><td char="." align="char"><p>3955.871</p></td><td char="." align="char"><p>0.988</p></td><td char="." align="char"><p>0.9304</p></td><td char="." align="char"><p>0.3750</p></td><td char="." align="char"><p>0.028</p></td></tr><tr><td align="left" colspan="8"><p><italic>Curiosity (Full sample, N</italic> = <italic>35)</italic></p></td></tr><tr><td align="left"><p>1</p></td><td char="." align="char"><p>3812.420</p></td><td char="." align="char"><p>3843.527</p></td><td char="." align="char"><p>3781.077</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>2<sup>a</sup></p></td><td char="." align="char"><p>3783.954</p></td><td char="." align="char"><p>3816.617</p></td><td char="." align="char"><p>3751.044</p></td><td char="." align="char"><p>0.992</p></td><td char="." align="char"><p>0.4099</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.200</p></td></tr><tr><td align="left"><p>3<sup>a</sup></p></td><td char="." align="char"><p>3819.658</p></td><td char="." align="char"><p>3855.431</p></td><td char="." align="char"><p>3783.614</p></td><td char="." align="char"><p>0.982</p></td><td char="." align="char"><p>0.2307</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.028</p></td></tr><tr><td align="left"><p>4<sup>a</sup></p></td><td char="." align="char"><p>3765.769</p></td><td char="." align="char"><p>3806.208</p></td><td char="." align="char"><p>3725.024</p></td><td char="." align="char"><p>0.984</p></td><td char="." align="char"><p>0.1998</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.028</p></td></tr><tr><td align="left"><p>5<sup>a</sup></p></td><td char="." align="char"><p>3734.728</p></td><td char="." align="char"><p>3779.833</p></td><td char="." align="char"><p>3689.281</p></td><td char="." align="char"><p>0.989</p></td><td char="." align="char"><p>0.5126</p></td><td char="." align="char"><p>0.0000</p></td><td char="." align="char"><p>0.028</p></td></tr><tr><td align="left"><p><bold>6</bold><sup><bold>a</bold></sup><bold>*</bold></p></td><td char="." align="char"><p><bold>3716.724</bold></p></td><td char="." align="char"><p><bold>3766.495</bold></p></td><td char="." align="char"><p><bold>3666.576</bold></p></td><td char="." align="char"><p><bold>0.985</bold></p></td><td char="." align="char"><p><bold>0.6015</bold></p></td><td char="." align="char"><p><bold>0.0000</bold></p></td><td char="." align="char"><p><bold>0.028</bold></p></td></tr></tbody></table> </ephtml> </p> <p>The final class solution is <bold>bolded</bold><emph>AIC</emph> Akaike information criteria, <emph>BIC</emph> Bayesian information criteria, <emph>Adj BIC</emph> Sample-size adjusted bayes information criteria, <emph>Adj LMR-LRT</emph> Adjusted Lo-Mendell-Rubin likelihood test, <emph>BLRT</emph> Bootstrapped likelihood ratio test, <emph>SC</emph> Smallest class a = Several models encountered the 'matrix (psi) is not positive definite' warning, indicating a negative variance/residual variance for a latent variable. Since the negative variance wasn't significant in these models, we fixed the variance to zero for either the intercept or slope of one or more classes (see Johnson, [<reflink idref="bib35" id="ref145">35</reflink>]; Ram & Grimm, [<reflink idref="bib75" id="ref146">75</reflink>]) <sups>*</sups>We tried models with more than six classes; however, these models had convergence issues in Mplus</p> <p>Graph: Fig. 4 Distinct latent trajectory classes of the character attributes of generosity and curiosity versus Idiographic Trajectories for full sample of participants within the intensive longitudinal design (N = 35). Note: The Red line indicates the class with the highest number of participants, followed by Blue, Green, Purple, Magenta, and Orange (Color figure online)</p> <p>Analyses of the first GMM with a single-group for the attribute of curiosity showed that both the mean of the intercept (93.942, <emph>SE</emph> = 2.669, <emph>p</emph> < 0.001) and the mean of the slope (− 0.654, <emph>SE</emph> = 0.286, <emph>p</emph> = 0.022) were statistically significant. These results indicate that the 35 participants reported very high levels of curiosity initially; however, these scores slightly decreased over time. Analyses of the GMMs with more than one-class solution suggested a model with six-class developmental trajectories for these participants. However, there was a possibility of identifying more classes. GMMs with more than six classes had convergence issues in Mplus, so we stopped with the six-class model, which included: Class 1, High-Increasing (intercept: 95.685, <emph>SE</emph> = 1.382, <emph>p</emph> < 0.001; slope: 0.281, <emph>SE</emph> = 0.102, <emph>p</emph> = 0.006; 60.2%), Class 2, Moderate-Stable (intercept: 73.620, <emph>SE</emph> = 2.463, <emph>p</emph> < 0.001; slope: -0.114, <emph>SE</emph> = 0.143, <emph>p</emph> = 0.426; 5.8%), Class 3, High-Decreasing (intercept: 90.097, <emph>SE</emph> = 2.884, <emph>p</emph> < 0.001; slope: − 0.607, <emph>SE</emph> = 0.158, <emph>p</emph> < 0.001; 10.5%), Class 4, Very Low-Stable (intercept: 30.699, <emph>SE</emph> = 1.672, <emph>p</emph> < 0.001; slope: 0.172, <emph>SE</emph> = 0.128, <emph>p</emph> < 0.001; 2.9%), Class 5, Very High-Decreasing (intercept: 108.914, <emph>SE</emph> = 2.344, <emph>p</emph> < 0.001; slope: − 3.404, <emph>SE</emph> = 0.249, <emph>p</emph> < 0.001; 17.9%), and Class 6, High-Decreasing (intercept: 93.882, <emph>SE</emph> = 6.775, <emph>p</emph> < 0.001; slope: − 4.760, <emph>SE</emph> = 0.530, <emph>p</emph> < 0.001; 2.9%) (see Fig. 4).</p> <hd id="AN0188240007-27">Person-Specific (Idiographic) Trajectories of Character Development</hd> <p>Figure 4 (Plots 2 and 4) depicts the person-specific pathway plots showing the trajectories of estimated means for distinct latent classes versus the person-specific trajectories of each participant for each character construct within the ILD (<emph>N</emph> = 35). The person-specific fluctuations (black lines) versus the trajectories of estimated means for distinct latent classes (colorful lines) in these plots confirm the specificity of character development.</p> <p>Figure 5 depicts the latent trajectory for Class 5 of the character attribute of generosity in the ILD versus the idiographic trajectories for three randomly selected participants within this class, and Fig. 6 shows the latent trajectory for Class 1 of the character attribute of curiosity in the ILD versus the idiographic trajectories for three randomly selected participants within this class. These plots further illustrate how the patterns of fluctuations differed among youth even when they were classified in the same group.</p> <p>Graph: Fig. 5 Distinct latent trajectory for class 5 of the character attributes of generosity in the intensive longitudinal design versus the idiographic trajectories for three randomly selected participants within this class. Note: The Green line indicates the distinct latent trajectory of Class 5, whereas the Black lines represent the idiographic trajectories (Color figure online)</p> <p>Graph: Fig. 6 Distinct latent trajectory for class 1 of the character attributes of curiosity in the intensive longitudinal design versus idiographic trajectories for three randomly selected participants within this class. Note: The Red line indicates the distinct latent trajectory of Class 1, whereas the Black lines represent the idiographic trajectories (Color figure online)</p> <hd id="AN0188240007-28">Discussion</hd> <p>Findings from the ILD, using the nomothetic approach, indicated that, on average, youth in the program reported very high levels of generosity and curiosity. Over time, curiosity slightly decreased, whereas generosity remained stable. These findings align with results from our traditional longitudinal design and previous studies, which suggest general stability or a decline in the average level of character constructs with age (see Brown et al., [<reflink idref="bib12" id="ref147">12</reflink>]; Ferragut et al., [<reflink idref="bib23" id="ref148">23</reflink>]). Analyses of the GMMs with more than one class (differential approach), however, shed light on completely different patterns of development for youth in the program. In particular, there were five distinct trajectories for the attributes of generosity versus six for the attribute of curiosity. These findings further support our results from the traditional longitudinal design, confirming the existence of distinct groups or classes of developmental trajectories in the character development of youth in the program. Accordingly, our first research hypothesis—that there would be group-based heterogeneity in the developmental trajectories of character constructs, both in terms of the number (e.g., two, three, or four) and the nature (e.g., increasing, decreasing, or stable)—was confirmed.</p> <p>Furthermore, findings from person-specific pathways plots demonstrated specificity in character development, indicating that the patterns of fluctuations for character constructs differed for every youth in the program. Findings from the idiographic trajectories of three randomly selected participants versus the trajectory of the latent class they were grouped into indicated that there was specificity in the developmental trajectories of youth in the program that couldn't be captured by the estimated mean trajectories of groups or classes. An important characteristic of mixture models is that there is always uncertainty in group memberships (i.e., they are probabilistic; Masyn, [<reflink idref="bib51" id="ref149">51</reflink>]). This probability in group membership highlights the variations that exist in person-specific trajectories. This finding means that although there can be homogonous groups, there is always heterogeneity in group membership among individuals, a point that again highlights the specificity of character development. Consequently, these findings support our last research hypothesis that character development is person-specific—each individual in the program has his or her specific change characteristics that rarely, if ever, conform to group averages (Molenaar & Nesselroade, [<reflink idref="bib58" id="ref150">58</reflink>], [<reflink idref="bib59" id="ref151">59</reflink>]; Rose, [<reflink idref="bib79" id="ref152">79</reflink>]).</p> <hd id="AN0188240007-29">General Discussion</hd> <p>Nomothetic, group differential, and idiographic approaches are all needed to fully understand youth development; however, most research on youth character development has traditionally relied on the nomothetic approach to study sample changes or on the application of differential approach on subsamples (e.g., gender, age) to study predefined group changes. Through this study, using two longitudinal data sets, and taking an integrated approach (Ram et al., [<reflink idref="bib76" id="ref153">76</reflink>]), we studied the overall/single group trajectories (nomothetic), distinct groups of individual trajectories (differential), and person-specific trajectories (idiographic) of character development among South African youth. We aspired to use this research to address the gap in studying distinct group-based trajectories and idiographic trajectories in character development. Results from both designs confirmed that there is group-based heterogeneity in character development. Findings from the ILD study additionally pointed to the specificity of character development and the fact that person-specific trajectories of the character attributes of youth in the program cannot be captured by the estimated mean trajectories of groups of classes.</p> <p>First, these results are consistent with dynamic, RDS-based ideas about the specificity of person ⬄ context relations (Bornstein, [<reflink idref="bib8" id="ref154">8</reflink>]). According to dynamic, RDS-based models (Overton, [<reflink idref="bib71" id="ref155">71</reflink>]; see too Lerner & Matthews, [<reflink idref="bib44" id="ref156">44</reflink>]), dynamic coactions occur continuously in the relational developmental system and can provide a basis for moment-to-moment, hourly, daily, weekly, etc. fluctuations in attributes of human functioning (see Deboeck et al., [<reflink idref="bib19" id="ref157">19</reflink>]; Ram & Gatzke-Kopp, [<reflink idref="bib77" id="ref158">77</reflink>]), including attributes related to character development. As such, given our RDS-based approach to developmental science, it is theoretically meaningful for each individual to show different trajectories as their personal dynamic developmental system continues to unfold across ontogeny. Therefore, the character development of a youth in a program cannot be adequately captured by a single score from a single or even from a few sparsely-collected occasions of measurement (Ram et al., [<reflink idref="bib76" id="ref159">76</reflink>]), which occur prototypically in variable-focused—cross-sectional or traditional longitudinal—studies that are limited to only between-person analyses.</p> <p>Second, these findings align with the Kluckhohn and Murray ([<reflink idref="bib37" id="ref160">37</reflink>]) idea regarding the possibility of existing homogeneous groups of individuals within the larger heterogeneous population or subpopulations. Accordingly, the findings of this study illuminate the necessity of integrating person-specific and differential information to provide a holistic understanding of character development for all youth in programs. Such research will avoid relying solely on nomothetic or group differential approaches to study changes on a sample or subsample of participants.</p> <hd id="AN0188240007-30">Limitations and Suggestions for Future Research</hd> <p>In this article, we studied the nomothetic, group differential, and idiographic development of character, identifying both group-based heterogeneity and person-specificity in character development of youth in Thanda programs. Although this study provided a comprehensive understanding of character development among South African youth, the implications of our research must be evaluated in the context of the study's limitations.</p> <p>Our study had limitations associated with sampling. We examined the group-based heterogeneity and person-specificity of character development only among South African youth enrolled in Thanda character development programs. Future research should include samples from different character development programs, and youth without a history of such program participation. In addition, the current sample had a higher number of dropouts among males and younger youth during Waves 2 and 3 of the traditional longitudinal design, which are important results for informing future studies on ways to reduce or prevent the systematic loss of participants among these groups, particularly by considering follow-up methods to maximize retention.</p> <p>Our study also included the well-known problems in character development and character education research and evaluation associated with the use of self-report measures (e.g., see Card, [<reflink idref="bib15" id="ref161">15</reflink>]; Clifton, [<reflink idref="bib17" id="ref162">17</reflink>]). These limitations include common-method variance, response-style bias, and social-desirability bias—particularly due to the one-on-one, interview-style format of survey administration; all of these issues may skew results from self-reported data. Future research should collect data from multiple observers (e.g., peer-, teacher-, and parent/guardian-reports) and aim to make the survey as anonymous as possible, allowing participants to respond freely and openly without feeling judged for their answers. In addition, future research should triangulate measurement by using multiple methods to obtain data about character constructs. Examples include data from behavioral observations as well as from qualitative interviews.</p> <p>Although mixture models have been successfully applied with as few as 34 participants (Ram and Grimm, [<reflink idref="bib75" id="ref163">75</reflink>]), the majority of methodological research indicates that several hundred participants are required for accurate estimation of mixture models (see Johnson, [<reflink idref="bib35" id="ref164">35</reflink>]; Nylund et al., [<reflink idref="bib69" id="ref165">69</reflink>]). The sample size must be sufficiently large to ensure adequate power for identifying the correct number of groups and accurately estimating model parameters. In the current study, the number of participants in the ILD was relatively small (<emph>N</emph> = 35), resulting in insufficient power to identify the correct number of groups, especially for the attribute of curiosity; models with more than six classes failed to converge in Mplus. Future studies should consider strategies for recruiting more participants (e.g., using remote data collection through mobile devices instead of one-on-one in-person surveys for convenient participation, or increasing incentives for continued participation; see Heron et al., [<reflink idref="bib29" id="ref166">29</reflink>]; van Roekel et al., [<reflink idref="bib95" id="ref167">95</reflink>]) to expand knowledge about the person-specificity and group-based heterogeneity of character development.</p> <p>In GMMs, the best practice for showing specificity in the developmental trajectories of individuals is to compare estimated individual trajectories with the estimated means of latent classes and ascertain if person-specific trajectories significantly vary from estimated means. In the current study, several models encountered the "matrix (psi) is not positive definite" warning in Mplus, indicating a negative variance/residual variance for a latent variable. Since the negative variance was not significant in these models, we fixed the variance to zero for either the intercept or slope of one or more classes (see Johnson, [<reflink idref="bib35" id="ref168">35</reflink>]; Ram & Grimm, [<reflink idref="bib75" id="ref169">75</reflink>]). However, due to this action, we were unable to develop plots with estimated individual trajectories versus estimated means for each latent class and instead relied on plots with idiographic trajectories versus the estimated means of latent classes to show the specificity in character development. Future research interested in studying person-specific variability in character development should consider developing plots with estimated individual trajectories versus estimated means of latent classes and reporting the significance of variance in the intercept and slope for the estimated means of the classes.</p> <p>Finally, in the current study, we primarily used GMMs as an exploratory method to identify the group-based heterogeneity and person-specificity of character development, given recent theoretical frameworks (e.g., Bornstein, [<reflink idref="bib9" id="ref170">9</reflink>]; Lerner & Matthews, [<reflink idref="bib44" id="ref171">44</reflink>]; Nucci, [<reflink idref="bib68" id="ref172">68</reflink>]; Witherington & McCready, [<reflink idref="bib98" id="ref173">98</reflink>]) that highlight heterogeneity and specificity in character development and the lack of previous research on this subject. Accordingly, we were not interested in exploring the effect of a specific intervention, or life event on changes in character constructs. However, many potential individual and contextual factors (e.g., antecedents or consequences) are pertinent to the dynamic and holistic development of character. Future studies should consider integrating these individual and contextual factors when studying the variability in character development. Such a step may offer deeper insight into the identified groups and thereby provide a basis for potentially greater generalizability.</p> <hd id="AN0188240007-31">Conclusions</hd> <p>The findings of this study underscore the importance of a holistic and integrative approach to studying character development and, as well, youth development more generally. The integrated use of nomothetic, differential, and idiographic assessments provides a valuable window for gaining an empirical understanding of such holism (Lerner, Yu, et al., 2024; Ram et al., [<reflink idref="bib76" id="ref174">76</reflink>]). In addition, when research uses multiple methods to interactively triangulate the study of holistic development of individual-context coactions, the developmental science of character development will be further enhanced (Lerner & Matthews, [<reflink idref="bib44" id="ref175">44</reflink>]). The results of such research will best illuminate the coactions involved in the bases of group-based heterogeneity as well as specificity in the dynamic process of character development. Therefore, character development research and the evaluation of character education programs should not focus solely on nomothetic or group differential approaches to study sample or subsample of participants. Instead, future character development scholarship and evaluations of the effectiveness of character education programs should be marked by researchers adding to their methodological toolbox procedures enabling the incorporation of person-specific designs, measures, and analyses. Such integrative research will provide important knowledge of dynamic wholeness of the character development pathway of every youth.</p> <hd id="AN0188240007-32">Funding</hd> <p>The writing of this paper was supported by a grant from the Templeton World Charity Foundation (TWCF0634).</p> <hd id="AN0188240007-33">Data Availability</hd> <p>The authors take responsibility for the integrity of the data and the accuracy of the data analysis.</p> <hd id="AN0188240007-34">Declarations</hd> <p></p> <hd id="AN0188240007-35">Conflict of interest</hd> <p>The authors declare that they have no conflict of interest.</p> <hd id="AN0188240007-36">Ethical Approval</hd> <p>All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.</p> <hd id="AN0188240007-37">Informed Consent</hd> <p>Informed consent was obtained from all individual participants included in this study.</p> <hd id="AN0188240007-38">Supplementary Information</hd> <p>Below is the link to the electronic supplementary material.</p> <p>Graph: Supplementary file1 (DOCX 515 kb)</p> <hd id="AN0188240007-39">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0188240007-40"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Allport GW. 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When children suffer from chronic undernutrition, their growth is hindered, leading to a condition known as 'stunting,' where they are shorter than expected for their age (Children's Institute, [16]).</bibtext> </blist> <blist> <bibtext> . Please refer to the Supplementary Materials document for figures of single-group trajectories.</bibtext> </blist> <blist> <bibtext> . Because some models encountered the 'matrix (psi) is not positive definite' warning, we fixed the variance to zero for either the intercept or slope in one or more classes (see Johnson, [35]; Ram & Grimm, [75]). Consequently, we were not able to develop plots comparing the estimated individual trajectories with the estimated means for the distinct classes. For more information, please refer to the limitations section of the paper.</bibtext> </blist> </ref> <aug> <p>By Roya Abbasi-Asl; Natasha Keces; Richard M. Lerner; Margaret Mackin; Dian Yu; Elizabeth M. Dowling; Jonathan M. Tirrell; Alexa Hasse; Kirsten Olander; Angela Larkan; Chuma Mashita; Raah Msimango; Sinenhlanhla Mkhithi and Tyler Howard</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib18" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib37" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib14" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib30" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib32" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib33" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib34" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib74" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib40" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib22" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib56" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib54" firstref="ref14"></nolink> <nolink nlid="nl13" 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  Label: Title
  Group: Ti
  Data: Nomothetic, Differential, and Idiographic Assessment of Character Development among South African Youth
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Roya+Abbasi-Asl%22">Roya Abbasi-Asl</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-1186-5424">0000-0003-1186-5424</externalLink>)<br /><searchLink fieldCode="AR" term="%22Natasha+Keces%22">Natasha Keces</searchLink><br /><searchLink fieldCode="AR" term="%22Richard+M%2E+Lerner%22">Richard M. Lerner</searchLink><br /><searchLink fieldCode="AR" term="%22Margaret+Mackin%22">Margaret Mackin</searchLink><br /><searchLink fieldCode="AR" term="%22Dian+Yu%22">Dian Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Elizabeth+M%2E+Dowling%22">Elizabeth M. Dowling</searchLink><br /><searchLink fieldCode="AR" term="%22Jonathan+M%2E+Tirrell%22">Jonathan M. Tirrell</searchLink><br /><searchLink fieldCode="AR" term="%22Alexa+Hasse%22">Alexa Hasse</searchLink><br /><searchLink fieldCode="AR" term="%22Kirsten+Olander%22">Kirsten Olander</searchLink><br /><searchLink fieldCode="AR" term="%22Angela+Larkan%22">Angela Larkan</searchLink><br /><searchLink fieldCode="AR" term="%22Chuma+Mashita%22">Chuma Mashita</searchLink><br /><searchLink fieldCode="AR" term="%22Raah+Msimango%22">Raah Msimango</searchLink><br /><searchLink fieldCode="AR" term="%22Sinenhlanhla+Mkhithi%22">Sinenhlanhla Mkhithi</searchLink><br /><searchLink fieldCode="AR" term="%22Tyler+Howard%22">Tyler Howard</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Child+%26+Youth+Care+Forum%22"><i>Child & Youth Care Forum</i></searchLink>. 2025 54(5):1203-1234.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 32
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
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  Label: Document Type
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  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Moral+Development%22">Moral Development</searchLink><br /><searchLink fieldCode="DE" term="%22Youth%22">Youth</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Development%22">Individual Development</searchLink><br /><searchLink fieldCode="DE" term="%22Personality+Traits%22">Personality Traits</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Differences%22">Individual Differences</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22South+Africa%22">South Africa</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s10566-025-09856-6
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1053-1890<br />1573-3319
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Nomothetic, group differential, and idiographic approaches are all needed to fully understand youth development. However, most research on youth character development has traditionally relied on either the nomothetic approach to study whole sample changes or the differential approach to study changes in predefined groups (e.g., involving gender or age). Objective: In this study, we used data from the Thanda Study of Character and Health to assess nomothetic, differential, and idiographic development of character attributes (generosity, curiosity) among South African youth. Methods: We measured character attributes using a traditional longitudinal design and an intensive longitudinal design (ILD). The traditional longitudinal design involved 222 youth (50% female; M[subscript age] = 9.82, SD[subscript age] = 1.39), and their scores of character attributes for three assessments separated by about half a year. A subset of 35 youth from this sample (48% female, M[subscript age] = 11.6, SD[subscript age] = 0.62) participated in the ILD, completing a survey of character attributes across 16 weekly assessments. Using Growth Mixture Modeling and person-specific pathways plots, we examined group-based heterogeneity and specificity in youth character development. Results: Results from both designs confirmed that there is group-based heterogeneity in character development. Findings from the ILD additionally pointed to the specificity of character development and the fact that person-specific trajectories of the character attributes cannot be captured by the estimated mean trajectories of groups. Conclusions: These findings underscore the importance of identifying both group-based and idiographic trajectories rather than relying solely on nomothetic or differential approaches on predefined groups when studying character development.
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  Data: As Provided
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  Label: Entry Date
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  Data: 2025
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  Data: EJ1488792
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        Value: 10.1007/s10566-025-09856-6
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      – Text: English
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        PageCount: 32
        StartPage: 1203
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      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Moral Development
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      – SubjectFull: Youth
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      – SubjectFull: Individual Development
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